Timezone: »
Workshop
Machine Learning in Public Health
Rumi Chunara · Daniel Lizotte · Laura Rosella · Esra Suel · Marie Charpignon
Tue Dec 14 05:55 AM -- 12:40 PM (PST) @
Event URL: https://sites.google.com/nyu.edu/mlph2021 »
Public health and population health refer to the study of daily life factors, prevention efforts, and their effects on the health of populations. Building on the success of our first workshop at NeurIPS 2020, this workshop will focus on data and algorithms related to the non-medical conditions that shape our health including structural, lifestyle, policy, social, behavior and environmental factors. Data that is traditionally used in machine learning and health problems are really about our interactions with the health care system, and this workshop aims to balance this with machine learning work using data on non-medical conditions. This year we also broaden and integrate discussion on machine learning in the closely related area of urban planning, which is concerned with the technical and political processes regarding the development and design of land use. This includes the built environment, including air, water, and the infrastructure passing into and out of urban areas, such as transportation, communications, distribution networks, sanitation, protection and use of the environment, including their accessibility and equity. We make this extension this year due to the fundamentally and increasingly relevant intertwined nature of human health and environment, as well as the recent emergence of more modern data analytic tools in the urban planning realm. Public and population health, and urban planning are at the heart of structural approaches to counteract inequality and build pluralistic futures that improve the health and well-being of populations.
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Tue 5:55 a.m. - 5:59 a.m.
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Welcoming Remarks
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Live
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SlidesLive Video » |
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Tue 6:00 a.m. - 6:30 a.m.
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Keynote #1 Dr. Subhrajit "Subhro" Guhathakurta
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Talk
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SlidesLive Video » |
Subhrajit Guhathakurta 🔗 |
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Tue 6:30 a.m. - 6:45 a.m.
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Keynote #1 Dr. Guhathakurta Live Q&A
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Live Q&A
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Tue 6:45 a.m. - 7:00 a.m.
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Deep Learning for Spatiotemporal Modeling of Urbanization
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Contributed talk 1
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SlidesLive Video » |
Tang Li 🔗 |
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Tue 7:00 a.m. - 7:05 a.m.
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Deep Learning for Spatiotemporal Modeling of Urbanization
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Contributed talk 1 Q&A
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Tang Li 🔗 |
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Tue 7:05 a.m. - 7:20 a.m.
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Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes
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Contributed talk 2
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SlidesLive Video » |
Aditya Mate 🔗 |
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Tue 7:20 a.m. - 7:25 a.m.
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Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes
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Contributed talk 2 Q&A
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Aditya Mate 🔗 |
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Tue 7:25 a.m. - 7:30 a.m.
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A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches
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Lightning talk 1
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SlidesLive Video » |
Ezgi Korkmaz 🔗 |
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Tue 7:30 a.m. - 7:35 a.m.
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Assisted Living in the United States: an Open Dataset
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Lightning talk 2
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SlidesLive Video » |
Anton Stengel 🔗 |
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Tue 7:35 a.m. - 7:40 a.m.
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A probabilistic approach to evaluating Cryptosporidium health risk in drinking water
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Lightning talk 3
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SlidesLive Video » |
Yirao Zhang 🔗 |
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Tue 8:00 a.m. - 9:00 a.m.
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ML in urban planning panel
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Discussion Panel
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SlidesLive Video » |
Rumi Chunara 🔗 |
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Tue 10:00 a.m. - 10:44 a.m.
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Keynote #2 Dr. Andrea Parker
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Talk
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SlidesLive Video » |
Andrea G. Parker 🔗 |
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Tue 10:30 a.m. - 10:45 a.m.
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Keynote #2 Dr. Parker Live Q&A
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Live Q&A
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Tue 10:45 a.m. - 11:00 a.m.
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Demand prediction of mobile clinics using public data
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Contributed talk 3
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SlidesLive Video » |
Haipeng Chen 🔗 |
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Tue 11:00 a.m. - 11:05 a.m.
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Demand prediction of mobile clinics using public data
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Live Q&A
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Tue 11:05 a.m. - 11:20 a.m.
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Role of Attachment Variables in Resilient Families
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Contributed talk 5
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SlidesLive Video » |
Tathagata Banerjee 🔗 |
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Tue 11:20 a.m. - 11:25 a.m.
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Role of Attachment Variables in Resilient Families
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Live Q&A
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Tathagata Banerjee 🔗 |
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Tue 11:25 a.m. - 11:30 a.m.
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Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
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Lightning talk 4
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SlidesLive Video » |
Ben Chugg 🔗 |
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Tue 11:30 a.m. - 11:35 a.m.
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An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test
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Lightning talk 5
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SlidesLive Video » |
Christine Herlihy 🔗 |
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Tue 11:35 a.m. - 11:40 a.m.
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Kronecker Factorization for Preventing Catastrophic Forgetting
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Lightning talk 6
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SlidesLive Video » |
Denis McInerney 🔗 |
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Tue 11:45 a.m. - 12:15 p.m.
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Keynote #3 Dr. Sanjay Basu
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Talk
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SlidesLive Video » |
Sanjay Basu 🔗 |
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Tue 12:15 p.m. - 12:30 p.m.
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Keynote #3 Live Q&A
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Live Q&A
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Tue 12:30 p.m. - 12:35 p.m.
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Learning after Deployment: The Missed Tale of Supervision
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Lightning talk 7
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SlidesLive Video » |
Aviral Chharia 🔗 |
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Tue 12:35 p.m. - 12:40 p.m.
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Contrastive Learning for PM2.5 Prediction from Satellite Imagery
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Lightning talk 8
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SlidesLive Video » |
Ziyang Jiang 🔗 |
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A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches
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Poster
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Ezgi Korkmaz 🔗 |
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A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches
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Oral
)
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Ezgi Korkmaz 🔗 |
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Assisted Living in the United States: an Open Dataset
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Poster
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Anton Stengel · Jaan Altosaar · Noemie Elhadad 🔗 |
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Assisted Living in the United States: an Open Dataset
(
Oral
)
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Anton Stengel · Jaan Altosaar · Noemie Elhadad 🔗 |
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A probabilistic approach to evaluating Cryptosporidium health risk in drinking water
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Poster
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Yirao Zhang 🔗 |
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A probabilistic approach to evaluating Cryptosporidium health risk in drinking water
(
Oral
)
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Yirao Zhang 🔗 |
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Kronecker Factorization for Preventing Catastrophic Forgetting
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Poster
)
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Denis McInerney · Luyang Kong · Byron Wallace · Parminder Bhatia 🔗 |
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Kronecker Factorization for Preventing Catastrophic Forgetting
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Oral
)
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Denis McInerney · Luyang Kong · Byron Wallace · Parminder Bhatia 🔗 |
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-
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Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
(
Poster
)
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Ben Chugg 🔗 |
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-
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Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
(
Oral
)
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Ben Chugg 🔗 |
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-
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Learning after Deployment: The Missed Tale of Supervision
(
Poster
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Aviral Chharia · Neeraj Kumar 🔗 |
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-
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Learning after Deployment: The Missed Tale of Supervision
(
Oral
)
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Aviral Chharia · Neeraj Kumar 🔗 |
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-
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Contrastive Learning for PM2.5 Prediction from Satellite Imagery
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Poster
)
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Ziyang Jiang · David Carlson 🔗 |
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-
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Contrastive Learning for PM2.5 Prediction from Satellite Imagery
(
Oral
)
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Ziyang Jiang · David Carlson 🔗 |
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-
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An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test
(
Poster
)
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Christine Herlihy · John Dickerson 🔗 |
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An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test
(
Oral
)
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Christine Herlihy · John Dickerson 🔗 |
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Exploring the Temporal Dynamics of County-Level Vulnerability Factors on COVID-19 Outcomes
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Poster
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Jing Zhang · Shivani Patel · Joyce Ho 🔗 |
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Modelling Patient Journeys with Sharded Encoder Blocks and Federated Split Learning
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Poster
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Jonathan Passerat-Palmbach · Francesca Anna-Sophia Beer 🔗 |
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Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network
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Poster
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Yash Sharma · Nicholas Coronato · Donald Brown 🔗 |
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Predicting Migraine Early from Fitbit Data with Deep Learning
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Poster
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Stephen Price · Raghu Kainkaryam · Arinbjörn Kolbeinsson · Luca Foschini 🔗 |
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A Recommendation System to Enhance Midwives’ Capacities in Low-Income Countries
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Poster
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Anna Guitart Atienza · Africa Perianez 🔗 |
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COVID-19 India Dataset: Parsing Detailed COVID-19 Data in Daily Health Bulletins from States in India
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Poster
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Mayank Agarwal · Tathagata Chakraborti · Sachin Grover 🔗 |
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Discovering Alternative Food Proteins with Manifold Exploration and Spectral Clustering
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Poster
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Geoffroy Dubourg-Felonneau 🔗 |
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Reaching out : Towards a sustainable allocation strategy between users and therapists
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Poster
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Prateek Chanda 🔗 |
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A Markov Chain Based Compartmental Model for COVID-19 in South Korea
(
Poster
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Sujin Ahn · Minhae Kwon 🔗 |
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General Framework for Evaluating Outbreak Prediction, Detection and Annotation Algorithms
(
Poster
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Stephane Ghozzi 🔗 |
Author Information
Rumi Chunara (New York University)
Daniel Lizotte (UWO)
Laura Rosella (University of Toronto)
Esra Suel (Imperial College London)
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.
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