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. Smita’s research focuses on applying machine learning methods to high-throughput high dimensional biological data. Smita has been focusing on using manifold learning and deep learning to develop unsupervised algorithmic approaches to naturally process data, visualize it, understand progressions , find phenotypic diversity, and infer patterns. Some of the key projects developed in her Lab include MAGIC (a tool for imputation and denoising of data), PHATE (a powerful new visualization method for high dimensional data that can unveil progression and cluster structures, and SAUCIE (an autoencoder-based deep learning approach for automatically batch correcting, visualizing, denoising and clustering data). These methods have been applied to a variety of biological applications including embryoid body differentiation, the epithelial-to-mesenchymal transition in breast cancer, lung cancer immunotherapy, infectious disease data, gut microbiome data and population genetics data. 

At Yale, Smita teaches two CS/Genetics/Computational Biology cross-listed courses. Advanced Topics in Machine Learning & Data Mining (Spring), and Machine Learning for Biology (Fall). 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 verification of nanoscale logic circuits that exhibit probabilistic effects.



David completed his PhD at the University of Amsterdam (with Professor Peter Sloot) and the Weizmann Institute of Science (with Professor Eran Segal) in Computational Biology. He used thermodynamic and kinetic models to understand how gene regulation, at the population and single-cell level, is encoded in DNA sequence. Currently he’s an Associate Research Scholar in the departments of Genetics and Computer Science at Yale University. His work focusses on developing new machine learning methods for big biological data, with a focus on applying spectral graph methods and deep learning on single-cell data. He co-developed MAGIC, a single-cell data imputation method, and PHATE, a dimensionality reduction and visualization method.


Guy Wolf, Yale Faculty collaborator

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.

You can visit Professor Wolf's website here:



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.



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.


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.


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.


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


Ofir Lindenbaum received B.Sc. degrees in 2010, in Electrical Engineering and in Physics (both summa cum laude), from the Technion - Israel Institute of Technology. He completed his Ph.D. in electrical engineering at the School of Electrical Engineering at Tel-Aviv University in 2017. His areas of interest include machine learning, applied and computational harmonic analysis, musical signals analysis.


Kevin completed his 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 is a post-graduate affiliate in the Krishnaswamy lab, working on applying deep learning methods to biological datasets. He graduated from Yale College in 2017 with a B.S. in Computer Science and Mathematics, and is currently applying to graduate programs in Computer Science. His other research interests include reinforcement learning and robotics, and is jointly working with Professor Aaron Dollar in the Mechanical Engineering Department.