The Humans of the Wyss (HOW) series features members of the Wyss community discussing their work, the influences that shape them as professionals, and their collaborations at the Wyss Institute and beyond.
Russell Gould wants to know why. Seeking a flexible, open career path after serving in the military, he gravitated toward science, where he was encouraged to ask questions about the unknown. Eventually, he combined that passion with his love of problem-solving to become a computational biologist. At the Wyss, he works on multiple projects, always identifying the most effective ways to use scientific data to solve problems. Learn more about Russell and his work in this month’s Humans of the Wyss.
What are you working on?
The simplest answer is figuring out how the various types of biological data generated at the Wyss can be used in a practical, predictive model to solve problems.
One current application is drug repurposing. I’m expanding upon an existing Wyss model to predict therapeutics for novel diseases by combining multiple data types. This can be applied across various projects. For example, as part of the DARPA ABC grant, we’re looking for drugs that could be applied as anesthetics for battlefield care. As part of the BARDA radiation countermeasures program, we’re searching for therapies for radiation sickness.
What real-world problem does your role and this work solve?
At the Wyss, there is a lot of cool instrumentation and novel ways of using existing instruments to get data, but the recurring question is, how do we best use the data to solve problems? I help scientists discover how to perform interesting computational analyses or processing of their data to build a model or use AI. Then we can use the model to solve the problem they’re trying to address.
In terms of the drug repurposing work, my job is to figure out how to get transcriptomic data, or information about RNA molecules, and proteomic data, or information about proteins, combined in a multi-omic, or holistic, way to predict whether these repurposed drugs could be used to treat a particular disease. This is an ongoing initiative building on a previous project, NeMoCAD, originally built by Richard Novak and Sahil Loomba, that could be applied to many projects at the Wyss.
What is the Translational AI Catalyst?
The Translational AI Catalyst is an institute-wide effort to organize and effectively channel the computational talent at the Wyss to enable and accelerate innovation through the application of AI and machine learning. Right now, four Advanced Technology Team members are mainly computational: Megan Sperry, who leads the Translational AI Catalyst; Chris Lee; Allison Grossberg; and me. Ally Chang from the business development team also provides extensive outreach and support for the Catalyst. But we’re not the only ones at the Wyss working on this. More and more, students are becoming aware of the necessity of computational biology. So we have graduate students and postdoctoral fellows joining the Institute, excited about its potential and eager to learn. They all approach it from different angles: some may be trained biologists new to computation, while others may be from more computational disciplines.
When discussing ideas for the Translational AI Catalyst, Megan and I envisioned recurring, open-door meetings where anyone doing computational work could come together to learn, share resources, and solve problems. Now, we hold workshops where invited speakers describe a project they’re working on or present on a topic relevant to the Wyss community. Researchers talk about challenges they face and ask for help. Anyone even tangentially connected to computation is welcome to join and connect.
It’s been really satisfying to be part of enabling people interested in computation to have a place to express their interest and interact with one another. It’s rewarding to see this expand and thrive.
It’s been really satisfying to be part of enabling people interested in computation to have a place to express their interest and interact with one another. It’s rewarding to see this expand and thrive.
Why did you want to work at the Wyss?
I was attracted to the Wyss because of its position between industry and academia. Here, I can see both sides of that world. I observe how industry approaches biology and the translation of innovations, while maintaining an academic foundation and a basic mechanistic understanding of human health. The Wyss is a great place to see the innovation pipeline and how new ideas turn into products that help people.
What is unique about the Wyss? How has that impacted your work?
There are few hard lines drawn between projects and teams, and there is a lot of crossover. When I speak to people about strictly industry positions, they tell me there are many silos and little freedom to move between projects. Here, it’s very fluid, and we are encouraged to collaborate, work on multiple relevant projects, and take risks. The Translational AI Catalyst is a perfect example of that. Because of this, I’ve had the chance to work on different types of projects and with many teams. It’s really unique and makes things more interesting.
What inspired you to get into this field?
In school, I chose math because I enjoyed problem-solving and uncovering the underlying justification or logic for a method or idea. If you could ask why, there would be a method to answering that question. I loved that.
I had been in the military, which, understandably, is a very rigid, top-down organization. Asking questions didn’t feel welcome. In searching for the opposite of that, I gravitated toward science. In research, you’re encouraged to ask questions and investigate phenomena. As an undergrad, I took a biology class and realized there’s a huge world out there, with so many unknowns.
Eventually, I recognized that you can combine math and biology in incredibly interesting ways, most of which involve a computer. So, I looked at applied computer science, modeling, and machine learning. My goal was to use existing data to predict something relevant. All of the projects I’ve worked on at the Wyss use predictions or data processing, so it was a very natural progression.
What continues to motivate you?
Of course, you’re working towards goals and meeting milestones, but you’re never done learning or figuring out biology. When you understand something to a certain point, it usually means you’ve uncovered new unknowns.
There’s no finish line in biology. Of course, you’re working towards goals and meeting milestones, but you’re never done learning or figuring out biology. When you understand something to a certain point, it usually means you’ve uncovered new unknowns.There’s always more to figure out, which is a constant motivator.
What excites you most about your work? What are some of the challenges that you face?
There’s a ton of interest, hype, and activity around AI, and specifically how we can apply it to biology, which is really exciting. The idea that we might be approaching a world of AI scientists is fascinating, if hubristic. But that’s also tied to the challenges, because that excitement is creating an unrealistic expectation that AI will solve all of our problems, from data analysis to hypothesis generation. It’s never that simple. The difficulty lies in managing expectations while keeping hope and enthusiasm alive. Are we actually creating a better world for humanity if we outsource our critical thinking?
How have your previous work and personal experiences shaped your approach to your work today?
Because of my background, I’ve always opposed the idea that there’s a definitive right and wrong way to do things. I want to understand the best theoretically or biologically motivated solution to a particular problem that makes the most sense in the context of a project, rather than being told what to do with no justification. That guides how I approach my work and the way I interact with colleagues.
What do you like to do outside of work?
I’m outdoorsy – I like to bike, rock climb, and hike. I’m from Portland, Oregon, so my favorite hiking is right outside of the city in the Columbia River Gorge. It’s incredible. There’s a place, right outside of Hood River, where you can see the Washington side of the river and the ground rising at this odd angle, and you get this visceral feeling of the massive glacier that dug this out like a spoon through ice cream. In the Northeast, my favorite place to hike is Acadia National Park.
1/4 Russell's favorite place to hike in the Northeast is Acadia National Park. Credit: Russell Gould 
2/4 Russell loves the outdoors. Here, he enjoying the snow in Grand Teton National Park. Credit: Russell Gould 
3/4 Russell taking in the scenery in Hetch Hetchy Valley in California. Credit: Russell Gould 
4/4 Russell also enjoys rock climbing. Here, he is getting ready to go bouldering at the Middlesex Fells Reservation. Credit: Russell Gould
I also enjoy baking and cooking. I spent a while trying to perfect the chocolate chip cookie. It seems simple, but then I learned that my partner and I fundamentally disagree on what that means. I prefer my cookies to have a crunch, and she likes them soft and gooey.
What’s something fun or unique about you that someone wouldn’t know from your resume?
I was previously fluent in Iraqi Arabic, a specific dialect of the language. I spent over a year studying eight hours a day at the Defense Language Institute and worked as a translator. It’s a little rusty now, but I can definitely still read and write.
If you had to choose an entirely different career path, what would it be?
Probably a lawyer. A friend once explained that there’s a connection between the rigid logic of the legal world and mathematics. He joked that a lot of people who drop out of math become lawyers. Sometimes, in my free time, I listen to Supreme Court oral arguments. It’s fascinating to hear the level of detail they get into, even arguing about the grammar in one sentence of a law.
What does it feel like to be working on cutting-edge technology that has the potential to have a real and significant impact on people’s lives and society?
It is fulfilling to be in an Institute that collaborates directly with hospitals, bringing in patient data. Our work feels immediately connected to the medical system and patients, or creating products that people can use.
Coming from math, my goal was to find something that was more relevant than theories and abstract models. So, it’s really satisfying to be closer to real-world applications. It is fulfilling to be in an Institute that collaborates directly with hospitals, bringing in patient data. Our work feels immediately connected to the medical system and patients, or creating products that people can use. It’s truly incredible.