Organizers

(alphabetically by last name)


Madalina Fiterau is a Postdoctoral Fellow in the Computer Science Department at Stanford University, working with Professors Chris and Scott Delp in the Mobilize Center. Madalina has obtained a PhD in Machine Learning from Carnegie Mellon University in September 2015, advised by Professor Artur Dubrawski. The focus of her PhD thesis, entitled “Discovering Compact and Informative Structures through Data Partitioning”, is learning interpretable ensembles, with applicability ranging from image classification to a clinical alert prediction system. Madalina is currently expanding her research on interpretable models, in part by applying deep learning to obtain salient representations from biomedical “deep” data, including time series, text and images. Madalina is the recipient of the GE Foundation Scholar Leader Award for Central and Eastern Europe. She is the recipient of the Marr Prize for Best Paper at ICCV 2015 and of Star Research Award at the Annual Congress of the Society of Critical Care Medicine 2016. She has organized two editions of the Machine Learning for Clinical Data Analysis Workshop at NIPS, in 2013 and 2014.


Jason Fries is a Postdoctoral Fellow in Computer Science at Stanford University. He works with Prof. Chris Ré and Scott Delp as part of Stanford's Mobilize Center, an NIH Big Data to Knowledge (BD2K) site of excellence that explores data science approaches to understanding diseases of human mobility. His research focuses on information extraction and predictive modeling using unstructured text and time series data from the electronic medical record. His most recent projects include phenotype discovery in chronic diseases like osteoarthritis and modeling postoperative trajectories of pain and function after joint replacement surgery. Jason received his PhD from the University of Iowa in 2015, co-advised by Alberto Segre and Dr. Phil Polgreen, working as part of Iowa's Computational Epidemiology Research Group. His thesis explored large-scale information extraction in electronic medical record text as well as machine learning approaches to public health surveillance using social media.


Marzyeh Ghassemi is a PhD student in the Clinical Decision Making Group (MEDG) in MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Prof. Peter Szolovits. Marzyeh’s research focuses on using machine learning techniques and statistical modeling to predict and stratify relevant human risks. Previous work has focused on creating probabilistic latent variable models to estimate the underlying physiological state of patients during critical illnesses, and understanding the development and progression of conditions like hearing loss and vocal hyperfunction using a combination of sensor data and clinical observations. Prior to MIT, Marzyeh received two B.S. degrees in computer science and electrical engineering with a minor in applied mathematics from New Mexico State University as a Goldwater Scholar, and a MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar. She also worked at Intel Corporation in the Rotation Engineering Program, and then as a Market Development Manager for the Emerging Markets Platform Group. While at MIT, Marzyeh has served on MIT’s Corporation Joint Advisory Committee on Institute-wide Affairs, and on MIT’s Committee on Foreign Scholarships.


David Kale is a fourth year PhD student in Computer Science at the University of Southern California. His research uses machine learning to extract insights from digital data in high impact domains, including but not limited to healthcare. He is especially interested in applications of modern deep learning architectures to complex, structured data with indirect or weak supervision. David is advised by Prof. Greg Ver Steeg of the USC Information Sciences Institute. He previously worked as a data scientist and researcher with the Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit (VPICU) at Children's Hospital LA. David helped found and organizes the annual Meaningful Use of Complex Medical Data (MUCMD) Symposium and the Machine Learning and Healthcare Conference (MLHC). He is a co-founder of Podimetrics and a judge in the Qualcomm Tricorder XPRIZE Competition. David is supported by the Alfred E. Mann Innovation in Engineering Fellowship.


Theofanis Karaletsos worked towards his PhD in computer science at the Max Planck Institute For Intelligent systems in Tübingen, Germany, supervised by Prof. Karsten Borgwardt and John Winn. He is currently at the Memorial Sloan Kettering Cancer Center in New York supervised by Prof. Gunnar Rätsch working on machine learning with a focus on healthcare including modeling of electronic health records, mining cancer pathology images and temporal progression models of patient records. His research interests revolve around probabilistic modeling of structured data to discover and quantify phenotypes from images, text and time­series. Before, Theofanis Karaletsos got his Diploma (MS equivalent) from the Technical University in Munich. Theofanis has received various fellowships, such as a Microsoft Research PhD Scholarship and the Ministerial Scholarship for undergraduate studies from the Bavarian Ministry Of Science, Research and the Arts.


Rajesh Ranganath is a PhD candidate in computer science at Princeton University. His research interests include statistics, machine learning with a focus on methods for healthcare including chronic kidney disease risk stratification and simultaneous survival analysis. He works in SLAP group under the supervision of David Blei. Before starting his PhD, Rajesh worked as a software engineer for AMA Capital Management. He obtained his BS and MS from Stanford University in computer science under the supervision of Andrew Ng and Dan Jurafsky. Rajesh has won several awards and fellowships including the NDSEG graduate fellowship and the Porter Ogden Jacobus Fellowship, the highest honor bestowed upon doctoral students at Princeton University.


Peter Schulam is a PhD student in the Computer Science Department at Johns Hopkins University where he is working with Suchi Saria. His research interests lie at the intersection of machine learning, statistical inference, and healthcare with an emphasis on developing methods to support the personalized medicine initiative. Before coming to JHU, he obtained his MS from Carnegie Mellon’s School of Computer Science and his BA from Princeton University. He is supported by a National Science Foundation Graduate Research Fellowship.


Uri Shalit is a postdoctoral researcher in the Courant Institute of Mathematical Sciences, New York University, working at David Sontag's Clinical Machine Learning Lab. His research is focused on creating new methods for finding causal relationships in large-scale high-dimensional observational studies. One of the major motivations for his research is applications in healthcare and clinical medicine. Uri completed his PhD studies at the School of Computer Science & Engineering at The Hebrew University of Jerusalem, under the guidance of Prof. Gal Chechik and Prof. Daphna Weinshall. His PhD focused on using Riemannian matrix manifold optimization tools to build better and faster machine learning algorithms. From 2011 to 2014 Uri was a recipient of Google's European Fellowship in Machine Learning. Previously he has received several fellowships and awards, including the Daniel Amit fellowship for significant contribution in theoretical or computational neuroscience, and the Alice and Jack Ormut Foundation PhD Fellowship.


Comments