Smita Krishnaswamy

Smita Krishnaswamy is an Assistant Professor in the department of Genetics at the Yale School of Medicine and Department of Computer Science in the Yale School of Applied Science and Engineering. She is also affiliated with the Yale Center for Biomedical Data Science, Yale Cancer Center, and Program in Applied Mathematics. Smita’s research focuses on developing unsupervised machine learning methods (especially graph signal processing and deep-learning) to denoise, impute, visualize and extract structure, patterns and relationships from big, high throughput, high dimensional biomedical data. Her methods have been applied variety of datasets from many systems including embryoid body differentiation, zebrafish development, the epithelial-to-mesenchymal transition in breast cancer, lung cancer immunotherapy, infectious disease data, gut microbiome data and patient data.

Smita teaches three courses: Machine Learning for Biology (Fall), Deep Learning Theory and applications (spring), Advanced Topics in Machine Learning & Data Mining (Spring). She completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She was trained as a computer scientist with a Ph.D. from the University of Michigan’s EECS department where her research focused on algorithms for automated synthesis and probabilistic verification of nanoscale logic circuits. Following her time in Michigan, Smita spent 2 years at IBM’s TJ Watson Research Center as a researcher in the systems division where she worked on automated bug finding and error correction in logic.


Jessie Huang, Postdoc

Jessie Huang is a postdoc in the department of computer science at Yale. She is generally interested in machine learning and its applications in medical and biological research. She was previously a postdoc in the Reasoning and Learning Lab at McGill University in Montreal. Her research at McGill focused on developing algorithms to learn robust policies with respect to misspecified rewards in reinforcement learning.

Jessie received her PhD in Mechanical Engineering from the University of Michigan, Ann Arbor. She worked as an engineering consultant at Exponent in the San Francisco bay area for two years before moving to Montreal. Before machine learning, she studied fracture mechanics and statistics and how to utilize the natural failure of materials to develop micro-scale tools for biomedical applications.


Roozbeh Yousefzadeh, Postdoc

Roozbeh received his PhD in Computer Science from University of Maryland, College Park. His main research interests are machine learning, scientific computing, and solving hard optimization problems. During his PhD, he developed mathematical methods to study deep learning models as nonlinear functions, interpret their behavior, and to improve and debug them. In summer 2018, he was at the Los Alamos National Laboratory for their Applied Machine Learning Fellowship. He completed a project about interpretation of Gaussian graphical models for unsupervised learning of data gathered by the Mars rover. He has also worked on real-time optimization problems in humanitarian aid delivery systems.


Dennis Shung, Clinical Fellow


Dennis is a board-certified physician trained in Internal Medicine and Gastroenterology. He is working on applying unsupervised machine learning techniques to electronic health record data.

Dennis grew up in Plano, Texas and attended Rice University and Baylor College of Medicine prior to his Internal Medicine residency training at Yale-New Haven Hospital. He is currently completing his clinical fellowship in Gastroenterology & Hepatology with a Master’s of Health Science in Clinical Informatics at Yale; in this capacity, he is developing machine learning models for predicting outcomes in acute gastrointestinal bleeding. His long-term goal is to become a physician data scientist who creates, validates, and tests integrated machine learning models to improve risk stratification for patients.



Daniel earned his B.S. in Microbiology at the University of Massachusetts, Amherst in 2015. He studied the effects of simulated climate warming on soil microbial communities with Dr. Kristen DeAngelis, and this work became the focus of his senior thesis. Daniel joined the PhD program in Genetics at Yale University in 2015, and he is jointly advised by Antonio Giraldez and Smita Krishnaswamy. Daniel is currently applying single-cell transcriptions to characterize the regulatory networks that drive neural differentiation in Zebrafish.


Jay Stanley, PH.D Candidate

Jay Stanley was born in Arkansas. He graduated with his BA in Biology from Hendrix College in 2016. At Yale, Jay is working on his PhD in Computational Biology and Bioinformatics. His research interests are Spectral Graph Theory, Signal Processing, Dimensionality reduction, data visualization

Jay is an avid music enthusiast, rock climber, and whitewater kayaker.



Matt Amodio is currently a doctoral candidate at Yale University in the Department of Computer Science. His research interest is in artificial intelligence, specifically deep learning. He enjoys developing new statistical and information theoretical techniques for use in neural networks, as well as practical optimization techniques general to any learning task.

Matt grew up in a suburb of Cleveland, Ohio. He completed his undergraduate studies at the Ohio State University before working in D.C. for two years. He is an ardent baseball fan as well as a trivia enthusiast.


Scott Gigante, PH.D Candidate

Scott is a PhD student in Computational Biology and Bioinformatics, and is jointly advised by Smita Krishnaswamy and Ronald Coifman. His research interests include deep learning and graph-based methods for the analysis of single-cell genomics data.

Scott completed his Bachelor of Science in Mathematics & Statistics at the University of Melbourne, Australia. Before coming to Yale, Scott was a research technician at the Walter and Eliza Hall Institute of Medical Research, where he developed computational methods for detecting and analysing epigenetic modifications to DNA in nanopore sequencing.

Scott is passionate about open,reproducible science and open source software. In his spare time, Scott enjoys singing with the Yale Camerata and cycling with the Yale Cycling Team.


Alex Tong, PhD Candidate

Alex is a second year computer science phd student at Yale University. His research interests are in machine learning and algorithms. Currently, he is working on structuring latent representations using ideas from graph theory and deep learning.

Alex grew up in Seattle, Washington. He completed B.S. and M.S. degrees in computer science from Tufts University in 2017. He splits his spare time between racing sailboats and trail running. You can visit his website here:



Egbert Castro is a PhD student in the Computational Biology and Bioinformatics track at Yale University. He is interested in methods for learning informative and useful representations of biomolecules. Currently his work in the Krishnaswamy lab focuses on learning latent representations of graph-structured data such as miRNA secondary structure ensembles.

Previously he majored in Pharmacological Chemistry with a minor in Mathematics at UC San Diego. While at UC San Diego, he performed research in the lab of Dr. Rommie Amaro applying molecular dynamics and Brownian dynamics in drug discovery. After graduating, he interned at Genentech applying machine learning to chemical property prediction tasks.



Manik Kuchroo is an MD candidate at Yale School of Medicine and is interested in the intersection of genomics, immunology and oncology. His current projects in the lab include developing a novel manifold based clustering algorithm for single cell genomics data, understanding tumor infiltrating immune cells developmental progression using deep learning and helping characterize interactions in the exoproteome using phage and yeast display technologies in collaboration with the Ring lab.

Manik is originally from Newton, Massachusetts and graduated from Harvard College with a BA in Neurobiology. Manik’s interest in the intersection of genomics and immunology was piqued during his time in Aviv Regev’s Lab at the Broad Institute and during his time with Phil De Jager and Nikolaos Patsopoulos at Harvard Medical School.

While not in the lab or in the hospital, Manik loves playing cricket and watching TV shows.


William Chen, MD Candidate

William Chen is an MD candidate at the Yale School of Medicine. Originally from Hawaii, he graduated with distinction from Stanford University with a B.S. in Computer Science. He is most interested in computational cancer genomics and has been developing novel algorithms for analyzing high-dimensional molecular profiling data. He is currently investigating the effects of various small-molecule inhibitors on epithelial-to-mesenchymal transition (EMT) in breast cancer using mass cytometry. When not in lab or in the hospital, Will can be found playing tennis or drinking bubble tea.


Alex Gonopolskiy, Software Developer

Alex Gonopolskiy is a software developer associated with the lab.  He works on developing novel algorithms for the analysis of biological data.  Prior to that he has worked in algorithmic trading for several years. He graduated from the University of Michigan in 2007 obtained and his MA degree in Computer Science specializing in Intelligent Systems.  He is currently working on an MA in Bioinformatics.

Krishnaswamy Lab Alumni


Guy Wolf received the M.Sc. and Ph.D. degrees in computer science from Tel Aviv University in 2009 and 2014, respectively. During his M.Sc. studies he served in the Israeli Defense Forces in software design and development roles. Between the years 2013 and 2015 he was a post-doctoral researcher in the Computer Science Department at Ècole Normale Supérieure in Paris. Since 2015, he is a Gibbs Assistant Professor in the Applied Math Program at Yale University. One of the main goals of his current research at Yale is to create meaningful interaction between data analysis aspects of machine learning, applied mathematics, and computational sciences, especially in Big Data applications. His research interests include exploratory & high-dimensional data analysis, manifold learning, diffusion geometries, machine learning, and deep learning. He now has his own lab at the University of Montreal.


Previously, David was a postdoc in the Krishnaswamy lab where he developed new machine learning tools for biomedical data analysis. David co-developed tools such as MAGIC, PHATE, and SAUCIE. David is now an Assistant Professor and started his own lab at Yale.


NV joined the lab as a senior in Yale College pursuing a combined B.S./M.S. in Computer Science and a B.A. in Statistics and Data Science. She is focused on studying and developing graph-based and deep learning methods. She is also interested in creating good platforms and tools for researchers, which was what she worked on while interning at Google Research and Facebook Feed Machine Learning. NV is currently working on speeding up kernel methods, including ones developed by Krishnaswamy Lab such as MAGIC and PHATE. While not coding, she spends lots of time ice skating with Yale Collegiate Figure Skating Club and playing percussion for Davenport Pops Orchestra. NV is currently a researcher at Google Brain.


Ofir Lindenbaum completed a postdoctoral fellowship in the Krishnaswamy Lab from 2017 to 2018, and is now a Gibbs Assistant Professor in the Yale University Applied Mathematics Program. Ofir’s areas of interest include machine learning, applied and computational harmonic analysis, musical signals analysis.


Kevin completed a postdoctoral fellowship in the Krishnaswamy Lab from 2016 to 2018, and is now an Assistant Professor in the Department of Mathematics and Statistics at Utah State University, where he focuses on the development of theory and applications in machine learning.


Alexander completed his undergraduate thesis in Applied Mathematics with the Krishnaswamy Lab in 2017. He is currently a PhD student in the Department of Computer Science at Princeton University, where he is focusing on utilizing Probabilistic Graphical Models to extract structure from Electronic Healthcare Records.


Krishnan completed his undergraduate thesis in the Krishnaswamy Lab in 2016 and 2017, and is now a PhD Student in the Computer Science Department at Stanford University, where he’s researching reinforcement learning and its applications to robot learning.


Jad completed a Computational Biology Lab Rotation with the Krishnaswamy Lab in 2017 and 2018, and is now a Masters' Student at Yale University, where he’s working on the intersection of Microbiology, Immunology, and Environmental Science research.