TRANSKRIPT 🔗
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SH: Hello everyone, it is your host Sam Hansen and I am back with a new series in
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Relatively Prime that we are going to be calling seminars and it's going to be
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going out into the Relatively Prime feed off and on and it's going to be
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tackling the big questions about mathematics, so not from mathematics.
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We're not going to be digging deep into specific mathematical concepts here, but
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we're going to be doing more of a meta conversation about what goes on in
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mathematics and our first conversation is going to be about funding in
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mathematics and if you find that you have a question that you want to hear a
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seminar about please just email seminar@acmescience.com that's seminar at A C M E
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science.com and it will be up for a potential seminar in the future. So
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without further ado to join me and discuss funding in mathematics I have
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Carrie Diaz-Eaton, a mathematician and the associate professor of digital and
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computational studies at Bates College as well as the executive director of the
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Rios Institute, Drew Lewis, a mathematician and independent consultant and Jude
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Higdon, the chief operation officers of the Institute for Quantitative Study of
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Inclusion, Diversity and Equity or QSide. Hello everyone, thank you so much for
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joining me.
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CDZ: Hello, thanks for having us.
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JH: Good to be here.
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SH: And so first off let's
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talk just about where funding for math has traditionally come from and I can
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give a short little rundown about 64% of the funding in math comes from the NSF,
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18% comes from the Department of Defense and these are the federal funders, not
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including the private funders, and the reason 18% is from DOD is that that is
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where the NSA or national security agency's money sits and 12% is from HHS
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which is where the NIH money sits. So has anyone here you know dealt with
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getting federal funding? What was it like?
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CDZ: I guess I can talk a little bit
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about the process. I'll start. There are many divisions within National
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Science Foundation that give federal funding for mathematics so one of them
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that I think folks are most familiar with is the DMS, Division of Mathematical
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Sciences, but I don't actually usually get funding from them at all. So in my
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world as a mathematician I work very interdisciplinary, I do a lot of education
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research, I also do some in math biology so I have been funded by programs in the
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education side of the house and some programs that are joint between
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education and biology. So each of those programs has like a flavor I think you
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know like you try to figure out you know what what are you doing? What kind of
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project do you have in mind? Is it going to be something that is focused on a
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particular research area? Is it going to be about bringing new trainees into your
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lab? Is it going to be about something you're doing for your institutional to
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create, institution to create capacity for something more? There are like
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all sorts of different ways that you can get or reasons why you might look to get
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funding for mathematics. My sort of experience of that is like sometimes
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putting in proposals that get shot down and then eventually getting proposals in
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after coming back to the drawing board and revising them and listening to feedback
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and getting funded, but it can be a very long process. It's a very lengthy, very
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intensive process. There are way more grants than can possibly get funded. It's
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very competitive. I've also served on the other side of things reviewing
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proposals and many proposals are great and we can't certainly fund them all and
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so it's overall a very difficult, rigorous, intensive project that gets
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measured all the way down to your margin size and point size and with some
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percentage chance of getting funded. But when it does it really can open the
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doors not just for mathematicians and for innovation but also for students and
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trainees that are also employed on projects as well.
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JH: I'm happy to jump in
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and talk about my experience there. I've been part of, similar to
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Carrie, I've been part of more application processes than funded
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processes. I think that's just sort of how it goes. Again, not being a
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mathematician myself, I sort of rep mathematicians. The role that I
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generally play in the proposal
Load full transcript
process is sort of narrative story building and
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trying to think about the why of the questions around this. My experience
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of federally funded grants as one of the, at least historically, we'll
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see where that goes, is that Federal grants particularly are incredibly
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onerous in terms of the strictures required. The hurdles you have to
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jump through both organizationally and as coming from a nonprofit, a 501c3,
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not from an academic institution that has that sort of baked into the sauce
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that's particularly onerous for my organization to sort of overcome those
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hurdles. So we have, and especially in the current political climate, largely
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walked away from even seeking federal grants. I've been part of several
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successful NSF grants, one successful NIH grants and more proposals than I can
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probably count or remember at this point. But they are, in my experience,
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federally funded grants are from federal institutions like the NSF, the NIH,
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the Department of Education, the Department of Defense. We don't tend to
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apply through the Department of Defense. I've never been part of that process.
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But it is, it's just a tough process, right? It's really hard to check all
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the boxes and to get everything in the exact right ways. There is a lot of
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bureaucratic structure to it. The payoff can be quite good, right?
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Because if you are successful, the numbers, you know, the amount of funding
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available there is quite good. And historically, it has also been incredibly
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stable once you're funded. I think the gravity, the sort of physics of that
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world are changing pretty quickly right now in ways that I think folks did not
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think they could be. But that's been sort of part of my experience with the
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funding landscape at that level.
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SH: Drew, is it possible for you to talk at all about
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sort of like what the impact of these grants means? Like what are some of the
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things that are actually happening when mathematicians are getting funding?
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CDZ: And I'll tell Drew to also talk about the institutes because I feel like that's a
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different kind of beast. Like we're getting federal funding by proxy, right?
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DL: Yeah, so the NSF funds all kinds of different things in math. So one thing
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that most mathematicians are maybe most familiar with there is the standard
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grants through DMS, the Division of Mathematical Sciences, which will
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directly fund mathematics research. So salary support and travel support for
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collaborations, right? For mathematicians to do math research, proving theorems,
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collaborating with others. Another big thing they fund is conference grants,
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right? This is across divisions and directorates, but they will fund directly
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conferences around different topics. So for instance, the Southern Regional
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Algebra Conference that helped organize one year, we got a DMS conference grant,
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helped support that and pay for particularly early career faculty to be
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able to attend the conference. Yeah, to Carrie's point, one of the big things
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that DMS funds is the, let me see if I can get the acronym right, is the
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Mathematical Sciences Infrastructure Program or something similar, which funds
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these big math institutes. So IMSI is one of them, right? And then there's AIM in
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Pasadena and ICERM at Brown University. And I think they also fund IAS at
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Princeton and one more, no, two more.
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CDZ: Ipam UCLA.
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DL: Yeah, and then the one in
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Minnesota, Minneapolis.
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JH: No, they don't fund that anymore.
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CDZ: SL Math is the one you're missing, I think.
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DL: There we go.
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SH: Yes, SL Math is the other main one, but they do still partially fund
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the Minnesota one. It's just not one of the big six Mathematical Sciences
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Research Institutes. Not to be confused with the Mathematical Science Research
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Institute, which is now SL Math.
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DL: Yeah, so these institutes are great because
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they host, in addition to shorter conferences and things, they also have
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longer programs and collaborations. So for instance, all three of us were
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part of one of those at IMSI a few years ago, where we all got to go to IMSI for a
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month and collaborate together in person. But they also do semester long or even
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year-long kind of collaborative programs where folks who are on sabbaticals can
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come and
really intensively work on these projects for an extended period of time.
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And this serves as a great kind of, I want to say incubator. That's not quite
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the right word, but maybe catalyst is the right word to really make significant
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progress on these hard problems and provide that time and space for people
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to think really deeply about these kinds of things.
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SH: And Jude, you mentioned Q-side,
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probably moving a little bit away from from federal funding right now. So could
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you talk a little bit about what the other options are? Because there are
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private funders. The Simons is the big one that pops into my head, but I mean
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Kavli and some of the others also do provide funding. So what's sort of
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different when you exit the federal and go to the private funders?
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JH: Well, there's also a missing link in there, which is state funding. And there's a
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huge amount of opportunity there. And I think sometimes folks forget that there
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are state and local grants. And so we've actually been wrapped into a lot of work
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around state-level grants. We're doing a ton of proposal work with the
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collaborative in the state of California around the Racial Justice Act and
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around some of that work that's coming out of the state. And then we've also got
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a proposal in with the state of Pennsylvania. So that's something that I
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do think a lot of folks don't even think about, right, is the state-level
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grants and those hurdles that I mentioned earlier being so onerous are
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much lower in those spaces. So that's something to think about. You know,
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primarily where we have looked for funding is to private foundations
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historically. And I think for nonprofits, that tends to be the place that they tend
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to look for state grants and for foundation grants. And so yeah, you've
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got, I mean, there's a ton of them for the kind of work that we do at QSide,
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even some of the traditional large funding foundations aren't really
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appropriate. So the places we tend to look are to mission-aligned foundations,
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right? So the big ones that do work related to like criminal illegal
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system reform, like the Arnold Foundation or Arnold Ventures, we've been
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funded pretty significantly historically from the Walmart Foundation. We've had
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funding from the Bloomberg Foundation. And I would say the difference, in my
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experience, between those kinds of funding is that my experience is that the
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foundation grants tend to be a lot more relationship driven. Like I sort of was
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invited into the Walmart Foundation criminal justice reform network where I
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participated and sort of got brought in to do some consulting and QSide for
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smaller scale things for two years before that we were sort of considered
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ready to be eligible to sort of be actually an independently funded
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organization from the foundation. That's not everyone's experience, but I do
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think that it is very relationship driven and sort of understanding the
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portfolio of the foundation and their theory of change. Like they tend to
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have a kind of a philosophy around the kind of work that they want to see done.
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And that can be scientific in nature or it can be supported in our case by data
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science tools and techniques. But it's just a different mission, right?
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And it's a different way of thinking about funding, thinking about
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cultivating relationships around that in those spaces. So that's been my
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experience. And I know Carrie also has a lot of experience in the foundation space.
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So I'd love to hear if anything I said rings true with her.
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CDZ: Yeah, no, 100%. I think you kind of get engaged in a process. And instead of the
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whole time you being like, I'm putting in all this work and I'm not sure if it's
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going anywhere, that certainty increases as the relationship builds. So at some
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point you decide, hey, like, I think we should keep in each other's circles, but
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I don't think we're quite aligned at this moment. Or you just continue building
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that relationship and that funding and then you have a pretty good relationship
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and that can get leveraged long term. But there are some foundations that
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still operate in a more like NSF-y kind of way. Like there are some that do
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have just open calls and lots of proposals and panel reviews as well and
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are highly competitive. But I think the ones I have really actually very much
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enjoyed sort of making a
relationship with my program officer at the Hewlett
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Foundation. And they operate with a philosophy that's very different from
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accounting for federal funds, which the back end of how you get your money and
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how it's dispersed and accounted for is very specific with federal funding.
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And there are some movements within philanthropy that are more relational
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and trust based that say we will give you some money for general operating costs
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and we know that you're going to use this money the way you need to use it when
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the time is called. So from my perspective in this moment it helps me to
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be able to quickly pivot my organization's actions based on what's going
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on in the world in a way that might be very difficult under another funding
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source.
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SH: Well if any foundation program officers are listening and you think
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that it's mission aligned for you to help spread mathematical stories, I am
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always looking for money. So one thing I do want to talk a little bit about is
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I mean there's this perception of mathematics, specifically theoretical
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mathematics, that it is one of those areas that's really nice because all you
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need is a writing utensil and something to write on and probably some books to
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read. And while I'm enough of a mathematician to know that that is not
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true, it is fundamentally different than some of the physical
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sciences or things like that where you say you are in physics, you're in
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high energy physics, you need a collider and that is not something that
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you can do easily and math can be a little bit easier to do without
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all those extra things. So I was digging into how different
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areas are funded federally. I chose federally because it's just a lot easier
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to get the numbers than to look at foundations. And so as I said before,
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math gets 64% from the National Science Foundation whereas across all of the
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science and engineering, medical, technology, the STEM fields, the average is
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27% from the NSF. So you can already see that math is getting
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more from the NSF than a lot of other fields. There are social sciences and
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psychology get about 68%. Biology but only basic biological research, so not
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applied biological research, so we're not including say
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medical fields in this, get 69% and computer science, again just basic
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computer science, so theoretical computer science gets 83%, but that's also a
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very small amount of the computer science research that happens.
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And then looking at the total like dollar amounts, and I found this
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wonderful breakdown that the NSF published and this is across the
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across the whole federal government. We give about,
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at least last year, we get there was about 45 billion dollars worth of funding.
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So we're not we're not talking about a small amount of funding, but
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in comparison with the federal budget, we're talking about
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a very small amount of money because that's in the
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trillions of dollars, so we're talking orders of magnitude.
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And there computer science got 2.3 billion,
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geosciences got 3.4 billion, life sciences got 19 billion, physics got
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9.5 billion, psychology 2.29 billion, engineering 3.8
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billion, social sciences only 766 million, and math and stats 608 million
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dollars. So there is another thing for other,
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which was 3.7 billion, but these are going to be smaller fields, which will get
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less money than math. So math is of all of the large fields,
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the least amount of money. Is anyone surprised?
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DL:No
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CDZ: No, I mean, and I think I think part of the reason is exactly what you said.
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Historically, hasn't required a lot of large equipment and costs and purchases.
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And so the vast majority of of math funding is about like
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conferences and collaborations and travel, and
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and that's why the math institutes are this overly large disproportionate
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amount of money that sort of acts as these mini hubs for a lot of that.
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And otherwise, equipment costs have been traditionally a lot lower.
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So I'm not surprised, but I think it is for the audience, it may be an
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interesting observation that we are maybe the lowest funding,
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but we also have a high dependence on federal funds.
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I think with the exception of folks who are actually, or
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mathematicians, getting
funded under biology like me,
00:18:23.800 --> 00:18:27.800
or something like that, a lot of this is really
00:18:27.800 --> 00:18:32.680
made possible just with federal monies, and there are fewer foundations out
00:18:32.680 --> 00:18:36.360
there and fewer different kinds of federal agencies that
00:18:36.360 --> 00:18:40.040
are supporting this work. Unlike, biology might also be supported
00:18:40.040 --> 00:18:44.280
by EPA or something. The landscape is shifting across all
00:18:44.280 --> 00:18:51.160
of those granting agencies, which is I think what we're getting to.
00:18:51.160 --> 00:18:55.800
JH: I think the only thing I would add is that
00:18:55.800 --> 00:19:01.000
one of the reasons why we as a mission-driven and applied,
00:19:01.000 --> 00:19:04.920
we're really a data science nonprofit, right? With the
00:19:04.920 --> 00:19:08.440
like the shorthand for QSide is data science plus social justice, right?
00:19:08.440 --> 00:19:12.680
That's like social justice problems examined and tried
00:19:12.680 --> 00:19:17.400
to solve social justice challenges through innovative uses and partnerships
00:19:17.400 --> 00:19:21.720
of using data science in that space. We have never found a home
00:19:21.720 --> 00:19:26.840
anywhere in the federal ecosystem for anything related to that,
00:19:26.840 --> 00:19:30.840
but my husband and I who co-founded QSide are, we are both scientists at our core.
00:19:30.840 --> 00:19:34.680
I'm a psychologist, my doctorate is in psychology, his is in applied math.
00:19:34.680 --> 00:19:38.920
Even in the applied math field, the true mathematicians in the group,
00:19:38.920 --> 00:19:43.800
correct me if I'm wrong, it can be even for sort of like more traditional
00:19:43.800 --> 00:19:47.640
applied math work. It's a heavier hill to climb in some
00:19:47.640 --> 00:19:54.280
ways in the federal ecosystem. Hyper-applied mission-driven work
00:19:54.280 --> 00:19:58.440
of using tools and using innovative tools or developing
00:19:58.440 --> 00:20:02.840
innovative mathematical tools that can sort of be used in social science
00:20:02.840 --> 00:20:07.400
contexts. We have yet to find a home or a kind of
00:20:07.400 --> 00:20:11.480
sympathetic landing spot for that within the federal ecosystem.
00:20:11.480 --> 00:20:15.080
We tried and sometimes we get subsumed in
00:20:15.080 --> 00:20:20.040
other types of grants like social science grants or something as
00:20:20.040 --> 00:20:24.520
kind of the resident quat nerds and to kind of bolster that,
00:20:24.520 --> 00:20:28.680
but it hasn't, we haven't really found a spot for this work
00:20:28.680 --> 00:20:32.520
within that landscape.
00:20:28.680 --> 00:20:32.520
SH: One thing that really strikes me
00:20:32.520 --> 00:20:37.800
when I'm thinking about all of this is that
00:20:37.800 --> 00:20:43.320
math, no and I don't want to get on, I don't want to get all soap boaxy, I also
00:20:43.320 --> 00:20:46.920
don't want to say things that are
00:20:46.920 --> 00:20:53.720
truthy and not truthful, but it really isn't that big of a stretch to say
00:20:53.720 --> 00:20:58.280
that mathematics is the thing that underpins most of this
00:20:58.280 --> 00:21:01.000
science at some level. Sometimes you have to dig
00:21:01.000 --> 00:21:05.240
really, really far down to get to it, but at some point there needs to be
00:21:05.240 --> 00:21:09.000
some math involved in most of this as long as there's any
00:21:09.000 --> 00:21:17.240
you know quantifiable research going on. And yet I've always felt a certain
00:21:17.240 --> 00:21:22.040
idea that a lot of people have that math is just sort of done,
00:21:22.040 --> 00:21:26.520
that it's this thing that exists and we have the math.
00:21:26.520 --> 00:21:31.000
And I worry about that some when we're thinking about
00:21:31.000 --> 00:21:37.160
funding in particular because as our university systems continue to move
00:21:37.160 --> 00:21:40.440
more and more in the corporatized direction that they
00:21:40.440 --> 00:21:45.000
have continually done under neoliberal leadership,
00:21:45.000 --> 00:21:51.080
I worry that without the funding with you know just being viewed as this
00:21:51.080 --> 00:21:56.760
thing that's sort of complete that math will atrophy a little bit.
00:21:56.760 --> 00:22:00.280
CDZ: So let me let me put a slightly different thing on it.
00:22:00.280 --> 00:22:04.040
I mean I think that's certainly something the students are taught in the
00:22:04.040 --> 00:22:07.800
classroom is like here are the rules apply the rules like math
00:22:07.800 --> 00:22:11.000
this has always been for 200 years in calculus. I mean I
00:22:11.000 --> 00:22:14.360
you know that is certainly a thing where we're teaching students actively
00:22:14.360 --> 00:22:17.240
whether or not we should do that is maybe a different podcast.
00:22:17.240 --> 00:22:24.920
But I think one of the things that I have been
00:22:24.920 --> 00:22:29.400
I wouldn't say struggling with but reprocessing I mean I think is the word
00:22:29.400 --> 00:22:34.120
to use is that traditionally DMS funding, mathematical funding
00:22:34.120 --> 00:22:39.400
direct from like the federal agencies has focused on
00:22:39.400 --> 00:22:44.280
mathematical innovation and not application. And when you go to
00:22:44.280 --> 00:22:48.200
application then you seek you know funding from the other divisions from
00:22:48.200 --> 00:22:53.000
the other places. While this is great for mathematical
00:22:53.000 --> 00:23:00.680
development I think it is part of the how we have
00:23:00.680 --> 00:23:05.000
not cultivated the relationship we need to have with our ultimate program
00:23:05.000 --> 00:23:11.960
officers which are the public. And I think that this is a huge problem
00:23:11.960 --> 00:23:16.520
how much are mathematicians the folks who are spotlighted
00:23:16.520 --> 00:23:20.600
when breakthroughs are made in all of these other areas right.
00:23:20.600 -->
00:23:24.360
You know if you're not supporting the applied work the work that's in the
00:23:24.360 --> 00:23:29.080
community because it's not innovation in
00:23:29.080 --> 00:23:35.400
in a traditional sense and is more about synthesis and border crossing
00:23:35.400 --> 00:23:39.400
from from the ivory tower to the public and to the community
00:23:39.400 --> 00:23:42.840
all of a sudden now you've missed this whole opportunity
00:23:42.840 --> 00:23:49.720
to tell people or engage people like with the joy of and the the excitement
00:23:49.720 --> 00:23:53.320
and the innovations and the insights and the power that you get from these
00:23:53.320 --> 00:23:57.160
techniques right. You've lost the right to claim it
00:23:57.160 --> 00:24:01.000
now you know that you're relevant and important to society.
00:24:01.000 --> 00:24:06.920
So I want to really appreciate how DMS funding has been used to really make
00:24:06.920 --> 00:24:11.240
sure that we are ahead on the innovation side that we're contributing to like
00:24:11.240 --> 00:24:14.600
thinking about like new complex problems before there are
00:24:14.600 --> 00:24:18.040
problem right. Just like the underpinnings of
00:24:18.040 --> 00:24:23.480
theory mathematical theory but I want to say at the same time I
00:24:23.480 --> 00:24:29.640
think we have created our own problem with who we're seen as and
00:24:29.640 --> 00:24:33.960
who we're seen for by the rest of the public.
00:24:29.640 --> 00:24:33.960
SH: Yes
00:24:33.960 --> 00:24:38.200
100 percent I mean that's that is part of why I do the work I do right like I
00:24:38.200 --> 00:24:42.440
want to tell these mathematical stories. It's one of my favorite things to do
00:24:42.440 --> 00:24:46.520
even though I do prefer telling theoretical stories that applied stories
00:24:46.520 --> 00:24:50.120
but that's just because that's the area of math that I always
00:24:50.120 --> 00:24:53.720
gravitated towards myself. The applied stories are tend to be much more
00:24:53.720 --> 00:24:57.640
enjoyable for everyone listening though.
00:24:57.640 --> 00:25:01.880
So now now as as Jude previously indicated let's
00:25:01.880 --> 00:25:05.960
let's move to today. Well actually I'm going to send us back to about October
00:25:05.960 --> 00:25:09.400
2024. Like I mean there's a long history that we
00:25:09.400 --> 00:25:15.240
could be talking about around different political parties views on
00:25:15.240 --> 00:25:18.920
scientific funding but we're not going to dig all the way into that because
00:25:18.920 --> 00:25:23.960
this podcast would be 45 hours long. So instead Drew
00:25:23.960 --> 00:25:30.280
can you tell me about the October 2024 commerce report from Senator
00:25:30.280 --> 00:25:37.000
Cruz?
00:25:30.280 --> 00:25:37.000
DL: Yeah so in October the the Senate
00:25:37.000 --> 00:25:41.640
Commerce Committee headed by Ted Cruz released a report.
00:25:41.640 --> 00:25:44.840
I forget the the title off the top of my head but
00:25:44.840 --> 00:25:49.080
essentially they went through the portfolio of NSF
00:25:49.080 --> 00:25:54.440
funding looking for keywords in a list. So any grant that used words like
00:25:54.440 --> 00:25:58.520
diversity, equity, inclusion, belonging,
00:25:58.520 --> 00:26:03.800
identity characteristics like black or latine and
00:26:03.800 --> 00:26:07.080
cooked up this database of all these grants that use these words. They did
00:26:07.080 --> 00:26:10.520
into some manual checking. They thought that black holes were
00:26:10.520 --> 00:26:14.200
important right so they took those off of the list right but
00:26:14.200 --> 00:26:16.520
you know if you're talking about supporting black people then that went
00:26:16.520 --> 00:26:20.040
on their list. And so they released this database of
00:26:20.040 --> 00:26:23.720
grants to target. I guess I should say they only very
00:26:23.720 --> 00:26:28.120
recently released a full database. They put out a report in October
00:26:28.120 --> 00:26:31.960
and where they kind of cherry picked some particular grants on there
00:26:31.960 --> 00:26:35.000
and lambasted them in their usual kind of
00:26:35.000 --> 00:26:39.160
rhetoric about how terrible it is to actually include and support
00:26:39.160 --> 00:26:43.640
people of various identities.
00:26:39.160 --> 00:26:43.640
SH: Yes so and that that report
00:26:43.640 --> 00:26:50.520
highlighted about two billion dollars of quote unquote woke NSF grants
00:26:50.520 --> 00:26:53.560
and just just know whenever we're talking about
00:26:53.560 --> 00:26:57.560
woke today there's likely scare quotes around it
00:26:57.560 --> 00:27:02.200
because it is very very scary. And so that's
00:27:02.200 --> 00:27:06.840
I mean two billion that's about a quarter a little bit under a quarter of
00:27:06.840 --> 00:27:13.640
of the NSF's annual outlay. So that happens in October and then
00:27:13.640 --> 00:27:18.600
there's an election in November and in late January
00:27:18.600 --> 00:27:25.160
our current president jumps into office and on the first day does any well
00:27:25.160 --> 00:27:28.840
first first day and then some days after is anyone want to
00:27:28.840 --> 00:27:33.800
you know give a colorful description of some of the executive orders that came
00:27:33.800 --> 00:27:38.040
through.
00:27:33.800 --> 00:27:38.040
CDZ: Sorry I was just flipping to another
00:27:38.040 --> 00:27:41.640
page I had with this like gorgeous timeline I wish you could see
00:27:41.640 --> 00:27:47.880
like how beautiful it is. So the it had this timeline of events
00:27:47.880 --> 00:27:52.360
and January 20th to 24th executive orders targeting federal spending
00:27:52.360 --> 00:27:58.200
workforce DEI etc are issued and and so I it's nice because it has a whole
00:27:58.200 --> 00:28:03.000
layout of all well nice tragic whichever the
00:28:03.000 --> 00:28:10.840
correct word is to use and January 27th was the memo
00:28:10.840 --> 00:28:15.800
directing federal funding pauses. So there were kind of these waves
00:28:15.800 --> 00:28:19.080
either were the executive orders that were like you got to stop
00:28:19.080 --> 00:28:23.080
spending we're gonna we're gonna start cutting people and we're going to like
00:28:23.080 --> 00:28:28.200
do the Ted Cruz like like anti DEI on everything
00:28:28.200 --> 00:28:33.960
contractors spending on DEI etc and then separately from that but only
00:28:33.960 --> 00:28:37.800
put only a little bit later it we were they were going to
00:28:37.800 --> 00:28:42.120
operationalize all of this through these like reviews of every single grant
00:28:42.120 --> 00:28:46.520
they've given of every funding the federal funding agency etc
00:28:46.520 --> 00:28:50.200
and in order to do that they were saying don't spend any
00:28:50.200 --> 00:28:53.560
money at all and I think folks probably have heard
00:28:53.560 --> 00:28:57.320
you know a lot like USAID was like one of the
00:28:57.320 --> 00:29:01.240
agencies that got a lot of play in in our academic world the
00:29:01.240 --> 00:29:07.000
pauses of NSF and of NIH and all of these others
00:29:07.000 --> 00:29:10.120
then almost immediately there were legal challenges
00:29:10.120 --> 00:29:16.440
and within two days the memo was rescinded but at the same time
00:29:16.440 --> 00:29:23.480
that that memo was rescinded they were still freezing the funding
00:29:23.480 --> 00:29:26.840
so there was even more
00:29:23.480 --> 00:29:26.840
SH: And the press secretary
00:29:26.840 --> 00:29:31.080
literally said no we're still we're still freezing the funding like
00:29:31.080 --> 00:29:35.640
at a press conference
00:29:31.080 --> 00:29:35.640
CDZ: Exactly exactly and so they were like yeah we're not
00:29:35.640 --> 00:29:39.160
going to pay attention to this which you know so they were more judges being
00:29:39.160 --> 00:29:43.080
like no no you can't like you cannot freeze and and we're
00:29:43.080 --> 00:29:48.760
talking about folks who just you know different institutions and
00:29:48.760 --> 00:29:51.960
funding agencies also like interpreted these memos very very
00:29:51.960 --> 00:29:54.600
differently and had different ways of operating
00:29:54.600 --> 00:29:58.280
there was a variety of responses some were like business as usual
00:29:58.280 --> 00:30:01.640
let's play it out in the court some were like freaking out and they were like
00:30:01.640 --> 00:30:05.720
stop using all your grants and and some of those institutions had real on the
00:30:05.720 --> 00:30:08.840
ground impacts immediately like folks were
00:30:08.840 --> 00:30:12.600
fired folks were like you got you lost your job today because we don't
00:30:12.600 --> 00:30:16.120
we're getting reviewed we were on the Ted Cruz list or
00:30:16.120 --> 00:30:21.400
whatever and some grants for example DOE grants have been
00:30:21.400 --> 00:30:25.640
straight out canceled so let's say you had a grant that was supposed to last
00:30:25.640 --> 00:30:31.160
three more years too bad it's done like there have been ones like that
00:30:31.160 --> 00:30:35.320
NSF has been differently impacted so as far as I'm aware
00:30:35.320 --> 00:30:39.400
so far folks have not been able to renew grants
00:30:39.400 --> 00:30:44.920
and grants that were going to end have been denied the ability to ask for a no
00:30:44.920 --> 00:30:48.200
cost extension but grants that have been
00:30:48.200 --> 00:30:52.680
promised up to a particular point have not yet been canceled I'm saying yet
00:30:52.680 --> 00:30:56.680
then canceled because I feel like it was they were trying
00:30:56.680 --> 00:31:02.920
but yeah it has been kind of halted so this is kind of spanned
00:31:02.920 --> 00:31:06.680
everything and I think one of the difficult parts is
00:31:06.680 --> 00:31:10.120
the DEI attack was maybe one of the first waves
00:31:10.120 --> 00:31:14.040
and then there was the the freezing on grants and then there was the firing of
00:31:14.040 --> 00:31:18.120
staff and and now more staff firing and budget cut
00:31:18.120 --> 00:31:21.720
allotments so I worry a little bit that by
00:31:21.720 --> 00:31:25.960
focusing on the the funding and the freezes that were also like we forget to
00:31:25.960 --> 00:31:30.360
talk about the DEI part as well like is there a list of words that you're not
00:31:30.360 --> 00:31:32.840
allowed to say and also you know question the
00:31:32.840 --> 00:31:38.040
legality of that core because at least for NSF where math is
00:31:38.040 --> 00:31:41.320
highly where we've established math is highly dependent on
00:31:41.320 --> 00:31:46.920
for anybody who's reviewed it's two criteria intellectual merit and
00:31:46.920 --> 00:31:50.440
broadening participation that are legislated by congress
00:31:50.440 --> 00:31:54.600
so when you throw out DEI when you throw out
00:31:54.600 --> 00:31:58.920
specific actions that are meant to make sure that we
00:31:58.920 --> 00:32:03.800
are competitive because everyone who wants to have a STEM career can have a
00:32:03.800 --> 00:32:07.640
STEM career and that's done through DEI
00:32:07.640 --> 00:32:12.120
actions right if you have removed that ability then
00:32:12.120 --> 00:32:17.640
you're not actually you know executing the instructions of congress
00:32:17.640 --> 00:32:21.880
SH: Yeah that that is a really important and good point
00:32:21.880 --> 00:32:25.080
like this has been legislated broad broader impacts
00:32:25.080 --> 00:32:29.880
is in the actual law but the executive borders are
00:32:29.880 --> 00:32:33.320
apparently trying to limit how you how you do it and
00:32:33.320 --> 00:32:38.680
as people on blue sky because we're not going to talk about the hell site
00:32:38.680 --> 00:32:42.840
have been pointing out specifically as say Chanda Prescod-Weinstein like this
00:32:42.840 --> 00:32:49.640
is effectively a resegregation effort in in many ways trying to limit
00:32:49.640 --> 00:32:53.640
higher education and the other groups that need this sort of funding to
00:32:53.640 --> 00:32:58.280
include people who have historically had the power and privilege to take part
00:32:58.280 --> 00:33:01.640
in those groups and not trying to keep the door open
00:33:01.640 --> 00:33:04.920
I do want to talk a little bit about that list of words so
00:33:04.920 --> 00:33:08.760
the list of words that were being searched for was
00:33:08.760 --> 00:33:12.520
actually gathered and shared by Michael Harris who is a
00:33:12.520 --> 00:33:16.920
mathematician who has a newsletter called the silicon reckoner talking about
00:33:16.920 --> 00:33:21.000
mathematics and artificial intelligence and machine learning but also
00:33:21.000 --> 00:33:24.360
has been covering some of this topic as well
00:33:24.360 --> 00:33:29.080
and the other mathematicians here I I would love to hear from y'all
00:33:29.080 --> 00:33:33.960
what it what it means if mathematicians are not allowed to use
00:33:33.960 --> 00:33:40.600
words like inclusion inequalities some things like polarization are those
00:33:40.600 --> 00:33:44.440
words that might trigger math grants to be looked at
00:33:44.440 --> 00:33:48.200
weird but yet have nothing to do with quote unquote
00:33:48.200 --> 00:33:52.840
wokeness
00:33:48.200 --> 00:33:52.840
DL: I mean I think if you just do the the naive search through the grants
00:33:52.840 --> 00:33:56.440
you're gonna find a lot of math grants with those words but
00:33:56.440 --> 00:33:59.320
at some point I think it's a little bit of a distraction to think about that
00:33:59.320 --> 00:34:02.520
because we already saw with the Ted Cruz report right he went through black was
00:34:02.520 --> 00:34:06.280
one of the words in there right and specifically in this report he said oh
00:34:06.280 --> 00:34:09.800
if they're just talking about black holes that's okay so they want to support
00:34:09.800 --> 00:34:12.920
black holes they don't want to support black people so I think the same thing
00:34:12.920 --> 00:34:15.480
with the math grants they're gonna look and if you're talking about inclusions
00:34:15.480 --> 00:34:18.440
of sets right they're gonna say okay this is fine
00:34:18.440 --> 00:34:22.600
if you're talking about including students from underrepresented groups in
00:34:22.600 --> 00:34:28.680
your work that's gonna get flagged
00:34:22.600 --> 00:34:28.680
SH: So I uh I hope that you are right I think
00:34:28.680 --> 00:34:34.920
that you are right right now but something that I worry about here is
00:34:34.920 --> 00:34:39.240
this push that seems to be coming from our unelected
00:34:39.240 --> 00:34:42.840
apartheid Clyde with the black hat and the
00:34:42.840 --> 00:34:49.960
fun adult boy jumps Elon Musk and of this this push to
00:34:49.960 --> 00:34:55.240
fire everyone and I think start using LLMs for everything so large language
00:34:55.240 --> 00:34:59.240
models for pretty much everything and I I I do think that as long as there's
00:34:59.240 --> 00:35:03.000
humans looking at these things we don't have much to worry about what
00:35:03.000 --> 00:35:06.520
happens if there aren't humans to looking to look at them because
00:35:06.520 --> 00:35:11.720
they've already fired 168 people from NSF
00:35:12.200 --> 00:35:20.200
so that was a what about 20% of the workforce and there's talk of cutting
00:35:20.200 --> 00:35:26.680
hundreds more people of like up to a 50 to 60 percent total cut of of workforce
00:35:26.680 --> 00:35:32.360
staff not to mention the fact that the house budget proposal probably will
00:35:32.360 --> 00:35:37.560
cut 50% to 75% percent of the funding that NSF gives out in
00:35:37.560 --> 00:35:41.480
grants so what if humans no longer look at them
00:35:41.480 --> 00:35:45.000
JH: Yeah so there's well there's a really quick
00:35:45.000 --> 00:35:48.600
anecdote that I'll share that speaks to exactly what you're saying
00:35:48.600 --> 00:35:52.120
Sam from the first Trump administration where
00:35:52.120 --> 00:35:57.320
they went through and they they preferred the word homosexual to gay
00:35:57.320 --> 00:36:00.440
and so they did these massive final replacements this is something that a lot
00:36:00.440 --> 00:36:03.400
of people in the queer community remember but I think folks who are not in the
00:36:03.400 --> 00:36:07.240
queer community may not remember and so they did this like
00:36:07.240 --> 00:36:10.840
in this mass way like across all of these documents
00:36:10.840 --> 00:36:15.800
and there were people whose names were gay who were then renamed homosexual
00:36:15.800 --> 00:36:24.280
in the process and I think that we even even with the human cognitive
00:36:24.280 --> 00:36:28.520
component to it I think that's just illustrative of the
00:36:28.520 --> 00:36:32.680
problem so I I want to quibble with one thing you said that
00:36:32.680 --> 00:36:36.840
even if it is the former and not the latter it is still deeply problematic and
00:36:36.840 --> 00:36:41.160
troubling I don't think like we're safe but even if we accept that
00:36:41.160 --> 00:36:45.320
one is okay and like more okay once in a while is more okay than the other you
00:36:45.320 --> 00:36:50.120
know like we'll put it there I think that as someone who
00:36:50.120 --> 00:36:55.720
grew up on Don't Ask Don't Tell and grew up under the regime of it is
00:36:55.720 --> 00:37:00.440
not being a thing but it is being known as that
00:37:00.440 --> 00:37:04.120
thing and using those words and having the taste of those words in your mouth
00:37:04.120 --> 00:37:10.280
linguistics matter language matters and the outlying of words like diversity
00:37:10.280 --> 00:37:14.520
equity inclusion and belonging is inherently deeply problematic
00:37:14.520 --> 00:37:20.360
not just for the tactical kind of results of that but it is a problem
00:37:20.360 --> 00:37:25.720
right like we we have to resist that that impulse like it is a problem
00:37:25.720 --> 00:37:30.360
globally to have people telling us what we can say and what we can't
00:37:30.360 --> 00:37:35.640
in those kind of ways so I'll get now I'll get down off my soapbox and
00:37:35.640 --> 00:37:40.120
relinquish the floor
00:37:35.640 --> 00:37:40.120
SH: It's it's a good soapbox and it's one I'm glad that
00:37:40.120 --> 00:37:43.320
glad that you reminded me of because I was I've been thinking so much about the
00:37:43.320 --> 00:37:47.640
funding over the last couple days I forgot that on day one
00:37:47.640 --> 00:37:52.040
he signed an executive order that removed my gender from the
00:37:52.040 --> 00:37:56.280
the legal framework of the United States which I'm gonna tell you felt
00:37:56.280 --> 00:38:01.480
felt real good that was a that was a very very positive moment for me
00:38:01.480 --> 00:38:05.160
so I'd love to talk a little bit more about the importance of program
00:38:05.160 --> 00:38:08.840
officers so we have we have already discussed them a bit
00:38:08.840 --> 00:38:12.200
we've discussed them both you know we've mentioned them on the federal and the
00:38:12.200 --> 00:38:17.480
philanthropy side so for those of you who dealt with them like
00:38:17.480 --> 00:38:21.720
what like what is the importance of a of a program officer like what does it
00:38:21.720 --> 00:38:25.880
mean that we might lose a huge amount of them I think it's really gonna
00:38:25.880 --> 00:38:31.240
DL: impact the quality of the funded projects we see so
00:38:31.240 --> 00:38:35.480
Carrie was kind of describing earlier kind of how the you know NSF process
00:38:35.480 --> 00:38:38.520
works but I think something that was kind of
00:38:38.520 --> 00:38:41.480
skipped over there is that it's not just a black box you don't just like write a
00:38:41.480 --> 00:38:44.760
proposal and then you send it in and then it comes back later
00:38:44.760 --> 00:38:49.320
right so most successful proposals there are several conversations with the
00:38:49.320 --> 00:38:52.360
program officers along the way you send them a one or two page
00:38:52.360 --> 00:38:55.560
summary you have a conversation Carrie was mentioning there's all these
00:38:55.560 --> 00:38:58.200
different programs that fund different sorts of things and the program
00:38:58.200 --> 00:39:02.280
officers help you figure out what program fits what you want to do and
00:39:02.280 --> 00:39:05.720
do you align what you want to do with the goals of the program
00:39:05.720 --> 00:39:11.640
so I think that is a huge part of what program officers do in that they help
00:39:11.640 --> 00:39:15.320
make the science and the math better
00:39:11.640 --> 00:39:15.320
CDZ: yeah no I was going to say also and not
00:39:15.320 --> 00:39:18.760
just at the proposal stage but they also travel to
00:39:18.760 --> 00:39:23.960
conferences there are also where people are and they might see a really good
00:39:23.960 --> 00:39:26.920
you know talk and say like hey like have you ever thought applying to our
00:39:26.920 --> 00:39:31.000
program or could you be a reviewer and get people involved in the system and
00:39:31.000 --> 00:39:35.160
and help to understand it I was recently at the AAAS
00:39:35.160 --> 00:39:38.280
conference which is the American Association for the Advancement of
00:39:38.280 --> 00:39:42.280
Science which is a DC policy broadly STEM professional
00:39:42.280 --> 00:39:45.320
society and I mean I was like wow there are a lot
00:39:45.320 --> 00:39:48.040
of booths missing because all the federal booths are gone like
00:39:48.040 --> 00:39:51.880
there's no you know if you did happen to have some
00:39:51.880 --> 00:39:56.040
sort of affiliation with NSF like that's not the hat you were wearing like so
00:39:56.040 --> 00:40:01.800
you couldn't tell I noticed that deeply the the absence of all
00:40:01.800 --> 00:40:07.480
sorts of federal agencies missing the agencies that
00:40:07.480 --> 00:40:11.160
could be talking to students about hey like you know here are some
00:40:11.160 --> 00:40:15.320
options for funding graduate school or might be talking to faculty about
00:40:15.320 --> 00:40:19.080
hey did you know that we have this new program in
00:40:19.080 --> 00:40:23.000
I don't know teaching AI and mathematics courses I don't know but like
00:40:23.000 --> 00:40:27.640
whatever the thing is they just weren't there anymore
00:40:23.000 --> 00:40:27.640
DL I think it's interesting
00:40:27.640 --> 00:40:31.800
so the the RUME conference the research and undergraduate math education
00:40:31.800 --> 00:40:35.880
conference is happening I think right now as we speak
00:40:35.880 --> 00:40:39.400
like a quarter of a mile from the NSF headquarters
00:40:39.400 --> 00:40:43.000
and the program officers were because of the travel ban right we're not allowed
00:40:43.000 --> 00:40:47.560
to walk down the street to the conference
00:40:47.560 --> 00:40:50.360
SH: one of the one of the other things that program officers do
00:40:50.360 --> 00:40:55.480
is that they help make sure that the review process
00:40:55.480 --> 00:41:00.200
keeps going and they you know help as Carrie mentioned find people to review
00:41:00.200 --> 00:41:02.440
they also make sure that all the meetings happen and that all the
00:41:02.440 --> 00:41:05.880
decisions are meeting their deadlines and things and
00:41:05.880 --> 00:41:09.240
one of the things that I really don't want us to
00:41:09.240 --> 00:41:13.800
miss out on is the idea that the current administration could essentially
00:41:13.800 --> 00:41:18.440
stop federal funding not by not spending it not by completely
00:41:18.440 --> 00:41:21.640
shuttering the agencies or anything like that you know such as they did with
00:41:21.640 --> 00:41:25.880
USAID they could effectively stop it by just
00:41:25.880 --> 00:41:30.040
making it so that there's so few employees that nothing can actually get
00:41:30.040 --> 00:41:34.840
done so they could claim look
we're trying to spend the money
00:41:34.840 --> 00:41:37.640
but if we don't have enough program officers to actually
00:41:37.640 --> 00:41:42.680
oversee all of the review processes and all of the grant applications and
00:41:42.680 --> 00:41:45.960
things it could be that some grants get given but
00:41:45.960 --> 00:41:49.960
you know it's a couple a year instead of the thousands now
00:41:49.960 --> 00:41:56.200
CDZ: yeah well even even the whole you know firing before they
00:41:56.200 --> 00:42:03.880
fired 10 of their staff they they were asked to review all I don't I
00:42:03.880 --> 00:42:07.560
can't I heard some number I think it was like 50 000 proposals that they
00:42:07.560 --> 00:42:10.760
have funded already so already they're like where are we going to
00:42:10.760 --> 00:42:15.000
find the time to do that right and then on top of that you fire 10
00:42:15.000 --> 00:42:18.840
percent of your staff right and and and many who I've talked to say
00:42:18.840 --> 00:42:24.040
that's just the first round right so you're actually creating extra work
00:42:24.040 --> 00:42:32.040
not efficient work and so you know I think I think that I have heard
00:42:32.040 --> 00:42:35.400
suggestions about using things like generative AI
00:42:35.400 --> 00:42:40.200
large language models to step in and fill these roles as if
00:42:40.200 --> 00:42:46.680
you know the quality of the mostly but not all a lot of PhDs
00:42:46.680 --> 00:42:51.640
is somehow can be replaced by a generative AI I think it shows
00:42:51.640 --> 00:42:58.440
a general disrespect for what program officers actually do right and
00:42:58.440 --> 00:43:03.320
and I think like I have heard some folks chime in with critiques about NSF
00:43:03.320 --> 00:43:07.240
and I'm like now is that like I get it there are always things that can be
00:43:07.240 --> 00:43:11.800
improved but like now's the time to like go on the other side and say like
00:43:11.800 --> 00:43:16.200
yes we're we critique because it's an important thing in our lives that we
00:43:16.200 --> 00:43:20.840
want to get better and and sometimes we need to also fight for it too
00:43:20.840 --> 00:43:24.600
right and don't forget that at the same time
00:43:20.840 --> 00:43:24.600
JH: I would
00:43:24.600 --> 00:43:28.040
totally agree with you Carrie and I think I would
00:43:28.040 --> 00:43:32.120
rather than saying I don't even know that it gets to
00:43:32.120 --> 00:43:36.360
the level of disrespect because I think to garner that
00:43:36.360 --> 00:43:41.320
type of language requires an understanding of something right and I
00:43:41.320 --> 00:43:44.920
think it's actually ignorance fueled contempt
00:43:44.920 --> 00:43:49.480
is is sort of how I would phrase it I I think it's like folks
00:43:49.480 --> 00:43:53.640
literally do not understand what is happening
00:43:53.640 --> 00:43:57.320
I think part of the disconnect Carrie
00:43:57.320 --> 00:44:02.200
sort of touched on earlier in the conversation but I do think that
00:44:02.200 --> 00:44:09.400
it is sort of a multi decades driven philosophy
00:44:09.400 --> 00:44:14.920
that government is inherently wrong, government it is inherently bad, anything
00:44:14.920 --> 00:44:18.760
funded by the government is inherently wasteful
00:44:18.760 --> 00:44:25.880
and that is the sort of like the noble truth that undergirds all of this
00:44:25.880 --> 00:44:30.200
and all the actions flow from there so it's like if that is the foundational
00:44:30.200 --> 00:44:34.840
sort of cosmological philosophical belief that you have
00:44:34.840 --> 00:44:39.240
anything is justified at that point and you know this is where we get into the
00:44:39.240 --> 00:44:42.680
scenarios that go far beyond the NSF where it's like oh yeah
00:44:42.680 --> 00:44:46.760
oh those folks were keeping our nukes safe oh yeah those folks were like helping
00:44:46.760 --> 00:44:50.840
us respond to make sure Ebola or the bubonic plague doesn't end up in
00:44:50.840 --> 00:44:53.640
the you know but you don't understand that that's
00:44:53.640 --> 00:44:56.360
actually a function of government because it is sort of
00:44:56.360 --> 00:44:59.800
ignorance, fueled contempt of everything governmental
00:44:59.800 --> 00:45:03.480
CDZ: yeah and and when it works you don't know it's there
00:45:03.480 --> 00:45:08.760
right like unfortunately
00:45:03.480 --> 00:45:08.760
JH: it's odorless and flavorless yeah yeah
00:45:03.480 --> 00:45:08.760
CDZ: yeah yeah I think
00:45:08.760 --> 00:45:11.960
we saw this we were seeing this also one of the other
00:45:11.960 --> 00:45:15.800
orders had to do with trying to cut the overhead to 15 percent and not really
00:45:15.800 --> 00:45:21.400
understanding what that actually means and and if you look at some of the DEI
00:45:21.400 --> 00:45:26.200
memorandums or memos or whatever kinds of things that have been sent
00:45:26.200 --> 00:45:30.040
they try to position DEI in particular as like
00:45:30.040 --> 00:45:35.560
taking away from low-income white people right like they see it as a taking away
00:45:35.560 --> 00:45:39.080
and and so what I think is really interesting is some folks made this
00:45:39.080 --> 00:45:43.640
really nice map of NIH funding where you can go and look to see top NIH
00:45:43.640 --> 00:45:47.320
funders in your state and it turns out that for Maine
00:45:47.320 --> 00:45:54.680
on that list of top three are two biological laboratories that are
00:45:54.680 --> 00:46:00.920
I would say economic powerhouses of our Republican Second District in a rural
00:46:00.920 --> 00:46:06.760
area that and we are talking about millions would be taken away from them
00:46:06.760 --> 00:46:12.600
by cutting the NIH overheads to 15 percent millions direct to them and
00:46:12.600 --> 00:46:16.920
multiply by at least a factor of two if not more
00:46:16.920 --> 00:46:24.200
for the impact to the local area and we're talking about jobs in
00:46:24.200 -->
00:46:28.520
what is like sometimes a touristy area that are year-round and are really hard
00:46:28.520 --> 00:46:33.800
to come by this would this would be devastating
00:46:33.800 --> 00:46:40.520
for rural low-income communities the same thing with DEI directly
00:46:40.520 --> 00:46:44.920
one of the things when I was an external visitor which is like an evaluator
00:46:44.920 --> 00:46:50.680
for a division program for IU's one of the things we recommended was to
00:46:50.680 --> 00:46:54.200
reinvest in EPSCOR states these are rural
00:46:54.200 --> 00:47:00.280
low-funded states exactly the kind of demographics that I think
00:47:00.280 --> 00:47:03.320
they are thinking are being left behind and so
00:47:03.320 --> 00:47:07.880
I think you know we need to spend a little bit of time talking about
00:47:07.880 --> 00:47:12.440
how these things are going to directly impact
00:47:12.440 --> 00:47:19.800
and take away from everyone as well
00:47:12.440 --> 00:47:19.800
SH: And so while the NSF money seems to still be
00:47:19.800 --> 00:47:24.760
flowing mostly there's probably some one-offs that we don't know about but
00:47:24.760 --> 00:47:29.320
seems to mostly be flowing there there are cases where it is already having
00:47:29.320 --> 00:47:34.120
direct impact and one of those is on the research
00:47:34.120 --> 00:47:38.600
experience for undergraduates area which is something that as far as I know
00:47:38.600 --> 00:47:41.640
actually started in mathematics it's definitely something that's very
00:47:41.640 --> 00:47:45.080
strong in mathematics and these are summer programs that
00:47:45.080 --> 00:47:49.480
undergraduate students get to go to and experience
00:47:49.480 --> 00:47:53.880
like doing mathematical research which is and I will say this from my
00:47:53.880 --> 00:47:57.800
personal experience not something that I knew anything about until
00:47:57.800 --> 00:48:02.280
after I had already been in grad school for a while
00:48:02.280 --> 00:48:05.640
it's not even like you start math grad school and you start in classes like you
00:48:05.640 --> 00:48:08.840
don't this is not this is not like biology or physics or
00:48:08.840 --> 00:48:11.720
something like that where there's labs that you go into right away and
00:48:11.720 --> 00:48:18.120
oftentimes you're already in in undergrad times so I would love to
00:48:18.120 --> 00:48:22.040
know if either of you have have had any experience with r.e.u.'s and
00:48:22.040 --> 00:48:26.520
what it could mean if these r.e.u.'s keep shuddering and what that could mean for
00:48:26.520 --> 00:48:30.600
mathematics and and also like the bigger picture of what it could mean
00:48:30.600 --> 00:48:35.000
beyond just our our parochial little area of academia
00:48:35.000 --> 00:48:38.200
DL: I wouldn't be talking to you today if it wasn't for an r.e.u.
00:48:38.200 --> 00:48:42.280
I wouldn't I wouldn't be a mathematician if it wasn't for an r.e.u. so when I was
00:48:42.280 --> 00:48:45.800
when I was an undergrad I was a computer science major and I was
00:48:45.800 --> 00:48:49.800
I did a math major too just for funsies to make myself a little different
00:48:49.800 --> 00:48:54.120
but one of the one of the elder math majors shared about what they did in a
00:48:54.120 --> 00:48:57.320
r.e.u. experience this summer before and I thought this was amazing that you
00:48:57.320 --> 00:49:01.720
could go get paid to do math so I went and did one of these and
00:49:01.720 --> 00:49:05.480
learned what math research was and then immediately decided I'm not going to go
00:49:05.480 --> 00:49:09.320
be a programmer bro for the rest of my life I'm going to apply to grad school
00:49:09.320 --> 00:49:14.600
in math and I don't think that's a unique story I
00:49:14.600 --> 00:49:18.680
I shared this on blue sky the other day and a bunch of people are
00:49:18.680 --> 00:49:22.920
applying yes me too I did a r.e.u. I would not be a scientist today if I had
00:49:22.920 --> 00:49:27.960
not had this r.e.u. experience back at back at the time
00:49:27.960 --> 00:49:31.880
and we're seeing so many of these are being canceled this summer because of a
00:49:31.880 --> 00:49:35.320
lack of funding and I think what's really crucial about the r.e.u.'s
00:49:35.320 --> 00:49:40.280
is they happen for students at a very particular time
00:49:40.280 --> 00:49:45.400
in their preparation career right you do these in the summer
00:49:45.400 --> 00:49:49.720
while you're an undergraduate and that often is the catalyst to get
00:49:49.720 --> 00:49:53.640
undergraduate students to think oh I could go get a PhD I could be a
00:49:53.640 --> 00:49:57.080
scientist and when you take that away at that critical time
00:49:57.080 --> 00:50:00.920
right they're never even going to think that they have this opportunity to
00:50:00.920 --> 00:50:04.120
come do that and so it's just going to be an incredible loss
00:50:04.120 --> 00:50:08.680
for us you know 10 20 years down the road when these would have been
00:50:08.680 --> 00:50:12.760
scientists with PhDs doing the the cutting-edge you know math and science
00:50:12.760 --> 00:50:17.000
research that we want to happen
00:50:12.760 --> 00:50:17.000
CDZ: Yeah back when I was talking about
00:50:17.000 --> 00:50:23.720
program officers at meetings like I was at a meeting an NIH program officer
00:50:23.720 --> 00:50:27.080
was there saying hey like did you know that if your
00:50:27.080 --> 00:50:31.000
advisor has NIH money he can write for a supplement
00:50:31.000 --> 00:50:38.040
to help hire you so that you can spend a semester or a year or however much time
00:50:38.040 --> 00:50:42.920
working on your dissertation and not have to teach and I was like what
00:50:42.920 --> 00:50:46.120
so I went and told my advisor you can hire me
00:50:46.120 --> 00:50:52.680
and he's like I can like like if I if that program officer hadn't been there
00:50:52.680 --> 00:50:56.360
you know I mean how much I
did not need extra years
00:50:56.360 --> 00:51:00.840
believe me to finish my PhD I don't know if I would have finished
00:51:00.840 --> 00:51:07.800
um so yeah these are I think REU's I think there's um I've also seen it in my
00:51:07.800 --> 00:51:11.240
own students grad school admissions are down
00:51:11.240 --> 00:51:16.040
particularly institutions that have that rely on research funds
00:51:16.040 --> 00:51:21.960
to do this I think for some places there is a little bit more going on there
00:51:21.960 --> 00:51:25.320
was renegotiation of union contracts that were already
00:51:25.320 --> 00:51:28.920
raising wages on this teaching and research assistant side
00:51:28.920 --> 00:51:33.160
and then when coupled with all of this uncertainty about federal funding
00:51:33.160 --> 00:51:37.000
it's just like cause some some some places are pausing
00:51:37.000 --> 00:51:42.280
entire graduate enrollments and so I think it is
00:51:42.280 --> 00:51:45.880
going to have impacts for a long long time
00:51:45.880 --> 00:51:52.200
beyond the immediate and what I will say is that even though we are
00:51:52.200 --> 00:51:57.320
talking primarily about federal funding the private sector has not remained
00:51:57.320 --> 00:52:00.840
unscathed so in my community working in inclusive
00:52:00.840 --> 00:52:07.400
STEM education it was a huge shock when the Howard Hughes Medical
00:52:07.400 --> 00:52:13.160
Institute's education foundation side of the
00:52:13.160 --> 00:52:16.600
operations because they have a research side and they have an
00:52:16.600 --> 00:52:21.080
outreach granting agency for education terminated its
00:52:21.080 --> 00:52:25.160
HHMI IE, inclusive excellence funding this means
00:52:25.160 --> 00:52:29.080
we they they had a few different cohorts go through the program
00:52:29.080 --> 00:52:32.840
cohort one has already been done I'm in cohort two
00:52:32.840 --> 00:52:37.640
our funding wasn't was in extension and it's final so we were told
00:52:37.640 --> 00:52:41.160
you know use up your money no final report that's it
00:52:41.160 --> 00:52:48.680
the next group after us was IE three they still had active awards ongoing that
00:52:48.680 --> 00:52:52.280
were closed immediately and again had staff
00:52:52.280 --> 00:52:56.200
working trying to make sure that you know students had
00:52:56.200 --> 00:52:59.960
good advisors or there was professional development for faculty or
00:52:59.960 --> 00:53:04.040
whatever else they had real jobs and didn't have on the next day
00:53:04.040 --> 00:53:07.320
and we're talking about a private funding agency
00:53:07.320 --> 00:53:12.440
that halted its programs Science coverage seems to suggest that this had to
00:53:12.440 --> 00:53:16.440
do with executive order orders targeting the threat of
00:53:16.440 --> 00:53:21.160
removing nonprofit status to foundations that funded DEI work
00:53:21.160 --> 00:53:25.880
I also imagine that because HHMI does both science work
00:53:25.880 --> 00:53:31.720
and has a foundation's arm this is me guessing this is but I think also was
00:53:31.720 --> 00:53:35.720
probably influenced by the instability of the the biomedical funding
00:53:35.720 --> 00:53:40.360
landscape as well so where some might be pivoting
00:53:40.360 --> 00:53:44.440
towards private grants some of those have also been withheld
00:53:44.440 --> 00:53:50.280
and we've seen Mark Zuckerberg's foundation CZI removed one of its
00:53:50.280 --> 00:53:53.880
fellows programs that were targeted towards
00:53:53.880 --> 00:53:57.560
supporting early career research fellows and other
00:53:57.560 --> 00:54:01.160
foundations I think Sloan for example has
00:54:01.160 --> 00:54:04.680
sort of said we don't expect to give any high number awards because we think
00:54:04.680 --> 00:54:07.480
we're going to get 10 times the applications and so
00:54:07.480 --> 00:54:10.680
we want to you know we're really probably going to fund
00:54:10.680 --> 00:54:15.640
try to fund more awards for less money and so
00:54:15.640 --> 00:54:19.640
this is having ripple effects throughout the entire ecosystem
00:54:19.640 --> 00:54:26.600
and I think for many years to come and has maybe opened our eyes
00:54:26.600 --> 00:54:33.560
to whether institutions were really fundamentally invested
00:54:33.560 --> 00:54:39.240
in equity and justice or just throwing money at it
00:54:33.560 --> 00:54:39.240
SH: So
00:54:39.240 --> 00:54:45.640
Carolyn Ibberson on Blue Sky I believe it was it was originally
00:54:45.640 --> 00:54:49.480
shared on February 22nd but the list keeps growing
00:54:49.480 --> 00:54:55.640
it shared a spreadsheet of reductions in admission and hiring at
00:54:55.640 --> 00:54:59.960
institutions as far as they are aware and so that now
00:54:59.960 --> 00:55:05.640
includes a reduction slash pause at University of Wisconsin Madison where
00:55:05.640 --> 00:55:11.960
I got my library degree across all programs there is 25 percent
00:55:11.960 --> 00:55:18.840
delaying offers at UNC Chapel Hill there's up to 60 percent admission cut
00:55:18.840 --> 00:55:23.320
at University of Pennsylvania and this is all this expectation of getting
00:55:23.320 --> 00:55:27.240
less and less money and there's also a number of
00:55:27.240 --> 00:55:34.920
places that are also instituting hiring freezes or slowdowns or pauses so
00:55:34.920 --> 00:55:41.880
well while I of course have been focusing us mostly on math today like this is a
00:55:42.680 --> 00:55:49.480
say landscape-wide sort of problem and one that if I'm going to be
00:55:49.480 --> 00:55:53.000
completely honest might actually start impacting math
00:55:53.000 --> 00:55:56.920
well not start that might impact math slightly less
00:55:56.920 --> 00:56:00.680
than the other fields just because math gets slightly less funding
00:56:00.680 --> 00:56:05.240
whereas you know in in health funding the the percentage of
00:56:05.240 --> 00:56:10.040
I mean in healthy
like there's just so much funding that it could
00:56:10.040 --> 00:56:15.480
have outsized impacts
00:56:10.040 --> 00:56:15.480
CDZ: Yeah though I think we have to say for whom
00:56:15.480 --> 00:56:18.600
so I think I think there's a huge asterisk here
00:56:18.600 --> 00:56:25.000
so for example LatMath this year couldn't provide funding for any faculty
00:56:25.000 --> 00:56:31.400
other than who was invited to speak that's a direct result of the freezes
00:56:31.400 --> 00:56:37.240
and the unknowingness of funding so now we're talking about specific
00:56:37.240 --> 00:56:41.160
initiatives that are meant to celebrate and
00:56:41.160 --> 00:56:44.360
build the capacity for mathematics research that
00:56:44.360 --> 00:56:48.840
that happens within the Latino community in the US
00:56:48.840 --> 00:56:52.360
and so I think the the impacts for whom have to be
00:56:52.360 --> 00:56:56.280
I think there has to be a huge asterisk there
00:56:52.360 --> 00:56:56.280
SH: Of course
00:56:56.280 --> 00:57:00.600
yes you are you are very correct because while you might be able to convince
00:57:00.600 --> 00:57:04.520
say your department to give you funding to go out to the joint math meetings
00:57:04.520 --> 00:57:08.200
because they are you know the big one the thing to do
00:57:08.200 --> 00:57:12.440
the smaller conferences could yeah the the for whom
00:57:12.440 --> 00:57:16.760
could impact them a lot more
00:57:12.440 --> 00:57:16.760
CDZ: Or some people don't have departments that
00:57:16.760 --> 00:57:20.760
have money to send them places I've been there before in my life
00:57:20.760 --> 00:57:23.880
and you know
00:57:20.760 --> 00:57:23.880
SH: Oh no no no that that of course I'm like
00:57:23.880 --> 00:57:27.720
but just if you're likely to get any money at all
00:57:27.720 --> 00:57:31.160
it would be probably easier for the for the larger
00:57:31.160 --> 00:57:36.600
conferences than a smaller ones that probably have outsized impact
00:57:36.600 --> 00:57:40.200
on the people who attend them as opposed to the
00:57:40.200 --> 00:57:44.120
jmm which could have impact or could just be a giant waste of money for the
00:57:44.120 --> 00:57:46.840
people who attend
00:57:46.840 --> 00:57:51.320
so I mean we've been going for a while so I think we probably need to
00:57:51.320 --> 00:57:56.840
wrap this seminar up and what I would like to do is to ask for any sort of
00:57:56.840 --> 00:58:00.200
final concluding thoughts potential next steps you could
00:58:00.200 --> 00:58:06.280
see for for what we could try to do to survive
00:58:06.280 --> 00:58:10.840
this this changing in the landscape
00:58:06.280 --> 00:58:10.840
JH: Well so I think that Carrie hit on
00:58:10.840 --> 00:58:14.520
something that you know Chad and I have been really sort of zeroing it on
00:58:14.520 --> 00:58:18.440
within our sort of ties to the academic community is
00:58:18.440 --> 00:58:22.040
the things that we believe in fundamentally are worth
00:58:22.040 --> 00:58:26.680
fighting for and being quiet being silent
00:58:26.680 --> 00:58:30.600
about those things is not going to save any of it any of the things that we
00:58:30.600 --> 00:58:34.520
care about right it's it's just not and I I will
00:58:34.520 --> 00:58:40.040
tell you that my personal posture on this is yes everyone has to sort of
00:58:40.040 --> 00:58:43.960
find their path here and I'm not trying to finger wag at anyone right it is a
00:58:43.960 --> 00:58:48.120
weird moment and gravity feels upside down
00:58:48.120 --> 00:58:52.920
right now everything is upturned but I would just encourage
00:58:52.920 --> 00:58:57.160
everyone to know what you stand for and stand for it now
00:58:57.160 --> 00:59:03.400
because it it it will get eroded right out from under you and then it
00:59:03.400 --> 00:59:06.760
there is a point at which it may be too late right and so
00:59:06.760 --> 00:59:10.440
you know again it's like stand for what you stand for now is your moment
00:59:10.440 --> 00:59:15.000
you know we all need to know and we need to stand up strongly and link arms
00:59:15.000 --> 00:59:19.640
with people who believe in science right and who believe in the fundamental
00:59:19.640 --> 00:59:25.000
value of this endeavor
00:59:19.640 --> 00:59:25.000
DL: I guess what I'll suggest for things we can do to survive
00:59:25.000 --> 00:59:28.920
that I found helpful is just finding community with the
00:59:28.920 --> 00:59:32.760
other folks like present company that are committed to
00:59:32.760 --> 00:59:36.840
doing the work no matter what's going on right
00:59:36.840 --> 00:59:40.760
and I think that's been really helpful for me just to just to stay engaged and
00:59:40.760 --> 00:59:44.680
know that there are other folks that still care about this that are still
00:59:44.680 --> 00:59:48.120
doing the work it has to look a little different right now
00:59:48.120 --> 00:59:51.800
we may have to rewrite what shows up on the the public web page
00:59:51.800 --> 00:59:55.160
about our work we're gonna do but we're still gonna show up and
00:59:55.160 --> 00:59:59.320
and do that do that work together so that's that's been giving me
00:59:59.320 --> 01:00:03.960
a little bit of hope as we try and navigate all of this
00:59:59.320 --> 01:00:03.960
CDZ: Yeah I'm gonna
01:00:03.960 --> 01:00:08.520
plus one what Drew said in terms of community I think
01:00:08.520 --> 01:00:12.040
not being alone in this having many community eyes
01:00:12.040 --> 01:00:18.040
in on this and giving updates and I I can speak about you know our RIOS
01:00:18.040 --> 01:00:21.720
community or the data science for social justice community but also the blue
01:00:21.720 --> 01:00:25.240
sky community like coming in at a big time where
01:00:25.240 --> 01:00:29.000
twitter used to be that place where I think it was happening and
01:00:29.000 --> 01:00:36.360
to have blue sky be be there has has been huge I'll also
01:00:36.360 --> 01:00:43.960
reflect on what Jude said know what you stand for and stand for that now
01:00:43.960 --> 01:00:49.000
one of the things that happened pretty early into this process
01:00:49.000 --> 01:00:55.640
was I feel like as I don't want to say an old person but an older person
01:00:55.640 --> 01:01:00.600
than my students or early career faculty I feel like this isn't the first time
01:01:00.600 --> 01:01:05.800
I've done you know been in the academic ecosystem and and had these you know
01:01:05.800 --> 01:01:09.720
trump as president and and saw climate change get removed from all the
01:01:09.720 --> 01:01:13.240
websites and and all of all of the things
01:01:13.240 --> 01:01:17.560
and I I kind of went back I know there's a lot of differences between what's
01:01:17.560 --> 01:01:21.480
going on today and what happened then in a lot of ways but
01:01:21.480 --> 01:01:26.680
I had done this really interesting study a few years back where I had looked at
01:01:26.680 --> 01:01:31.720
DEI language in a journal archive of educational lesson
01:01:31.720 --> 01:01:35.640
plans and looked at how that journal language changed over time
01:01:35.640 --> 01:01:41.160
and one of the predominant narratives that's been given around our attitudes
01:01:41.160 --> 01:01:44.920
towards DEI is that we've had this shift in 2020 and then people started paying
01:01:44.920 --> 01:01:48.200
attention and so I feel like if I were to look at archives of published lesson
01:01:48.200 --> 01:01:51.880
plans post 2020 I would see this sort of signal of increased
01:01:51.880 --> 01:01:57.160
DEI language in fact what I noticed was earlier
01:01:57.160 --> 01:02:01.480
and I had you know based on the timeline of submission and review and
01:02:01.480 --> 01:02:04.200
everything that this was actually happening closer
01:02:04.200 --> 01:02:08.680
the submissions were happening closer to 2018 2019
01:02:08.680 --> 01:02:12.600
and so I had thought about okay well what's happening in 2018 like what's
01:02:12.600 --> 01:02:16.200
what's going on at NSF or you know released its includes program which was
01:02:16.200 --> 01:02:20.680
focused on on inclusion there was like some real
01:02:20.680 --> 01:02:24.040
funding you know activities and spotlights
01:02:24.040 --> 01:02:27.400
because of that includes program of such a big launch it wasn't the only thing
01:02:27.400 --> 01:02:33.320
that was happening but it was a thing and that's kind of when I had
01:02:33.320 --> 01:02:36.520
done that study that's kind of where I had landed like okay like
01:02:36.520 --> 01:02:39.960
there was a lot of enthusiasm we had a lot of real fine finally some
01:02:39.960 --> 01:02:43.640
investment in that area and then we we we got some
01:02:43.640 --> 01:02:47.320
good research and professional development and then and then this shift
01:02:47.320 --> 01:02:49.800
happened I've been thinking about it a lot
01:02:49.800 --> 01:02:53.960
differently I've been thinking about who I was as an educator
01:02:53.960 --> 01:03:02.600
and where my socio-political turn was and I I think that I doubled down on that
01:03:02.600 --> 01:03:07.960
commitment right after the 2016 election I think the result
01:03:07.960 --> 01:03:12.760
the investments what what we saw happening at the level of NSF and
01:03:12.760 --> 01:03:18.360
all of these other things were not things of 2018
01:03:18.360 --> 01:03:26.360
they were reactions from a community to what was happening in 2016 and 2017
01:03:26.360 --> 01:03:31.480
and we activated we had the women's march we had march for science
01:03:31.480 --> 01:03:38.040
we fought our federal cuts we stood up and I think many of us
01:03:38.040 --> 01:03:43.800
ground down and recommitted to a new vision of what this future could be
01:03:43.800 --> 01:03:51.000
and so when I landed on that narrative I started seeing that this is a place
01:03:51.000 --> 01:03:56.200
of opportunity also for us to do something even better and remake this even
01:03:56.200 --> 01:03:59.800
better than it was before
01:03:56.200 --> 01:03:59.800
SH: Well don't think we're going to get any
01:03:59.800 --> 01:04:03.400
better closing than that so I want to thank
01:04:03.400 --> 01:04:07.160
Jude Drew and Carrie so much for joining me thank you
01:04:07.160 --> 01:04:10.760
for taking part in this seminar
01:04:07.160 --> 01:04:10.760
DL: Thank you it's been a pleasure
01:04:10.760 --> 01:04:13.560
thanks so much Sam