Arizona State University Network Science Seminar Series

Upcoming Seminar: On the Challenge of Gene Regulatory Network Reconstruction from High-Throughput Sequencing Data

Speaker Xiaohan Kang (University of Illinois at Urbana–Champaign (UIUC))
Date 1:30 p.m., April 9th, 2018
Location GWC 487
Short Bio
Xiaohan Kang is a postdoctoral research associate with the Coordinated Science Laboratory at the University of Illinois at Urbana–Champaign working with Prof. Bruce Hajek. He received the Ph.D. degree in Electrical Engineering at Arizona State University under the supervision of Prof. Lei Ying in 2015. He received the B.E. degree in Electronic Engineering at Tsinghua University in China in 2009. His research interests include bioinformatics, algorithm design and analysis, resource allocation in data networks, and game theory.
Abstract
Gene regulatory networks are central to the functioning of organisms. High-throughput sequencing technology like RNA-Seq has enabled the production of massive amounts of data pertaining to activation levels of genes, which has the potential to help scientists reverse engineer gene regulatory networks. In this talk I will briefly explain the steps from data collection to network modeling and analysis that go into the process for a particular study examining soybean plants.

Seminars

Title Speaker Time Location
Collimated light propagation: The next frontier in underwater wireless communication Mohamed-Slim Alouini (King Abdullah University of Science and Technology (KAUST)) 1:00 p.m., Jan. 12th, 2018 GWC 487
Where is the Quantum Flyball Governor? Robert Kosut (SC Solutions) 1:00 p.m., Mar. 12th, 2018 GWC 487
Distributed Control Design for Balancing the Grid Using Flexible Loads Sean Meyn (University of Florida) 3:00 p.m., Mar. 16th, 2018 GWC 487
Automatic Detection of Manipulated Images Using Deep Learning and A-Contrario Analysis Arjuna Flenner (U.S. Navy) 1:30 p.m., Mar. 29th, 2018 SS 208
On the Challenge of Gene Regulatory Network Reconstruction from High-Throughput Sequencing Data Xiaohan Kang (University of Illinois at Urbana–Champaign (UIUC)) 1:30 p.m., April 9th, 2018 GWC 487

Collimated light propagation: The next frontier in underwater wireless communication

Speaker Mohamed-Slim Alouini (King Abdullah University of Science and Technology (KAUST))
Date 1:00 p.m., Jan 12th, 2018
Location GWC 487
Short Bio
Mohamed-Slim Alouini was born in Tunis, Tunisia. He received the Ph.D. degree in Electrical Engineering from the California Institute of Technology (Caltech), Pasadena, CA, USA, in 1998. He served as a faculty member in the University of Minnesota, Minneapolis, MN, USA, then in the Texas A&M University at Qatar, Education City, Doha, Qatar before joining King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah Province, Saudi Arabia as a Professor of Electrical Engineering in 2009.

Professor Alouini has won several awards in his career. For instance, he recently received the 2016 Recognition Award of the IEEE Communication Society Wireless Technical Committee, the 2016 Abdul Hameed Shoman Award for Arab Researchers in Engineering Sciences, and the Inaugural Organization of Islamic Cooperation (OIC) Science & Technology Achievement Award in Engineering Sciences in 2017.

Other recognitions include his selection as (i) Fellow of the Institute of Electrical and Electronics Engineers (IEEE), (ii) IEEE Distinguished Lecturer for the IEEE Communication Society, (iii) member for several times in the annual Thomson ISI Web of Knowledge list of Highly Cited Researchers as well as the Shanghai Ranking/Elsevier list of Most Cited Researchers, and (iv) a co-recipient of best paper awards in eleven IEEE conferences (including ICC, GLOBECOM, VTC, PIMRC, ISWCS, and DySPAN).
Abstract
Traditional underwater communication systems rely on acoustic modems due their reliability and long range. However their limited data rates, lead to the exploration of alternative techniques. In this talk, we briefly go over the potential offered by underwater wireless optical communication systems. We then summarizes some of the underwater channel challenges going from severe absorption and scattering that need to be surpassed before such kind of systems can be deployed in practice. We finally present some of the on-going research directions in the area of underwater wireless optical communication systems in order to (i) better characterize and model the underwater optical channel and (ii) design, develop, and test experimentally new suitable modulation and coding techniques suitable for this environment.

Where is the Quantum Flyball Governor?

Speaker Robert Kosut (SC Solutions)
Date 1:00 p.m., Mar. 12th, 2018
Location GWC 487
Short Bio
Robert Kosut received a B.S. from the City College of New York in 1965 and a Ph.D. from the University of Pennsylvania in 1969. In 1996 he co-founded the Systems & Control Division of SC Solutions to develop and license physics-based real-time control software to the semiconductor and LED equipment manufacturing industry. Prior to this he was Director of the Research Department at Integrated Systems Inc. (ISI) and a Consulting Professor in the Electrical Engineering Department of Stanford University. At ISI he led the development of commercial software tools for control design, system identification, and optimization. He is a Fellow of the IEEE for fundamental research in adaptive control and system identification for robust control, and is currently a Visiting Research Scholar at Princeton University. His recent work has been as a PI working jointly with numerous Universities and Research Labs under government funded research programs on the control and identification of quantum systems.
Abstract
In a 1985 paper in Optics News entitled "Quantum Mechanical Computers", Richard Feynman described how a computer could be built upon the mathematical principles of quantum mechanics. He also heralded the difficulties in an actual physical implementation: "In a machine such as this there are very many other problems due to imperfections. . . . At least some of these problems can be remedied in the usual way by techniques such as error correcting codes. . . But until we find a specific implementation for this computer, I do not know how to proceed to analyze these effects. . . . This computer seems to be very delicate and these imperfections may produce considerable havoc."

In this talk I will describe some of the on-going efforts that I and my co-authors have employed (see quant-ph/arXiv) in an attempt to alleviate the possibility of "considerable havoc".

Distributed Control Design for Balancing the Grid Using Flexible Loads

Speaker Sean Meyn (University of Florida)
Date 3:00 p.m., Mar. 16th, 2018
Location GWC 487
Short Bio
Sean Meyn is now Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida, the director of the Laboratory for Cognition & Control, and director of the Florida Institute for Sustainable Energy.His academic research interests include theory and applications of decision and control, stochastic processes, and optimization. He has received many awards for his research on these topics, and is a fellow of the IEEE. His award-winning 1993 monograph with Richard Tweedie, Markov Chains and Stochastic Stability, has been cited thousands of times in journals from a range of fields. For the past ten years, his applied research has focused on engineering, markets, and policy in energy systems.
Abstract
Inexpensive energy from the wind and the sun comes with unwanted volatility, such as ramps with the setting sun or a gust of wind. Controllable generators manage supply-demand balance of power today, but this is becoming increasingly costly with increasing penetration of renewable energy. It has been argued since the 1980s that consumers should be put in the loop: "demand response" will help to create needed supply-demand balance. However, consumers use energy for a reason, and expect that the quality of service (QoS) they receive will lie within reasonable bounds. Moreover, the behavior of some consumers is unpredictable, while the grid operator requires predictable controllable resources to maintain reliability. The goal of this lecture is to describe an emerging science for "demand dispatch" that will create virtual energy storage from flexible loads. By design, the grid-level services from flexible loads will be as controllable and predictable as a generator or fleet of batteries. Strict bounds on QoS will be maintained in all cases. The technical foundation is primarily a new approach to distributed control.

Automatic Detection of Manipulated Images Using Deep Learning and A-Contrario Analysis

Speaker Arjuna Flenner (U.S. Navy)
Date 1:30 p.m., Mar. 29th, 2018
Location SS 208 (Social Sciences Building)
Short Bio
Arjuna Flenner is a senior research physicist in the U.S. Naval Air Systems Command facility in China Lake, California. He holds a PhD in Theoretical and Mathematical Physics from the University of Missouri-Columbia. His activities at China Lake entail algorithmic research and development in machine learning, statistical pattern recognition, image processing and computer vision. His work focuses particularly on representations of data that enable efficient representation and algorithmic exploitation, especially in the context of Naval applications. He has won the Delores M. Etter Navy Top Scientist and Engineers award as well as a SIAM outstanding paper award. In addition to his individual research activities, Dr. Flenner oversees a small group of researchers that interfaces with academia, grant sponsors, and transition partners to identify Navy specific problems and develop machine learning and related algorithms.
Abstract
Images are a prominent way to convey information and is often used in promotional material. With the advent and popular use of consumer image manipulation technology it has become difficult to determine if an image represents the real world or has been manipulated. This talk discusses automatically determining if an image has been manipulated using a three step analysis. The first step consists of computing the Radon transform of resampling features on overlapping image patches and a deep learning classifiers is trained on these patches. We observed that this procedure still obtains a high number of false alarms. We then use a-contrario hypothesis testing to both identify if the patterns of the manipulated blocks indicate if the image has been tampered and to localize the manipulation. We demonstrate that this strategy is effective in indicating if an image has been manipulated and localizing the manipulations.
 
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