Olgica Milenkovic,
Associate Professor, Electrical and Computer Engineering,
University of Illinois at Urbana-Champaign.

Coding Techniques for Emerging DNA-Based Storage Systems

Abstract. Despite the many advances in traditional data recording techniques, the surge of Big Data platforms and energy conservation issues have imposed new challenges to the storage community in terms of identifying extremely high volume, non-volatile and durable recording media. The potential for using macromolecules for ultra-dense storage was recognized as early as in the 1960s, when the celebrated physicists Richard Feynman outlined his vision for nanotechnology in the talk "There is plenty of room at the bottom." Among known macromolecules, DNA is unique in so far that it lends itself to implementations of non-volatile recoding media of outstanding integrity (one can still recover the DNA of species extinct for more than 70,000 years) and extremely high storage capacity (a human cell, with a mass of roughly 3 pg, hosts DNA with encoding 6.4 GB of information).

Building upon the rapid growth of biotechnology systems for DNA synthesis and sequencing, we developed and implemented a new DNA-based rewritable and random access memory. Our system is based on DNA editing and constrained and error-control coding techniques that ensure data reliability, specificity and sensitivity of access, and at the same time, provide exceptionally high data storage capacity. The coding methods used range from traditional prefix-synchronized codes to newly introduced DNA profile, asymmetric Lee distance and Damerau codes. As a proof of concept, we encoded in DNA parts of the Wikipedia pages of six universities in the USA, selected specific content blocks and edited portions of the text within various positions in the blocks.

A Joint Work with Ryan Gabrys, Han Mao Kia, Jian Ma, Gregory Puleo, Hussein Tabatabaei Yazdi, Eitan Yaakobi, Yongbo Yuan and Huimin Zhao.

Biography. Olgica Milenkovic is a professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign (UIUC), and Research Professor at the Coordinated Science Laboratory. She obtained her Masters Degree in Mathematics in 2001 and PhD in Electrical Engineering in 2002, both from the University of Michigan, Ann Arbor. Prof. Milenkovic heads a group focused on addressing unique interdisciplinary research challenges spanning the areas of algorithm design and computing, bioinformatics, coding theory, machine learning and signal processing. Her scholarly contributions have been recognized by multiple awards, including the NSF Faculty Early Career Development (CAREER) Award, the DARPA Young Faculty Award, the Dean's Excellence in Research Award, and several best paper awards. In 2013, she was elected a UIUC Center for Advanced Study Associate and Willett Scholar. In 2015, she became Distinguished Lecturer of the Information Theory Society. From 2007 until 2015, she has served as Associate Editor of the IEEE Transactions of Communications, Transactions on Signal Processing and Transactions on Information Theory. In 2009, she was the Guest Editor in Chief of a special issue of the IEEE Transactions on Information Theory on MolecularBiology and Neuroscience.

Stephen J. Wright,
Professor, Computer Sciences Departmentat
University of Wisconsin Madison.

Fundamental Optimization Methods in Data Analysis

Abstract. Optimization formulations and algorithms are vital tools for solving problems in data analysis. There has been particular interest in some fundamental, elementary, optimization algorithms that were previously thought to have only niche appeal. Stochastic gradient, coordinate descent, and accelerated first-order methods are three examples. We outline applications in which these approaches are useful, discuss their basic properties, and survey some recent developments in the analysis of their convergence behavior.

Biography. Stephen J. Wright is the Amar and Balinder Sohi Professor of Computer Sciences at the University of Wisconsin-Madison. His research is on computational optimization and its applications to many areas of science and engineering. Prior to joining UW-Madison in 2001, Wright was a Senior Computer Scientist at Argonne National Laboratory (1990-2001), and a Professor of Computer Science at the University of Chicago (2000-2001). He has served as Chair of the Mathematical Optimization Society and as a Trustee of the Society for Industrial and Applied Mathematics (SIAM). He is a Fellow of SIAM. In 2014, he won the W.R.G. Baker award from IEEE. Wright is the author or coauthor of widely used text / reference books in optimization including "Primal Dual Interior-Point Methods" (SIAM, 1997) and "Numerical Optimization" (2nd Edition, Springer, 2006, with J. Nocedal). He has published widely on optimization theory, algorithms, software, and applications. Wright is editor-in-chief of the SIAM Journal on Optimization and has served as editor-in-chief or associate editor of Mathematical Programming (Series A), Mathematical Programming (Series B), SIAM Review, SIAM Journal on Scientific Computing, and several other journals and book series.

Yannis Paschalidis,
Professor, College of Engineering
Boston University.

Predictive Health Analytics

Abstract. In 2014, the United States spent $3 trillion in health care, equivalent to 17.2% of the US GDP. About one third of this amount ($971.8 billion) is attributed to hospital care. Evidently, even modest efforts for preventing and/or streamlining care in a hospital setting matter.

In this talk, I will outline our recent work leveraging Electronic Health Records at the Boston Medical Center to predict hospitalizations and re-hospitalizations. Our current work makes hospitalization predictions within the next year for heart-disease patients and patients with diabetes, as well as, re-hospitalization predictions within 30 days of general surgery. There are interesting lessons to be learned by working with such data. Specifically, very sparse classifiers that focus on a small feature subset for each patient perform remarkably well. Yet, there are no features that can be eliminated for all patients. In fact, our methods substantially outperform classifiers based on a small set of medically recommended risk factors. It is also true that there are patients with very similar features. This has led us to develop a new joint clustering and sparse classification method which produces strong predictions while discovering hidden patient clusters. Cluster membership of positive predictions can be used to justify them, which is particularly useful to clinicians.

Biography. Yannis Paschalidis is a Professor and Distinguished Faculty Fellow of Electrical and Computer Engineering, Systems Engineering, and Biomedical Engineering at Boston University. He is the Director of the Center for Information and Systems Engineering (CISE). He obtained a Diploma (1991) from the National Technical University of Athens, Greece, and an M.S. (1993) and a Ph.D. (1996) from the Massachusetts Institute of Technology (MIT), all in Electrical Engineering and Computer Science. He has been at Boston University since 1996. His current research interests lie in the fields of systems and control, networks, applied probability, optimization, operations research, computational biology, medical informatics, and bioinformatics.

Prof. Paschalidis' work on communication and sensor networks has been recognized with a CAREER award (2000) from the National Science Foundation, the second prize in the 1997 George E. Nicholson paper competition by INFORMS, and the best student paper award at the 9th Intl. Symposium of Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2011) won by one of his Ph.D. students for a joint paper. His work on protein docking (with his collaborators) has been recognized for best performance in modeling selected protein-protein complexes against 64 other predictor groups (2009 Protein Interaction Evaluation Meeting). He was an invited participant at the 2002 Frontiers of Engineering Symposium organized by the National Academy of Engineering, and at the 2014 National Academies Keck Futures Initiative (NAFKI) Conference. Prof. Paschalidis is a Fellow of the IEEE and the Editor-in-Chief of the IEEE Transactions on Control of Network Systems.

Dean Foster,
Professor, Statistics Department (currently at Amazon.com)
University of Pennsylvania

Linear methods for large data

Abstract. Using random matrix theory, we now have some very easy to understand and fast to use methods of computing low rank representations of matrices. I have been using these methods as a hammer to improve several statistical methods. I'll discuss several of these in this talk. First, I'll show how these ideas can be used to speed up regression. Then I'll turn to using them to construct new linear features motivated by CCA's. Finally, I'll use these methods to get a fast way of estimating an HMM.

Biography. Dean has pioneered two areas in game theory: stochastic evolutionary game dynamics and calibrated learning. In both cases he worked on the theory necessary to show convergence to equilibrium. The calibrated learning strategies he developed grew out of his work on individual sequences. In his work with Rakesh Vohra he coined the ideas of no-internal-regret and calibration. It is these learning rules that can be shown to converge to correlated equilibrium.

Much of his current work is on statistical approaches to NLP problems and other issues in big data. He has come up with several algorithms for fast variable selection in regressions and has proven these to have nice theoretical properties. He has used vector models for words to allow them to be more easily manipulated using statistical technology. These often end up using spectral techniques, for example, as he has used them to fit HMMs and probabilistic CFG.