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Group News
(08/2025) We received a three-year grant, "Passive Source Quantum Superresolution Assisted by Physics-Informed Robust Deep Learning," from the National Science Foundation (NSF).
(07/2025) We received a four-year NSF grant (with Ohio State University), "SHF: Medium: OASIS: An Open-Source AI-Driven EDA Tool for Real-Time Synthesis of Short-Distance Wireless Interconnects on Silicon" from the Division of Computing and Communication Foundations (CCF).
(07/2025) Our papers, "dyGRASS: Dynamic Spectral Graph Sparsification via Localized Random Walks on GPUs" and "HyperEF 2.0: Spectral Hypergraph Coarsening via Krylov Subspace Expansion and Resistance-based Local Clustering", have been accepted to the IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
We’re excited to welcome Jasmine Xu, a first-year computer science student at Cornell University, who will join our lab as a summer intern.
(06/2025) Our latest work (code release), "SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning", has been accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). We obtained state-of-the-art partitioning solutions for multiple public-domain VLSI circuit partitioning benchmarks, such as the ISPD98 and TITAN23 datasets. Congratulations!
(04/2025) Wuxinlin Cheng successfully defended his Ph.D. dissertation titled "Stability Analysis of Machine Learning Models on Manifolds". Congratulations!
(02/2025) Our collaborative work (with Professor Zhiru Zhang's group and code release), "CirSTAG: Circuit Stability Analysis on Graph-based Manifolds" (slides), has been accepted to the ACM/IEEE Design Automation Conference (DAC). This paper was nominated for the Best Paper Award (7 out of 1862 submissions). Congratulations!
(11/2024) Invited seminar: Professor Feng gave a seminar talk at Nvidia Research.
(09/2024) Our collaborative work (with Professor Yue Ning's group), "A Topology-aware Graph Coarsening Framework for Continual Graph Learning", has been accepted to the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24). Congratulations!
(06/2024) Invited seminar: Professor Feng gave a seminar talk at Lawrence Berkeley National Laboratory.
(06/2024) We received a three-year NSF grant, "SHF: Small: High-Performance Incremental Spectral Algorithms for Efficient Modeling and Simulation of Large-Scale Integrated Circuits", from the Division of Computing and Communication Foundations (CCF).
(06/2024) Our latest research focuses on the stability analysis/enhancement of Graph Neural Networks (GNNs), which is now available online: (1) The SAGMAN framework estimates and enhances the stability of GNNs' node classification tasks by examining the distance distortions of the maps between the input and output manifolds; (2) The Graph Geodesic Distance (GGD) metric is introduced for assessing the stability of GNNs in graph classification tasks by leveraging spectral graph matching and the geodesics defined on the Riemannian manifold.
(05/2024) Three new Ph.D. students, Lizhou Qi, Philip Mascaro, and Jinwen Wu, will join us this Fall semester. We extend a warm welcome to them!
(02/2024) Our papers "inGRASS: Incremental Graph Spectral Sparsification via Low-Resistance-Diameter Decomposition" and "SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks" have been accepted to the ACM/IEEE Design Automation Conference (DAC). Congratulations!
(12/2023) Our paper "diGRASS: Directed Graph Spectral Sparsification via Spectrum-Preserving Symmetrization" has been accepted to the ACM Transactions on Knowledge Discovery from Data (TKDD). Congratulations!
(11/2023) Hamed Sajadinia joined our group as a Ph.D. student. Welcome!
(06/2023) Yihang Yuan joined our group as a Ph.D. student. Welcome!
(05/2023) Ali Aghdaei successfully defended his Ph.D. dissertation entitled "High-Performance Spectral Algorithms for Hypergraph Compression". Congratulations!
(03/2023) Invited seminar: Professor Feng gave a seminar talk at Rutgers University.
(11/2022) Our collaborative work (with Professor Zhiru Zhang's group), "GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks", has been accepted to the Learning on Graphs Conference (LoG) as an oral presentation. Congratulations!
(10/2022) We received a three-year grant, "SHF: Small: Learning Circuit Networks from Measurements" from the Division of Computing and Communication Foundations (CCF) of the National Science Foundation (NSF).
(09/2022) Invited seminar: Professor Feng gave a seminar talk at Temple University.
(09/2022) Soumen Shuvo joined our group as a new Ph.D. student. Welcome!
(08/2022) Ying Zhang successfully defended her Ph.D. dissertation. Congratulations!
(07/2022) Our work "HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering" has been accepted to the IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
(07/2022) Our work, "SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements", has been accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). Congratulations!
(07/2022) We received a four-year grant (with Cornell University), "SHF: Medium: Co-optimizing Spectral Algorithms and Systems for High-Performance Graph Learning" from the Division of Computing and Communication Foundations (CCF) of the National Science Foundation (NSF).
(04/2022) Our work, "A Multilevel Spectral Framework for Scalable Vectorless Power/Thermal Integrity Verification", has been accepted by ACM Transactions on Design Automation of Electronic Systems (TODAES). Congratulations!
(02/2022) Prof. Feng will be serving as a guest editor for the VLSI Journal – Elsevier Special Issue of the Asia and South Pacific Design Automation Conference 2022. Please consider submitting your work to this journal.
(10/2021) Our work "Scalable Graph Topology Learning via Spectral Densification" has been accepted by WSDM'22. Congratulations!
(09/2021) John Anticev joined our group as a new Ph.D. student. Welcome!
(07/2021) Our work "HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering" has been accepted to the IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
(07/2021) Invited seminar: Professor Feng gave a seminar talk at Nvidia Research (slides).
(05/2021) Our collaborative work (with Professor Zhiru Zhang's group at Cornell University), "SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation", has been accepted to the International Conference on Machine Learning (ICML). Congratulations!
(02/2021) Our collaborative work (with Professor Wenjian Yu's group at Tsinghua University), "feGRASS: Fast and Effective Graph Spectral Sparsification for Scalable Power Grid Analysis", has been accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Congratulations!
(02/2021) Our work "SGL: Spectral Graph Learning from Measurements" has been accepted to the ACM/IEEE Design Automation Conference (DAC). Congratulations!
(02/2021) Invited seminar: Professor Feng gave a seminar talk at the Department of Computer Science and Engineering, University of California at San Diego.
(01/2021) Wuxinlin Cheng joined our group as a new Ph.D. student. Welcome!
(12/2020) Mr. Yongyu Wang successfully defended his Ph.D. dissertation. Congratulations!
(10/2020) Mr. Zhiqiang Zhao successfully defended his Ph.D. dissertation and will join our group as a postdoc researcher. Congratulations!
(10/2020) Our work "Towards Scalable Spectral Embedding and Data Visualization via Spectral Coarsening" has been accepted by WSDM'21. Congratulations!
(07/2020) Our work, "SF-GRASS: Solver-Free Graph Spectral Sparsification," has been accepted to ICCAD'20. Congratulations!
(04/2020) Our collaborative work (with Professor Chaoli Wang's group at the University of Notre Dame), "Spectrum-preserving sparsification for visualization of big graphs", has been published in Computers & Graphics. Congratulations!
(12/2019) Our collaborative work (with Professor Zhiru Zhang's group at Cornell University), "GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding", has been accepted to ICLR'20 for oral presentation. Congratulations!
(12/2019) Our work, "GRASS: Graph Spectral Sparsification Leveraging Scalable Spectral Perturbation Analysis", has been accepted for publication in the Transactions on Computer-Aided Design of Integrated Circuits and Systems. Congratulations!
(11/2019) Our work, "A Spectral Approach to Scalable Vectorless Thermal Integrity Verification", has been accepted to DATE'20. Congratulations!
(06/2019) We presented our work, "Effective-Resistance Preserving Spectral Reduction of Graphs," at the Design Automation Conference. (Slides)
(05/2019) We received a three-year CCF grant, SHF: Small: Spectral Reduction of Large Graph and Circuit Networks from the National Science Foundation (2019-2022, single PI).
(12/2018) Our latest papers, "Towards Scalable Spectral Sparsification of Directed Graphs", and "Nearly-Linear Time Spectral Graph Reduction for Scalable Graph Partitioning and Data Visualization", are now available on arXiv!
(10/2018) Invited seminar: Professor Feng gave an HC Torng Lecture at the Department of Electrical and Computer Engineering, Cornell University.
(06/2018) Ying Zhang and Ali Aghdaei joined our group as new Ph.D. students. Welcome!
(12/2017) We received a research award from Keysight Technologies that will support our work on spectral methods for radio-frequency integrated circuit partitioning and analysis.
(11/2017) Invited seminar: Professor Feng gave a seminar talk at the Department of Computer Science and Engineering, University of California, San Diego.
(11/2017) Our latest paper, "Similarity-Aware Spectral Sparsification by Edge Filtering", is now available on arXiv!
(10/2017) Our latest paper, "Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification", is now available on arXiv!
(10/2017) Invited seminar: Professor Feng gave a seminar talk at the Department of Electrical and Computer Engineering, Duke University.
(09/2017) Our spectral graph sparsification engine is now available for download. It can robustly preserve important spectral (structural) graph properties while dramatically reducing the complexity of general large-scale networks, such as social networks, big data graphs, and VLSI circuit networks. Check it out!
(09/2017) Prof. Feng is organizing the International Workshop on Design Automation for Analog and Mixed-Signal Circuits. This workshop is co-located with the 2017 International Conference on Computer-Aided Design (ICCAD). The deadline for poster abstract submissions is September 25, 2017.
(07/2017) Prof. Feng gave a research talk entitled: "Towards Practically-Efficient Spectral Sparsification of Graphs" at the SIAM Annual Meeting (AN17) and Carnegie Mellon University, Pittsburgh, PA.
(06/2017) Our paper, "SAMG: Sparsified Graph-Theoretic Algebraic Multigrid for Solving Large Symmetric Diagonally Dominant (SDD) Matrices", has been accepted to the IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2017. Congratulations!
(02/2017) Our paper, "A Spectral Graph Sparsification Approach to Scalable Vectorless Power Grid Integrity Verification", has been accepted to the IEEE/ACM Design Automation Conference (DAC), 2017. Congratulations!
(11/2016) Prof. Feng and Prof. Xin Li (Carnegie Mellon University) are organizing an International Workshop on Design Automation for Analog and Mixed-Signal Circuits, co-located with the International Conference on Computer-Aided Design (ICCAD), 2016.
(06/2016) We received a research grant from the National Science Foundation (NSF) for the proposal entitled "SHF: Small: Scalable Spectral Sparsification of Graph Laplacians and Integrated Circuits" (2016-2019, single PI).
(04/2016) Congratulations to Mr. Xueqian Zhao and Mr. Lengfei Han for passing their Ph.D. defense!
(02/2016) Mr. Lengfei Han will join Intel Corporation, OR. Congratulations!
(09/2015) Mr. Yongyu Wang joined our group as a Ph.D. student.
(08/2015) Mr. Xueqian Zhao will join Synopsys Inc., CA. Congratulations!
(11/2014) Prof. Feng became the principal investigator of the CUDA Research Center at Michigan Technological University, named by Nvidia Corporation.
(07/2014) Mr. Zhiqiang Zhao joined our group as a Ph.D. student.
(06/2014) Prof. Feng received the Research Enhancement Fund from Michigan Technological University.
(05/2014) Prof. Feng received a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF) for the proposal entitled: "CAREER: Leveraging Heterogeneous Manycore Systems for Scalable Modeling, Simulation and Verification of Nanoscale Integrated Circuits"(2014-2019).
(10/2013) Prof. Zhuo Feng accepted an invitation to serve as a member of the technical program committee of the 2014 ACM/IEEE Design Automation Conference (DAC) to be held in San Francisco.
(09/2013) Prof. Feng received a research grant from the National Science Foundation (NSF) for the proposal entitled: "SHF: Small: Graph Sparsification Approach to Scalable Parallel SPICE-Accurate Simulation of Post-layout Integrated Circuits" (2013-2016, single PI).
(08/2013) We received a research grant from Intel Corporation for power grid verifications (2013-2014, single PI).
(07/2013) Our paper, "An Efficient Graph Sparsification Approach to Scalable Harmonic Balance (HB) Analysis of Strongly Nonlinear RF Circuits", has been accepted to the 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
(06/2013) Our paper, "Scalable Vectorless Power Grid Current Integrity Verification", received the prestigious best paper award at the 2013 ACM/IEEE Design Automation Conference (DAC). This paper was the sole best paper award winner selected from eight nominated candidates, 162 accepted technical papers, and 747 papers submitted this year. More than 6,000 people attended the 2013 DAC conference. Congratulations!
(02/2013) Our papers on GPU-based SPICE simulator (TinySPICE) and vectorless power grid verification have been accepted to the 2013 ACM/IEEE Design Automation Conference (DAC). The paper on vectorless current integrity verification has been nominated for the DAC Best Paper award. Congratulations!
(08/2012) Arash Qodratnama and Jehanzeb Ashraf joined our group.
(06/2012) Our paper, "GPSCP: A General-Purpose Support-Circuit Preconditioning Approach to Large-Scale SPICE-Accurate Nonlinear Circuit Simulations", has been accepted to the IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
(02/2012) Our paper, "Towards Efficient SPICE-Accurate Nonlinear Circuit Simulation with On-the-Fly Support-Circuit Preconditioners", has been accepted to the 2012 ACM/IEEE Design Automation Conference (DAC). Congratulations!
(12/2011) Bojun Ma and Lengfei Han joined the group as Ph.D. students.
(06/2011) Our paper, "Power Grid Analysis with Hierarchical Support Graphs", has been accepted to the 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
(02/2011) Our paper, "Fast Multipole Method on GPU: Tackling 3-D Capacitance Extraction on Massively Parallel SIMD Platforms", has been accepted to the 2011 ACM/IEEE Design Automation Conference (DAC). Congratulations!
(09/2010) Jennifer Winikus joined the group.
(09/2010) Liang Ma joined the group.
(06/2010) Our paper, "Fast thermal analysis on GPU for 3D-ICs with integrated microchannel cooling", has been accepted to the 2010 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). Congratulations!
(02/2010) Three papers were accepted to the 2010 ACM/IEEE Design Automation Conference (DAC). Congratulations!
(08/2009) Xueqian Zhao joined the group as the first Ph.D. student.
(08/2009) Our research website has been created.
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