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.

February 15, 2019

Predictive Modeling of Aperiodic Astrophysical Behavior

Speaker: Matthew Graham, Research Professor of Astrophysics, CalTech

The majority of variable astronomical sources are aperiodic but represent a wide range of physical processes and scales. They can play a key role in our understanding of complex dynamic physical environments from stellar photospheres to accretion disks to merging galactic systems. However, they remain poorly studied in comparison to periodic sources, partly due to a lack of suitable statistical tools and methodologies. The best known aperiodic classes are quasars and young stellar objects (YSOs) but in both cases fundamental questions remain about the physical mechanisms behind their optical variability. A new generation of sky surveys is enabling systematic studies of astrophysical variability and discovering as many new phenomena as it seeks to explain: in quasars, we have discovered sub-parsec separated binaries, major multi-year long flares attributable to microlensing and explosive stellar-related activity in the accretion disk, and changing-state sources indicative of thermal fronts propagating through the accretion disk. In this talk, Professor Graham will discuss new approaches to characterize aperiodic variability using generative data-derived models and predict the future behavior of aperiodic sources. This allows them to be monitored in real-time with new synoptic facilities thus providing a more powerful way to detect unexpected behavior than differential photometry.

March 15, 2019



Speaker: Benjamin Brown, Assistant Professor, University of Colorado

Advances in theoretical astrophysics are powered by computational tools.  Here we discuss the Dedalus project, a flexible, open-source and spectrally accurate framework for solving partial differential equations.  In Dedalus, equations are separated from solution techniques, allowing rapid comparison of different approximations within a consistent numerical framework.  We discuss recent advances in representing spherical geometries and the sparse solution of non-constant coefficient systems, with illustrations drawn from our team's work on stellar astrophysics, convection and magnetic dynamo processes.  Further details are at http://dedalus-project.org/.

March 29, 2019



Speaker: Yael Grushka-Cockayne, Associate Professor, Harvard Business School

In collaboration with Heathrow airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces quantile forecasts for the number of passengers arriving at the immigration and security areas. Airports and airlines have been challenged to improve decision-making by producing accurate forecasts in real time. Our work is the first to apply machine learning for predicting real-time quantile forecasts in the airport. We focus on passengers’ connecting journeys, which have only been studied by few researchers. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. The predictive model developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict complete distributions, moving beyond point forecasts. To derive insights from the tree, we introduce the concept of a stable tree that can be summarized by its key variables’ splits. We identify seven key factors that impact passengers’ connection times, dividing passengers into 16 passenger segments. We find that adding correlations among the connection times of passengers arriving on the same flight can improve the forecasts of arrivals at the immigration and security areas. When com- pared to several benchmarks, our model is shown to be more accurate in both point forecasting and quantile forecasting. Our predictive system can produce accurate forecasts, frequently, and in real- time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late connecting passengers and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger arrivals. Our approach can be generalized to other domains, such as rail or hospital passenger flow.

April 12, 2019



Speaker: Isaac Lagaris, Professor of Computer Science and Engineering, University of Ioannina

The universal approximation capability of neural networks is exploited to recover solutions of Differential Equations.  The process of solving a Differential Equation is reduced to that of training a Neural Form.  Boundary conditions may be satisfied either by proper construction of the neural form, or alternatively, by treating them as constraints.