| |
|
|
The Department of Electrical Engineering and Computer Science at the University of Wyoming is offering the EECS Colloquium series as a service to all who are interested in Electrical Engineering and Computer Science. Most seminars in Fall 2026 are scheduled for Mondays 3:10PM -- 4:00PM in EERB 255. For help finding the locations of our seminar meetings, consult the on-line UWyo campus map. For questions about this page or to schedule talks, please contact Diksha Shukla: dshukla@uwyo.edu. Here is a list of seminar schedules. |
EECS Colloquium Schedule, Fall 2026
|
|
EECS Colloquium EECS Research Spotlight: Cybersecurity Faculty Talks Speakers: Dr. Diksha Shukla, Dr. Yibo Wang, and Dr. Sudipta Banerjee, EECS, University of Wyoming. When: 3:10PM ~ 4:00PM, Monday, September 28, 2026 Abstract: This EECS Research Spotlight session will feature cybersecurity research by Dr. Diksha Shukla, Dr. Yibo Wang, and Dr. Sudipta Banerjee, faculty members affiliated with the Cyber Intelligence and Security Innovation (CISI) Lab at the University of Wyoming. The speakers will highlight their current research, the real-world security challenges motivating their work, and opportunities for collaboration and student engagement. Bio: Dr. Diksha Shukla, Dr. Yibo Wang, and Dr. Sudipta Banerjee are faculty members in the Department of Electrical Engineering and Computer Science at the University of Wyoming and are affiliated with the Cyber Intelligence and Security Innovation (CISI) Lab. Their work advances cybersecurity research, education, and solutions to emerging security challenges. |
|
|
|
EECS Colloquium EECS Research Spotlight: Faculty Talks Speaker: Dr. Robin Hill, Dr. Vinit Katariya, and Dr. Abul Hasnat, EECS, University of Wyoming. When: 3:10PM ~ 4:00PM, Monday, October 05, 2026 Abstract: The EECS Research Spotlight: Faculty Talks offers an opportunity to explore the breadth of research taking place within the Department of Electrical Engineering and Computer Science at the University of Wyoming. In this session, Dr. Robin Hill, Dr. Vinit Katariya, and Dr. Abul Hasnat will highlight their ongoing research, the real-world challenges that motivate their work, and opportunities for collaboration and engagement with their research groups. The session aims to foster connections, spark new research interests, and strengthen the broader EECS research community. Bio: Dr. Robin Hill is a Lecturer, and Dr. Vinit Katariya and Dr. Abul Hasnat are Assistant Professors in the Department of Electrical Engineering and Computer Science at the University of Wyoming. Their teaching and scholarship reflect the interdisciplinary breadth of electrical engineering and computer science. During this faculty spotlight, they will introduce their current research and opportunities for students, faculty, and colleagues to connect with their research groups. |
|
|
|
EECS Colloquium Efficient Image Restoration for Adverse Weather Speaker: Dr. Judith N. Njoku When: 3:10PM ~ 4:00PM, Monday, October 12, 2026 Abstract: Modern vision systems rely on images to observe, interpret, and reconstruct the physical world. However, adverse conditions such as rain, fog, snow, and low light can severely degrade image quality, obscuring important scene information and reducing the reliability of applications ranging from autonomous navigation and surveillance to remote sensing and three-dimensional reconstruction. Image restoration, a field of artificial intelligence concerned with recovering clean and usable images from degraded inputs, offers a promising path toward more reliable visual perception. This talk introduces image restoration as a core challenge in computer vision and traces its evolution from classical signal-processing techniques to modern deep-learning approaches. It examines why models designed for a single degradation often fall short in real-world deployment and considers what a practical, unified restoration system must learn to handle diverse and potentially unknown conditions. The talk then explores how advances in multimodal learning and efficient neural architectures are enabling multiple degradations to be addressed within a single compact model. OmniRestore, our research in universal adverse-weather image restoration, serves as a case study for illustrating how these ideas move from theory to practical model design. Bio: Dr. Judith Nkechinyere Njoku is a Postdoctoral Research Associate at the University of Wyoming, where she is affiliated with the Secure Sensing and Learning (SSL) Research Lab. Her research lies at the intersection of artificial intelligence, digital twins, and cyber-physical systems, with a focus on developing intelligent and resilient solutions for real-world environments. She combines machine learning, explainable AI, and computer vision to address challenges in autonomous systems, smart infrastructure, energy technologies, and environmental resilience. Dr. Njoku received her Ph.D. in IT Convergence Engineering and her M.Sc. in Electronics Engineering from Kumoh National Institute of Technology, South Korea. |
|
|
|
EECS Colloquium From Splats to Objects: Segmenting 3D Gaussian Scenes for Interactive VR Speaker: Paul Sansah Gyreyiri, Ph.D. Candidate, EECS, University of Wyoming. When: 3:10PM ~ 4:00PM, Monday, October 26, 2026 Abstract: 3D Gaussian Splatting (3DGS) has quickly become a leading representation for photorealistic, real-time 3D scene reconstruction. However, a reconstructed scene is only a starting point: to edit it, interact with it, or bring it into VR, we must identify which Gaussians belong to which object. This is inherently challenging because Gaussians lack discrete surfaces, often overlapping and blending across object boundaries, while 2D masks from foundation models like SAM remain inconsistent across views. Existing strategies address this by lifting 2D masks onto Gaussians or distilling learned features into the scene, but they often fall short of the precision needed for physical interaction. In this talk, we introduce RigidGS, our physics-aware approach to Gaussian object segmentation. RigidGS bridges this gap by refining SAM2 masks with depth and temporal cues and assigning each Gaussian a fixed object identity via multi-view voting before geometric optimization. Finally, we demonstrate how this framework connects to an end-to-end VR pipeline, enabling users to seamlessly remove, inpaint, and manipulate segmented objects as rigid bodies directly on VR headsets. Bio: Paul Sansah Gyreyiri is a PhD candidate in Electrical Engineering and Computer Science at the University of Wyoming, where he works in the Secure Sensing and Learning (SSL) Research Lab under the supervision of Dr. Diksha Shukla. His research spans three iterconnected areas: security vulnerabilities in augmented and virtual reality, multimodal behavioral biometric authentication, and physics-aware 3D scene reconstruction using Gaussian Splatting. His broader interests include machine learning security, security and privacy in wearable devices, 3D computer vision for Immersive Systems, and 3D generative models. Paul earned his BSc with First Class Honors from the University of Mines and Technology, Tarkwa, Ghana, and is preparing to defend his PhD in November 2026. |
|
|
|
EECS Colloquium Agentic Modeling Workflows for Thermal Energy Harvesting Materials Speaker: Dr. Wabi Demeke, Postdoctoral Fellow, University of Wyoming. When: 3:10PM ~ 4:00PM, Monday, November 02, 2026 Abstract: Computational materials modeling employs quantum simulations to evaluate prospective structures for thermal energy harvesting applications. However, executing these multi-step simulations for complex systems such as metal-organic frameworks (MOFs) remains challenging due to the steep learning curve involved. Even with efficient methods like Density Functional Tight Binding (DFTB), configuring calculations, diagnosing runtime errors, and linking outputs to transport solvers still require specialized expertise and manual scripting. Autonomous multi-agent systems (MAS) powered by large language models (LLMs) present an attractive path for automating scientific workflows. Building on recent advances in agentic orchestration, this talk examines how integrating LLM reasoning with deterministic physics tools can bridge the automation gap in computational modeling. Specifically, we show how an agentic workflow can take a single crystal structure and run the entire simulation pipeline autonomously. A central Orchestrator sets the parameters and recovers from simulation failures, while specialized agents handle structure relaxation, HPC job submission, and transport calculations before compiling the results into user-friendly reports. We validate this pipeline across a diverse set of MOFs, showing how it reliably extracts key electronic transport properties without manual scripting and easily extends to other materials like covalent organic frameworks (COFs). Bio: Dr. Wabi Demeke Diriba is a postdoctoral research associate for AI-enabled interdisciplinary research at the University of Wyoming, where he is affiliated with the Computational Design of Inorganic Materials Lab in the Department of Chemistry. His core expertise lies in Scientific Machine Learning (SciML), computational modeling, and AI-driven design, with a focus on accelerating materials discovery and optimizing energy harvesting systems. Dr. Diriba received his Ph.D., M.S., and B.S. degrees in Mechanical Engineering from the Korea Advanced Institute of Science and Technology (KAIST), South Korea. |
|
|
|
EECS Colloquium TBA Speaker: Dr. Ruben Gamboa When: 3:10PM ~ 4:00PM, Monday, November 09, 2026 Abstract: TBA. Bio: TBA. |
|
|
|
EECS Colloquium Foundation Models for Precision Poultry Farming Speaker: Dr. Xiao Yang When: 3:10PM ~ 4:00PM, Monday, November 16, 2026 Abstract: Monitoring poultry behavior can help farmers understand their birds and make better management decisions. However, developing AI tools for this purpose often requires substantial manual labeling and training. This talk introduces foundation models and explains how they can make poultry monitoring easier and more flexible. Research examples include combining video and sound to recognize behavior, tracking individual chickens, and generating additional videos for training AI models. The talk also explores how language models could bring together monitoring results and farm records to provide useful explanations for farmers. These examples highlight both the potential of foundation models and the need to test them carefully under real farm conditions. Bio: Xiao Yang, Ph.D., is a Research Associate in the Department of Electrical Engineering and Computer Science at the University of Wyoming. Yang’s research interests include precision livestock farming, artificial intelligence, computer vision, robotics, and smart sensing technologies, with applications in poultry, swine, cattle, and wildlife monitoring. Yang is author/coauthor of 40 peer-reviewed journal articles，1 book chapter and 32 conference papers/abstracts. He has secured a total of $152,400 in research funding as PI and has participated in multiple research projects supported by USDA-NIFA, the Georgia Research Alliance, the Egg Industry Center, Oracle America, and industry partners. Yang is actively involved in extension and outreach activities, including the Wyoming State Science Fair, Georgia 4-H, etc. He also serves as a journal and conference reviewer, an Early Career Editorial Board Member of Animals, and a Special Issue Editor for topics related to intelligent animal monitoring and poultry farming. Yang’s research and professional activities have been recognized with several awards and honors, including the 2024 AOC Graduate Academic Achievement Award, 2024 AOC Student Research Presentation Award (2nd Prize), and 2023 Student Hackathon Poultry Track Competition (1st Prize). |
|
|
|
EECS Colloquium TBA Speaker: Dr. Judith N. Njoku When: 3:10PM ~ 4:00PM, Monday, December 07, 2026 Abstract: TBA. Bio: TBA. |
