On the Passing of Former FAS Board Member David Hafemeister

The Federation of American Scientists (FAS) is saddened to learn of the recent death of David Hafemeister, a former FAS Board member who served the organization for 8 years. Dr. Hafemeister was 88 years old.

Hafemeister’s decorated career working on nuclear proliferation and arms control included stints in the office of Senator John Glenn, the State Department, and the National Academy of Sciences (NAS). In 2022, Hafemeister received the University of Illinois Alumni Achievement Award for his work on international arms control.

Daniel Correa, FAS CEO, said “David Hafemeister spent his long and impressive career dedicated to things FAS is still focused on: sound public policy and cutting-edge science. We honor his contributions.”

Steven Aftergood, former director of FAS’ Government Secrecy Project, said “[David] was part of a generation of scientists that took public policy very seriously, and he was deeply involved in nuclear arms control. He worked on the issues from multiple angles — as an advocate (with FAS and APS), as a policy maker at the State Department, and as an influential congressional staffer. I think he represented the heritage of FAS at its best.”

The Federation of American Scientists, founded in 1945, is a catalytic, non-partisan, and nonprofit organization committed to using science and technology to benefit humanity through national security transparency and policy agenda-setting. While continuing its proud tradition of nuclear weapons analysis, FAS now also works to embed science, technology, innovation and experience into a wide range of policy areas to build a healthy, prosperous and equitable society.

Empowering First Generation Scientists with Gabriel Reyes

For many students in the U.S. a career in science is out of reach. Too often young people interested in science never get the chance to pursue their dreams simply because they come from low-income families or live in parts of the country where opportunities to engage in scientific research are limited. This leads to a critical lack of diversity in the scientific community that stifles creativity, innovation and progress. 

FLi Sci, short for first-generation/low-income scientists, is an education nonprofit that addresses the root causes of lack of diversity in the scientific profession. The organization provides financial support for high school and college students in poverty to access, pursue, and engage in scientific opportunities. The flagship program is a two-year paid fellowship during which students are able to conduct their own independent research projects.  

FLi Sci recently became a member of FAS’s Fiscal Sponsorship Program which identifies burgeoning entrepreneurs in science and technology policy and supports their philanthropic endeavors. Gabriel Reyes, FLi Sci’s founder and Executive Director, sat down with us to discuss his organization’s pioneering work and how his own experiences as a scientist inspired him to make a difference.


FAS: Before we start talking about FLi Sci – tell us a little bit about your science story. How did you first discover your love for science – was it one class or one teacher?

Gabriel Reyes: I loved math a lot – to the point where when my parents were grocery shopping and they would take me, I would go to the book section, and try to find one of those books that had math problems and I would try to finish as many math problems as I could before my parents picked me up. And I would be really mad at myself if I didn’t finish all the problems. But the real point of discovery was probably in my rising sophomore year of high school, when I took an intro to psychology course at the University of New Mexico. I really took that course because there was a condition at the summer program at the University that if I wanted to have a free lunch, I needed to take a morning class. And so I was like, “Ugh, fine, I’ll take this psych class.”  I ended up falling in love with the material.

FAS: And from there was it just a logical transition into neuroscience?

GR: I went to college and became more mature about what science is and what research in neuroscience was; I thought I was interested in psychology, but I realized I was actually interested in neuroscience – because the parts of psychology that I was most excited about were the brain, and the neurons, and topics like brain development.

FAS: Your organization, FLi Sci, focuses on first-generation, low-income, budding scientists. You describe yourself as proud to have come from a first-generation, low-income background. Tell us more about your childhood and upbringing.

GR: My parents were Mexican immigrants. Now they have visas, so they have a residency status in the U.S., but for so long, we grew up in a mixed-status household. As a result of that, it was very difficult for my parents to access careers that offered affordable wages. And so all my life I lived in economic scarcity. That was one of the parts that contributed to us moving constantly when I was a child – my dad kept getting fired from his job, or he wasn’t getting paid enough to make rent. So we had to move. 

FAS: You have said that your parents came to the U.S. in part because they had seen such a lack of educational opportunity in Mexico, and didn’t want the same for their children. But New Mexico presented its own challenges, at least compared to other parts of the U.S, right?

GR: Yes, I am the first in my family to go to college. And yes, another unique aspect about me is growing up in a place like New Mexico. Other places like New York or the Bay Area have a myriad of pre-college programs; like teaching low income students how to code, or having them do research with a professor from NYU or Stanford or other big universities nearby. I’m a little envious of high schoolers that grow up in those places. But there are a lot more places like New Mexico, or like Alabama or Kentucky that similarly do not have the same sort of abundance of nonprofits or educational opportunities. It’s important that people like myself and others can engage in science early enough to know science, and careers in science, exist. 

This is a focus of the work that my organization does and I hope other organizations in the future do more of this sort of landscape analysis. Which schools and cities already have resources? How do we maybe amplify those existing resources, or strategically work with them to target groups that may not be accessing them, and extend opportunities in areas that don’t already have them.

FAS: The aim of your organization, FLi Sci, is to make science more accessible to help low-income, first-generation American high school students who attend high schools with severely limited resources. You put a specific emphasis on research – why is that?

GR: I say ‘research’ specifically because there are already many pre-college science programs that put a big focus on ‘industry.’ They might teach students, for example, how to code, so that they can pursue careers in tech at Google or Amazon, or to become engineers so that they work in the field of engineering. But FLi Sci specifically is hoping to get a group of students interested in pursuing a PhD or a medical degree. 

I think that focus is because along my journey I’ve seen a lot less first generation and low income students and students of color following my path. Every year we talk about the lack of diversity in science, but I don’t see enough action to actually combat the root causes of why there’s not a lot more diversity in the profession. We want to provide students early exposure to these career pathways, and set them up for success by having them do their own research projects. That way they’ll be able to access further science opportunities the minute they get to college.

I want to move away from a model of where low income students are sort of given these science kits; you know, it’s like, ‘Here’s a chemistry kit!’ and that’s supposed to be the thing that inspired them to do science. Instead, I really want to see what people produce –  like what are these young people chasing? Because for me, I became a scientist because I got to decide what questions I wanted to ask. But many students in high school that I’ve been interacting with don’t have that same privilege. 

FAS: FAS’ Fiscal Sponsorship program basically allows you to fundraise in a way you couldn’t on your own. Are their specific ambitions or initiatives you are aiming for with more funding?

GR: Our flagship program is the FLi Sci Scholars Program, which is right now a two-year fellowship. We’re in the process of recruiting the next cohort, but our true goal is for this to be a longer multi-year fellowship program where we get students at the high school level and support them until they apply to medical school or a PhD program. We know that it can be very easy to fall off the path to a career in science; because access to science isn’t just about getting opportunities, it’s about when you get those opportunities. 

The other thing is we are a virtual program primarily because we started during the pandemic, but also because, again, one of our goals is to target and reach students that don’t have access to science, and virtual outreach has been great for that. But with more funding, it would be great to be able to provide some in-person activities. So for example, one of the things that we’re thinking about is a summer conference, either for all of our FLi Sci scholars or a summer conference for people before their first year of college, so that we can do some preparation and support before they start their very first day in higher education. 

Then I think the last thing that is a long-term goal that we’re trying to achieve once we’re at a more financially stable place is to start training teachers. Our curriculum is designed to help students pursue their own independent capstone project – but ultimately, I don’t want to be the sole keeper of that curriculum. If we can train astute teachers, we can reach more individuals. Maybe we could even help high school teachers who want to pursue their own scientific research – teachers who never had that opportunity themselves because a program like FLiSci didn’t exist.

FAS: That gets to our last question: what does it feel like to be helping students who remind you of yourself in high school – and trying to give them opportunities that you didn’t have yourself at their age? Rewarding? More challenging than you thought?

GR: It’s a range of emotions, and it varies from day to day. It’s very humbling because one of the things that I try to tell myself is that I worked very hard and I am good at my craft and I’ve created this organization, but there were people before me who did the hard work to provide that access that I’ve been given. Not too long ago the Ivy League schools I attended did not have generous financial aid programs for people like myself to be able to go to school for free.  Other people had to advocate for that. They may have taken out loans that they may still be paying on but I was able to go and graduated debt free because of the work others did. So in those moments where I feel sad or angry that there wasn’t a program like FLi Sci when I was in high school, I have to remember that.

I also still get questions about whether I think children from low-income backgrounds have what it takes to excel in science. These students always have to justify, you know, having a morsel of opportunity, whereas people who are wealthier – they just have to write a check or swipe their credit card.

I have some friends who do not share my identity, either race or my gender or my sexuality or my class background. For them, sometimes as little as proposing an idea is enough for them to get a substantial amount of funding that I haven’t been able to obtain. Whereas in my case, the scrutiny is a lot more intense. Like if I don’t have a perfect model, then I’m not ready for funding. Or if I haven’t tried this with at least 100 students, I’m not ready for funding. So it’s very fascinating to see, as an entrepreneur of color, just how different it is for me to get traction in the organization that I’m building. So I imagine that when I was a student, the lack of programming wasn’t because no one cared; I’m sure it was because someone was blocking the emergence of such an opportunity.

Prepping for the CLIMATE TIPPING POINTS TOURNAMENT with Metaculus’ Gaia Dempsey

The concept of forecasting is pretty familiar to anyone who’s flipped on their local news to get a sense of the week’s weather. But the broader science of forecasting, which is being applied to policy-relevant topics such as epidemiology, energy, technology progress, and many more topics  – – has never been in a more exciting place. In just a few weeks, FAS, along with Metaculus, will hold a forecasting tournament (The Climate Tipping Points Tournament) aimed at demonstrating just how powerful a tool forecasting can be for policymakers trying to effect change.

Metaculus is a forecasting platform – their unique system aggregates and scores forecasts and forecasters. Metaculus’ global forecasting community correctly predicted the outbreak of Russia’s Ukraine invasion, and its models have also helped state governments make better real-time decisions regarding COVID-19 response.

Metaculus’ CEO, Gaia Dempsey, sat down with us recently to discuss her organization’s work, and why forecasting holds such promise for better public policy.

FAS: Gaia, thank you for making the time for this. To start, could you talk about why you think forecasting, which at first blush can seem like peering into a crystal ball – not scientific at all – is actually very aligned with public policy based on scientific evidence?

Gaia Dempsey: At Metaculus we care a lot about ‘epistemics’ and epistemology.  Epistemology is a branch of philosophy that deals with knowledge itself, and how we form beliefs – how we come to believe something to be true.  Forecasting is a way of continuously improving our epistemics. It’s connected to the fundamental basis of the scientific method itself, which is this essential idea that our trust in any given theory about the world should increase when it’s able to predict the result of an experiment or the future state of the  given system or environment that’s being studied.  So this agreement between the theory and the experiment – that’s predictive accuracy.  That’s the gold standard for what we trust as a valid explanation for anything in the world – any given phenomenon. 

What we do on our platform is bring that mindset into more complex environments — outside of the laboratory and into society, into public policy.

FAS: You discuss Metaculus as both a forecasting platform and a community. What makes your platform unique?

GD: You can think of it as a network of citizen scientists, or a decentralization of the role of the analyst in a way that rewards and gives credence to analysis on the basis of its accuracy rather than the person’s position or title. Everyone has the ability to be an astute observer of the world. Metaculus is kind of like infrastructure that facilitates the collaboration of thousands of people and aggregates their insights. Our platform has a set of scoring rules – every time you make a forecast, you get rigorous, quantitative feedback. When you’re making a prediction, you have to take into account all of the factors that affect the outcome.  If you haven’t, your score is going to let you know that you’ve missed something. 

FAS: In your community you’ve said there are casual forecasters and hardcore forecasters – and recently you’ve even assembled a team of 30  Pro Forecasters – people with a track record of accuracy and prescience in their forecasts. Aside from a familiarity with and facility with data science, you’ve also said that humility is a good trait for a forecaster. Can you elaborate?

GD: I’d say it’s ‘epistemic humility’: you need to be able to update on new evidence. If you hold on really tightly to a belief, you’re going to be biased by that desire to believe that the world works one way, while the evidence is actually telling you something else.

FAS: That makes sense. In terms of why forecasting is such an exciting field right now, it seems as if we’re reaching this point in forecasting science because of the confluence of human learning coupled with new technologies that allow us to aggregate information at these incredible scales. Is that a fair way to think about it?

GD: Yes, I would say so. Metaculus mimics the structure of a neural network. We aggregate statistically independent forecasts, and our scores and tournament prizes serve as reward functions. It really is sort of like a hive mind. Within this system, accurate signals are cross-validated, while errors cancel each other out, and since we’re in effect running hundreds of trials at any given time, the system is designed to get more and more accurate over time. Our aggregation algorithm gives more weight to forecasters who have a track record of accuracy. We are basically weighting the probability of a forecaster being more accurate based on their past performance, so in a way it’s actually quantitatively controlling for cognitive biases across a population of forecasters.

FAS: Our upcoming Climate Tipping Points Tournament is aimed at showing how useful forecasting can be for policymakers, looking at questions like “What will the Zero-Emission Vehicle Adoption Rate be in X years?” or “If X policy is implemented, what will the charging infrastructure look like in year X?”  It’s focusing on something known as the “conditional approach” to forecasting. How is this different from what Metaculus has done in the past?

GD: Conditional forecasts give you the delta – the difference – between taking an action, or not taking it. It’s going to be the first time we’re really doing this at scale like this. The thought process behind this particular project was: how can we leverage the talent and the analytical capacity of forecasters to actually be as useful as possible to the policy community? We want to help answer questions like, “Which policy actions are really feasible?” or “What may actually happen in the time period of interest that policymakers are concerned about?” If you’re asking, “What would happen if we just outlawed all cars tomorrow?” Yes, we could produce an estimate that could tell you what would happen to global CO2 emissions, but it wouldn’t be very useful. So it’s really about considering the policy interventions that are realistic, and developing a quantitative analysis that can tell us what the likely impact will be of these policy actions on the outcomes we really care about, such as the reduction of CO2 emissions. Our conditional forecasting methodology will make the relative expected value of various policy actions unmistakably clear, using an empirically grounded methodology.

FAS: And as the title of the tournament suggests, it will also highlight a fairly new idea to U.S. climate policy: positive tipping pointsFAS’ own Erica Goldman is very excited about this area of study, but how does it relate to forecasting and the work that Metaculus does?

GD: It’s something I find really exciting about this particular project; it brings together research from the University of Exeter on positive tipping points, FAS’ policy expertise, and our forecasting methodology. The goal of this tournament is to assess what climate policies may present positive tipping points toward decarbonization. We know that tipping points are a real phenomenon in lots of different complex systems. The work that our partners at Exeter University did to pinpoint and really identify positive tipping points in our current understanding of climate science is so exciting.  But we want to look at how we can then leverage those tipping points to achieve goals in terms of reducing the impact of climate change—and maybe even reversing it.

FAS: Before we let you go, could you talk about how to participate in a forecasting tournament? Once the questions and topics are released, how much expertise is required to engage with this?

GD: Laymen can totally participate – it just requires intellectual curiosity and a willingness to engage in the forecasting process. Most forecasters benefit from building some kind of model or by explicitly implementing Bayes’ rule. People who are already familiar with data science techniques, people who are familiar with modeling, people who have some kind of science background – they tend to be able to jump right in and feel comfortable, but you don’t necessarily need to have that background at the start. It’s something where you become a part of a community. If you comment on the public forecasting questions, people will engage with you very sincerely.  It’s just like if you’re a new software developer: People don’t expect you to get everything right away. But if you’re genuinely putting in the effort, they will come and support you.

FAS: On that inviting note – we’ll let you go prepare those tournament questions! Thanks.

GD: Thank you!