Todd Coleman, PhD, a bioengineer with an interest in using neural data science to understand nerve signaling in the gut-brain axis, has joined the Wu Tsai Neurosciences Institute faculty as our newest Institute Scholar. His home department, Stanford Bioengineering, recently asked him a to share a few stories about himself and his interests:
What initially got you interested in Bioengineering?
I am a bit of an outsider to bioengineering whose career path naturally evolved towards the field. When I was in middle school in one of the Dallas Public Schools, my teachers noticed I was good at math and science. They encouraged me to explore applying to a Science and Engineering Magnet high school in Dallas. At the time, it was really unique because you spent half a day at your home high school in your neighborhood and the other half of the day at this magnet school. My high school experience was great because I had great teaching and encouragement from math teachers (and others) at my home high school. Along with that, my experience being introduced to science and engineering at the magnet school, around other students with like-minded interests, created a fertile environment. As such, I would say that my path to becoming an engineer was set once I was in high school because of that unique situation.
In college and beyond, I was originally trained as an unapologetic electrical engineer (the last time I formally took a biology course was AP Biology in high school). My PhD work was in the field of information theory which is more on the theoretical side of applied probability, at the intersection of engineering and mathematics. Near graduation, my PhD advisor at MIT, Muriel Medard, gave me the best piece of advice in my career. She encouraged me to go do a postdoctoral study in something "wildly different.” So I began to explore biology in part because a lot of my buddies in grad school were working on things that intersected biology and engineering and they used to give me the "side-eye" suggesting that I should take all that math I am using and apply it to benefit mankind. That was part of the reason I ended up doing a postdoctoral study in quantitative neuroscience, under Emery Brown (an anesthesiologist and computational neuroscience) at Mass General Hospital and MIT. Interestingly, he has a PhD in statistics and so we had a common language of probability and statistics to speak. This allowed me to learn about the dynamics of neural systems and quantify uncertainty in interpreting neural data. Once that began, my path to bioengineering was inevitable. I really enjoyed my time there and continued this research area when I started my career.
My experience of "feeling comfortable being uncomfortable" in that postdoc gave me the confidence to explore new areas even if I have not been formally trained in them. That allowed me to have a bit of a "maverick" mindset (it might have been subconsciously in my mind since Dallas sports teams include "Cowboys" in football and "Mavericks" in basketball) and realize that just because I don't have formal training in an area doesn't mean I can't go into it, as long as I have a perspective that can add value to that field and help solve important unaddressed problems.
What excites you about working at Stanford?
Stanford is great because of the amazing colleagues and students as well as the well-resourced environment within the university as well as its surroundings to encourage innovation and "outside of the box" thinking. It is also unique for my interests specifically because engineering, biological & mathematical sciences, medicine, and business are all at the very top of their respective fields at Stanford. As such, operating at their intersection will allow one to work with people from each of those areas who are stellar.
What will your research at Stanford focus on?
I can never predict in the long term what I will be working on, as my research directions evolve with my interests. That uncertainty is what is exciting.
In the short term, I plan to continue efforts of using techniques in applied probability and data science along with technology development to "turn data into information" involving problems at the interface of the central nervous system, the autonomic nervous system, and the enteric nervous system (or nervous system of the gut). Some of the efforts will be in basic science and others will be directly translational. For instance, we recently have been developing technology to record the electrical patterns of the gut underlying digestion and applying modern statistical signal processing on the generated datasets to address clinical gastroenterology problems involving understanding disease etiology, tracking disease progression, and predicting treatment response of novel therapies.
It is exciting because it involves my expertise in electrical engineering, the coupling between the digestive system and the brain, and the very dynamic nature of symptoms of patients with GI problems. The unmet needs create opportunities to build upon my previous efforts in signal processing and technology development associated with brain science and neurology.
More importantly, there's a personal connection that motivates me: I began to explore this area after I lost my dad to pancreatic cancer in 2011, especially since he himself lost his mom to stomach cancer. When I began to poke around and look into this area, and I learned that the gut had a "brain" that generates electrical signals, my curiosity radar began buzzing.
We have many other current research pursuits we will continue, including building novel technologies and algorithms with size/softness/timeliness constraints for the pediatric population as well as developing high dimensional uncertainty quantification algorithms using techniques from "optimal transport theory" within the field of mathematics to create novel algorithms with provably good performance in data science and machine learning. We also recently have been pursuing synthetic biology tool development that includes developing novel methods to control genes with red light and targeting the delivery of large genes to cells of interest.
I think Stanford will be a natural fertile ground to continue these endeavors and apply them, given its strength in these areas. As more tools for optical, sound, and magnetic control of biological activity are being developed, I can see how my interests in technology development and applied statistics can be used with synthetic biology to build new approaches to understand basic science and solve translational medicine challenges.
What do you want the Stanford/BioE community to know about you?
Along with research, I really enjoy teaching and working with students. I have a reputation of teaching tough classes in bioengineering but also of genuinely caring about the long-term best interests of the students. I like to have former students come back and explain to the current students in class how what they are experiencing is a form of “delayed gratification” with a story of how it wasn’t clear to them while taking the course, but the content they learned helped them in their career. Some of my former trainees are actually at Stanford so it will be fun to catch up! As my former students go along in their careers, in many cases our roles reverse and they educate me on things, making me the "student" and them the "professor".
I am also a big fan of mentorship more broadly, because I reflect upon my training and career trajectory and find myself extremely lucky that I had wonderful mentors at every step. As such, I try to "pay it forward" by doing my part for the next generation.
What are some of your favorite past times/hobbies?
I love sports, enjoy exercising when I can, and love reading about history and politics. I guess one common thread that links them is that it is really important that one knows their history because in many situations, those lessons can come back and provide insight about current situations. In another life, I likely would have been a sports commentator, a historian, or a political scientist.