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: September 8th, 2025
Time: 5:00 PM - 6:00 PM
Place: ESB 501
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: Shomir Wilson
Date: October 17th
Time: 4:00 PM - 5:00 PM
Place: ESB 501
Abstract: Natural language processing (NLP) holds an increasingly significant role in our information society, but privacy risks and inappropriate biases emerge when NLP becomes part of sociotechnical systems. I will share my lab’s research to use NLP to push in a positive direction, toward privacy empowerment and fairness for technology users. Results of this work include PrivaSeer (https://privaseer.ist.psu.edu/), a search engine and corpus that represent over 3M website privacy policies, made available and explorable for privacy stakeholders. I will also describe our work to identify sociodemographic biases in large language models, showing the need for careful attention to the diversity of users when developing human language technologies.
Speaker Bio: Shomir Wilson is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University, where he leads the Human Language Technologies Lab. Prior to becoming faculty he held postdoctoral positions at Carnegie Mellon University and the University of Edinburgh, and he received his Ph.D. in Computer Science from the University of Maryland in 2011. His research interests span natural language processing, privacy, and computational social science. His work has been funded by the National Science Foundation and the National Institutes of Health. For more about his work, visit https://shomir.net/.
Speaker: Juggy Jagannathan
Date: Monday, October 20th, 2025
Time: 5:00 PM - 6:00 PM
Place: https://wvu.zoom.us/j/9188836315
Abstract: AI’s rapid evolution—from large language models to agentic systems—has magnified risks of exploitability, bias, and misinformation. This talk examines real-world examples of AI misuse and emerging strategies to safeguard ethical AI use. Dr. Jagannathan will discuss frameworks for responsible AI design, red- and violet-teaming, and adaptive governance models that balance innovation with accountability. The session emphasizes the role of interdisciplinary collaboration and continuous auditing in building trustworthy AI ecosystems.
Bio: Dr. Vasudevan (“Juggy”) Jagannathan is an AI Evangelist with over four decades of experience in academia and industry. He has led AI initiatives at 3M, Solventum, and NextGen Federal Systems, focusing on generative and agentic AI in healthcare and government. With 80+ publications and multiple patents, he continues to teach and research AI ethics, NLP, and healthcare systems engineering as an Adjunct Professor at WVU.
Speaker: Subhonmesh Bose
Date: October 27th
Time: 5:00 PM - 6:00 PM
Place: https://wvu.zoom.us/j/9188836315
Abstract: Key to autonomous design is the ability to control a system in dynamic non-stationary environments. While off-the-shelf models may not be available for these environments, it is vitally important to build models on the fly in a way where models can be compared, and learned controllers in environments can be repurposed to warm-start reinforcement learning algorithms in similar environments. In this talk, we will present a framework for autonomous design in piecewise-stationary environments, offer simulation results, and identify key theoretical analysis required to study the computational considerations for said design. Specifically, we will present two sets of theoretical results that define key elements of this design. Namely, they are characterization of computational complexity of sparse model learning in reproducing kernel Hilbert spaces and an analysis of change detection in transition kernels of Markov decision processes.
Speaker Bio: Subhonmesh Bose is an Associate Professor and Stanley Helm Fellow in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at University of Illinois Urbana-Champaign (UIUC). His research lies in the intersection of optimization, control theory, game theory, and machine learning, with applications in power system operations and transportation electrification. Before joining UIUC, he was a postdoctoral fellow at the Atkinson Center for Sustainability at Cornell University. Prior to that, he received his MS and Ph.D. degrees from Caltech in 2012 and 2014, respectively. He received the NSF CAREER Award in 2021. His research projects have been supported by grants from the NSF, PSERC, Siebel Energy Institute, and C3.ai, among others.
Speaker: Scott Adams and Emmanuel Oleka
Date: November 10th
Time: 5:00 PM - 6:00 PM
Place: https://wvu.zoom.us/j/9188836315
Abstract: Synchrophasors are a decades old technology, having been invented in the late 1980s, with the first commercial PMU released in 1992. Although Synchrophasors offer significant advantages over traditional Supervisory Control and Data Acquisition (SCADA) systems - such as higher resolution, time-synchronized measurements, and improved visibility - they remain underutilized across the power industry, particularly within Control Centers. As power systems evolve toward greater complexity and responsiveness, integrating synchrophasor technology into grid operations has become both a technical necessity and a cultural shift.
This presentation explores Dominion Energy’s journey of deploying a Synchrophasor-based Energy Management System (EMS) to enhance real-time situational awareness, decision-making, and control across the grid. We begin with a high-level overview of EMS architecture and the rationale behind incorporating synchrophasor data, highlighting its unique advantages over traditional SCADA systems.
A deep dive into our Synchrophasor PowerFlow (SPF) initiative reveals the challenges of merging disparate data streams—SCADA and PMU— to increase observability and the innovative methodologies we employed, including the use of fictitious VAR generators to reconcile temporal mismatches that result from this merger. We then transition to Wide Area Monitoring Systems (WAMs), examining how Real-Time Dynamics Monitoring System (RTDMS) is being configured to support operational workflows and alerting mechanisms.
Finally, we confront the often-overlooked human dimension: the differing priorities and perspectives of grid operators versus engineers. While engineers may pursue analytical elegance and exploratory insights, operators demand clarity, speed, and functional relevance. This tension—between what’s fascinating and what’s functional—shapes not only the tools we build but the culture of grid modernization itself.
Bio: Scott Adams is the Manager of Dominion Energy’s System Operation Center’s Operations Engineering team. He has worked at Dominion Energy since 2011, where he has held roles within the Substation Data Communications team, the project team to commission a new Data Historian, the Data Analytics team, and more recently leadership roles on the Data Communications and now SOC Operations Engineering teams. He has spent the majority of his career focused on data and its usage within Power Utilities. He is a graduate of West Virginia University where he earned his Bachelors in Electrical Engineering. Scott is a member of IEEE.
Emmanuel Oleka serves as a System Operations Center (SOC) Staff Engineer at Dominion Energy. In this role, he oversees the integration of synchrophasor Wide Area Monitoring Systems (WAMS) into operational processes. Emmanuel brings extensive expertise in power and energy, with experience spanning academia, the automotive sector, and utilities. He earned his Ph.D. in electric power systems from North Carolina A&T State University. His dedication and significant contributions to electrical engineering have led to his recognition as a Senior Member of the IEEE.
Speaker: Sumit Paudyal
Date: November 17th
Time: 5:00 PM - 6:00 PM
Place: ESB 501
Abstract: This talk aims at highlighting the need of models, methods, and tools for managing large penetration of Smart Inverters (SIs) in power distribution networks. Small-sized SIs will constitute up to 100% on the low voltage (LV) side of the distribution system feeders in the near future as the conventional inverters phase out. Utilities adopt non-optimal, default, and often fixed droop settings of SIs, which is a conservative solution that may lead to violation of operational constraints and unnecessarily high energy curtailments from the distributed energy resources (DERs). In this context, this talk will focus on the development of efficient optimization models, phasor-based dynamic models, and Neural Network based approaches for the management of SIs for optimal steady state operations and dynamic analyses.
Bio: Sumit Paudyal is Professor in the Department of Electrical and Computer Engineering at Florida International University. He received MS in Electrical Engineering from the University of Saskatchewan, Canada in 2008, and PhD in Electrical Engineering from the University of Waterloo, Canada in 2012. He was a faculty member in the Department of Electrical and Computer Engineering at Michigan Technological University from 2012 to 2019. He is the recipient of National Science Foundation Faculty Early CAREER Award in 2018, and Eta Kappa Nu (HKN) Best Professor of the Year for teaching (Michigan Tech , 2018), and Top Scholar Award (FIU, 2023). He is currently serving as an Associate Editor of the IEEE Transactions on Smart Grid. Dr. Paudyal’s recent research activities include distribution grid modeling, optimization in Smart Grids, photovoltaic (PV) integration issues, power system control and protection.
Speaker: Rahul Mangharam
Date: December 1st
Time: 5:00 PM - 6:00 PM
Place: Zoom-https://wvu.zoom.us/j/9188836315
Abstract: : The critical challenge in deploying autonomous systems is achieving peak performance without compromising safety. Autonomous racing crystallizes this challenge, as it punishes timid policies and demands robust, adaptive strategies in multi-agent settings. Current approaches often fail by either oversimplifying the behavior of other agents or lacking mechanisms for real-time adaptation. This talk presents research that pushes the boundaries of perception, planning, and control. We will explore how to develop highly competitive agents through:
1. Adversarial Training: Leveraging game theory and distributionally robust online adaptation to create agents that dynamically balance safety and assertiveness.
2. Adaptive Safety: Using conformal prediction, control barrier function and imitation learning we show how multiple imperfect experts train an AI to perform better than any single expert.
3. Safe MPC Frameworks: Implementing an iterative control strategy for nonlinear stochastic systems to handle constrained, real-world uncertainty.
All research is implemented on our F1Tenth/RoboRacer.ai platform—1/10th the size, but 10x the fun. The key takeaway is a deeper understanding of how to build and validate safe autonomous systems for complex, interactive environments.
Bio: Rahul Mangharam is a Professor in the departments of Electrical and Systems Engineering and Computer and Information Science at the University of Pennsylvania, where he directs research on the formal verification and synthesis of safe autonomous systems. His work bridges formal methods, machine learning, and control theory to create provably safe systems for applications including autonomous vehicles, urban air mobility, and life-critical medical devices.
Dr. Mangharam serves as the Penn Director for the $20 million Safety21 National University Transportation Center, a US DOT initiative for safe and efficient mobility. He also directs the Autoware Center of Excellence, an open-source autonomous driving consortium of over 100 industry and academic partners, and is the founder of the F1TENTH Autonomous Racing Community, now active in over 90 universities worldwide
Speaker: Amin Marvasti
Date: December 8th
Time: 5:00 PM - 6:00 PM
Place: https://wvu.zoom.us/j/9188836315
Abstract: Optimization plays a central role in managing and operating complex cyber-physical systems such as airline networks, gas infrastructures, and power grids. However, solving these large-scale problems often demands significant computational resources and time, frequently exceeding the limits of classical computing. Emerging paradigms such as quantum computing and machine learning offer powerful new tools to overcome these challenges. This talk presents an integrated framework that combines quantum computing, learning-based models, and decomposition techniques to develop next-generation algorithms for complex optimization and decision-making in cyber-physical infrastructure systems.
Speaker Bio: Amin Kargarian is an Associate Professor in the Department of Electrical and Computer Engineering at Louisiana State University. His research integrates optimization, machine learning, and quantum computing to advance the operation and resilience of power and energy infrastructure systems. He is a recipient of the NSF CAREER Award for his pioneering work in learning-assisted decomposition and distributed optimization. His contributions to research and education have been recognized with the LSU Rising Faculty Research Award and the LSU Instructor Excellence Award.