New Directions in IT Society Research Jeff Andrews, UT Austin Andrea Montanari, Stanford Michelle Effros, Caltech Olgica Milenkovic, UIUC Alex Dimakis, UT Austin Lara Dolecek, UCLA Muriel Medard, MIT Sriram Vishwanath, UT Austin ISIT 2013

Background IEEE has asked its societies to come up with a document detailing “Future Directions” This would presumably go into an IEEE level report Gerhard asked me to lead this effort We could also use the outcome for our own purposes, inc. IT Society newsletter, seeding funding agency ideas, etc. (ideas welcome) Seems a useful exercise, albeit a challenging one If all this was clear and obvious, we’d have a lot more open faculty positions and NSF funding for information theory Related to our Outreach endeavors I formed the committee, aiming for diversity of research areas, blend of youth and senior people, top minds in variety of topics

Plan Emphasize the generality and universality of IT as a serious scientific discipline Tout its past triumphs and established intersections with and contributions to other fields Provide concise conjecture (total report < 10 pages) on areas for potential growth, new synergies, and articulate exciting open areas Avoid buzzwords, stick to fundamentals This requires more vision than any small group of people can provide We approach this with humility, and greatly appreciate inputs from the BoG and beyond Ideally, we can advance a conversation that will be helpful

An Inspiration and Framework Develop a 2013 update of such a figure Articulate existing intersections (with a bit more detail) Conjecture on future intersections Figure 1 of Cover and Thomas (1991)

Current Outline/Ideas Communications Mostly reviewing past triumphs Networking Fundamental properties of large (general) networks and graphs Nano-circuits, distributed systems Control theory Signal Processing Compressed sensing Lossy compression, inc. for huge data sets Implementation of IT-inspired ideas Human information acquisition Physics Statistical physics, entropy Quantum information theory Statistics and Learning Theory Includes application to enormous data sets E.g. High-dimensional statistics, PCA Computer Science Seen as a major area of blurring with us (list decoding, security, etc.) Computation as a constraint? Genetics and Molecular Biology DNA detection, processing, computing Virology Neuroscience Encoding, storage, processing of information in neural networks Economics and Finance (See graph theory above) Universal investment theory

Discussion/Questions for BoG Are key areas missing? Can any be combined? To cite or not? Is this a worthy exercise in your opinion?

Definition of Information Theory (back up slide) Definition. Information theory is a mathematical science that studies the ultimate limits of, and optimal methods and algorithms for: 1. The representation of information; 2. The communication of information; 3. The processing and utilization of information.