About me

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I am an Associate Professor in the School of Computing with a joint appointment in the Department of Electrical and Computer Engineering at the College of Engineering, Computing and Applied Sciences, Clemson University. Prior to this position, I was a Senior Research Scientist at Pacific Northwest National Laboratory.

I received my bachelor's degree in Computer Science from Wuhan University in 2001, a Master's degree in Computer Science from Wahsington State University in 2003, and a Ph.D in Computer Science from Washington State University in 2007.

My speciality lies in high-performance computing (HPC), including distributed and parallel computing, general-purpose computation on graphical processing units (GPGPU), as well as HPC-based big data analysis, machine learning, scientific computation, and visualization.

I'm particularly interested in applying these HPC-based technologies to pressing science and engineering fields such as electrical engineering (power and energy systems, power electronics), automotive engineering, systems biology, and computer graphics, etc. ​

Research

Computer science is a discipline that spans theory and application, with strong connections to all other fields.

Moreoever, when addressing large-scale domain problems with high-throughput real-time datasets, HPC-based parallel computation proves extremely beneficial in providing fast and efficient technical solutions. ​

My research goal is to build a bridge between computer science and other disciplines, applying effective and efficient HPC-based solutions. ​

Currently, my research primarily focuses on developing optimized HPC-based parallel programming algorithms and architectures to solve complex scientific and engineering domain problems. Examples include smart grid modeling and simulation, power electronics reliability assessment, ground vehicle systems prototyping, and advanced grid analytics. For instance, we accelerate large-scale power system simulations to leverage the computing capabilities of HPC resources, enabling real-time or faster-than-real-time computation. This involves implementing Open Multi-Processing (OpenMP), Message Passing Interface (MPI), Pthreads, and CUDA/OpenCL on shared-memory supercomputers, distributed-memory clusters, inter-connected workstations, or graphical processing units (GPUs).

In my research lab, High-Performance Computing Enabled Science and Engineering (HPCeSE) Lab, I currently supervise six PhD students. They are involved in projects such as HPC implementations for power system dynamic simulation, GridPACK application development, data-driven model-based smart control of power electronics converters, learning to adapt and control for complex power systems, virtual prototyping of autonomy-enabled ground systems, virtual reality enabled electric power grid cyber training and testing platforms, and edge computing for grid edges.

I welcome highly motivated prospective PhD students with strong interests in HPC applications, particularly those with background in Computer Science or Electrical and Computer Engineering, to contact me and join my research group.

Teaching

CPSC/ECE 4780/6780: General-Purpose Computation on Graphical Processing Units (GPGPU)

Fall 2017, Fall 2018, Summer 2020, Fall 2020, Fall 2021, Fall 2022, Fall 2024

Course Description: Graphics processing units (GPUs), a term introduced by NVIDIA in the late 1990s, were initially designed to handle computation solely for computer graphics. However, following advancements such as programmable shaders and floating-point support on graphics processors after 2001, general-purpose computing on GPUs became both practical and popular for scientific computing applications due to its increasing speed and computational capacity. Nevertheless, maximizing the performance of a GPU presents challenges. While parallel algorithms are essential, they are not sufficient on their own. Careful consideration of both control flow patterns and memory access patterns is necessary to avoid flow divergence and bank conflicts, which can significantly impede computational threads. Understanding memory hierarchies, memory staging techniques, and available synchronization primitives is crucial for achieving significant performance improvements over conventional CPU programming techniques. This course is designed to provide instruction in designing and implementing GPU-based solutions to computationally intensive problems across various disciplines. Both NVIDIA’s CUDA and OpenCL will be utilized as programming languages, with interoperability with the open standard graphics language, OpenGL, for massive data visualization.

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CPSC 4770/6770: Distributed and Cluster Computing

Spring 2019, Fall 2019, Fall 2020, Fall 2021

Course Description: This course will explore contemporary challenges in distributed platforms by delving into significant technologies within the realms of parallel and distributed computing, focusing on computational and data-intensive problems. By the course's conclusion, students will be proficient in applying mathematical foundations, algorithmic principles, and computer science theory to model and design of computer-based systems. They will demonstrate an understanding of the tradeoffs inherent in design choices, posses the ability to analyze problems and define appropriate computing requirements for their solutions, apply design and development principles to construct large-scale computing systems, and effectively collaborate within teams to achieve shared objectives.

CPSC 8810/ECE 8930: High-Performance Computing for Power System Modeling and Simulation

Spring 2018, Spring 2020, Spring 2023

Course Description: With the escalating demand, scale, and complexity of power systems, the need for fast real-time modeling and simulation tools has grown significantly in power system operation and control. Enhancing the computational efficiency of power system applications necessitates the parallel computing implementation of solution methods leveraging high-performance computing (HPC) capabilities. This course aims to provide comprehensive instruction in the design and implementation of HPC technologies in power system modeling and simulation. Through extensive examples, studies, and project demonstrations, students will gain hands-on experience in parallel programming to tackle large-scale, real-world complex power grid problems. The course will feature extensive case studies at the intersection of HPC and energy systems. These studies will encompass various aspects, ranging from the parallel development of standalone power system applications (e.g., power flow, dynamic simulation, contingency analysis, state estimation) and scalable power grid frameworks (e.g., GridPACK, GridLAB-D) to co-simulation infrastructure. Co-simulation is an analysis technique enabling simulators from different domains to interact, exchanging values throughout the simulation process. Examples include HELICS, an open-source cyber-physical-energy co-simulation framework tailored for energy systems, with a strong emphasis on the electric power system.

CPSC 8810/ECE 8930: High-Performance Computing for Computational Science and Engineering

Spring 2022

Course Description: With the increasing demand, scale, and complexity of computational science and engineering, rapid modeling and simulation have become integral for real-time monitoring, analytics, and decision-making processes. This course is meticulously crafted to offer comprehensive instruction in formulating, designing, analyzing, implementing, and applying High-Performance Computing (HPC) approaches in science and engineering. Students will delve into multithreading, Open Multi-Processing (OpenMP), Message Passing Interface (MPI), as well as CUDA/OpenCL/OpenACC programming techniques across various shared, distributed, or hybrid memory architectures as essential parallel programming tools. Additionally, they will be introduced to popular parallel scientific libraries and advanced computational frameworks (such as SuperLU, PETSc, Julia, RAPIDS, etc.) to facilitate the rapid and efficient development of parallel applications tailored for tackling large-scale, complex real-world problems. The transformative HPC techniques and hands-on parallel programming experiences acquired from this course are not only applicable within the realm of computational science and engineering but can also be seamlessly extended to address large-scale, real-world computational challenges across diverse disciplines for in-depth research and development purposes.

Recent Publications

  • C Wang, S Wang, S Jin, “Acceleration of Learning-based Load-Shedding Scheme using Reduced Ybus Modeling Method”, 2024 IEEE Power & Energy Society General Meeting, July 2024.
  • L Wang, S Jin, Z Zhang, “Sample-reduced Uncertainty Quantification Method on PV Inverter Reliability Assessment using Unscented Transformation”, the 52nd IEEE Photovoltaic Specialists Conference (PVSC 52), June 2024.
  • L Zheng, Y Cui, S Jin, Y Chen, “High-Performance Computing-based Open-Source Power Transmission and Distribution Grid Co-Simulation”, IEEE PES Transactions on Power Systems, January 2024.
  • C Wang, S Liang, X Jia, S Jin, “High-Performance Computing on Power System Transient Stability Analysis: A Review”, the 55th Annual North American Power Symposium (NAPS-2023), October 2023.
  • C Wang, L Wang, S Jin, “A Single-Source Multiprocessing Parallelism for Heterogeneous Acceleration of Power System Dynamic Simulation”, the 55th Annual North American Power Symposium (NAPS-2023), October 2023.
  • S Amara, Y Li, C Wagner, S Jin, Z Zhang, C Edrington, “High-Performance Computing-based Fast Virtual Prototyping of Power Electronics Converters for Ground Vehicle Powertrain Systems”, the 55th Annual North American Power Symposium (NAPS-2023), October 2023.
  • Y Li, Z Zhang, C Edrington, S Jin, “Accelerating Switching Model-based Simulation Through Parallel Computing”, 2023 IEEE Energy Conversion Congress and Exposition, October (2023).
  • L Wang, D Clemens, J Hodges, A Reeves, J Ozbeytemur, S Jin, Z Zhang, “Integrated Large-Scale Data Management Platform for Photovoltaic PCE Reliability Data”,50th IEEE Photovoltaic Specialists Conference (PVSC 50), June (2023).
  • C Wang, S Jin, R Huang, Q Huang, Y Chen, "A Configurable Hierarchical Architecture for Parallel Dynamic Contingency Analysis on GPUs", IEEE Open Access Journal of Power and Energy (OAJPE), November 2022.
  • Y Chen, Z Huang, S Jin, A Li, "Power System Computing: Then, Now, and the Future", iEnergy, September 2022.
  • L Wang, R Thiagarajan, S Jin, Z Zhang, “Accelerating Simulation for High-fidelity PV Inverter System Reliability Assessment with High-Performance Computing”, 49th IEEE Photovoltaic Specialists Conference (PVSC 49), June 2022.
  • Y Li, C Wagner, S Jin, Z Zhang, “Quantitative Analysis of Accelerated Power Electronics Simulation Using Advanced Computing Technology”, 37th Annual IEEE Applied Power Electronics Conference & Exposition (APEC), March 2022.
  • F Tooryan, H HassanzadehFard, V Dargahi, S Jin, “A Cost-Effective Approach for Optimal Energy Management of a Hybrid CCHP Microgrid with Different Hydrogen Production Considering Load Growth Analysis”, International Journal of Hydrogen Energy, January 2022.
  • C Wang, L Wang, S Jin, “Adaptive Development of Parallel Power System Dynamic Simulation Application in Python”, 2021 OkIP International Conference on Advances in High-Performance Computing (AHPC).
  • S Abhyankar, R Huang, S Jin, B Palmer, W Perkins, Y Chen, “Implicit-Integration Dynamics Simulation with the GridPACK Framework”, 2021 IEEE Power & Energy Society General Meeting, July 2021.
  • D Qin, L Wang, S Jin, Z Zhang, “Data-driven Model-based Smart Control of Intelligent Gate Drive for Converter Operational Performance Improvement”, 36th Annual IEEE Applied Power Electronics Conference & Exposition (APEC), June 2021.
  • F Tooryan, H HassanzadehFard, R Collins, S Jin, B Ramezani, “Smart Integration of Renewable Energy Resources, Electrical, and Thermal Energy Storage in Microgrid Applications”, Energy, Volume 212, December 2020.
  • H HassanzadehFard, F Tooryan, R Collins, S Jin, B Ramezani, “Design and Optimum Energy Management of a Hybrid Renewable Energy System Based on Efficient Various Hydrogen Production”, International Journal of Hydrogen Energy, Volume 45, Issue 55, November 2020.
  • F Tooryan, H HassanzadehFard, R Collins, S Jin, B Ramezani, “Optimization and Energy Management of Distributed Energy Resources for a Hybrid Residential Microgrid”, Journal of Energy Storage, Volume 30, August 2020.
  • C Wang, S Jin, Y Chen, “Directive-based Hybrid Parallel Power System Dynamic Simulation on Multi-Core CPU and Many-Core GPU Architecture”, PDPTA'20- The 26th Int'l Conf on Parallel and Distributed Processing Techniques and Applications, July 2020.
  • S Jin, S Abhyankar, B Palmer, R Huang, W Perkins, Y Chen, “Toward a Numerically-Robust and Efficient Implicit Integration Scheme for Parallel Power Grid Dynamic Simulation Development in GridPACK”, PDPTA'20- The 26th Int'l Conf on Parallel and Distributed Processing Techniques and Applications, July 2020.
  • L Wang, Q Zhou, S Jin,“Physics-Guided Deep Learning for Power System State Estimation", Journal of Modern Power Systems and Clean Energy, Volume 8, Issue 4, July 2020.
  • Q Huang, R Huang, BJ Palmer, Y Liu, S Jin, R Diao, Y Chen, Y Zhang, “A Generic Modeling and Development Approach for WECC Composite Load Model”, Electric Power Systems Research, Volume 172, Pages 1-10, July 2019.
  • A Ahmadi, S Jin, M Smith, R Collins, “Parallel Power Flow based on OpenMP”, 50th North American Power Symposium, Fargo, North Dakota, September 2018.
  • R Diao, Z Huang, Y Makarov, Y Chen, B Palmer, S Jin, J Wong, "An HPC Based Realtime Path Rating Calculation Tool for Congestion Management with High Penetration of Renewable Energy", CSEE Journal of Power and Energy System, Volume 3, Issue 4, December 2017.
  • R Huang, S Jin, Y Chen, R Diao, B Palmer, Q Huang, and Z Huang, "Faster Than Real-time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models Using High-Performance Computing Platform", 2017 IEEE Power & Energy Society General Meeting, Pages 1-5, July 2017.
  • R Diao, S Jin, F Howell, Z Huang, L Wang, D Wu, Y Chen, "On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software", IEEE Transactions on Power Systems, Volume 32, Issue 3, Pages 2225-2233, May 2017.
  • S Jin, Z Huang, R Diao, D Wu, Y Chen, "Comparative Implementation of High Performance Computing for Power System Dynamic Simulations", IEEE Transactions on Smart Grid, Volume 8, Issue 3, Pages 1387-1395, March 2017.

Latest News

  • [July 2024] Our paper "Acceleration of Learning-based Load-Shedding Scheme using Reduced Ybus Modeling Method" has been accepted by the IEEE Power & Energy Society General Meeting, July 2024. Congratulations to Cong Wang!
  • [June 2024] Our paper "Sample-reduced Uncertainty Quantification Method on PV Inverter Reliability Assessment using Unscented Transformation" has been accepted by the 52nd IEEE Photovoltaic Specialists Conference (PVSC 52), June 2024. Congratulations to Liwei Wang!
  • [May 2024] Congratulations to Xun Jia for starting a graduate student internship at Hexagon Manufacturing Intellegence in Summer 2024!
  • [March 2024] Gave a talk on “Vehicle Propulsion Digital Twins: High-Performance Computing (HPC)-based Next Generation High-Fidelity Powertrain Co-Simulation for Ground Vehicle Systems” at FGVSC VIPR-GS Annual Review Meeting, March 2024.
  • [January 2024] Our paper "High-Performance Computing-based Open-Source Power Transmission and Distribution Grid Co-Simulation" has been accepted by the IEEE PES Transactions on Power Systems, January 2024. Congratulations to Lei Zheng!
  • [December 2023] Congratulations to Cong Wang on his graduation with a PhD in Computer Science and on starting as a computational scientist at UT Southwestern!
  • [April 2023] Served as a Panelist on NSF POSE.
  • [October 2023] Three papers have been accepted and will be presented in October at the NAPS-2023. Congratulations to Cong and Sushma!
  • [September 2023] Congratulations to Liwei Wang, Xun Jia, and Cong Wang for receiving the Zucker Graduate Education Center PhD Grant!
  • [August 2023] Congratulations to Cong Wang for receiving the 2023-2024 Doctoral Dissertation Completion Award!
  • [June 2023] Congratulations to Xun Jia for receiving the Churchill Carter Fellowship 2022-2023!.
  • [May 2023] Congratulations to Sushma on her graduation with a Master's Degree in Computer Science!
  • [April 2023] Served as a Panelist on NSF OAC Core.
  • [January 2023] Gave two presentations "Vehicle Propulsion Digital Twins: High-Performance Computing (HPC)-based Next Generation High-Fidelity Powertrain Co-Simulation for Ground Vehicle Systems" and "High-Speed Virtual Prototyping of Power Electronics Intensive Energy Systems Using High Performance Computing" at the GVSC VIPR-GS Annual Review Meeting, March 2023.
  • [January 2023] Welcome Prashanth Kadire (MS Candidate) and Joshua Smith (PhD Candidate) to join our HPCeSE Lab in Spring 2023!
  • [November 2022] Our paper "A Configurable Hierarchical Architecture for Parallel Dynamic Contingency Analysis on GPUs" has been accepted by the IEEE Open Access Journal of Power and Energy (OAJPE), November 2022. Congratulations to Cong Wang!
  • [November 2022] Welcome Shiyang Liang (PhD Candidate) to join our HPCeSE Lab in Spring 2023!
  • [September 2022] Received grant from DOD U.S. Army through GVSC on "Vehicle Propulsion Digital Twins: High-Performance Computing (HPC)-based Next Generation High-Fidelity Powertrain Co-Simulation for Ground Vehicle Systems" project (PI).
  • [August 2022] Received grant from DOE WETO and AGM through PNNL on "GridPACK-Wind: High-Performance Modeling and Simulation Tool for Wind Integration" project (PI).
  • [June 2022] Congratulations to Liwei Wang for starting a graduate student internship at Pacific Northwest National Laboratory in Summer 2022!
  • [June 2022] Congratulations to Cong Wang for starting a graduate student internship at Fermi National Accelerator Laboratory in Summer 2022!
  • [June 2022] Our paper "Accelerating Simulation for High-fidelity PV Inverter System Reliability Assessment with High-Performance Computing" has been presented in 49th IEEE Photovoltaic Sepecialists Conference (PVSC 49), June 2022. Congratulations to Liwei Wang!
  • [May 2022] Welcome Veera-Venkata-Hanuma Sushma-Amara (MS Candidate) to join our HPCeSE Lab in Summer 2022!
  • [May 2022] Congratulations to Cayden Wagner on his graduation with a Master's Degree in Computer Science!
  • [May 2022] Xun Jia and Dr. Jin joined a two-week NSF CyberTraiing on Employing Proper Orthogonal Decomposition (POD) and High-Performance Computing (HPC) in Advanced CI conducted by Clarkson University.
  • [April 2022] Congratulations to Cayden Wagner for receiving the "Outstanding Masters Student in Computer Science" award!
  • [November 2021] Received 2021 Junior Faculty Excellence in Teaching award from College of Engineering, Computing and Applied Sciences (CECAS) at Clemson University!
  • [November 2021] Our paper “Adaptive Development of Parallel Power System Dynamic Simulation Application in Python” has been published in 2021 OkIP International Conference on Advances in High-Performance Computing (AHPC), November 2021. Congratultions to Cong Wang!
  • [February 2021] Served as an independent merit reviewer for the U.S. Department of Energy's (DOE's) Office of Technology Transitions (OTT) for the Technology Commercialization Fund (TCF) FY21 proposals.
  • [January 2021] Welcome Cayden Wagner (MS Candidate) to join our HPCeSC Lab in Spring 2021!
  • [December 2020] Welcome Lei Zheng (MS Candidate) to join our HPCeSC Lab in Spring 2021!
  • [November 2020] Received grant from DOE Office of Energy Efficiency and Renewable Engergy Solar Energy Technologies Office (SETO) Year 2020 Fuding Program on "Tool for Reliability Assessment of Critical Electronics in PV (TRACE-PV)" project (Co-PI).
  • [November 2020] Gave a talk on "Tutorial - Integrated Planning Tools" at 2020 Fall CAPER General Meeting, November 10th, 2020.
  • [October 2020] Received grant from DOD U.S. Army through GVSC on VIPR-GS Program Focus Area 2 Propulsion Systems and Smart Energy "Electrical Power Architectures and Power Electronics" project (Co-PI).
  • [August 2020] Served as a technical reviewer for the 5th Workshop on the Electronic Grid.
  • [August 2020] Congratulations to Cong Wong for being selected as a student volunteer for Supercomputing 2020!
  • [August 2020] Congratulations to Ray Crane for receiving a Dr. Robert M. Geist, III Annual Fellowship in Computing!
  • [August 2020] Welcome Ray Crane (PhD Candidate) to join our HPCeSC Lab in Fall 2020!
  • [August 2020] Received grant from DOE AGM through PNNL on "Learning to Adapt and Control for Complex Power Systems" project (PI).
  • [August 2020] Contributed to a panel session presentation on “Novel Interdisciplinary Approaches for Power and Energy Research and Education” at IEEE PES General Meeting 2020, August 5th, 2020.
  • [July 2020] Our paper “Directive-based Hybrid Parallel Power System Dynamic Simulation on Multi-Core CPU and Many-Core GPU Architecture” has been accepted to PDPTA'20- The 26th Int'l Conf on Parallel and Distributed Processing Techniques and Applications, and presented at the 2020 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE'20), July 27-30, 2020. Congratultions to Cong Wang!
  • [July 2020] Our paper “Toward a Numerically-Robust and Efficient Implicit Integration Scheme for Parallel Power Grid Dynamic Simulation Development in GridPACK” has been accepted and presented on PDPTA'20- The 26th Int'l Conf on Parallel and Distributed Processing Techniques and Applications, and presented at the 2020 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE'20), July 27-30, 2020.
  • [February 2020] Served as an independent merit reviewer for the U.S. Department of Energy's (DOE's) Office of Technology Transitions (OTT) for the Technology Commercialization Fund (TCF) FY20 proposals.
  • [January 2020] Served as a reviewer for Department of Energy (DOE) FY2020 Phase II Small Business Innovation Research (SBIR)/Small Business Technology Transfer (STTR) proposals.
  • [October 2019] Jointly appointed to Holcombe Department of Electrical & Computer Engineering as an Associate Professor at Clemson University.
  • [August 2019] Welcome Cong Wang and Liwei Wang (PhD Candidates) to join our HPCeSC Lab in Fall 2019!
  • [July 2019] Received grant from DOE AGM through PNNL on "GridPACK DAE Solver Interface Development" project (PI).
  • [June 2019] Gave a talk on “High-Performance Computing (HPC) Software Framework Development for Electric Power Grid Modeling and Simulation” at Clarkson University Electrical & Computer Engineering Seminar for NSF REU site, June 10, 2019.
  • [May 2019] Received Clemson Faculty SUCCEEDS awards on Integrated High-Performance Data-Driven Analytics Framework Development (PI) and CPS for Power System Control and Operation (Co-PI).
  • [February 2019] Received the Certificate of Successful Completion on 2018 E4 Carolinas Emerging Leaders Program after one year committment.
  • [February 2019] Served as an independent merit reviewer for the U.S. Department of Energy's (DOE's) Office of Technology Transitions (OTT) for the Technology Commercialization Fund (TCF) FY19 proposals.
  • [November 2018] Served as a reviewer for Department of Energy (DOE) FY2019 Phase I Small Business Innovation Research (SBIR)/Small Business Technology Transfer (STTR) proposals.
  • [September 2018] Hosted a Panel Session on "High-performance Computing for Future Power and Energy System" at Clemson University Power System Conference, September 4-7, 2018.
  • [August 2018] Gave a talk on “The Development of a High-Performance Computing (HPC) Software Framework for Power Grid Simulations and Beyond” at Fall 2018 School of Computing Seminar, Clemson University, August 2018.
  • [July 2018] Gave a talk on “GridPACK – A Shortcut to Parallel Power Grid Simulation Development” at CAPER Summer 2018 Workshop, Raleigh, NC, July 2018.
  • [July 2018] Joined USEA Big Data and Machine Learning Workshop on July 12th, 2018 in Washington D.C.
  • [August 2017] Joined Clemson University as an Associate Professor in the School of Computing.
Last Updated on May 16, 2024 9:15am EST