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Poster Session I
Shuangjia Zheng · Arnav Kapur · Umar Asif · Eyal Rozenberg · Cyprien Gilet · Oleksii Sidorov · Yogesh Kumar · Tom Van Steenkiste · William Boag · David Ouyang · Paul Jaeger · Sheng Liu · Aparna Balagopalan · Deepta Rajan · Marta Skreta · Nikhil Pattisapu · Jann Goschenhofer · Viraj Prabhu · Di Jin · Laura-Jayne Gardiner · Irene Li · sriram kumar · Qiyuan Hu · Mehul Motani · Justin Lovelace · Usman Roshan · Lucy Lu Wang · Ilya Valmianski · Hyeonwoo Lee · Sunil Mallya · Elias Chaibub Neto · Jonas Kemp · Marie Charpignon · Amber Nigam · Wei-Hung Weng · Sabri Boughorbel · Alexis Bellot · Lovedeep Gondara · Haoran Zhang · Taha Bahadori · John Zech · Rulin Shao · Edward Choi · Laleh Seyyed-Kalantari · Emily Aiken · Ioana Bica · Yiqiu Shen · Kieran Chin-Cheong · Subhrajit Roy · Ioana Baldini · So Yeon Min · Dirk Deschrijver · Pekka Marttinen · Damian Pascual Ortiz · Supriya Nagesh · Niklas Rindtorff · Andriy Mulyar · Katharina Hoebel · Martha Shaka · Pierre Machart · Leon Gatys · Nathan Ng · Matthias Hüser · Devin Taylor · Dennis Barbour · Natalia Martinez · Clara McCreery · Benjamin Eyre · Vivek Natarajan · Ren Yi · Ruibin Ma · Chirag Nagpal · Nan Du · Chufan Gao · Anup Tuladhar · Sam Shleifer · Jason Ren · Pouria Mashouri · Ming Yang Lu · Farideh Bagherzadeh-Khiabani · Olivia Choudhury · Maithra Raghu · Scott Fleming · Mika Jain · GUO YANG · Alena Harley · Stephen Pfohl · Elisabeth Rumetshofer · Alex Fedorov · Saloni Dash · Jacob Pfau · Sabina Tomkins · Colin Targonski · Michael Brudno · Xinyu Li · Yiyang Yu · Nisarg Patel

Author Information

Arnav Kapur (MIT)
Umar Asif (IBM Research Australia)
Eyal Rozenberg (Technion - Israel Institute of Technology)
Cyprien Gilet (University of Côte d'Azur CNRS, I3S Laboratory)
Oleksii Sidorov (Facebook)
Yogesh Kumar (Aalto University)

Doctoral Student working on Machine Learning at Aalto University, Finland

Tom Van Steenkiste (Ghent university - imec)
William Boag (MIT)
David Ouyang (Stanford University)
Paul Jaeger (German Cancer Research Center (DKFZ))
Sheng Liu (New York University)
Aparna Balagopalan (University of Toronto / WinterLight Labs)
Deepta Rajan (IBM Research)
Marta Skreta (University of Toronto)
Nikhil Pattisapu (IIIT Hyderabad)
Jann Goschenhofer (Ludwig-Maximilians-University Munich)
Viraj Prabhu (Georgia Tech)
Viraj Prabhu

I am a fourth year CS Ph.D. student at Georgia Tech, advised by Judy Hoffman. My research interests are in developing data-efficient and reliable computer vision systems that can be deployed in the real world. Specifically, I am interested in sample-efficient learning (particularly few-shot and active learning), adaptation across visual tasks and domains, and reliable and calibrated uncertainty estimation from deep neural networks.

Di Jin (MIT)
Laura-Jayne Gardiner (IBM Research UK)

Dr. Laura-Jayne Gardiner is a computational biologist with a background in molecular biology . Laura develops tools and pipelines to analyse large scale next-generation sequencing (NGS) datasets for life science research. Currently, Laura is a Postdoctoral Researcher at IBM Research UK within the life sciences team and is applying machine learning to genomics datasets to make predictions, tackling issues of resource integration and large-scale network analysis.

Irene Li (Yale University)
sriram kumar (PerceptiMed)
Qiyuan Hu (University of Chicago)
Mehul Motani (National University of Singapore)

Mehul Motani received the B.E. degree from Cooper Union, New York, NY, the M.S. degree from Syracuse University, Syracuse, NY, and the Ph.D. degree from Cornell University, Ithaca, NY, all in Electrical and Computer Engineering. Dr. Motani is currently an Associate Professor in the Electrical and Computer Engineering Department at the National University of Singapore (NUS) and a Visiting Research Collaborator at Princeton University. Previously, he was a Visiting Fellow at Princeton University. He was also a Research Scientist at the Institute for Infocomm Research in Singapore, for three years, and a Systems Engineer at Lockheed Martin in Syracuse, NY for over four years. His research interests include information theory, machine learning, wireless and sensor networks, and energy harvesting communications. Dr. Motani was the recipient of the Intel Foundation Fellowship for his Ph.D. research, the NUS Annual Teaching Excellence Award, the NUS Faculty of Engineering Innovative Teaching Award, and the NUS Faculty of Engineering Teaching Honours List Award. He actively participates in the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). He is a Fellow of the IEEE and has served as the Secretary of the IEEE Information Theory Society Board of Governors. He has served as an Associate Editor for both the IEEE Transactions on Information Theory and the IEEE Transactions on Communications. He has also served on the Organizing and Technical Program Committees of numerous IEEE and ACM conferences.

Justin Lovelace (Texas A&M University)
Usman Roshan (New Jersey Institute of Technology)
Lucy Lu Wang (Allen Institute for Artificial Intelligence (AI2))
Ilya Valmianski (Kaiser Permanente)

I am a Physicist turned Machine Learning Scientist. My current work is focused on developing deep learning models for natural language processing of free text in Electronic Health Records (EHR). Clinical notes present a special challenge for NLP as they often contain many idiosyncratic abbreviations, technical terms, and incomplete sentence structure. This makes it very hard to process using classical techniques. At KP, we have one of the largest in the world EHR clinical note corpora, which allows us to leverage Deep Learning techniques and achieve state of the art performance on auditing and clinical decision support tasks.

Hyeonwoo Lee (Allen Institute for Cell Science)
Sunil Mallya (AWS)
Elias Chaibub Neto (Sage Bionetworks)
Jonas Kemp (Google Health)
Marie Charpignon (MIT)

Marie grew up in Burgundy, France and studied engineering in Paris. She moved to the US to study Computational and Mathematical Engineering at Stanford University for her master’s (’16). She is passionate about statistics for education and healthcare. After graduation, she joined Microsoft as a data scientist focusing on education technology. There, she built models to better understand online collaboration, studied the impact of technology usage at school and organized workshops for high school girls. She is currently a second-year graduate student in the MIT PhD program in Social & Engineering Systems. Her work on causal inference for drug repurposing using Electronic Health Records combines mathematical modelling, data analysis and policy.

Amber Nigam (Georgia Institute of Technology)
Wei-Hung Weng (MIT)
Sabri Boughorbel (Sidra Medicine)
Alexis Bellot (University of Cambridge / Alan Turing Institute)
Lovedeep Gondara (Simon Fraser University)
Haoran Zhang (University of Toronto)
Taha Bahadori (Amazon)
John Zech (Columbia University Medical Center)
Rulin Shao (Xi'an Jiaotong University)
Edward Choi (Google)
Laleh Seyyed-Kalantari (University of Toronto)
Emily Aiken (University of California, Berkeley)
Ioana Bica (University of Oxford)
Yiqiu Shen (New York University)
Kieran Chin-Cheong (ETH Zurich)
Subhrajit Roy (Google)
Ioana Baldini (IBM Research)
So Yeon Min (MIT)
Dirk Deschrijver (Ghent University)
Pekka Marttinen (Aalto University)
Damian Pascual Ortiz (ETH Zurich)
Supriya Nagesh (Georgia Institute of Technology)
Niklas Rindtorff (Harvard Medical School, DKFZ Heidelberg)

Niklas Rindtorff, MBI, is a joint MD/PhD student at the Max Planck Institute for Medical Research and the German Cancer Research Center, Heidelberg. Here he combines high-throughput drug testing, image analysis and patient derived organoids to study treatment responses of heterogenous cancer models ex-vivo. His prior research at the Broad Institute, the German Cancer Research Center and the ETH Zurich is focused on improving treatment recommendations in precision oncology based on uncertainty-aware predictive algorithms, functional drug testing of patient derived cancer samples, allele-specific genome editing and microscopy image analysis. As a Fulbright scholar, he received a masters degree in biomedical informatics from Harvard Medical School in 2019. The title of his thesis under Jesse Boehm at the Broad Institute was: "Living Biosensors: Predicting Drug Vulnerabilities from Living Tumor Samples by Single-Cell, Label-free imaging. Niklas is a member of the MIT Critical Data initiative and regularly co-organizes Healthcare Datathon events across Europe. Having led a student-run retail company during medical school, he plans to work at the intersection of the digital health and pharmaceutical industry after completion of his clinical training.

Andriy Mulyar (Virginia Commonwealth University)

undergraduate researcher in language processing and machine learning

Katharina Hoebel (Massachusetts Institute of Technology)
Martha Shaka (The University of Dodoma)
Pierre Machart (Universitätsklinikum Hamburg-Eppendorf / ZMNH / Institute for Medical Systems Biology)

Machine Learning researcher currently in a post-doc, focusing on developping and using cutting edge Machine Learning methods (deep generative models) to solve complex biomedical problems (augmentation, debiasing, prediction of unseen events on omics data).

Leon Gatys (Apple)
Nathan Ng (University of Toronto)

I am a first year PhD student with Prof. Marzyeh Ghassemi. I am broadly interested in NLP and machine learning for healthcare. Specifically I am interested in building systems that can understand and build intelligent representations of noisy or unstructured text, especially in a clinical setting.

Matthias Hüser (ETH Zürich)
Devin Taylor (University of Cambridge)

Master's student focussing on the application of machine learning to medicine. Specific interests include cross-modal deep learning and interpretability in deep learning.

Dennis Barbour (Washington University in St. Louis)
Natalia Martinez (Duke University)
Clara McCreery (Stanford University)

Clara McCreery is a master's student in computer science at Stanford University, graduating in June 2020. Her research focuses on how re-structuring data can be used to improve machine learning models. In addition to computer science and data science, Clara enjoys racing with the Stanford Triathlon team and playing the violin.

Benjamin Eyre (Winterlight Labs)
Vivek Natarajan (Google Brain)

Working on AI + healthcare at Google AI Brain. Previously: Facebook AI Research (FAIR) Engineer where I worked on speech, conversational AI and multimodal models

Ren Yi (New York University)
Ruibin Ma (University of North Carolina at Chapel Hill)

I am a PhD student in CS Department of UNC-Chapel Hill. My research interest is in medical image analysis and 3D computer vision.

Chirag Nagpal (Carnegie Mellon University)
Nan Du (Google Brain)
Chufan Gao (Carnegie Mellon Univeristy)
Anup Tuladhar (University of Calgary)

My focus is on machine learning solutions for medicine, with specific focus on convolutional neural networks and distributed learning. I come from an alternative background: My PhD thesis was on a biomaterials-based drug delivery system for local drug release in the stroke-injured brain.

Sam Shleifer (Stanford University)
Jason Ren (Harvard University)
Pouria Mashouri (The Hospital For Sick Children)
Ming Yang Lu (Pathology, Brigham and Women's Hospital, Harvard Medical School)
Farideh Bagherzadeh-Khiabani (University of Alberta)
Olivia Choudhury (IBM Research)

I am a postdoctoral researcher at IBM Research Cambridge. My areas of interest include federated learning, blockchain technology, healthcare informatics, cloud computing, data privacy, and genomics. My current research focuses on developing privacy-preserving federated learning models to analyze large-scale distributed data. I am also interested in blockchain technology and its application in healthcare and composite cloud solutions. I am a Visiting Scientist at the Broad Institute of MIT and Harvard, where I am designing methods to improve the predictive power of polygenic risk scores to help clinicians identify patients at serious risk for cardiovascular disease. I received my Ph.D. in Computer Science and Engineering from University of Notre Dame, IN. My doctoral thesis focused on designing cloud computing-based infrastructures to expedite analysis and learning-based algorithms to improve quality of large-scale genomic data. Prior to joining IBM Research, I worked at the Broad Institute of MIT and Harvard on comparative genomics to control Zika outbreak. During my internship at IBM Watson, I built automated tools to optimize cloud-based resource allocation for SoftLayer Infrastructure as a Service (IaaS) and deploy Watson applications (Watson Oncology and Watson Engagement Advisor) on the cloud.

Maithra Raghu (Cornell University and Google Brain)
Scott Fleming (Stanford University)
Mika Jain (Stanford University)
Alena Harley (Human Longevity Inc)
Stephen Pfohl (Stanford University)
Elisabeth Rumetshofer (LIT AI Lab / University Linz)
Alex Fedorov (Georgia Institute of Technology)
Saloni Dash (Birla Institute of Technology and Science Pilani - K.K. Birla Goa Campus)
Jacob Pfau (UCSF)
Sabina Tomkins (Stanford University)
Colin Targonski (JP Morgan Chase)
Michael Brudno (University of Toronto)
Xinyu Li (Carnegie Mellon University)
Yiyang Yu (University Paris Diderot)
Nisarg Patel (University of California, San Francisco)

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