Speaker: Sarika Khushalani Solanki
Date: January 12, 2026
Time: 5:00 PM - 6:00 PM
Place: ESB 801
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.
.
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.