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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 VAN DIJK, ASSOCIATE RESEARCH SCHOLAR

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.

 

 

 

 

 

Kevin Moon, Postdoctoral Associate

Co-advised by Ronald Coifman in the Mathematics and Computer Science departments. He completed my PhD in Electrical Engineering and Computer Science (EECS) under Dr. Alfred Hero at the University of Michigan in Ann Arbor in 2016. He completed an MS (2016) in Mathematics at Michigan and an MS (2012) and BS (2011) in Electrical Engineering from Brigham Young University in Provo, Utah.

Kevin's general research interests are in the development of theory and applications in machine learning, big data, information theory, manifold learning, statistical signal processing, statistical learning theory, estimation, graphical models, and random matrix theory. His main projects currently focus on data visualization, data denoising, and nonparametric estimation of information theoretic measures such as entropy, mutual information, and information divergence. I focus primarily on single-cell data (e.g. CyTOF and single-cell RNA-sequencing).

 

 

 

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: http://users.math.yale.edu/users/gw289/

 

 

 

 

Ofir Lindenbaum, Postdoctoral researcher

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 has submitted 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.

 

 

 

 

 

 

 

MATT AMODIO, PH.D CANDIDATE

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 BURKHARDT, PH.D CANDIDATE

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 completed his BSc in Mathematics & Statistics at the University of Melbourne, Australia in 2016. After graduating, Scott joined Terry Speed's lab at the Walter and Eliza Hall Institute of Medical Research, studying computational methods for detecting epigenetic modifications to DNA in nanopore sequencing. Scott began his PhD in Computational Biology and Bioinformatics in 2017, and is currently completing a rotation project with Smita Krishnaswamy, in which he is looking to generalize autoencoding neural networks to model time series data.

 

 

 

 

 

 

 

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.

 

 

 

 

 

 

 

Krishnan Srinivasan, Post-Graduate researcher

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.

 

 

 

 

 

 

 

 

Alexander Strzalkowski,YALE College UNDERGRADUATE STUDENT

Alexander Strzalkowski is a Senior Undergraduate at Yale pursuing a B.S. in Applied Mathematics. His current research interests revolve around applying and innovating unsupervised learning algorithms on biological and biomedical data in order to aid in answering the various scientific questions such data poses. In the Krishnaswamy Lab, his efforts have primarily been focused on analyzing Imaging CyTOF (IMC) data, which is a novel biomedical imaging technology. IMC can take high dimensional images of tissues being able to currently measure 32 proteins at every pixel with a resolution of 1 micrometer. More recently, he has taken on exploring possible extensions of Autoencoders to a generative model capable of generating transitions.

Alexander was born and raised in Bradenton, Florida and does not enjoy long walks on the beach. Instead, he enjoys learning about random facts from various fields, especially mathematics, computer science, and biology. In his free time, he also likes to play guitar and trumpet.