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 30th, 2021
Time: 5:00 PM - 6:00 PM
Place: AERB 135
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: Zouheir Rezki
Date: Monday, September 13, 2021
Time: 5:00 PM
Place: Online via Zoom. Please register here to get the attendance instructions.
Abstract: Requirements of 5G and beyond, in terms of high throughput, low latency, high reliability, broadband coverage, etc., can only be met via seamless integration of diverse technologies and exploration of higher frequency bands, from 30 to 300 Gigahertz (GHz), or even around few hundreds of Terahertz (THz). This explains the increasing popularity of technologies such as optical wireless communications (OWC), using an RF link as a backup. In particular, OWC exhibits the following features 1) a large unlicensed available spectrum in the range from 350-1500 nanometers that can support high data rates in the order of Gigabits per sec (Gbps), 2) a high energy-efficiency with low transmission power and interference-free communication, 3) a high level of security against eavesdroppers due to its natural behavior of being highly directive and inability for wall penetration, and 4) a low deployment cost compared to RF networks.
In this talk, I will focus on the class of Intensity-Modulation Direct Detection (IM-DD) OWC, overview the most popular underlying channel models, provide recent fundamental results related to secure communications in various contexts. Towards the end of my talk, I will introduce new OWC learning-based designs for single and multiple user settings.
Bio:Dr. Rezki is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California Santa Cruz (UCSC). Before joining UCSC in July 2020, he has been an Assistant Professor at the University of Idaho (August 2016 - June 2020), a Senior Research Scientist (July 2014 - June 2016), a Research Scientist (July 2012 - June 2014), a Postdoctoral Fellow (November 2009 - June 2012) in the Computer Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST), and a Postdoctoral Fellow (September 2008 - August 2009) at the University of British Columbia (UBC).
He received his PhD from University of Montreal, Polytechnique Engineering School in 2008 where his thesis was nominated for “Best Thesis of the Year”. Honors also include the “Best Paper Award’’ at the 2008 Personal Indoor Mobile Radio Communication (PIMRC’2008), The Fonds Québécois de la recherche sur la nature et les technologies “Postdoctoral Research Fellowship” in 2009, the 2020 (2020 - 2025) “NSF CAREER Award”. Dr. Rezki is a Senior Member of IEEE (2013 - present), has been an Editor of IEEE Wireless Communications Letters (2014 - 2017) and served as a Symposium Chair/Co-Chair of many IEEE flagship conferences in communication, signal processing, and networking. His current research covers a wide range of topics in wireless communications and networking including security and privacy of data networks, applying machine-learning techniques to design and optimize communication systems, information theory, optical communication, and application of communication as an enabling technology for the smart grids.
Speaker: John D. McDonald
Date: Monday, October 11, 2021
Time: 5:00 PM
Place: Online via Zoom. Please register here to get the attendance instructions.
Abstract: This talk is designed to discuss managing your career. There are 12 important things to keep in mind when living and managing your career to achieve your goals. These will be explained and explored with examples and photographs based on my 47 years as an engineer, manager and executive managing people's careers, and 50 years as an IEEE member (IEEE Life Fellow) and 15 years as a CIGRE member (CIGRE Honorary Member). Your career is your career and understanding your priorities (which can change) and your company's objectives can help you have a rewarding and fulfilling career.
Speaker Bio: John D. McDonald, P.E., is Smart Grid Business Development Leader for GE’s Grid Solutions business. John has 47 years of experience in the electric utility transmission and distribution industry. John received his B.S.E.E. and M.S.E.E. (Power Engineering) degrees from Purdue University, and an M.B.A. (Finance) degree from the University of California-Berkeley. John is a Life Fellow of IEEE (member for 50 years), and was awarded the IEEE Millennium Medal, the IEEE Power and Energy Society (PES) Excellence in Power Distribution Engineering Award, the IEEE PES Substations Committee Distinguished Service Award, the IEEE PES Meritorious Service Award, the 2016 CIGRE Distinguished Member Award, the 2016 CIGRE USNC Attwood Associate Award, and the 2021 CIGRE Honorary Member Award. John is Past President of the IEEE PES, the VP for Technical Activities for the US National Committee (USNC) of CIGRE, the Past Chair of the IEEE PES Substations Committee, and the IEEE Division VII Past Director. John was on the Board of Governors of the IEEE-SA (Standards Association) and is an IEEE Foundation Director. John received the 2009 Outstanding Electrical and Computer Engineer Award from Purdue University. John teaches a Smart Grid course at the Georgia Institute of Technology, a Smart Grid course for GE, and Smart Grid courses for various IEEE PES local chapters as an IEEE PES Distinguished Lecturer (since 1999). John has published one hundred fifty papers and articles, has co-authored five books and has one US patent.
John has been married for 41 years, has two children and two grandchildren, and works out regularly with a trainer (for 11 years to date).
Speaker: Ashutosh Trivedi
Date: Monday, October 18, 2021
Time: 5:00 PM
Place: Online via Zoom. Please register here to get the attendance instructions.
Abstract: Reinforcement learning (RL) is a sampling-based approach to optimization on Markov decision processes (MDPs), where learning agents rely on scalar reward signals to discover optimal solutions.
When combined with powerful approximation schemes (e.g., deep neural networks), RL has been effectively deployed to perform highly complex tasks traditionally considered beyond the ambit of AI. At the same time, its sensitivity to the choice of approximation parameters makes RL difficult to use (significant ML expertise is demanded of the
programmer) and difficult to trust (manual approximations lose guarantees). One of the key reasons for the need of approximation in RL is that RL algorithms with guaranteed convergence work on finite MDPs. In a recent breakthrough, Dr. Trivedi has developed a convergent model-free (tabular) RL algorithm for branching Markov decision processes (BMDPs): an extension of finite MDPs with parallel process creation and deletion. BMDPs generalize finite MDP in a similar fashion as context-free grammars generalize finite automata. The existence of a convergent RL algorithm for BMDPs makes one hopeful about convergent RL for other classes of MDPs more expressive than finite MDPs.
Speaker Bio: Ashutosh Trivedi is an assistant professor of computer science at the University of Colorado Boulder. His research interests lie at the intersection of computer science, control theory, and machine learning. His research focuses on developing and applying rigorous mathematical reasoning techniques to design and analyze learning-enabled cyber-physical systems. He received his doctorate in computer science with a focus on game theory and optimization from the University of Warwick. Before joining the University of Colorado Boulder, Ashutosh worked as an assistant professor of computer science at the Indian Institute of Technology Bombay and a postdoctoral research associate at the University of Pennsylvania and the University of Oxford.
Speaker: Guannan Qu
Date: Monday, October 25, 2021
Time: 5:00 PM
Place: Online via Zoom. Please register here to get the attendance instructions.
Abstract: Artificial Intelligence (AI), particularly Reinforcement Learning (RL), has achieved great success in domains such as gameplay. However, RL has scalability and reliability issues which makes it challenging for RL to make an impact in safety-critical and large-scale systems such as power grids, transportation, smart city. In this talk, we show that integrating RL with model-structure and model-based control can address the scalability and reliability issues of RL. In the first part of the talk, we consider a networked multi-agent setting and we propose a Scalable Actor Critic framework that provably addresses the scalability issue of multi-agent RL. The key is to exploit a form of local interaction structure widely present in networked systems. In the second part, we consider a fundamental voltage control problem in the smart grid, where RL is a promising tool but it suffers from potential instability issues. We show that exploiting a known Lyapunov function for this problem can ensure provable stability for policy learning in this problem.
Speaker Bio: Guannan Qu has been an Assistant Professor at the Electrical and Computer Engineering Department of Carnegie Mellon University since September 2021. He received his B.S. degree in Electrical Engineering from Tsinghua University in Beijing, China in 2014, and his Ph.D. in Applied Mathematics from Harvard University in Cambridge, MA in 2019. He was a CMI and Resnick postdoctoral scholar in the Department of Computing and Mathematical Sciences at California Institute of Technology from 2019 to 2021. He is the recipient of Caltech Simoudis Discovery Award, PIMCO Fellowship, Amazon AI4Science Fellowship, and IEEE SmartGridComm Best Student Paper Reward. His research interest lies in control, optimization, and machine/reinforcement learning with applications to power systems, multi-agent systems, Internet of things, smart city, etc.
Speaker: Arvind Tiwari
Date: Monday, November 1, 2021
Time: 5:00 PM
Place: Online via Zoom. Please register here to get the attendance instructions.
Abstract: Increased penetration of Renewables (wind and solar) will continue to stress grid integration and control. Faster pace of renewable deployment and limited /slower pace of transmission evacuation risks must run operability of renewable plants. Intermittent nature of renewable sources restricts its participation in varied nature of continuous energy demand. Across the globe regulators are bringing operation mechanism for renewable plants to be in line with conventional generation plants. This necessitates incorporation of plant control mechanism at design stage. Proposed presentation focuses on scalable and flexible control architecture considering energy mix profile and continuously evolving load impacts. Typical asset control enables individual asset operation capturing variability in local perspectives. Significant benefit from renewable plant can be extracted with coordinated operation and knowledge-based solutions.
Speaker Bio: Arvind Tiwari has 19+years of experience in industrial research, innovation, analysis and product design. Currently he is leading outcome-oriented research lab, focused on technology and concepts in Renewable domain at Global Research. Prior to this he was technology lead and program manager at GE Global Research supporting broader industrial businesses since 2003. Prior to role at GE, he was at Indian Institute of Technology, BHU, India. Recognized for building & leading R&D team focused on conceptualization of new technologies in highly matrixed, global organizations. Distinguished academic record with patents (30+) to credit, international publications (20+) and coauthored a book. Regular invitee to deliver technical talks at select industry and academic forums.
Speaker: Pritika Dasgupta
Date: Monday, November 8, 2021
Time: 5:00 PM
Place: Online via Zoom. Please register here to get the attendance instructions.
Abstract: In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of the motor skill of walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the body motion’s translation during walking. Gait measures from accelerometers can enrich measurements of walking and motor performance. This talk will categorize the aspects of the motor skill of walking and review how trunk-acceleration gait measures during walking can be mapped to motor skill aspects, satisfying a clinical need to understand how well accelerometer measures assess gait. Using machine learning, this talk will clarify how to leverage more complicated acceleration measures to make accurate motor skill decline predictions, thus furthering fall research in older adults.
Speaker Bio: Pritika (Pri) uses she/they pronouns and is a recent Ph.D. graduate from the Department of Biomedical Informatics at the University of Pittsburgh. She holds a bachelor’s degree in Biological Engineering from Cornell University, a master of public health degree in epidemiology from the University of Michigan – Ann Arbor, and a master of health informatics degree from the University of Michigan – Ann Arbor. Pri is currently a Data Scientist at the University of Pittsburgh Epidemiology Data Center, where she analyzes data from a myriad of clinical trials and studies. She is enthusiastic about her research and is always trying to innovate ways to analyze and describe her insights. Her doctoral research was about analyzing gait measures from accelerometer signals. She hopes that this research will help the geriatric population and prevent falls from using machine learning and artificial intelligence algorithms. In her free time, she is the chair of the Women in Engineering affinity group at the IEEE Pittsburgh section. Finally, she is actively passionate about social justice and reform in both academic and community spaces.