Members Only Roundtable - AI, Perturbation, Integration, Discovery
The pharmaceutical industry has experienced an extraordinary rise in the generation and use of enormous datasets. Nevertheless, there remain great challenges on this front regarding everything from target identification to understanding the performance of marketed products. In the context of this broad impact, we have assembled a group of leading researchers and executives from the MIT-connected community who will address questions of discovery, data integration, and system perturbation analysis, including use of artificial intelligence and machine learning techniques. What is the impact of current developments on high-throughput profiling, computational biology, and validation of gene targets? How do these developments impact the use of chemical libraries, drug-delivery systems, and patient-facing objectives? These are the types of questions that will be addressed in this exciting panel discussion.
Join the MIT Industrial Liaison Program on March 25, 2021 for our ILP Members-only Health Science Technologies webinar, an installment of our Spring 2021 virtual conference series. This webinar follows the Health Science Technologies webinar on March 23 and will be followed by a MIT Digital Health Startup Workshop on March 26.
Manolis Kellis is a professor of computer science at MIT, a member of the Broad Institute of MIT and Harvard, a principal investigator of the Computer Science and Artificial Intelligence Lab at MIT, and head of the MIT Computational Biology Group (compbio.mit.edu). His research includes disease circuitry, genetics, genomics, epigenomics, coding genes, non-coding RNAs, regulatory genomics, and comparative genomics, applied to Alzheimer's Disease, Obesity, Schizophrenia, Cardiac Disorders, Cancer, and Immune Disorders, and multiple other disorders. He has helped lead several large-scale genomics projects, including the Roadmap Epigenomics project, the ENCODE project, the Genotype Tissue-Expression (GTEx) project, and comparative genomics projects in mammals, flies, and yeasts. He received the US Presidential Early Career Award in Science and Engineering (PECASE) by US President Barack Obama, the Mendel Medal for Outstanding Achievements in Science, the NIH Director’s Transformative Research Award, the Boston Patent Law Association award, the NSF CAREER award, the Alfred P. Sloan Fellowship, the Technology Review TR35 recognition, the AIT Niki Award, and the Sprowls award for the best Ph.D. thesis in computer science at MIT. He has authored over 280 journal publications cited more than 148,000 times. He has obtained more than 20 multi-year grants from the NIH, and his trainees hold faculty positions at Stanford, Harvard, CMU, McGill, Johns Hopkins, UCLA, and other top universities. He lived in Greece and France before moving to the US, and he studied and conducted research at MIT, the Xerox Palo Alto Research Center, and the Cold Spring Harbor Lab. For more info, see: compbio.mit.edu
Ernest Fraenkel is a professor of biological engineering at MIT. His laboratory seeks to understand diseases from the perspective of systems biology. They develop computational and experimental approaches for finding new therapeutic strategies by analyzing molecular networks and clinical and behavioral data. Fraenkel received his PhD in biology from MIT after graduating summa cum laude from Harvard College with an AB in chemistry and physics.
Juan C. Caicedo is a Schmidt Fellow at the Broad Institute of MIT and Harvard. He is pioneering the use of deep learning and machine learning methods to analyze microscopy images and high-resolution genetic data. He is also exploring reinforcement learning, a method of training algorithms, as a way to optimize biological experiments. He collaborates with the Cell Circuits and Epigenomics Programs and the Imaging Platform.
Caicedo received his Ph.D. in computer engineering from the National University of Colombia. He completed internships at Google Research, Microsoft Research, and Queen Mary University of London as a grad student. As a postdoctoral researcher at the University of Illinois at Urbana-Champaign, he studied object detection problems in internet scale image collections with deep reinforcement learning. He most recently held a postdoc position with the Imaging Platform at Broad.
Caroline Uhler joined the MIT faculty in 2015 and is currently the Henry L. and Grace Doherty associate professor in EECS (Electrical Engineering & Computer Science) and IDSS (Institute for Data, Systems and Society). She is a member of LIDS (Laboratory for Information and Decision Systems), the Center for Statistics, Machine Learning at MIT, and the ORC (Operations Research Center).
Prof. Uhler holds an MSc in mathematics, a BSc in biology, and an MEd in mathematics education from the University of Zurich, and a PhD in statistics from UC Berkeley. Before joining MIT, she spent a semester in the "Big Data" program at the Simons Institute at UC Berkeley, postdoctoral positions at the IMA and at ETH Zurich, and 3 years as an assistant professor at IST Austria.
Prof. Uhler is an elected member of the International Statistical Institute and a recipient of a Simons Investigator Award, a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation.
Her research focuses on statistics, machine learning and computational biology, in particular on graphical models, causal inference, algebraic statistics and applications to genomics, for example on linking the spatial organization of the DNA with gene regulation.
Dipen Sangurdekar is the Head of Oncology Translational Genomics team at Takeda which leverages high-dimensional pre-clinical and real-world datasets and computational biology to enable translational drug development. Dr. Sangurdekar’s prior work focused on genomics/bioinformatics, statistical learning, predictive analytics, biomarker discovery and R&D data infrastructure strategy. Before joining Takeda, Dr. Sangurdekar was most recently director of data sciences at bluebird bio, a gene therapy company focusing on oncology and severe genetic disorders. He has a PhD in Chemical Engineering from University of Minnesota, post-doctoral training from Princeton University and an MBA from Babson College.