About SRC Lab
The Structured Representation and Computing (SRC) Lab, located in Rutgers University, studies how the inherent structure of data, signals, models, and physical systems can be made explicit through representation and translated into efficient computation. Our research spans algorithms, AI, computer architecture, and hardware-software co-design, with a focus on developing structure-aware methods and systems that improve efficiency, scalability, and intelligence.
Our Research Philosophy
At SRC Lab, we view computation as fundamentally a problem of structure. The world is not unstructured: data, signals, models, and physical processes all embody regularities, constraints, symmetries, and hierarchies. The central challenge of computing is therefore not simply to process information faster, but to find representations that make these hidden structures explicit, so that meaningful computation becomes possible, efficient, and scalable. In this view, representation is not a neutral encoding; it determines which structure is preserved, which is suppressed, and which forms of reasoning become natural. Algorithms operate on these representations, systems and hardware determine the true cost of exploiting them, and intelligence emerges when structure, representation, computation, and physical realization are brought into alignment. Our research is driven by the belief that the deepest advances in AI and computing will come not only from greater scale, but from better internal forms through which the world becomes intelligible to computation.
Current Research Interests
We have broad interests in various technical fields and layers towards building next-generation intelligent and emerging systems. Specifically, our research areas include:
1) Efficient AI: Efficient AI Algorithm, Hardware and System Design
2) Embodied AI: Efficient and Intelligent Robotic Computing
3) AI for X: AI-powered Scientific and Engineering Applications (Wireless, Transportation, Manufacturing)
4) Quantum: Quantum Computing and Quantum-Inspired Computing
Recent News
03/2026 One collaborative paper is accepted by ACM Intl. Symp. on Computer Architecture (ISCA)
01/2026 One paper is accepted by IEEE Intl. Conf. on Robotics & Automation (ICRA)
10/2025 One collaborative paper is accepted by IEEE/ACM Asia and South Pacific Design Auto. Conf (ASP-DAC)
07/2025 One paper is accepted by IEEE/ACM Intl. Symp. on Microarchitecture (MICRO)
05/2025 One paper is accepted by Trans. on Machine Learning Research (TMLR)
02/2025 Two papers are accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
01/2025 One collaborative paper is accpeted by ACM Trans. on Design Automation of Electronic Systems (TODAES)
09/2024 One paper is accepted by Conference on Neural Information Processing Systems (NeurIPS)
09/2024 One paper is accepted by Empirical Methods in Natural Language Processing (EMNLP)
07/2024 One paper is accepted by European Conference on Computer Vision (ECCV)
06/2024 One collaborative paper is accpeted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
06/2024 One collaborative paper is accpeted by IEEE/ACM Intl. Conf. on Computer Aided Design (ICCAD)
Acknowledgement
Our research is generously supported by National Science Foundation, Department of Energy, US Air Force, US Army and SNAP.
Opening for Ph.D. positions
We am looking for self-motivated Ph.D. students to join SRC lab with full financial support. Applicants with B.S. or M.S. degree of Electrical Engineering, Computer Science, Computer Engineering, Applied Math/Physics and Statistics are welcome. Interested applicants can send CV to PI Yuan (bo.yuan@soe.rutgers.edu). Students in Rutgers can stop by CoRE 715.