Fall 2020 Seminars

Speaker: Sarika Khushalani Solanki

Date: August 31st, 2020

Time: 5:00 PM - 6:00 PM

Place: Online via Zoom. Please fill this form to get the attendance instructions.

Abstract: Introduce the graduate seminar series and welcome students.

Speaker Bio: Biography: Sarika Khushalani Solanki received B.E. and M.E. degrees from India in 1998 and 2000 respectively. She received Ph.D. in Electrical and Computer Engineering from Mississippi State University, USA in 2006. She is currently an Associate Professor in Lane Department of Computer Science and Electrical Engineering at West Virginia University, Morgantown, WV, since August 2009. Prior to that, she worked for Open Systems International Inc, Minneapolis, MN as a Senior Engineer for three years. She has served as reviewer in National Science Foundation and Department of Energy and is past president of IEEE Distribution Systems Analysis Subcommittee and IEEE Career Promotion and Workforce Development Subcommittee and is editor of Transactions in Smart Grid. She is a recipient of Honda Fellowship award and NSF Career Award. Her research interests are Smart Grid, Power Distribution System, computer applications in power system analysis and power system control.

Speaker: Martin Dunlap

Date: August 27th, 2020

Time: 3:00 PM - 4:00 PM

Place: Online via Zoom. Please fill this form to get the attendance instructions.

Abstract: He will introduce the services and resources available through the WVU Libraries. These library resources may be critical to your graduate research.

Speaker Bio: He joined WVU in 1998 and has spent 10+ years working in the swamps of Florida as an environmental consultant. Since then he has worked in libraries first in Cleveland, Ohio and then here at WVU in various capacities. He recently got promoted to be the Engineering Librarian at WVU.

Speaker: WVU IT

Date: NA

Time: NA

Place: At your desk

Abstract: There is an online plagiarism tutorial at https://wvu.qualtrics.com/jfe/form/SV_6W3rGjsAaEenYgd

Here are the steps:
View videos.
Take a self-test.
Repeat steps for each module.
Take the Plagiarism Avoidance Test.

How do you progress through this tutorial?
View videos or read material in a module. Take a self-test after reading and viewing materials in a module. This self-test is for practice and taking it will open the next module. Repeat steps for each module, five modules in all. After viewing / reading the material in each module and taking the self-tests, take the Plagiarism Avoidance Test.

Speaker: Hari Kalva

Date: Monday, September 14, 2020

Time: 5:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract: Video streaming now accounts for over 60% of all the Internet traffic. This large-scale use of video in services such as Netflix and YouTube has contributed to higher data usage costs and uneven video quality. This talk will present an overview of video compression and the characteristics of human vision that impact video compression. A few results on improving video quality by exploiting visual perception will be discussed. The talk will also summarize the latest standardization activities in the field and the impact of these new developments on the cost and quality of video streaming services.

Bio: Dr. Kalva is a Professor and Associate Chair in the Dept of Computer & Electrical Engineering and Computer Science at the Florida Atlantic University. Dr. Kalva has been working on video compression and communication for over 25 years and has made key contributions to research, development, standardization, and commercialization of video technologies. Dr. Kalva has published over 200 papers and has over 20 granted patents. He is a co-inventor of MPEG standards related patents that are being used in broadcast TV, mobile devices, Blu-ray, and online video streaming systems. Dr. Kalva is also the founder of Videopura Inc, a video technology startup based in Boca Raton, FL.

Speaker: Milorad Papic

Date: September 21, 2020

Time: 5:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract: Power grid today is an automated cyber-physical infrastructure that is growing in complexity and changing rapidly with increased penetration of renewable energy sources and with implementation of new technologies. A strong and resilient power system with all three segments (generation, transmission, distribution) serves as a vital societal function and plays an essential role in the economy and to social wellbeing. Power grid may become increasing vulnerable to extreme events and in particular to High-Impact Low-Probability (HILP) category events. Therefore, power system resilience today is receiving more attention by regulators and the utility industry as a key factor of the defense against HILP events that have significant economical and societal impact. Assessment of reliability and resilience is of vital importance to both actual system operation and adequate planning of the network reinforcements. Maintaining an adequate level of reliability and resilience in the planning and operation of the power grid is a challenging problem that operating entities face today due to frequent extreme events (e.g., failure of multiple physical components, natural disasters, cyber-attacks) and the increasing complexity of energy system infrastructure. The application of probabilistic techniques to the composite generation and transmission system planning becomes increasingly important because of probabilistic behavior of power system and since the generation resource mix continues to change due to the addition of wind and solar, the retirement of conventional generation, increasing DR, and distributed resources. The talk will try to address the following questions: • What is present status of probabilistic reliability evaluation of power systems? • Do we need new methodologies and tools for evaluating the bulk power system (BES)? • Is resilience different than reliability? • How can we evaluate and measure the cascading? • Does industry needs new standards for resilience? • How do we balance infrastructures reliability and resilience with a cost?

Bio: Dr. Milorad Papic, (M’88, SM’05, F’19) is an independent consultant in the fields of power systems planning, reliability and cascading. After receiving his M.Sc. degree from Zagreb University and D.Sc. degree from Sarajevo University, Milorad worked in the System Planning department at Idaho Power for 25 years, until his retirement in April 2020. Prior to arriving at Idaho Power in 1996, Dr. Papic held an Associate Professor position at Sarajevo University; he is also an Adjunct Professor of Electrical Engineering at the University of Idaho. His areas of expertise include power system planning, reliability modeling and evaluation of power systems, risk assessment in electric power systems, cascading, and resilience. To date, he has published more than 100 technical papers in Refereed Journals and Conference Proceedings in the aforementioned areas. He is recipient of the 2020 IEEE PES Roy Billinton Power System Reliability Award. Milorad has been a speaker, invited presenter, and panelist at numerous IEEE PES conferences and panels, presenting on a diverse range of topics in reliability and cascading.

Speaker: Lingling Fan

Date: Friday, September 25, 2020

Time: 5:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract:Synchronous generator has been the major source of power grids since its invention in the 19th century. In 2008, the size of a synchronous generator reached 2000 MW. On the other hand, things have changed quite a bit in the past decade. More and more wind and solar are integrated into power grids. On a day in 2019 in Texas, 56% of power came from wind farms. Unprecedented dynamics also occurred in power grids. Examples include 20-30 Hz oscillations that occurred in 2009 and 2017 in Texas when wind farms radially connected to series capacitors, 9 Hz oscillations that occurred in August 2019 in an offshore wind farm in UK before the power disruption, and subcyle overvoltage dynamics which triggered large-scale solar PV tripping in California in 2017 and 2018. Power engineers act as House MD for power grids to diagnose and mitigate those dynamics. This talk reveals the discovery process on new dynamics with knowledge and tools introduced in fundamental EE courses (e.g., classical control). The main tool for diagnosis is dynamic model building along with linear system analysis whilst the main validation tools are computer simulation and hardware experiments. Wind Energy

Speaker Bio: Dr. Lingling Fan is a Professor at the Dept of Electrical Engineering at the University of South Florida, where she has been since 2009. She was a Senior Engineer in the Transmission Asset Management Department, Midwest ISO, St. Paul, MN, from 2001 to 2007, and an Assistant Professor with North Dakota State University, Fargo, from 2007 to 2009. Her research interests include power systems, power electronics and electric machines. Dr. Fan serves as the editor-in-chief for IEEE Electrification Magazine and associate editor for IEEE Trans. Energy Conversion. She is the founding co-chair of IEEE Power and Energy Society’s Wind SSO Task Force. Dr. Fan received the B.S. and M.S. degrees in electrical engineering from Southeast University, Nanjing, China, in 1994 and 1997, respectively, and the Ph.D. degree in electrical engineering from West Virginia University, Morgantown, in 2001.

Speaker: Rahul Panat

Date: Monday, October 05, 2020

Time: 5:00 PM - 6:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract: In this research, we develop a novel nature-inspired droplet-based nanoparticle 3D printing method to create highly complex three-dimensional microarchitected metal and polymer structures. A balance between inertia forces and surface forces for the microdroplets, along with rapid solvent evaporation at microscales is used to stack nanoparticles in 3D space. This work extends ink-based printing methods into 3D space and opens up a new dimension in enabling various microelectronic devices.We then use the above 3D printing method to realize a new class of bioelectronic and biosensing devices. First, we show a novel electrochemical sensor that can detect COVID-19 antibodies within 10 seconds. This is the fastest detection time for this disease yet reported in literature and will have a significant positive impact on the course of this pandemic. We also use the same 3D printing method to create fully customizable brain-computer interfaces having recording densities > 2500 electrodes/cm2, which is an order of magnitude higher than the current state-of-the-art BCI technologies. The BCIs were successfully inserted into mouse and macaque brains. Recording of action potentials from the brain of anesthetized mice was achieved with a high signal to noise ratio. We also use the nanoparticle printing method to demonstrate high-gauge factor strain sensors, flexible temperature sensors, and touch sensors via the printing method developed in the lab.

Speaker Bio: Prof. Panat is an Associate Professor of Mechanical Engineering and Biomedical Engineering at Carnegie Mellon University. His background is in applied mechanics and he worked in industry for a long time. He came to academia in 2014 from Intel Corporation, where he spent a decade working on microprocessor manufacturing research. At Intel, Dr. Panat led a team of engineers that developed the manufacturing process for world’s first halogen-free IC chip. After spending a few years at Washington State University, he joined CMU in fall 2017. His research is focused in the areas of additive manufacturing, stretchable electronics, and Li-ion batteries. His research is funded by NIH, DOE, and NSF. Dr. Panat is a recipient of several awards, including MRS gold medal, Mavis Memorial Award, and an award at Intel for his work on the halogen-free chip. Dr. Panat received a Ph.D. in Theoretical and Applied Mechanics from University of Illinois at Urbana Champaign in 2004, a MS in Mechanical Engineering from the University of Massachusetts at Amherst, and BE in Mechanical Engineering from Pune University, India.

Speaker: Serge Belongie

Date: Monday, October 12, 2020

Time: 5:00 PM - 6:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract: : In collaboration with the Global Biodiversity Information Facility (GBIF), iNaturalist, and Visipedia, we have introduced a new workflow for biodiversity research institutions who would like to make use of Machine Learning. With its billion+ species occurrence count contributed by thousands of institutions around the globe, GBIF is playing a vital role in enabling this workflow, whether in terms of data aggregation, collaboration across teams, or standardizing citation practices. In the short term, the most important role relates to an emerging cultural shift in accepted practices for the use of mediated data for training of ML models. In the process of data mediation, GBIF helps ensure that training datasets for ML follow standardized licensing terms, use compatible taxonomies and data formats, and provide fair and sufficient data coverage for the ML task at hand by potentially sampling from multiple source datasets.

Speaker Bio: Serge Belongie received a B.S. (with honor) in EE from Caltech in 1995 and a Ph.D. in EECS from Berkeley in 2000. While at Berkeley, his research was supported by an NSF Graduate Research Fellowship. From 2001-2013 he was a professor in the Department of Computer Science and Engineering at University of California, San Diego. He is currently the Andrew H. and Ann R. Tisch Professor in the Department of Computer Science at Cornell University and an Associate Dean at Cornell Tech. His research interests include Computer Vision, Machine Learning, Crowdsourcing and Human-in-the-Loop Computing. He is also a co-founder of several companies including Digital Persona, Anchovi Labs and Orpix. He is a recipient of the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review “Innovators Under 35” Award and the Helmholtz Prize for fundamental contributions in Computer Vision.

Speaker: Alvaro Velasquez

Date: Monday, October 26, 2020

Time: 5:00 PM – 6:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract: Reinforcement learning and planning have been revolutionized in recent years, due in part to the mass adoption of deep convolutional neural networks and the resurgence of powerful methods to refine decision-making policies. However, the problem of sparse reward signals and their representation remains pervasive in many domains. While various reward-shaping mechanisms and imitation learning approaches have been proposed to mitigate this problem, the use of human-aided artificial rewards introduces human error, sub-optimal behavior, and a greater propensity for reward hacking. In this talk, research addressing these issues is presented. In particular, we mitigate these issues by representing objectives as automata in order to define novel reward shaping functions over this structured representation. In doing so, we address the sparse rewards problem within a novel implementation of Monte Carlo Tree Search (MCTS) by proposing a reward shaping function which is updated dynamically to capture statistics on the utility of each automaton transition as it pertains to satisfying the goal of the agent. We further demonstrate that such automaton-guided reward shaping can be utilized to facilitate transfer learning between different environments when the objective is the same. Multi-agent settings are also considered.

Speaker Bio: Alvaro Velasquez leads the machine intelligence sub-portfolio of investments for the Information Directorate of the Air Force Research Laboratory. In this capacity, he manages and proposes new research directions and technology transitions for the Air Force in the fields of artificial intelligence and autonomous systems. This entails close collaboration with both the academic and private sectors towards the goal of strengthening the defense of our nation. Alvaro is also a research scientist with an interdisciplinary research record, including publications in artificial intelligence, combinatorial optimization, networking, cloud computing, and logic and circuit design. Alvaro is a recipient of numerous awards, including the National Science Foundation Graduate Research Fellowship Program (NSF GRFP) award as well as the University of Central Florida 30 Under 30 award and his research is currently funded by the Air Force Office of Scientific Research.

Speaker: Raman Kannan

Date: Monday, November 2, 2020

Time: 5:00 PM - 6:00 PM

Place: Online via Zoom. Please register here to get the attendance instructions.

Abstract: Machine Learning is commercial technology and engaging in ML tasks using a single learner is not anymore frontier technology, as it used to be. Given advances in substrate technologies we now have the opportunity to incorporate and leverage parallelism and elastic computing medium. Pursuing better performance while maintaining strict compliance with both the so called No Free Lunch Theorem and Ockham’s Razor are plausible. Effective and performant machine learning tasks can be deployed in both MIMD and SIMD configurations. We will present an experimental framework to process non-trivial datasets in MIMD/SIMD configurations. We will review realized performance improvements both from learning and computing perspective. The framework is configurable and dataset independent. We will present few target applications and challenges to validate the framework.

Speaker Bio: Dr. Raman Kannan, received his PHD in Physics from West Virginia University 1987 and a Masters in Computer Science from the Lane Department of Computer Science in 1988. His pursuit of a Masters in Classical Music (piano) was cut short by Professor X because his music bathered every student and anyone around him. He worked in the DICE (Concurrent Engineering Project, led by Professor Ramana Reddy) project. Raman considers himself a student of Frank Hiergiest, Dr. Atkins, Dr. Mooney, Dr. Sumitra Reddy, Professor Dodrill, Professor Vanscoy and Dr. Ramana Reddy and the Physics department faculty. Raman is and has always been interested in distributed computing, mechanisms facilitating distributed computing. Dr. Kannan, then went on to work for Citi and several other firms in capital markets. Raman has returned to academics and now is an adjunct, teaching Machine Learning, Big Data and polygot persistence in and around NYC. His research interest continues to be applied NLP, NLG, semantics, making sense of the vast information assets available to all of us and computational finance. Ancient cultures, philosophy, travel, western classical music are his passion besides academics.