IACS Seminars

IACS seminars are generally held every other Friday at lunchtime during the academic year. (Lunch is served at 12:30pm on a first-come, first served basis, with the seminar beginning promptly at 1pm.) Unless otherwise indicated, all seminars will be held in Maxwell Dworkin G115. Students, faculty and others interested in computational science and applied computation are welcome to attend. See the calendar page for ways to find out about future seminars.

2014-15 IACS Seminar Series

September 12, 2014: Mauricio Santillana (Harvard SEAS; HMS)

September 19, 2014: Chris Rycroft (Harvard SEAS) ** Pierce 209**

October 3, 2014: Nima Dehghani (Wyss Institute)

October 10, 2014: Gennette Gill & Alexander Ramek (D.E. Shaw Research) ** Pierce 209**

October 17, 2014: Ashish Mahabal (Caltech)

October 31, 2014: Chris Wiggins (The New York Times)

November 14, 2014: William D. Henshaw (Rensselaer Polytechnic Institute)

November 21, 2014: Aaron Adcock and Shankar Kalyanaraman (Facebook) 

September 12, 2014

Mauricio Santillana
Lecturer in applied mathematics at the Harvard School of Engineering and Applied Sciences and an instructor at the Harvard Medical School

Title: Using big data in epidemiology for digital disease detection: Lessons learned and new directions
VIDEO: .MP4

Preventing outbreaks of communicable diseases is one of the top priorities of public health officials from all over the world. Although traditional clinical methods to track the incidence of diseases are essential to prevent outbreaks, they frequently take weeks to spot critical epidemiological events. This is mainly due to the multiple clinical steps needed to confirm the appearance and incidence of diseases. Recently, the real time analysis of big data sets such as search queries from Google, posts from Facebook, tweets from Twitter, and article views from Wikipedia, has allowed researchers to identify epidemic events in multiple communities, giving rise to the creation of internet-based public health surveillance tools. These new tools often provide timely epidemiological information to public health decision makers up to two or three weeks ahead of traditional reports.

September 19, 2014

Chris Rycroft
Assistant Professor of Applied Mathematics, Harvard SEAS

Title: High-throughput screening of crystalline porous materials
VIDEO: .MP4

Crystalline porous materials, such as zeolites, contain complex networks of void channels that are exploited in many industrial applications. A key requirement for the success of any nanoporous material is that the chemical composition and pore topology must be optimal for a given application. However, this is a difficult task, since the number of possible pore topologies is extremely large: thousands of materials have been already been synthesized, and databases of millions of hypothetical structures are available.

This talk will describe the development of tools for rapid screening of these large databases, to automatically select materials whose pore topology may make them most appropriate for a given application. The methods are based on computing the Voronoi tessellation, which provides a map of void channels in a given structure. This is carried out using the software library Voro++, which has been modified to properly account for three-dimensional non-orthogonal periodic boundary conditions. Algorithms to characterize and screen the databases will be described, and an application of the library to search for materials for carbon capture and storage will be discussed.

October 3, 2014

Nima Dehghani
Wyss Institute
VIDEO: .MP4

Title: Computational network dynamics of the neocortex

Network activity is a key aspect of neocortical computation. Whether the system portrays spatiotemporal assemblies, acts in a balanced regime, or if it follows a self-organized critical regime, are all among the fundamental organizing principles of neocortical computation. This talk will overview our recent findings from high-density ensemble recordings from the neocortex of humans and higher mammals such as monkey and cat. I will portray a detailed morpho-functional characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex. The discussion will extend to the examination of self-organized criticality in neural avalanche dynamics in different in vivo preparations during wakefulness, slow-wave sleep, and REM sleep, from cat to monkey and man. I will then show that the large ensemble of units show a remarkable excitatory and inhibitory balance, at multiple temporal scales, and for all brain states, except seizures, showing that balanced excitation-inhibition is a fundamental feature of normal brain activity.

October 10, 2014

D.E. Shaw Research
Gennette Gill & Alexander Ramek

Title: D. E. Shaw Research Information Session

D. E. Shaw Research is an independent research laboratory that conducts basic scientific research in the field of computational biochemistry under the direct scientific leadership of Dr. David E. Shaw. Our group is currently focusing on molecular simulations involving proteins and other biological macromolecules of potential interest from both a scientific and a pharmaceutical perspective. Members of the lab include computational chemists and biologists, computer scientists and applied mathematicians, and computer architects and engineers, all working collaboratively within a tightly coupled interdisciplinary research environment.

Our lab has designed and constructed multiple generations of a massively parallel supercomputer called Anton specifically for the execution of molecular dynamics (MD) simulations. Each Anton computer can simulate a single MD trajectory as much as a millisecond or so in duration -- a timescale at which biologically significant phenomena occur. Anton has already generated the world’s longest MD trajectory.

Join us for an overview of our work on parallel algorithms and machine architectures for high-speed MD simulations and a description of the simulations that have helped elucidate the dynamics and functional mechanisms of biologically important proteins.

October 17, 2014

Ashish Mahabal
Staff Scientist in Computational Astronomy at Caltech

Title: Marrying domain knowledge and computational methods

Astronomy datasets have been large and are getting larger by the day (TB to PB). This necessitates the use of advanced statistics and machine learning for many purposes. However, the datasets are often so large that small contamination rates imply large number of wrong results. This makes blind applications of methodologies unattractive. Astronomical transients are one area where rapid follow-up observations are required based on very little data. We show how the use of domain knowledge in the right measure at the right juncture can improve classification performance. We will bring up various computational methods, some established, some not so established, that are being used for detecting outliers and choosing optimal ones for best science returns. With an eye on PB-sized datasets coming up soon, we use time-series data from existing sky-surveys like the Catalina Real-Time transient Survey (along with auxiliary data) which has covered 80% of the sky several tens to a few hundreds of times over the last decade. We will also bring up an unconnected problem with some parallels - our JPL collaboration for the search of Cancer biomarkers in Early Detection Research Network (EDRN).

October 31, 2014

Chris Wiggins
Chief Data Scientist, The New York Times

Title: Data Science at The New York Times

The New York Times is a technology company which aims not only to produce great content, but also to ensure the reach and impact of its journalism.  In terms of machine learning tasks, there is a growing effort within the engineering division to reframe many of the central and most necessary business goals to maximize the paper's reach.  Chris will give examples of machine learning challenges he has addressed in his role as Chief Data Scientist at The New York Times, and illustrate how they compare with Data Science as understood in the natural sciences. He will also answer questions about working at NYT.

November 14, 2014

William D. Henshaw
Margaret A. Darrin Distinguished Professor in Applied Mathematics at Rensselaer Polytechnic Institute

Title: Over-coming the fluid-structure added-mass instability for incompressible flows

The added-mass instability has, for decades, plagued partitioned fluid-structure interaction (FSI) simulations of incompressible flows coupled to light solids and structures. Many current approaches require tens or hundreds of expensive sub-iterations per time-step. In this talk two new stable partitioned algorithms for coupling incompressible flows with both compressible elastic bulk solids and thin structural shells are described. These added-mass partitioned (AMP) schemes require no sub-iterations, can be made fully second-or higher-order accurate, and remain stable even in the presence of strong added-mass effects. Extensions of the schemes to treat large solid motions using deforming overlapping grids and the Overture framework will also be described.

November 21, 2014

Aaron Adcock and Shankar Kalyanaraman
Facebook

Talk 1: Tree-like Structure in Social and Information Networks
Presenter: Aaron Adcock

It is often noted that social and information networks exhibit tree-like structure and properties.  In the past few years several tools have been developed to more closely quantify this structure.  I will discuss some of the results of applying these tools to real-world social and information networks.  In particular, I will discuss two alternatives for measuring this structure: Gromov hyperbolicity and tree-width.

Talk 2: Data-mining for development
Presenter: Shankar Kalyanaraman

Over the last few years, we have witnessed innovative uses of big data to model and predict complex human behavior and patterns. Google's use of search query data to accurately forecast flu incidence and Ushahidi's crowdsourced crisis maps following the Haiti earthquake in 2010 for quicker and more effective deployment of humanitarian aid are two leading examples in this domain. My research interests draw inspiration from these examples; and in this talk, I will showcase some previous work I have done in disease surveillance and post-conflict violence prevention.

In the end, time-permitting, we’ll briefly chat about data science at Facebook.

2013-14 IACS Seminars
2012-13 IACS Seminars
2011-12 IACS Seminars