Upcoming Seminar

Past Seminars

Date: Sept 21, 2016

Speakers: T.J. Lane

Title: Can machine learning teach us physics? Using Hidden Markov Models to understand molecular dynamics.

Abstract: Machine learning algorithms are often described solely in terms of their predictive capabilities, and not utilized in a descriptive fashion. This “black box” approach stands in contrast to traditional physical theories, which are generated primarily to describe the world, and use prediction as a means of validation. I will describe one case study where this dichotomy between prediction and description breaks down. While attempting to model protein dynamics using master equation models — known in physics since the early 20th century — it was discovered that there was a homology between these models and Hidden Markov Models (HMMs), a common machine learning technique. By adopting fitting procedures for HMMs, we were able to model large scale simulations of protein dynamics and interpret them as physical master equations, with implications for protein folding, signal transduction, and allosteric modulation.

Date: Aug 31, 2016

Speakers: Apurva Mehta

Title: On-the-fly unsupervised discovery of functional materials

Abstract: Solutions to many of the challenges facing us today, from sustainable generation and storage of energy to faster electronics and cleaner environment through efficient sequestration of pollutants, is enabled by the rapid discovery of new functional materials. The present paradigm based on serial experimentation and serendipitous discoveries takes decades from initiation of a new search for a material to marketplace deployment of a device based on it. Major road-blocks in this process arise from heavy dependence on humans to transfer knowledge between interdependent steps. For example, currently humans look for patterns in current knowledge-bases, build hypotheses, plan and conduct experiments, evaluate results and extract knowledge to create the next hypothesis. The recent insight, emerging from the materials genome initiative, is that rapid transfer of information between hypothesis building, experimental testing and scale-up engineering can reduce the time and cost of material discovery and deployment by half. Humans, though superb at pattern recognition and complex decision making, are too slow and the major challenge in this new discovery paradigm is to reliably extract high-level actionable information from large and noisy data on-the-fly with minimal human intervention. In here, I will discuss some of the strategies and challenges involved in construction of unsupervised machines that perform these tasks on high throughput and large volume X-ray spectroscopic and scattering data sets.

Date: Aug 17, 2016

Speakers: Anna Leskova, Hananiel Setiawan, Tanner M. Worden, Juhao Wu

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