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: January 26th, 2026
Time: 5:00 PM - 6:00 PM
Place: ESB 801
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: Amin Kargarian
Date: February 16th
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 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.
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Speaker: Robert Mnatsakanov
Date: Monday, February 23rd
Time: 5:00 PM - 6:00 PM
Place: ESB 801
Abstract: In this talk the problem of estimating the moment-determinate functions and their derivatives given the information contained in the sequence of moments is discussed. Moment type reconstructions are of interest in many areas of mathematics and statistics. For example, as one of the main applications in statistics, we will show how our approximations, based on estimated moments of the model, yields a new type of non-parametric estimates of the quantile, conditional quantile, and regression functions. In addition, the moment method is applied to the problem of estimating the joint density function of the random coefficients in the linear regression model. Under the assumptions that coefficients of the regression function are non-negative random variables, the new non-parametric density estimator of the unknown density function of coefficients is derived. As another example, it is worth mentioning the area of Computed Tomography, where the moment methods are very useful. One can establish the relationship between the moments of observed Radon transform (projections) of function f(x) and the moments of original function f(x) (image) itself for recovery of the image f from the values of its Radon transform. We implemented new algorithm to reconstruct f(x) from the values of its Radon transform. Numerical and graphical convergences of our constructions are illustrated by means of tables and graphs.
Bio: Robert Mnatsakanov, PhD, is the Professor of Mathematics at the School of Mathematical and Data Sciences, WVU. He received his PhD in Physics and Mathematics from Moscow Institute of Electronics and Mathematics. Mnatsakanov’s research interests are concentrated in different areas of statistics and mathematics, such as the change-set problem, entropy estimation in multidimensional space, on recovering the distributions in the framework of multidimensional Hausdorff and Stieltjes moment problems, the nonparametric estimation of unknown mixing distributions in Poisson mixture models. He works on reconstructions of unknown intensity functions in Computed Tomography by inverting the Radon and the Laplace transforms using newly developed the moment-recovered approximations.
Speaker: Jona Balle
Date: April 27th
Time: 3:00 PM - 4:00 PM
Place: https://wvu.zoom.us/j/9188836315
Abstract: Since its emergence roughly a decade ago, the field of learned data compression has attracted considerable attention from the machine learning, information theory, and signal processing communities. Data-driven coding promises better adaptation to novel types of media as well as to improved models of human perception. In this talk, I will review nonlinear transform coding (NTC), a framework that has surpassed hand-crafted image compression methods in terms of rate–distortion and subjective visual quality, and has led to the recent JPEG AI standard. I will briefly highlight how learned compression methods can discover behaviors that closely resemble information-theoretically optimal strategies, and discuss remaining challenges to achieving general optimality. I will then shift focus to perceptual modeling, an area of growing importance. Inspired by advances in generative models, recent work has focused on increasingly accurate probabilistic models of natural images, yielding hyper-realistic outputs at very low bitrates. However, these techniques often come at a computational cost far beyond that of conventional codecs. I will show how improved models of human perception, specifically optimization for Wasserstein Distortion, a perceptual measure grounded in models of peripheral vision, can offer a better trade-off between realism, fidelity, and complexity. These models rival the performance of generative approaches while remaining computationally practical.
Speaker Bio: Jona Ballé is an Associate Professor at New York University and a Group Lead at Fraunhofer Heinrich Hertz Institute, studying data compression, information theory and models of visual perception. She defended her master's and doctoral theses on signal processing and image compression under the supervision of Jens-Rainer Ohm at RWTH Aachen University in 2007 and 2012, respectively. This was followed by a brief collaboration with Javier Portilla at CSIC in Madrid, Spain, and a postdoctoral fellowship at New York University’s Center for Neural Science with Eero P. Simoncelli, where Jona studied the relationship between perception and image statistics. While there, she pioneered using machine learning for end-to-end optimized image compression – this work ultimately led to the JPEG AI standard, finalized in 2025. From 2017 to 2024, Jona deepened her ties to industry as a Research Scientist at Google, before returning to NYU. Jona has served as a reviewer for top-tier publications in both machine learning and image processing, such as NeurIPS, ICLR, ICML, Picture Coding Symposium, and several IEEE Transactions journals. She has been active as a co-organizer of the annual Challenge on Learned Image Compression (CLIC) since 2018, and on the program and steering committees of the Data Compression Conference (DCC) since 2022 and the Picture Coding Symposium (PCS) since 2025, respectively.
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