Graduate Spotlight: Raghav Sehgal's Journey Through Yale's CBB PhD Program

Raghav Sehgal, PhD '25, recently completed his doctoral work in Yale’s Computational Biology and Biomedical Informatics program, where he focused on developing advanced biomarkers for aging and human health. In this interview, he reflects on his research journey, what inspired his path, and the impact he hopes his work will have on the future of medicine.

A: Thank you! My research focuses on developing biomarkers of aging using computational biology and large-scale multi-omic data. Specifically, I’ve worked on algorithms that use DNA methylation and other biological signals to quantify how different organ systems age—an effort to move from one-size-fits-all metrics of aging to individualized, system-level diagnostics. This work sits at the intersection of geroscience, machine learning, and translational medicine.

A: Early on, I was fascinated by the idea that aging is not just inevitable—it’s quantifiable. Discovering that molecular features could be used to track biological age more accurately than the calendar led me into this field. Computational biology gave me the tools to integrate and interpret complex biological data, and biomedical informatics showed me how these tools could translate into clinical insights.

A: At its core, my work is about shifting medicine from reactive to proactive—redefining how we measure, monitor, and ultimately slow aging. Rather than waiting for disease to appear, we can now detect subtle signs of physiological decline years in advance and tailor interventions accordingly. These aging biomarkers are already being used in clinical trials to test longevity drugs and in health optimization programs to personalize care. Some have even made their way into the clinic and marketplace—moving from algorithm to action in record time. It’s about giving people more healthy years, not just more years.

A: I’m most proud of developing SystemsAge—a platform that quantifies aging across 11 distinct physiological systems from a single blood draw. It goes beyond asking if someone is aging faster to pinpointing where—whether it's immune, metabolic, cardiovascular, or another domain. What started as a research framework has now been commercialized and licensed for clinical use, with adoption by longevity clinics, biotech companies, and academic groups. It’s being used to stratify patients in clinical trials, guide personalized interventions, and validate the effects of next-generation gerotherapeutics. Seeing it move from code to clinic has been one of the most rewarding parts of my journey.

A: The Forbes recognition highlighted my contributions at the intersection of artificial intelligence and human health, particularly in the development of aging clocks and foundation models for epigenomic data. My work has focused on using machine learning to quantify biological aging, model intervention effects, and build tools that are both scientifically rigorous and clinically useful. This recognition reflects years of work aimed at bridging computational methods with translational impact—turning complex biological data into actionable insights for optimizing health span.

A: CBB provided a rare environment where methodological rigor in data science is balanced with deep biological grounding. It gave me the flexibility to explore everything from epigenomics to clinical trials, while also training me to ask the right scientific questions. The BIDS community added an incredible layer of interdisciplinary thinking—bringing together engineers, statisticians, and biologists under one roof.

A: Absolutely. Working under Albert Higgins-Chen, MD, PhD, was transformative—his mentorship style encouraged both independence and deep scientific rigor. I also received invaluable support from Graeme Mason, PhD, and Steve Kleinstein, PhD, who helped me refine my thinking around data integration and computational modeling. From the Department of Psychiatry, Rajita Sinha, PhD, provided crucial guidance in bridging biological aging with stress and behavioral health research. I also learned a great deal from Morgan Levine, PhD, whose work on aging clocks laid the foundation for much of what I do. Being immersed in Yale’s vibrant aging biology community gave me access to world-class expertise across psychiatry, genomics, and translational medicine. Courses in systems biology and Bayesian modeling were especially pivotal in shaping my interdisciplinary approach.

A: It wasn’t just encouraged—it was essential. My work wouldn’t exist without blending computational methods with biological insight and clinical relevance. CBB gave me access to experts in each of these domains and the space to build bridges between them. That interdisciplinary fluency became one of the most valuable outcomes of my PhD.

A: One of the most challenging aspects of my PhD was navigating the inherent uncertainty of interdisciplinary research. Working at the edge of multiple fields—computational biology, clinical medicine, and aging science—often meant there were no clear templates. I had to build tools, assemble datasets, and validate methods from scratch, which was both exciting and demanding.

Another pivotal challenge came when my original mentor, Morgan Levine, PhD, transitioned out of academia. Morgan was instrumental in shaping my early direction and intellectual foundation, and I’ll always be grateful for her guidance. Her departure required me to reorient within my research program and find new mentorship to continue pushing forward.

I was incredibly fortunate to have found that in Albert Higgins-Chen, PhD, whose mentorship and leadership helped me grow as a scientist. The support from the CBB leadership and the broader Yale aging biology community also made a big difference. Ultimately, what helped me most was having a strong network—mentors, peers, and collaborators—who created a sense of continuity and community. And learning to ask for help, early and often, became one of the most valuable lessons of my PhD.

A: One of the biggest lessons I’m taking with me is that translation is the true measure of scientific impact. It’s easy to get caught up in papers, metrics, and academic cycles—but what ultimately matters is whether your work leaves the lab and enters the world. Yale taught me to value rigor and reproducibility, but also to constantly ask: How will this change practice? Who does it help? I’ve learned to pursue research that doesn’t just generate knowledge, but moves it—into clinics, products, and public health systems. That translational mindset will continue to guide me wherever I go next.

A: I’m continuing in academia as faculty at Yale, where I’ll be leading research at the intersection of computational biology, aging biomarkers, and translational medicine. In parallel, I advise investment groups focused on identifying and evaluating emerging longevity technologies. My work centers on scientific and translational due diligence—assessing omics-driven biomarkers, mechanistic targets, and platform therapeutics—to guide capital deployment across preclinical, clinical, and regulatory stages of development. I also direct a translational longevity clinic, based in Prague and Florida, that integrates omics-based biomarkers with individualized gerotherapeutic strategies to bridge frontier science and preventive care.

Beyond academia and investing, I’m deeply involved in the global longevity innovation ecosystem. Our team was recently selected as a semi-finalist in the $101 million XPRIZE Healthspan Challenge, where we’re working to demonstrate a 20-year reversal in biological aging across cognitive, immune, and musculoskeletal domains. It’s an ambitious effort that combines clinical trials, AI-based biomarkers, and systems-level thinking to move the field from promise to proof.

At every level—research, clinical, entrepreneurial—my goal is to accelerate the development, validation, and equitable deployment of aging interventions that can meaningfully improve human health and resilience.

A: A major challenge I want to address is population-level personalization of aging interventions. Right now, most longevity trials and tools are developed for a narrow slice of humanity. I’m working on generative models that simulate how interventions affect people of different ancestries, gender, and life histories—so that aging science becomes equitable by design.

A: The Forbes recognition has opened doors to cross-sector collaborations and public visibility. I hope to use it to advocate for evidence-based longevity interventions and to make aging science more accessible to younger scientists, clinicians, and entrepreneurs.

A: Stay curious and be fearless about exploring adjacent fields. The most impactful science happens at the interfaces—of disciplines, datasets, and ideas. Also, don't be afraid to build your own datasets or tools. And finally, find mentors who not only guide your research but also support your growth as a thinker and leader.

A: Running the “Decoding Longevity” podcast was a creative outlet for me—I got to interview scientists and entrepreneurs working on aging from around the world. Outside the lab, I found community through teaching, mentoring undergraduates, and organizing informal dinners with peers to talk science and life. And of course, regular walks around New Haven helped clear the head.