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
Title: Using big data in epidemiology for digital disease detection: Lessons learned and new directions
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
Title: High-throughput screening of crystalline porous materials
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
Title: Computational network dynamics of the neocortex
October 10, 2014
D.E. Shaw Research
Title: D. E. Shaw Research Information Session
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
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
Title: Data Science at The New York Times
November 14, 2014
William D. Henshaw
Title: Over-coming the fluid-structure added-mass instability for incompressible flowsThe 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
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.