2018-2019

 

September 14, 2018

Learning to Rank an Assortment of Products


Speaker: Kris Ferreira, Assistant Professor of Business Administration in the Technology and Operations Management (TOM) Unit, Harvard Business School


This talk will highlight the joint work of Kris Johnson Ferreira, Assistant Professor of Business Administration in the TOM Unit at HBS, and Shreyas Sekar, postdoc at the Laboratory for Innovation Science at Harvard.

Kris Johnson Ferreira and Shreyas Sekar's research considers the product ranking challenge that online retailers face when their customers typically do not have a good idea of the product assortment offered. Customers form an impression of the assortment after looking only at products ranked in the initial positions, and then decide whether they want to continue browsing all products or leave the site. Ferreira and Sekar propose to resolve this challenge with a class of online algorithms that prescribe a ranking to show each customer with the goal of maximizing customer engagement. Over time, the algorithm learns about customer interest/engagement via clicks and uses this information to inform rankings offered to subsequent customers. Kris will prove that their algorithm converges to the best known ranking for the full-information setting, and share simulation results that highlight its performance on data from a large online retailer.

September 28, 2018

Fluid Mechanics with Turbulence, Reduced Models, and Machine Learning


Speaker: David Sondak, Lecturer in Computation, Institute for Applied Computational Science, Harvard University
 

Fluids are everywhere. As humans, we are constantly surrounded by them, including the air we breathe, the blood in our bodies, the water in the oceans, and the solar wind bombarding the Earth. Indeed, fluids impact every area of science from the biological to the geophysical and astrophysical. Understanding and controlling fluid behavior has an immense impact on human society from more eective drug delivery techniques through more ecient energy harvesting technologies. However, the desire to understand and control fluid behavior gives rise to signicant mathematical challenges in the form of multiscale behavior, complex geometries, and complex fluids.

Dr. Sondak will begin his talk with an introduction to fluid mechanics and why it is an important field of study. He will then motivate numerical methods and multiscale phenomena before giving an overview of turbulence. He will discuss reduced order models from the perspective of turbulence, including turbulence modeling. Dr. Sondak will utilize the last portion of his talk to present recent progress on using machine learning to develop and improve turbulence models.

October 19, 2018

Computational Perception with Applications to Graphic Design


Speaker: Zoya Bylinskii, Research Scientist at Adobe Research & MIT Postdoctoral Affiliate
 

What makes an image memorable? Which parts of a display or interface capture attention? How can a visualization be designed to be impactful and educational? At the core of computational perception, Zoya's work focuses on understanding human memory and attention, using computational approaches (e.g., information theoretic models, deep learning, etc.) for modeling, and, coming full circle, using the findings about human perception to improve user interfaces. During her talk, Zoya, will demonstrate applications of this work to interactive design tools and automatic graphic design summarization, and talk about the future of A.I. for creativity.

October 26, 2018

Data Science for Game Development


Speaker: Dean Wyatte, Lead Data Scientist, Activision
 

Online games are capable of generating vast amounts of data ranging from aggregate player behavior to low-level instrumentation from the game engine and back end services. Modern games are also designed from large amounts of data -- think textures, models, and photogrammetry; animation, physical systems, and motion capture. This talk will describe the role of data science in supporting these multiple stages of game development. Come learn about some of the specific challenges of making games at Activision and the data-driven solutions that the Activision team has built.

November 2, 2018

Machine Learning in the Healthcare Enterprise


Speaker: Mark H. Michalski, Executive Director of the MGH & BWH Center for Clinical Data Science
 

Machine learning is an emerging technology with promise to impact a wide variety of areas throughout the healthcare enterprise. In this discussion, Dr. Michalski will review advances in machine learning and their potential impact on several areas of healthcare, with special focus in diagnostic areas. In addition, he’ll discuss some of the challenges and approaches that have been taken to translate this technology at the Partners organization.

November 9, 2018

Bottlenecks, Representations, and Fairness: Information-Theoretic Tools For Machine Learning


Speaker: Flavio P. Calmon, Assistant Professor of Electrical Engineering, Harvard University
 

Information theory can shed light on the algorithm-independent limits of learning from data and serve as a design driver for new machine learning algorithms. In this talk, Dr. Calmon will discuss a set of flexible information-theoretic tools that can be used to (i) understand fairness and discrimination by machine learning models and (ii) characterize data representations learned by complex learning models. He will illustrate these techniques in both synthetic and real-world datasets, and discuss future research directions.

November 30, 2018

Machine Learning for Materials Discovery


Speaker: Julia Ling, Director of Data Science, Citrine Informatics
 

Materials science presents a unique set of challenges and opportunities for machine learning methods in terms of data size, data sparsity, available domain knowledge, and multi-scale physics.  In this talk, Dr. Ling will discuss how machine learning can be used to accelerate materials discovery through a sequential learning workflow.  You'll examine how domain knowledge can be integrated into data-driven models, the role of uncertainty quantification in driving exploration of new design candidates, and how to forecast the impact of a data-driven approach on a given materials discovery campaign.