Smita Krishnaswamy is an Associate Professor in the departments of Computer Science (SEAS) and Genetics (YSM). She is part of the programs in Applied Mathematics, Computational Biology & Bioinformatics and Interdisciplinary Neuroscience. She is also affiliated with the Yale Institute for the foundations of data science, Wu-Tsai Institute, Yale Cancer Center. Smita's lab works on fundamental deep learning and machine learning developments for representing and learning from big data. Her techniques incorporate mathematical priors from graph spectral theory, manifold learning, signal processing, and topology into machine learning and deep learning frameworks, in order to denoise and model the underlying systems faithfully for predictive insight. Currently her methods are being widely used for data denoising, visualization, generative modeling, dynamics. modeling, comparative analysis and domain transfer in datasets arising from stem cell biology, cancer, immunology and structural biology (among others).
Smita teaches several courses including: Deep Learning Theory and Applications, Unsupervised learning, and Geometric and Topological Methods in Machine Learning. Prior to joining Yale, Smita 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 obtained her Ph.D. from EECS department at University of Michigan 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. Smita's work over the years has won several awards including the NSF CAREER Award, Sloan Faculty Fellowship, and Blavatnik fund for Innovation.

Ian Adelstein is a Lecturer and Associate Research Scientist in the Mathematics Department at Yale University. He is also part of the Applied Math program at Yale where he serves as Associate Director of Undergraduate Studies. He is the Director of the SUMRY REU program at Yale. His research brings theoretical methods from Riemannian geometry to the field of geometric manifold learning, informing dimension reduction, metric recovery, and curvature computations. He received his PhD in mathematics from Dartmouth where he used variational methods to study closed geodesics on Riemannian manifolds.

Arman Afrasiyabi is a postdoctoral associate at Yale University in the Krishnaswamy Lab. He is interested in the development of representation learning algorithms designed to process multi-modal data, specifically those requiring minimal to no supervision. In 2022, Arman received his Ph.D. from Université Laval & Mila-Montréal Canada, where he concentrated on research in meta-learning representations. During his spare time, he enjoys hiking and playing soccer.

Dhananjay Bhaskar is a postdoc in Genetics at the School of Medicine and a Yale-Boehringer Ingelheim Biomedical Data Science Fellow. His interdisciplinary research combines topological data analysis, machine learning, and mathematical modeling with applications in biophysics and biomedical research. Previously he worked on quantitative analysis of pattern formation and phase transitions in active matter, automated embryo selection for IVF, and unsupervised methods for analyzing cell shape and motility in time-lapse microscopy.Dhananjay received his Ph.D. in Biomedical Engineering and Sc.M. in Data Science from Brown University. Prior to Brown, he studied Computer Science and Applied Mathematics at the University of British Columbia. When he is not busy transforming coffee beans into code, Dhananjay enjoys hiking, swimming, and playing badminton.

Edward is a post-doctoral associate at Yale university and joined the Krishnaswamy lab in April 2023. His research combines dynamical systems, geometric deep learning and causality with a strong focus on healthcare applications.
Previously, Edward has received his PhD in machine learning for healthcare from KU Leuven, Belgium. During his PhD, he was a visiting fellow at ETH Zürich, the University of Toronto, and MIT. He was also a summer inter at Google X and Microsoft research, where he worked on biomedical applications. He holds a Master in Financial Mathematics and in Electrical Engineering. In his free time, Edward composes music about the Anthropocene and is a part-time undergrad student in philosophy.

Holly is a postdoctoral fellow in Genetics and Human Translational Immunology at Yale School of Medicine. Her research is focused on geometric deep learning for various kind of biomedical data to identify cellular populations, gene expression patterns, and metabolic programs operative in different biological processes. Applications include predicting metabolic activity of cells using deep learning on biochemical networks represented as directed graphs and geometric feature extraction and interpretation on point clouds constructed from single-cell gene expression data. She holds a PhD in Immunobiology from Yale where she studied metabolic regulation of immunity advised by Drs. Richard Flavell and Joe Craft as an NSF GRFP fellow, prior to which she spent 1 year working with Dr. Marion Pepper in UW Immunology and 2 years working with Dr. Justin Taylor at Fred Hutch Cancer Research Center. As an undergraduate at University of Washington she initially majored in English where she became interested in philosophy of science and the evolving reciprocal relationship between scientific rationality and culture. This ultimately led her to change majors and complete a B.S. in molecular, cell, and developmental biology, however her research interests continue to be motivated by interdisciplinary context and implications.

Rahul is a postdoc in Computer Science Department and a Wu Tsai Postdoctoral Fellow. He is also associated with the Brain Function Laboratory at Yale School of Medicine. His research interests are in the areas of signal processing, graph neural networks, and machine learning applications in bioinformatics and neuroscience. He received is PhD in Machine Learning from Georgia Institute of Technology in 2023. During his PhD, he worked on various topics including signed graph neural networks, collective graphical models, optimal transport, and distributional reinforcement learning.

Scott is an Associate Research Scientist at Yale University in the Krishnaswamy Lab and Giraldez Lab. As a computational biologist from a molecular biology lab background, his role bridges computational and biological domains, developing and applying novel tools for biomedical data analysis. Scott is motivated by the thrill of biological discovery and my fascination with finding coherent patterns within complex data. He loves the challenge of communicating science in a way that can connect with the intellect of people from outside his field. The tenets of Scott's career are integrity, enjoyment and discovery.

Aarthi is a PhD student in Computational Biology and Bioinformatics at Yale University. She is interested in developing improved methods to study cancer immunogenomics. To this end, her research focuses on machine learning and graph-based methods that leverage the rich properties of single-cell RNA sequencing data.Aarthi graduated from UC San Diego with a B.S. in Bioengineering: Bioinformatics. While there, she performed resesarch in Dr. Ferhat Ay's lab, where she analyzed Hi-C sequencing data to interpret the conserved organizational features of parasite genomes. She also worked with Dr. Theresa Gaasterland to study cancer genomics and structural variation. Over the summers, Aarthi interned at Auris Health and Regeneron, where she further grew her skillset in synergistic approaches to computer science and biological discovery.

Chen Liu joined the lab as a PhD student in Computer Science at Yale University starting 2022. His research interest is machine learning for healthcare. He primarily worked on radiological images in the past but also enjoys exploring new areas such as computational biology and genomics. He is excited about developing generalizable machine learning techniques that are widely applicable.
Chen grew up in Shanghai, China. He completed his B.S. and M.S. degrees at Bucknell University and Columbia University respectively. Prior to joining Yale, he was a senior Research Scientist at GE Healthcare, working on deep learning in X-ray and MRI data.

Danqi Liao is a Ph.D. student in the Computer Science department at Yale University. Her research interests include geometric, manifold learning, and deep learning. Specifically, she is interested in studying the characteristics that make expressive and generalizable embeddings and developing efficient and principled methods to achieve such representations.
Danqi graduated Magna Cum Laude from Northwestern University and obtained her master's degree from Princeton University. While at Princeton, she worked on computer perception and video recognition in Dr. Olga Russakovsky's lab. Before graduate school, she worked as a machine learning engineer at Meta for two years. Outside of research, she is a big fan of soccer, tennis, and video games. Her favorite video game series is The Last of Us.

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

Kevin is a MD-PhD student with an interdisciplinary interest in developing deep learning models for therapeutic design, with an emphasis in cancer immunotherapeutics. He is co-mentored by Akiko Iwasaki. Prior to his time at Yale, Kevin spent 3 years in the biotech industry conducting research at companies like ImmunityBio (cancer genomics) and Genentech (antibody immunogenicity). His graduate work focuses on developing interpretable models for therapeutic protein design and immunogenicity prediction, for applications such as cancer neoantigen vaccines and immunotherapy target discovery. In his free time, Kevin enjoys playing on the Yale club soccer team, playing piano, and eating chocolate chip cookies.

Siddharth studied Computer Science at UC Irvine and is currently a CS PhD student at Yale. Siddharth works on directed graph methods to model metabolic networks and work on geometric scattering methods in the lab. In his free time, he play the guitar, cricket and daydream.

Xingzhi is a PhD student of Computer Science at Yale University. His interests lie in deep learning and signal processing for biological problems. He has done research on interpretable machine learning, single-cell label transfer, graph spectral denoising, and neural differential equations for EHR data. He obtained his master's in Statistics and Data Science from Yale and his undergraduate degree in Mathematics and Applied Mathematics from the University of Chinese Academy of Sciences. He also interned at Microsoft Research Asia.

Charles is an undergraduate at Yale studying applied math and chemistry. He is generally interested in developing tools to model biological processes. In the lab he is currently working on graph signal processing. Outside of research, Charles’s interests in chemistry manifests itself in a love of cooking and eating food.

Kincaid is an undergraduate at Yale studying mathematics, computer science, philosophy, and exploring their practical intersection in artificial intelligence. He is particularly interested in developing mathematical tools to shine light into the "black box" of modern deep neural networks.