Principal Investigator · Department of Systems Biology, Harvard Medical School
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Debora is a mathematician and computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for interpreting genetic variation in a way that impacts biomedical applications. During her PhD, she quantified the potential pan-genomic scope of microRNA targeting and combinatorial regulation of protein expression and co-discovered the first microRNA in a virus. As a postdoc she and her colleagues cracked the classic, unsolved problem of ab initio 3D structure prediction of proteins using a maximum entropy probability model for evolutionary sequences. She has developed this approach to determine functional interactions, biomolecular structures, including the 3D structure of RNA and RNA-protein complexes and the conformational ensembles of apparently disordered proteins. Her lab is interested in developing methods in deep learning to address a wide range of biological challenges including predicting the effects of genetic variation and sequence design for biosynthetic applications.
joint with Chris Sander
Asif's background is in machine learning, with research interests in representation learning and how models learn invariances and equivariances. He now develops machine learning methods for cancer research and clinical applications, including scalable models of longitudinal electronic health records for pan-cancer risk stratification and models of drug and CRISPR perturbation data to guide combination therapies.
Coming from a background in computer science and biology, Rose was a Systems Biology graduate student in the Marks Lab, defedning her PhD in August 2025.
Delaram’s background spans biology and computer science, and she has worked at their interface since her undergraduate studies. She completed her PhD at the University of Toronto, developing interpretable statistical and machine-learning methods to study sources of variation in single-cell RNA-seq data, followed by a bridge postdoc focused on computational approaches for spatial transcriptomics. She has joined the Marks Lab as a postdoc and is excited to explore the world of proteins and immunology through machine learning.
Kai’s background spans physical chemistry and biology, and he has been working at the intersection of these fields since his master’s studies. He earned dual PhD degrees in Singapore and the Netherlands, where his research focused on applying AI to understand protein dynamics. He is excited to join the Marks Lab and use machine learning to explore enzyme function and design.
Biological & Biomedical Sciences
Murphy is a Biological & Biomedical Sciences Ph.D. student. Previously, he studied computer science at Northwestern, explored the game industry, and then completed a post-bac at Indiana University School of Medicine. He’s interested in using molecular modeling and evolution to understand and exploit the behavior of complex systems.
Systems, Synthetic, and Quantitative Biology
Leo is a PhD student in the Systems, Synthetic, and Quantitative Biology program at Harvard University. His research focuses on biomolecular design, modeling, and discovery. Before Harvard, he completed his undergraduate and master’s studies at Duke Kunshan University and Duke University. Outside the lab, he enjoys exploring food, music, and sports.
Medical Engineering and Medical Physics (bioengineerings concentration) - Harvard-MIT Health Sciences and Technology program
Abigail is a Ph.D. student in the Harvard-MIT HST program in the Marks Lab. She previously graduated from the University of Southern California with degrees in Computational Neuroscience and Philosophy and conducted research in structural virology and immunology at The Scripps Research Institute under Andrew Ward. Abigail is interested in studying how viral evolution can inform gene therapy and vaccine design.
Bioinformatics and Integrative Genomics
Navami is a PhD student in the Bioinformatics and Integrative Genomics Program at Harvard Medical School completing a rotation in the Marks Lab. She is interested in applying machine learning to improve potency of vaccines and therapeutic antibodies.
Systems, Synthetic, and Quantitative Biology
Gaeun is a first year PhD student at Harvard, co-advised by Sophia Liu. Previously, she developed microfluidic assays for protein engineering at Stanford University, and explored molecular dynamics for drug development at D.E. Shaw Research. She's now interested in using ML models to improve our understanding of immune repertoires. Outside of lab, Gaeun loves skiing uphill, really loves skiing downhill, and copes with the warmer months by distance running and cooking.
Harvard-MIT Health Sciences and Technology
Rachit is a first-year Ph.D. student at Harvard and MIT, co-advised by Gaurav Gaiha. His research focuses on developing machine learning methods to guide the design of T-cell receptor therapies and T cell-based vaccines for infectious diseases. He previously studied bioengineering at MIT, where he developed high-throughput experimental tools for T cell antigen screening. Outside of lab, Rachit enjoys playing the viola, composing/arranging music, hiking in the White Mountains, cooking, and following Formula 1.
Systems, Synthetic, and Quantitative Biology
Fiona studied Chemistry at the University of Illinois where she did protein biochemistry and antibiotic drug discovery. After a few years in industry as a protein engineer, she joined the Systems Biology program at Harvard where she’s excited to dive further into protein design and synthetic biology. Outside of science, she likes playing bassoon in community orchestras, making functional pottery, and knitting/crocheting.
Computational Biology and Quantitative Genetics
Khaoula completed a double degree in applied mathematics and machine learning at Institut Polytechnique de Paris and École Normale Supérieure Paris-Saclay (ENS Cachan), and is now in the CBQG program at Harvard. She is interested in using machine learning to advance biology, improve health outcomes, and inform policy. Before Harvard, she worked on AI modeling of protein-small molecule binding at AQEMIA and led their internal hit identification pipeline. She also spent time as a venture investor in AI and Sciences.
Computational Science and Engineering
Zilin is a master’s student in Computational Science and Engineering at Harvard University, researching AI for biomolecular discovery and design, particularly RNA and protein modeling. Before Harvard, she studied Computer Science at Worcester Polytechnic Institute. Her previous research spans language model reasoning, robotics, and robust machine learning.
Computational Biology and Quantitative Genetics
Sree studied Data Science and Biology at Northeastern University, where he worked on computational biology in industry-focused research roles. At Nvelop Therapeutics, he developed genomic analysis pipelines and built tools for visualizing data, while at Cellino Biotech he fine-tuned machine-learning models for stem-cell imaging. Now in the CBQG program, he’s excited to keep advancing big-data and machine-learning approaches in biology. Outside of science, he enjoys basketball, cooking, gaming, traveling, and is always looking for new movies, tv shows and music.
University of Cambridge
Sergio is a PhD student at the University of Cambridge and the Wellcome Sanger Institute. He is interested in using machine learning to engineer biology, and is working on enzyme design.
University of California San Francisco
Noah is a visiting postdoc in the lab. He did his PhD at Stanford where he analyzed multiplexed microscopy image data in the Angelo and Curtis labs. He then went to UCSF, where he is currently doing a postdoc in the Coyote-Maestas lab. Noah is visiting the lab to work on the analysis and interpretation of deep mutational scanning datasets. Noah is interested in cancer, human genetics, machine learning, and proteins.
Seoul National University
Sunny is a visiting PhD student in the Interdisciplinary Program in Bioinformatics at Seoul National University, where she is advised by Martin Steinegger. She did her undergraduate studies in Computer Science and Biology, and has a background in software development and data visualization. She is now interested in developing pipelines for viral analysis and applying machine learning to predictive viral forecasting, with a focus on pandemic preparedness and zoonotic spillover. Outside the lab, she loves playing with her cat, drinking coffee, touring cafes, and keeping up with the latest kpop/kdramas.
MIT Undergraduate Intern
Dean is an undergraduate student at MIT studying Computer Science and Molecular Biology. He is building a benchmarking platform to characterize the failure modes of supervised machine-learning models for protein engineering, exploring how factors like sequence encoding, dataset size, and train/test splits shape model performance and generalizability. Outside of lab, Dean rows for the MIT varsity lightweight rowing team, plays with his dog, spends time with his family, and likes eating food.
Harvard University
Julia is an undergrad at Harvard studying Molecular and Cellular Biology with a secondary in Computer Science. Currently, she’s working on computationally parsing viral sequences and structures to predict B-cell epitopes. Beyond science, she also loves making digital animations, reading, banging on the timpani in Harvard Pops Orchestra, and going on runs around the Charles!
Software Engineer
Ethan has worked on a variety of biological machine learning problems in various groups, including Neil King's lab at the Institute for Protein Design and the Molecular AI team at Novo Nordisk. His past projects have involved biophysical property prediction, protein nanoparticle design, and antibody-antigen binder prediction.
Project Manager
Jake has been a project manager for various university and healthcare systems in the US and Europe since 2016. He has a background in Dramatic Art, Chemistry, and Higher Education Student Affairs. He has enjoyed expanding his network in biology, computer science, mathematics, and education-based research projects.
The Marks lab works very closely with the lab of Chris Sander. Find all our collaborators from the Sander lab at sanderlab.org.