A new $5 million grant from the Warren Alpert Foundation was recently awarded to the Department of Biomedical Data Science (DBDS) at the Stanford School of Medicine. The grant will fund the training of 15 graduate scholars over the next five years to enhance the education and retention of scholars in computational biology/artificial intelligence (CBAI).
If you are interested in the Warren Alpert CBAI Scholars Program, we will be selecting MS trainees for the 2025-2026 cohort during the graduate application period. Please visit the Prospective Students page for more info.
Meet the 2024/2025 Warren Alpert Computational Biology/AI Scholars Cohort
Justin Adjasu: MS trainee, 1st year
What you will be working on?
What I find the most fascinating is all the ways that seemingly different aspects of our lives & health intersect and influence one another. With that in mind, I plan on engaging on work that incorporates data from various range of modalities, from genomic sequencing data, to diet, to survey data on community & familial support structures. Specifically, I’m interested in tackling problems capable of improving quality of life & long-term health for people with conditions that disproportionately affect historically underserved communities. For example, right now I’m in the beginning stages of a project aimed at using clinical & genomic data to better match hypertensive patients to a regimen that effectively manages their condition with minimal side effects.
What are you looking forward to the most in your experience as a WA Scholar?
What I’m looking forward to the most is having the chance to learn from and work with some of the best minds in the space of biomedical informatics and computational biology here at Stanford. It doesn’t take much investigation to realize that, amongst both students and faculty here, around every corner is someone who has made a foundational discovery, published a method that’s seen widespread adoption, or is on the cusp of a finding that could reshape some aspect of how we think about using computational methods to support the mission of healthcare. From research seminars, to lecture breakouts, to informal discussions with peers and faculty, there’s no shortage of opportunity to share ideas, pitch questions, and collaborate on meaningful and engaging projects.
How you see this shaping your research and ultimately, career?
Within the Warren Alpert program, in DBDS, and here at Stanford more generally, one thing that’s been very clear to me is the emphasis on taking interdisciplinary approaches and viewing the problems we want to solve in healthcare from all applicable lenses. Who will derive benefit from the problems we work to address? How will proposed innovations fit into real clinical practice? How would these developments fit into the existing payor structures? Even in the short time I’ve been here to this point, I’ve been well trained in how to begin thinking about the problems I want to tackle with a more holistic view. My ultimate goal is to become teaching faculty, as I’ve rarely found experiences more rewarding than those I’ve had in teaching and mentorship, especially when both parties share the same passion for the subject matter. As I continue working towards this goal, I think that this program’s focus on honing the thinking and skills necessary to taking a comprehensive view on work in this space will be invaluable for me. I’ll be taking to heart all the mentorship and instruction I’m able to receive in learning the right ways to think about and solve problems in the domain of precision medicine, and I very much look forward to the day I’ll be able to pass that knowledge on to others.
From your perspective, what does it mean to you to be a WA Scholar?
To me, being a Warren Alpert Scholar means being granted the opportunity to play my part during a revolutionary time in the healthcare domain. As humans we all come from unique and diverse backgrounds, whether the signs and labels of each part of our identity and history be visible or not. In the age of precision medicine and personalized health, providing the ideal healthcare experience for the individual requires properly contextualizing and accounting for these differences between us. The breadth of available data and the scale of modern computational tools position us to be well equipped to do just that, but these tools and the solutions they give birth to can only be as respectful and conscientious of individuals’ differences as the researchers and innovators building and utilizing them.
As a Warren Alpert Scholar, I’ll have the opportunity to be one amongst many voices and listeners in this space. By leveraging a research community that is growing to be as rich in culture and diversity as the populations it serves, we can address the biggest problems in healthcare via computational solutions whose benefits are shared equitably. At the stages of medical problem selection, cohort selection in data collection, feature selection in model training, model evaluation, clinical deployment, and so many other steps in the pipeline from biomedical research to impactful healthcare solutions, we have the responsibility to make sure that the knowledge and methods we develop bring value to everyone in some way. My goal as a Warren Alpert Scholar is to not lose sight of what I use the education and training I receive to drive meaningful discovery and change for the communities dearest to me, and support others in doing so for the communities closest to them.
Stephanie Arteaga: Ph.D trainee, 2nd year, PI – Russ Altman
What you will be working on?
I will focus on developing artificial intelligence (AI) and machine learning (ML) methods to predict pharmacogenetic phenotypes in underrepresented populations. My work aims to address the biases that exist in current pharmacogenetic research, which have historically focused on individuals of European ancestry. By utilizing large biobank datasets such as the All of Us Research Program and the Million Veteran Program, I plan to identify and characterize genetic variations in diverse populations and develop computational models to predict how these variations impact drug response and safety. The ultimate goal is to create tools that help regulators, such as the FDA, make more informed decisions, ensuring that medical products are safe and effective for all groups.
What are you looking forward to the most in your experience as a WA Scholar?
I am most excited about the opportunity to engage with other scholars and leading academics in the field through the Warren Alpert community. Collaborating with experts on integrating multimodal data—from genetic to patient-level information—will deepen my expertise in developing AI/ML methods that merge data science with therapeutic development.
How do you see this shaping your research and ultimately, career?
This fellowship will be instrumental in expanding my research capabilities and professional network. The opportunity to work on human-centered research will provide me with the hands-on experience needed to develop AI/ML methods that can directly influence therapeutic decision-making. By collaborating with experts and having access to cutting-edge resources, I will build a strong foundation for a career focused on developing methods that bridge the gap between technology, medicine, and equity.
From your perspective, what does it mean to you to be a WA Scholar?
Being a WA Scholar represents a commitment to developing innovative healthcare solutions by integrating diverse biomedical data and translating them into actionable therapeutic insights. For me, it is also an opportunity to contribute to equitable healthcare advancements, ensuring that computational methods are developed and validated across diverse populations. This aligns with my passion for inclusive research that drives real-world impact.
Jacob Chang: Ph.D trainee, 3rd year, PI – Sylvia Plevritis
What you will be working on?
I am planning to use Bayesian nonparametrics to model the spatial and temporal cellular organization of tumor and stromal cells in response to drug treatment in assembloid models.
What are you looking forward to the most in your experience as a WA Scholar?
I’m looking forward to building connections and collaborations with computational biology communites at Stanford and beyond.
How you see this shaping your research and ultimately, career?
I am grateful to the WA fellowship and community for funding and supporting an early-stage researcher like myself while I consider future careers in industry or academia.
Cally Lin: MS trainee, 1st year
What you will be working on?
My research interest is in the application of machine learning and artificial intelligence in the biomedical sciences to improve prevention, diagnosis, and treatment. I will be working on multi-modal modeling for oncology.
What are you looking forward to the most in your experience as a WA Scholar?
To flourish and grow as a student and researcher!
I am excited to connect with and learn from leading researchers in the computational biology and artificial intelligence field. I also look forward to joining a community of students who share similar backgrounds and aspirations.
How you see this shaping your research and ultimately, career?
As a first-generation, low-income student, being a Warren Alpert Scholar is a transformative opportunity for me. I never imagined reaching this point—a college graduate, an engineer, and, now, a graduate student at Stanford University. The access to resources and mentorship through this program will open doors that might have otherwise remained closed due to my unfamiliarity with navigating research and graduate school. This support will provide essential guidance throughout my academic journey and beyond. I am confident that the Warren Alpert program will help me to achieve my goal of making a meaningful impact in the field of cancer research.
Edric Tam: Postdoc trainee, PI – Barabara Engelhardt
What you will be working on?
I will be working with Prof. Barbara Engelhardt on using graph machine learning and statistics to analyze spatial genomics data. I am also interested in modern inferential paradigms such as prediction powered inference, conformal prediction, and how to connect these approaches to my background in Bayesian inference.
What are you looking forward to the most in your experience as a WA Scholar?
I am most looking forward to meeting lots of students and collaborators, and getting broadly exposed to the interesting research problems inspired by modern medicine/biology, as well as approaches to tackle them using statistics, ML and AI.
How do you see this shaping your research and ultimately, career?
The WA postdoctoral fellowship will help me develop as a scientist. It provides broad application areas for my research that could inspire new methodology and theory. It also allows for a lot of opportunities to interact with students and collaborators. My experience as a WA postdoctoral fellow will be invaluable for my academic career and my research as I work towards a tenure-track faculty position.
From your perspective, what does it mean to you to be a WA Scholar?
It means a commitment to interdisciplinary innovation and collaboration, as well as generous mentorship.