Web Science and Digital Libraries Research Group

VDMC and Q.E.D. Systems teams at the Q.E.D. Center for Training and Development. How do we really know when someone has learned a skilled trade? Is completing a task enough, or should we also understand how the person performed it, where they struggled, what demanded their attention, and how their performance changed as they gained experience? These questions came to life during a recent hands-on maritime workforce visit to Q.E.D. Systems, Inc ., by members of the Virginia Digital Maritime Center (VDMC) Workforce Innovation team. For the day, we stepped away from the roles we normally hold as researchers, developers, and workforce professionals and became novice maritime trainees. Instead of only discussing workforce training and performance from the outside, we experienced parts of the learning process firsthand. The experience gave us an opportunity to learn several maritime skilled trades firsthand while also observing how skills are introduced, practiced, coached, and ultimately a...

IEEE Quantum Week  2026  in Toronto From September 13-18, I attended the IEEE International Conference on Quantum Computing and Engineering (QCE), also known as IEEE Quantum Week, in Toronto, Canada. The main reason for the trip was to present our paper, " Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search ". QCE was especially interesting for me because essentially everyone there was working somewhere in the quantum computing ecosystem. There were people with backgrounds in physics, computer science, electrical engineering, AI/ML, and pretty much everything in between.  Throughout the week, I attended keynotes and technical sessions, spent too much time walking around the exhibit hall, and had the chance to meet people working across quantum hardware, software, algorithms, HPC, and AI.  My parents and brother also tagged along for the trip, so outside the conference we had some time to explore Toronto tog...

As stated in the paper, " A self-correcting multi-agent LLM framework for language-based physics simulation and explanation " [1], physics simulations are essential in science and engineering, but creating them often requires expert level knowledge of a variety of domains. For example, the development of a fluid simulation requires a strong understanding of the Navier-Stokes equations and the appropriate numerical solvers surrounding that area. In addition, users require proficiency in a programming language and familiarity with physics libraries, as building a simulation entirely from scratch is rarely, if ever, practical. The figure below illustrates the outcome of their research. Most notably, the user provides a basic ‘layman’ input and a basic set of parameters, while being afforded the luxury of omitting critical information required to develop a comprehensive solution. This is all accomplished using the Memory-Coordinated Physics-Aware Simulation (MCP-SIM), a self-c...