Prodan published a paper in PhysRevLett that explored how a microtubule’s structure and its ability to store energy along its edges could be useful in areas like cancer research.

“My hypothesis is that through evolution, cancer-derived microtubules actually found a way to get rid of these energy storage methods,” she said.

One of her goals is to research ways to physically manipulate a cancerous microtubule into one that is noncancerous.

“Then you have another method to treat cancer,” she said.

Connecting Fields

Prodan’s work connects biology with materials science, a field that combines areas like physics and biology to better understand the properties of different materials and how they can be used. This field is useful in areas like engineering, energy conversion, and telecommunications.

“I have two areas that seem disconnected, cancer research and engineering new materials, but they are highly interconnected,” said Prodan, who came to Fordham from the New Jersey Institute of Technology this fall. “The main relationship between them is physics.”

In the course of researching microtubules, Prodan began noticing similarities between them and a new type of material, topological insulators. A topological insulator is a material whose surface behaves as an electrical conductor while its interior behaves as an electrical insulator, she said.

The possibilities of topological insulators became even more clear in 2016, when a team of scientists was awarded the Nobel Prize for its work on topological materials that “could be used in new generations of electronics and superconductors, or in future quantum computers.”

The microtubules’ ability to store energy on the outside of their structure, similar to a topological insulator, makes them a promising subject for future research, Prodan said.

Prodan said that physics has an important role to play in serving humanity, such as by assisting in drug discovery and helping run the communication technology behind programs such as Zoom.

Helping to Enhance STEM Efforts

Prodan said that she was drawn to Fordham because of the University’s expanding STEM offerings, particularly in physics.

That’s why she’s teaching an introductory physics course to undergraduates this summer. The hope is that students get excited about STEM at the beginning of their time at Fordham, before moving on to upper-level courses.

Prodan said that her lab is currently under construction but hopes that it will be ready by the summer or sooner, which would allow her to provide hands-on learning and research opportunities to undergraduate students, as well as local high school students.

“If you want people to have a better life, a healthier life, a happier life, physics and STEM in general are really important,” she said.

“In general, the discoveries that happen in physics don’t have an impact right away. It’s a long-term impact, but they’re essential.

In a recent conversation, Joshua Schrier, Ph.D., spoke about the science that has been taking place in his lab since he became the first Kim B. and Stephen E. Bepler Chair in Chemistry last fall. His research blends three scientific branches: quantum mechanics, chemistry, and computational science. This month, his research on biases in chemical reaction data was published in the journal Nature. Fordham News spoke to him about his work.

The Bepler endowed chair has allowed you more time to tackle research projects. That includes your $7.4 million project, “Discovering reactions and uncovering mechanisms of perovskite [mineral]  formation,” funded by the Defense Advanced Research Projects Agency. Tell me about this project. 

Most experiments are designed, conducted, and interpreted by humans. The goal of this project is to create the capability of having machine-specified experiments so that computer algorithms can select new experiments to perform that accelerate the scientific discovery process.

What does “machine-specified” mean? 

We want to give computer algorithms the ability to perform experiments in the real world. But to do this, we need to make sure that the specifications of what to do are completely unambiguous. Humans are pretty good about working with imprecise instructions about what to do. If I say, “Hey, let’s go to the zoo,” you would infer it’s the Bronx Zoo and that “us” includes you and I and other individuals within earshot. But a computer is not going to know what I meant: What zoo? What entrance? How do we get there? Who should go? We are working to develop software that allows people (or computers) to specify the experiments they want to be performed. The software turns that into a set of instructions in an unambiguous way. This might include a mixture of instructions for human operators and for machines—just like the way that specifying where you want to go in an Uber ride fills in the details of how to get there. Finally, we want to make it easy to collect all of the things that happen during the process so that we can learn from that data.

Like programming a self-driving car, but for science experiments? 

Yeah. That’s the high-level goal: a “self-driving” or autonomous laboratory. Just like a self-driving car, we have to be able to “steer” the experiments (specify what to do) and “see” the world. So we are also collecting as much information about everything that happens in the laboratory so that the algorithms can make sense of what is happening when devising new experiment plans. Experiment specifications are the steering wheel, so to speak. As new experiments are performed, machine-learning models get trained on the new data. This is a general problem across many areas of science—how do we use data to more efficiently get scientific insight? Because of the scale of the data, we use algorithms to sift through the data and identify anomalies, and use the insights latent in that data to devise the next round of experiment plans. 

There’s another part to your project: using this “self-driving laboratory” to develop as many different types of perovskites—minerals that help create solar cells—as possible, and then identify the most useful perovskites. 

Yes. Essentially, what we’ve cooked up—in collaboration with researchers at Lawrence Berkeley National Laboratory and Haverford College—is a way to do these types of [perovskite]syntheses using commercially-available laboratory robots. More specifically, organohalide perovskite materials are hybrid materials that have both organic and inorganic building units—and changing these changes their electronic and optical properties. As a result, there is a general interest in using perovskites for high performance, low-cost solar cells. We are using the robotic system [called RAPID]to try to discover new materials that will have higher performance. But just to be clear, our focus for now is on discovering new compounds. We don’t yet build devices from these discoveries, although we are expanding work in that direction [in collaboration with researchers from MIT]. It would be neat if we also found some really great high-performance perovskites—but even if we do not, we’ll still be able to learn rules about how they form, and demonstrate this toolbox which can be applied to other scientific problems.

Another ongoing research project is the National Science Foundation-funded “Dark Reaction Project.” What is that about?

“Dark reactions” sounds mysterious, right? But it’s a simple idea. Most of the experiments performed in laboratories are never reported. Journals tend to publish only a single example of “success.” So this vast, unreported collection of marginal successes and failures never gets exposed to the world. So by analogy to the astronomer’s “dark matter,” we like to think of “dark reactions” as this vast majority of scientific experiments that aren’t seen directly [in journal articles], but yet influences scientists’ decisions in a non-obvious way.

The good news is that scientists keep good laboratory notebooks, so the “dark reactions” are in principle available. This project is an initiative to harness the unpublished failures and marginal successes [dark reactions]in laboratory notebooks, turn them into digital data, and use that to advance hydrothermal synthesis of oxides. Once you digitize the results, you can use that database to build a machine-learning model. With that machine-learning model, you can recommend reactions to perform in the laboratory. 

So the machine-learning model is learning from “dark reactions,” or our mistakes—what not to do? 

Correct. And you can only do this if you’ve got the complete record of success and failures. 

If you look at all the published scientific literature, all you see are successes. You never see any of the failures. So if you’re trying to identify a mathematical function that divides success and failure—and that’s really all that you’re doing with machine-learning, is finding the mathematical function—then your algorithm is going to look at all of these examples in the published literature and say, “Oh, good news, everything is successful.” Because all the examples that it sees are only examples of success. 

Lastly, you have a paper that was recently published by Nature: “Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis.” How does it relate to dark reactions? 

This work is supported by the same project from the National Science Foundation, and is a natural continuation. “Dark reactions” are the experiments that have been tried in the laboratory, but not reported because they are “failures” or marginal successes. But what about the “extra dark” reactions that don’t even get attempted? In practice, chemical experiments are planned by human scientists and thus are subject to a variety of human cognitive biases, heuristics, and social influences that might lead to some reactions being systematically excluded. What we were able to show in this study is that such biases are present in the chemical reaction literature, and that the underrepresented reactions are not being excluded for any “good” reason—it’s not because they are more expensive, or more difficult, or more prone to failure, but rather simply because humans tend to get stuck in a rut when planning reactions. This might just be a curiosity, except for the fact that these anthropogenic (human-generated) data are now being widely used to train machine-learning models to predict chemical syntheses. The hazard is that we end up making the machine in our own image, so to speak, rather than letting it perform as well as it could. We were able to show that indeed, human-selected experiments were inferior to randomly-generated experiments for building machine learning models, even if you gave the humans many more reaction data.  

This interview has been edited and condensed for clarity.

Hein, a Princeton-educated mathematician who moved to the U.S. from his native Germany more than a decade ago, is the first Kim B. and Stephen E. Bepler Chair in Mathematics at Fordham University. Since his appointment last fall, he’s had more time to pursue his research in differential geometry, or geometry in any dimension. 

On a daily basis, he develops methods and equations that lead to new shapes. These shapes can surpass three-dimensional space. It’s impossible to sketch some of them. But the point of his papers, often with titles as abstract as “A Liouville Theorem for the Complex Monge-Ampere Equation on Product Manifolds,” is to explore uncharted territory in the realm of mathematics and develop new ways of thinking that can describe complex phenomena like black holes, though perhaps only decades or centuries from now. 

“[Mathematicians] try to figure out patterns, describe certain things that they observe, purely within math,” Hein said. “These methods and equations have a life of their own. They exist abstractly, without any specific application. And then 20, 30, 50 years later, it may turn out that this is exactly the right kind of math that you need to describe something that actually exists in the real world—like gravitational waves or black holes.” 

How did your love for math begin? 

There used to be these TV programs in Germany for people who didn’t finish high school or wanted to brush up on high school material before they went to college. I started watching the trigonometry program, just out of curiosity, when I was 9 or 10. I liked the shapes. They were explaining how to graph sin and cosine. I sat down after the lesson and tried to recreate that on paper on my own. And I got a shape that looked like the thing that I saw on TV. 

How do you define mathematics? 

Simple ideas that solve problems, that, in the end, are correct. It doesn’t depend on anybody’s opinion. It’s some pattern or idea that’s going to be correct a thousand years from now, if humanity still exists. 

Your branch of math is differential geometry. What does that mean? 

There’s the more elementary stuff, like basic differential geometry that actually happens in three-dimensional space, that actually exists in the real world, that engineers and physicists use all the time. Then there’s my work—the rarified, cutting-edge stuff in theoretical math.

What does it look like for you to do research? It’s obvious for chemists and biologists. They have microscopes, petri dishes, beakers—things like that. But for you, a mathematician, how does that work? 

I lie on the couch all day. I imagine shapes and connections between shapes and quantities and try to figure out if some quantity is going to be large or small—how different quantities interact with each other. It’s a little bit like art in the sense that you create shapes and patterns. And then if I have the complete picture in my mind, I’m usually able to see the solution.

My wife is a mathematician, too, so at least it’s not weird for her. She knows what’s going on … that I’m actually working. 

When you’re brainstorming, do you map out your thoughts on a blackboard? 

No. It’s just in my head. If I’ve really thought something through, I can just go to my laptop and write like 10 pages of equations and formulas and arguments and reasoning, based on what I have been imagining. Sometimes I have to do some calculations on paper, but that usually comes later.

What’s the most difficult part of your research?

That you’re stuck constantly. You don’t know what you’re doing most of the time. It’s not like you’re applying some method that you learned in grad school, and you’re trying to use that to create something new. I mean, sometimes it’s like that. But more often, you’re working on some problem that nobody’s really thought about before—that certainly no one has ever solved before. What that usually means is that the methods that exist aren’t sufficient enough to solve that problem. 

So you have to come up with new methods? 

Right. Usually it’s a tweak on some method that you learned in grad school or from somebody else’s paper. But, you know, once in a while, you have to create something completely new. 

To a non-mathematician, how do you explain the importance of your work? 

This kind of math that I do is incredibly abstract. Right now, nobody knows if it’s ever going to have an application to anything real. Much of the math is developed completely independently of any applications to physics [for example]. We often create new ideas for their own sake. And then [decades or even centuries later]it turns out to be exactly the right math that’s needed to make sense of things like quantum mechanics. 

You discover these new beasts, specimens. You can see them in your head. Somehow, they’re out there.

This interview has been edited and condensed for clarity.

Since she became the first Kim B. and Stephen E. Bepler Chair in Biology last fall, she has used her new position to pursue new or previously underfunded research projects and dedicate more time toward her student mentees in her Larkin Hall retina cell biology laboratory. And soon, she’ll be showcasing her research to scientists in Japan. 

Your lab specializes in retinal neurobiology. What does that mean? 

We study the long-term maintenance of the retina, the neural tissue in our eyes that allows us to see. How does our retina manage to function for life? What are the cellular and tissue mechanisms that are responsible for life-long visual function? 

What made you interested in studying the human eye? 

The eye is a particularly beautifully organized organ. Also, our visual sense is enormously important to most of us. If you ask people what they are afraid of when it comes to their health, they say cancer and blindness. To this day, even in developed countries, the risk of having visual impairment with age is very high. Diseases are not necessarily hereditary, and they affect a very large percentage of the elderly—age-related macular degeneration is diagnosed in about 25 percent of the U.S. population over 75. So there is a medical need. 

As a scientist, I also like the eye because unlike the brain, it’s easy to access. You can look at part of the central nervous system by shining light into the eye. You can’t use eye drops on brains. The accessibility of the eye, from an experimental point of view, is a huge advantage. There’s no other part of our central nervous system—brain, neural retina, spinal cord—that you can manipulate in real time and take a look at. 

What brought you to Fordham? 

When I came to Fordham in the fall of 2008, I was already an established principal investigator. I had been Assistant and Associate Professor at Weill Cornell Medical College for several years. The move to Fordham was really motivated by the opportunity to integrate research and science education at the immersion level, where students come into the lab, participate, and understand what professional lab research is really about. That is what makes my Fordham lab different, not only from my previous lab at the medical school but also from many other labs of my colleagues and competitors around the world.

How has the Bepler endowed chair changed your life as a scientist and professor? 

The Bepler endowment allows me to be more present in the lab and work on experiments directly on a daily basis and often with my mentees. For instance, I used to ask my students to email me their research results at night. Now I can actually be there in real time. 

I also have the opportunity to participate more often in international conferences. For instance, this fall, I’m invited to participate in a workshop called “The retina—Mechanism of photoreceptor degeneration and regeneration, and roles of immune system” hosted by the Okinawa Institute of Science and Technology Graduate University in Japan. I will teach budding retina cell biologists and physicians about the work in my lab, possibly recruit new investigators and collaborators, and spread the word about Fordham as an institution with a vibrant Ph.D. program, where graduate students join labs like mine. The flexibility that I have, supported by the Bepler endowment, makes that a lot easier. 

With funding from the endowment, I was also able to push an important project to completion. We found that by using biosensor eye drops alone—a non-toxic, gentle procedure—we can detect early-onset retinal degeneration in experimental models at a stage where rescue, and thus prevention of vision loss, may still be possible. Using these diagnostic eye drops, we can monitor blinding disease and maybe make decisions on therapy without having to do any kind of invasive testing. 

At the moment, what’s the most exciting thing happening in your lab? 

A second, new eye drop study. Unlike the first study, these aren’t diagnostic eye drops. This time, we’re actually providing therapy. We’re delaying blindness in an animal model that carries a mutation also found in human patients. The animal we are studying for this particular project is called the Royal College of Surgeons rat. It’s a very well characterized, classic animal model for retinitis pigmentosa, which is a hereditary form of blindness that is very frequent in the human population. The idea is that anything we give to the animal that is actually working will then be picked up by physicians and used to delay blindness in human patients.

What is the most rewarding part of working with young scientists? 

The students, for the very first time in their lives, are realizing how it feels to perform an experiment to the best of their abilities and to obtain a result that nobody else in the whole world has ever seen before. To make scientific progress means discovery at a very fundamental level. And that is a thrill—the thrill of discovery in and of itself. 

This interview has been edited and condensed for clarity.