Events

Amy Abernethy, MD, PhD, President of Clinical Studies Platforms at Verily

Landmark Ideas Series

January 20, 2022 at 4:00PM - 5:30PM

Clinical research is undergoing a major shift, as regulators and sponsors move towards continuous evidence generation to study how interventions perform over time. In this talk, we’ll explore what’s accelerating this trend, how new tools and technologies are advancing the space, and how it can ultimately enable more personalized care.

Byron Wallace, PhD, Associate Professor at Northeastern University

BCH AI & Machine Learning Working Group

December 14, 2021 at 2:00PM - 3:00PM

Dr. Wallace will talk about their work in text summarization and simplification, and issues related to factual accuracy of generated texts: - https://arxiv.org/abs/2104.05767- https://arxiv.org/abs/2008.11293

Alal Eran, PhD, Faculty, Computational Health Informatics Program at Boston Children's Hospital

BCH AI & Machine Learning Working Group

November 30, 2021 at 2:00PM - 3:00PM

Dr. Eran presented her paper in Nature Medicine entitled "A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia": https://www.nature.com/articles/s41591-020-1007-0.

Alina Chan, Molecular Biologist at the Broad Institute at MIT and Harvard University

Landmark Ideas Series

November 15, 2021 at 4:00PM - 5:30PM

In May 2020, I co-authored a preprint that asked whether SARS-CoV-2 might have emerged naturally or from a lab. The preprint propelled me into an unpredictable year-and-a-half journey of searching for the origin of Covid-19. In May 2021, I agreed to write down what I had learnt in a book that will be released on November 16, VIRAL: The Search for the Origin of Covid-19. In this Landmark talk, I will discuss the role of scientists in the investigation of how SARS-CoV-2 emerged in Wuhan in late 2019. I will also describe the available data relevant to the origin of Covid-19, and how scientists can and must play an important role in what is possibly the most important mystery of our generation. This virus will be with us forever. We need to know where it came from.

Michael Kremer, PhD, 2019 Nobel Laureate, Director of the Development Innovation Lab, University Professor in Economics and the College and the Harris School of Public Policy at the University of Chicago

Landmark Ideas Series

November 4, 2021 at 4:00PM - 5:30PM

Stephen Wolfram, PhD, Founder and CEO at Wolfram Research

Landmark Ideas Series

October 7, 2021 at 4:00PM - 5:30PM

Dr. Stephen Wolfram  –  creator of Mathematica, Wolfram|Alpha and the Wolfram Language; the author of A New Kind of Science; the originator of the Wolfram Physics Project; and the founder and CEO of Wolfram Research – will speak about the computational future and biomedicine. Dr. Wolfram will share a roadmap for recentering biomedicine around computation and give insights into harnessing data driven science to transform the biomedical landscape. This should be a relevant and an illuminating talk from one of the foremost leaders in computational health.  

Aaron Kesselheim, MD, JD, MPH, Professor of Medicine at Harvard Medical School

Frontline Dispatch Series

September 23, 2021 at 4:00PM - 5:30PM

Dr. Aaron Kesselheim, former member of the FDA’s Peripheral and Central Nervous System Drugs Advisory Committee who resigned over the agency’s approval of Aducanumab, will speak about the FDA’s accelerated approval pathway, how it is implemented, and how it was applied in the controversial Aducanumab case – which he dubbed the “worst drug approval decision in recent U.S. history.” Dr. Kesselheim will also give suggestions for the future. This should be a fascinating talk with wide implications for all future accelerated drug approval pathways.

William La Cava, PhD, Faculty, Computational Health Informatics Program at Boston Children's Hospital

BCH AI & Machine Learning Working Group

September 17, 2021 at 09:30AM - 10:30AM

Most interpretable machine learning research focuses on explaining the outputs of black-box models. A different, and promising, approach is to use machine learning to find the simplest possible model that meets certain performance criteria; this is the pursuit of symbolic regression. In this talk I will discuss the concepts of interpretability and explainability, and how they are used in the machine learning world. I will then discuss a pre-print that will be published in the Neurips Datasets and Benchmarks track later this year. In it, we attempt to benchmark many different approaches to symbolic regression on hundreds of problems in order to determine the strengths and weaknesses of current methods. I will discuss what lies ahead and implications for how clinicians and patients receive and process models that increasingly appear in the health system.   This event is only open to Boston Children's staff. If you would like to attend the Zoom details, please email [email protected]. 

Ben Reis, PhD, Assistant Professor of Pediatrics, Computational Health Informatics Program at Boston Children's Hospital

Frontline Dispatch Series

July 22, 2021 at 3:00PM - 4:00PM

The Delta (B.1.617.2) variant of the SARS-CoV-2 virus has rapidly emerged as the dominant strain spreading in many countries worldwide. Dr. Ben Reis led a discussion reviewing the latest findings on the Delta variant, with a focus on the effectiveness of approved COVID-19 vaccines against this emerging viral strain. Dr. Reis reviewed the evidence available from scientific publications, preliminary studies and public health reports, in the context of the inherent challenges involved in real-world vaccination effectiveness studies. He discussed the lessons learned from the nation-wide mass-vaccination experience in Israel and other highly vaccinated countries such as the UK, and provided an update on how these countries are responding dynamically to the threats posed by this emerging variant.  

Lucy Gao, PhD, Assistant Professor of Statistics at the University of Waterloo

BCH AI & Machine Learning Working Group

May 11, 2021 at 2:00PM - 3:00PM

Dr. Gao will discuss the following article: Gao, Bien, and Witten (2020). Selective inference for hierarchical clustering. arXic:2012.02936. This journal club is only available to the BCH community. If you would like to be sent a calendar invite please email [email protected].