collaborators

7 papers

cs.AI2026

AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents

Xi Yu, Dmitrii Torbunov, Soumyajit Mandal +1

The design of Analog and Mixed-Signal (AMS) integrated circuits remains heavily reliant on expert knowledge, with transistor sizing a major bottleneck due to nonlinear behavior, hi…

cs.CV2026

CircuitSense: A Hierarchical MLLM Benchmark Bridging Visual Comprehension and Symbolic Reasoning in Engineering Design Process

Arman Akbari, Jian Gao, Yifei Zou +6

Engineering design operates through hierarchical abstraction from system specifications to component implementations, requiring visual understanding coupled with mathematical reaso…

cs.LG2026

Parameter Inference and Uncertainty Quantification with Diffusion Models: Extending CDI to 2D Spatial Conditioning

Dmitrii Torbunov, Yihui Ren, Lijun Wu +1

Uncertainty quantification is critical in scientific inverse problems to distinguish identifiable parameters from those that remain ambiguous given available measurements. The Cond…

cs.CV2025

IE2Video: Adapting Pretrained Diffusion Models for Event-Based Video Reconstruction

Dmitrii Torbunov, Onur Okuducu, Yi Huang +4

Continuous video monitoring in surveillance, robotics, and wearable systems faces a fundamental power constraint: conventional RGB cameras consume substantial energy through fixed-…

nucl-ex2025

Robust and Generalizable Background Subtraction on Images of Calorimeter Jets using Unsupervised Generative Learning

Yeonju Go, Dmitrii Torbunov, Yi Huang +8

Accurate separation of signal from background is one of the main challenges for precision measurements across high-energy and nuclear physics. Conventional supervised learning meth…

cs.AI2025

Diffusion Model-based Parameter Estimation in Dynamic Power Systems

Feiqin Zhu, Dmitrii Torbunov, Zhongjing Jiang +4

Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness presents…