Fall 2018 TEER Talks

Gaurav Arya is an Associate Professor of Mechanical Engineering and Material Science at Duke University. Prior to joining Duke in Fall 2017, he was an Assistant Professor and then Associate Professor in the Department of NanoEngineering at UC San Diego. He obtained his B.Tech. degree in Chemical Engineering from IIT Bombay in 1998, and Ph.D. degree, also in Chemical Engineering, from the University of Notre Dame in 2003. He did postdoctoral research at Princeton University and New York University. Professor Arya’s research focuses on the development and application of computational methods to gain a molecular-level understanding of various nanoscale systems of interest, including biomolecular motors, DNA-based devices, and nanoparticle-polymer composites.

Nanotechnology deals with the science and engineering of functional materials and devices at the nanometer length scales. This rapidly growing field harnesses the unique properties and behavior of matter that emerge when it is confined to nanoscale dimensions. The potential applications of nanotechnology are enormous, impacting almost all aspects of our daily life, including the medicines we take, the electronic items we enjoy, the quality of air we breathe, and the energy we consume. In this talk, I will describe a relatively new class of computational techniques known as molecular simulations that our laboratory is using to understand and design nanoscale systems at the molecular scale. More specifically, I will show how we are using such computational tools to advance three different areas in soft nanotechnology: assembling polymer-embedded plasmonic nanoparticles into unique spatial arrangements relevant for optical applications; introducing dynamic mechanisms into rigid DNA origami nanostructures for applications in sensing and actuation; and elucidating the molecular mechanisms by which powerful motor proteins package DNA into viral capsids.

Dr. Shani B. Daily is an associate professor of the practice in Electrical and Computer Engineering. Previously she was the Director of Human-Centered Computing and Digital Arts and Sciences in the Department of Computer and Information Science and Engineering at the University of Florida. Previously as well as an associate professor and interim co-chair in the Human-Centered Computing Division in the School of Computing at Clemson University. She received her Ph.D. and S.M. from the Massachusetts Institute of Technology Media Lab and her B.S. and M.S. in Electrical Engineering from the Florida Agricultural and Mechanical University - Florida State University College of Engineering. Her research interests include the design, development, and evaluation of interactive technologies to support shifts in perspectives, attitudes, emotional dispositions, and cognitive habits as well as broadening participation in STEM.

The ubiquity of information technology creates opportunities for students to engage with and design interactive media. Unfortunately, students are typically consumers rather than producers of social media, video games, and other online media. In this talk, I describe how tools can be used to support students in shifting from this predominantly consumption based role, to one where they are actively designing their own media using software tools. I present experiences from the past fifteen years where diverse groups of students have utilized different environments to create expressive stories, games, animations, community solutions, and dance-based performances. I end with a discussion of student participation in computing moving toward the future.

Michael Rubinstein received B.S. with honors in physics from Caltech in 1979, M.A. in 1980, and Ph.D. in physics from Harvard University in 1983 specializing in soft condensed matter theory in the group of D. R. Nelson. Between 1983 and 1985 Michael Rubinstein was a post-doctoral fellow with E. Helfand at AT&T Bell Laboratories in Murray Hill, NJ where he started his research in polymer physics. In 1985 Michael Rubinstein joined Research Laboratories of Eastman Kodak Company in Rochester, NY where he worked for 10 years in different areas of polymer theory. In 1987 he received C.E.K. Mees Award “In Recognition of Excellence in Scientific Research and Reporting”. In 1994 he was Juliot Curie Visiting Professor at Ecole Superieure de Physique et de Chimie Industrielles in Paris. In 1995 Michael Rubinstein started his academic career at the University of North Carolina at Chapel Hill and in 2018 he moved to Duke where he is currently on the faculty of Departments of Mechanical Engineering and Materials Science, Biomedical Engineering, Physics, and Chemistry. In 1998 he was Visiting Professor at College de France.  In 2001 Michael Rubinstein was elected a Fellow of the American Physical Society. From 2001 through 2004 he was an Associate Editor of Macromolecules. In 2003 he published a textbook “Polymer Physics” with R. H. Colby. In 2004 Michael Rubinstein was a co-chair of the Gordon Research Conference on Macromolecular, Colloidal and Polyelectrolyte Solutions. In 2008-2009 he was a Chair of the Division of Polymer Physics of the American Physical Society. In 2010 Michael Rubinstein received the Polymer Physics Prize of the American Physical Society.  In 2013-2017 Rubinstein served as the Chair of the Editorial Board of Soft Matter. In 2017 he founded and became a Chair of IUPAP Soft Matter Working Group. Rubinstein was awarded Bingham Medal of the Society of Rheology in 2018.

Rubinstein’s research interests are in the area of soft condensed matter physics with an emphasis on polymer physics. His main scientific contributions include theories of polymer entanglements, dynamics of reversible networks, and models of charged polymers. His recent scientific interests are in applications of polymer physics to biological systems, such as airway surface layer of a lung and development of molecular models of polymer gels and networks including those with self-healing properties.

How can meters of DNA pack into the micron-size nucleus in a way that it can easily unpack without getting tangled and, at the same time, to make every part of it accessible? How the structure of this densely packed library of instructions allows a section of DNA control another section thousands of nucleotides away?

Leila Bridgeman joined Duke’s Mechanical Engineering and Materials Science department as a postdoctoral researcher in 2017 and became an assistant professor in January 2018. She received B.Sc. and M.Sc. degrees in Applied Mathematics in 2008 and 2010 from McGill University, Montreal. In 2016, she completed a Ph.D. in Mechanical engineering, also at McGill University. Her graduate studies involved research semesters at University of Michigan, University of Bern, and University of Victoria, along with an internship at Mitsubishi Electric Research Laboratories (MERL) in Boston. Dr. Bridgeman's doctoral research extending and applying the foundational work of George Zames was awarded McGill’s 2017 D. W. Ambridge Prize.

Through her research, Dr. Bridgeman strives to bridge the gap between theoretical results in robust and optimal control and their use in practice. She explores how the tools of numerical analysis and stability theory can be applied to the most challenging of control problems. Resulting publications have considered applications of this work to robotic, process control, and time-delay systems.

The study of feedback control is arguably the most influential of engineering disciplines. Autonomous driving, spacecraft pointing, indoor temperature and humidity control, and modern cancer radiation therapy all hinge on the ability of a control system to robustly and reliably regulate system behavior. Despite its diverse areas of application, the desire to optimize performance and guarantee acceptable behavior in the face of inevitable uncertainty is pervasive throughout control theory. This creates a fundamental challenge since the necessity of stable yet robust control schemes often favors conservative designs, while the desire to optimize performance typically demands the opposite. This talk will discuss how a return to one of the foundational results of input-output stability theory, George Zames' Conic Sector Theorem, has led to new controller design methods that aid in solving the most challenging of modern control problems.

David Carlson is an Assistant Professor in the Department of Civil and Environmental Engineering and the Department of Biostatistics and Bioinformatics.  He is also a member of the Duke Clinical Research Institute.  He previously completed postdoctoral training at Columbia University in the Data Science Institute and the Department of Statistics in addition to postdoctoral training at Duke University in the Department of Electrical and Computer Engineering and the Department of Psychiatry and Behavioral Sciences.  He received his Ph.D., M.S., and B.S.E. in Electrical and Computer Engineering from Duke University, where he won awards for both graduate scholarship and teaching.  His research is focused in machine learning and data-driven science.  He is very interested in how modern machine learning and statistical techniques can be used not only for the analysis of large data sets, but also in the design of novel experiments to elucidate scientific understanding.  He has developed algorithms and analysis methods for diverse engineering and health applications, including several recent developments in neuropsychiatric disorders.

There is an extensive literature in machine learning demonstrating extraordinary ability to predict labels based off an abundance of data, such as object and voice recognition.  Furthermore, machine learning is increasingly utilized in health and scientific applications, and data collection in some fields is drastically increasing to feed such approaches.  However, often the explicit goal of machine learning techniques is to predict well, whereas in scientific applications we care about our ability to understand the underlying nature of the problem.  Towards this end, I will briefly introduce my work on developing interpretable machine learning techniques for the analysis of neural signals and how such analyses can be used to create testable, data-driven hypotheses.  With collaborators, we have already (successfully) validated some of these approaches with optogenetic techniques.  I will also highlight some of the unique challenges that commonly arise with the “little big data” in these applications.

Kenneth Brown has been working in the area of quantum information since his PhD at UC Berkeley. After a postdoc at MIT and a visiting scientist position at Osaka University, he started his own group constructing quantum information experiments and developing quantum information theory at Georgia Tech in 2007. In 2015, he was awarded an Alexander von Humboldt Experienced Research Fellowship to construct a quantum sensing experiment at the University of Duesseldorf. He has successfully constructed lasers, trapped single atomic ions, and developed quantum error correction theory on 3 continents.  Prof. Brown joined the Duke faculty in January 2018. He leads the multi-institute NSF Ideas Lab STAQ collaboration to build a practical quantum computer and was recently selected to be an APS Fellow for his work in quantum information.

Quantum computers promise to transform scientific and mathematical computing. The challenge is building machines both large enough and reliable enough to outperform today's conventional supercomputers. The goal of my research is to overcome this challenge.

Manolis Veveakis is Assistant Professor of Civil and Environmental Engineering as of June 2018. Being Greek by origin, Manolis earned a Ph.D. in 2010 from the Department of Mechanics of the National Technical University of Athens, Greece. Before joining Duke University, he was a Senior Lecturer at UNSW's School of Petroleum Engineering since 2014 and a Research Scientist in CSIRO's Division of Earth Sciences and Resource Engineering before that. He holds a Diploma (BSc+MEng) in Applied Mathematics and Physics (MEng in Materials Engineering), an MSc in Applied Mechanics and a PhD in Geomechanics.

Predicting the time, size and location of natural disasters like landslides, earthquakes and volcanic eruptions has been the holy grail of earth sciences and engineering for over 200 years. Today, having more tools and technology available than all these past years combined, we have understood that the main reason this question remains unanswered is because the question itself is not specific enough. In this talk a new question will be presented, that will be shown to be more precise in the approach required to be answered. Can we prevent natural disasters? For this question to be answered, the mechanics and physics of natural disasters need to be understood in depth, so that appropriate monitoring and decision-making approaches are designed. This talk will give a brief overview of the engineering, mathematical and computational complexity such scientific question entails and to the state-of-the-art knowledge we have on the topic today.