This year we are excited to host a virtual CRCS Rising Stars Speaker Series! Each talk in the series will feature 12 minute presentations from 4 PhD students and postdoctoral candidates who were nominated by experts as demonstrating exemplary research in advancing AI for Social Good. Talks will be followed by a panel discussion with the speakers. Please check back at this address in the future for more information on our Rising Star speakers, and send any questions via email to email@example.com.
This session's topic: Health
Sonali Parbhoo (Harvard University)
Talk Title: Robust Machine Learning Methods for Targeted Healthcare
Across several fields in science and engineering, we are increasingly turning to machine learning solutions for making decisions that can affect our lives in profound ways.
Unlike many of these success stories, machine learning has had limited success in healthcare. Yet the vast volumes of medical data currently recorded are far beyond what medical experts can analyse. In this talk, I will discuss the importance of building robust tools that can communicate their decisions and limitations to human decision-makers. I will demonstrate how building small, inspectable models that humans can understand can help us manage hypotension in the ICU, and show how incorporating human input into off-policy evaluation can help us find better strategies for managing illnesses such as HIV. Throughout the talk I will highlight several interesting questions that could have a profound impact on healthcare.
Bio: Sonali is a postdoctoral research fellow at Harvard, working with Prof Finale Doshi-Velez. Her research focuses on decision-making in uncertainty, causal inference and building interpretable models to improve clinical care and deepen our understanding of human health, with applications in areas such as HIV and critical care. Her work has been published at a number of machine learning conferences (NeurIPS, AAAI, ICML, AISTATS) and medical journals (Nature Medicine, Nature Communications, AMIA, PLoS One, JAIDS). Sonali received her PhD (summa cum laude) in July 2019 from the University of Basel, Switzerland, where she built intelligent models for understanding the interplay between host and virus in the fight against HIV. She was also a recipient of the Swiss National Science Foundation (SNSF) Mobility Fellowship for her research at Harvard. Prior to this, Sonali received her B.Sc. and M.Sc. in Johannesburg, South Africa where she specialised in Molecular Biology, Computer Science and Mathematics. Apart from her research, Sonali is also passionate about encouraging more discussion about the role of ethics in developing machine learning technologies to improve society.
Paidamoyo Chapfuwa (Duke University)
Talk Title: Counterfactual Survival Analysis with Balanced Representations
Abstract: Survival analysis or time-to-event studies focus on modeling the time of a future event, such as death or failure, and investigate its relationship with covariates or predictors of interest. Specifically, we may be interested in the causal effect of a given intervention or treatment on survival time. A typical question may be: will a given therapy increase the chances of survival of an individual or population? Such causal inquiries on survival outcomes are common in the fields of epidemiology and medicine. In this talk, I will introduce our recently proposed coun- terfactual inference framework for survival analysis which adjusts for bias from two sources, namely, confounding (from covariates influencing both the treatment assignment and the outcome) and censoring (informative or non- informative). I will then present extensive results on challenging datasets, such as the Framingham Heart Study and the AIDS clinical trials group (ACTG).
Bio: Paidamoyo Chapfuwa received B.S.E. with distinction, M.S., and Ph.D. degrees in electrical and computer engineering from Duke University, Durham, NC, USA, in 2013, 2018, and 2021 (expected), respectively. Paidamoyo has been advised throughout her Ph.D. by Drs. Lawrence Carin and Ricardo Henao. Her research focuses on developing modern machine learning approaches, i.e., representation and deep learning, to characterize individualized survival (event times) from clinical data such as electronic health records and more recently, immunomics. Her work incorporates statistical techniques from causal inference, generative modeling, and Bayesian nonparametrics. Her work has culminated in publications at prestigious venues such as IEEE, ACM, ACL, and ICML. See https://paidamoyo.github.io for more information.
Irene Chen (MIT)
Talk Title: Beyond Bias Audits: Building an Ethical Machine Learning for Health Pipeline
Abstract: Machine learning has demonstrated the potential to fundamentally improve healthcare because of its ability to find latent patterns in large observational datasets and scale insights rapidly. However, the use of ML in healthcare also raises numerous ethical concerns, often analyzed through bias audits. How can we address algorithmic inequities once bias has been detected? In this talk, we consider the pipeline for ethical machine learning in health and focus on two case studies. First, cost-based metrics of discrimination in supervised learning can decompose into bias, variance, and noise terms with actionable steps for estimating and reducing each term. Second, deep generative models can address left-censorship from unequal access to care in disease phenotyping. The talk will conclude with a discussion of directions for further research along the entire model development pipeline including problem selection and data collection.
Bio: I’m a Ph.D. student in computer science at MIT, advised by David Sontag in the Clinical Machine Learning group. I work on machine learning methods to advance understanding of health and reduce inequality. Prior to MIT, I completed a joint AB/SM degree at Harvard. I also worked at Dropbox as a data scientist, machine learning engineer, and chief of staff.
Charles C Onu (MILA)
Talk Title: Robust algorithms for the analysis of infant cry sounds to detect pathologies
Abstract: My research is inspired by the goal of developing accurate and robust algorithms for the analysis of infant cry sounds to detect pathologies in the real world. I will discuss our work in learning in the small data setting, model compression and task-invariant representations of cry sounds. I will also describe our ongoing effort, in collaboration with clinicians across 3 countries to collect a large database of newborn cry sounds that are fully-annotated with clinical indications. Such a database will facilitate the development and validation of effective models for pathology detection.
Bio: I conduct my research at the intersection of artificial intelligence and healthcare at Mila and the Reasoning and Learning (RL) lab, McGill University. My supervisor is Prof. Doina Precup, co-director of RL lab and director of the DeepMind lab in Montreal. The overarching theme guiding my work is advancing machine learning to positively impact healthcare. Specific areas I work on include classical ML, deep learning, speech, physiological signal processing and tensor decomposition techniques. I hold a Vanier Canada Graduate Scholarship.
I founded and lead AI Research at Ubenwa. The Ubenwa project is aimed at developing cry-based, low-cost tools for early diagnosis of conditions that affect the central and autonomic nervous systems in newborns. Our work is funded by generous grants from Mila, Ministère de l’Économie et d’Innovation (MEI) du Québec, District 3 Innovation Centre, and MIT Solve.
All events will take place 12–1:30pm ET
Public health: Tuesday, March 30
Conservation: Thursday, April 8
Fairness: Tuesday, April 20
Tech + Society: Thursday, April 29