IACS Seminars

IACS Seminar Series

2018 Fall Seminars

2018 Sep 28

Fluid mechanics with turbulence, reduced models, and machine learning | David Sondak, Harvard SEAS

Location: 

Harvard University, TBA
1:30pm to 2:30pm

Abstract: 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...

Read more about Fluid mechanics with turbulence, reduced models, and machine learning | David Sondak, Harvard SEAS
2018 Oct 19

Computational Perception with Applications to Graphic Design | Zoya Bylinskii, MIT

Location: 

Harvard University, Maxwell Dworkin G115, 33 Oxford Street, Cambridge, MA 02138
1:30pm to 2:30pm

Abstract: 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...

Read more about Computational Perception with Applications to Graphic Design | Zoya Bylinskii, MIT
2018 Oct 26

Data Science for Game Development | Dean Wyatte, Activision

Location: 

Harvard University, Maxwell Dworkin G115, 33 Oxford Street, Cambridge, MA
1:30pm to 2:30pm

Abstract: 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...

Read more about Data Science for Game Development | Dean Wyatte, Activision
2018 Nov 02

Machine Learning in the Healthcare Enterprise | MGH & BWH Mark Michalski, Center for Clinical Data Science

Location: 

Harvard University, Maxwell Dworkin G115, 33 Oxford Street, Cambridge, MA
1:30pm to 2:30pm

Abstract: 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.

Refreshments will be served from 1-1:30pm on a first-come, first-served basis.

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Read more about Machine Learning in the Healthcare Enterprise | MGH & BWH Mark Michalski, Center for Clinical Data Science
2018 Nov 09

Bottlenecks, Representations, and Fairness: Information-Theoretic Tools for Machine Learning | Flavio Calmon, Harvard SEAS

Location: 

Harvard University, Maxwell Dworkin G115, 33 Oxford Street, Cambridge, MA
1:30pm to 2:30pm

Abstract: 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.

Refreshments will be...

Read more about Bottlenecks, Representations, and Fairness: Information-Theoretic Tools for Machine Learning | Flavio Calmon, Harvard SEAS
2018 Nov 30

Machine Learning for Materials Discovery | Julia Ling, Citrine Informatics

Location: 

Harvard University, TBA
1:30pm to 2:30pm

Abstract: 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...

Read more about Machine Learning for Materials Discovery | Julia Ling, Citrine Informatics