I am an Associate Professor in School of Computing with a joint appointment in the Department of Electrical and Computer Engineering at 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, Master's degree in Computer Science from Wahsington State University in 2003, and Ph.D in Computer Science from Washington State University in 2007.
My speciality lies in high-performance computing (HPC) (e.g., distributed and parallel computing, general-purpose computation on graphical processing units (GPGPU)), and HPC-based big data analysis, machine learning, and scientific computation and visualization, etc.
I'm particularly interested in applying these HPC-based technologies into pressing science and engineering such as electrical engineering (power and energy systems, power electronics), automotive engineering, system biology, and computer graphics, etc.
Computer science is a discipline that spans theory and application, which has a strong connection with all other disciplines.
Furthermore, when large-scale domain problems with high-throughput real-time dataset are concerned, HPC-based parallel computation is extremely helpful in providing a fast and efficient technical solution.
My research goal is thus building a bridge between the fields of computer science and other disciplines, and applying effective and efficient HPC-based solutions.
My current research is primarily focused on developing optimized HPC-based parallel programming algorithms and architectures to solve complex scientific and engineering domain problems such as smart grid modeling and simulation, power electronics reliability assessment, ground vehicle systems prototyping, and advanced grid analytics, etc. For example, we can speed up large-scale power system simulations to take advantage of the superb computing capabilities of HPC resources, thus enabling real time or faster-than-real-time computation with the implementation of 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 (GPU).
My research lab, High-Performance Computing Enabled Science and Engineering (HPCeSE) Lab, now has four PhD and Master students working with me on 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, and Virtual Reality Enabled Electric Power Grid Cyber Training and Testing Platform projects.
Highly motivated prospective PhD students with strong interests in HPC applications with background in Computer Science or Electrical and Computer Engineering are very welcome to contact me and join my research group.
CPSC/ECE 4780/6780: General-Purpose Computation on Graphical Processing Units (GPGPU)
Fall 2017, Fall 2018, Summer 2020, Fall 2020, Fall 2021, Fall 2022
Course Description: Graphics processing units (GPU) is a term introduced by NVIDIA in the late 1990s which typically handles computation only for computer graphics. After 2001, with the advent of both programmable shaders and floating-point support on graphics processors, general-purpose computing on GPUs became practical and popular for scientific computing applications with its increasing speed and volume of computation. Nevertheless, extracting full performance from a GPU is challenging. Parallel algorithms are necessary but far from sufficient. Careful layout of both control flow patterns and memory access patterns is required to avoid flow divergence and bank conflicts, which can severely stall computational threads. Memory hierarchies, memory staging techniques, and the available synchronization primitives must be thoroughly understood to provide tremendous performance improvements over conventional programming techniques on CPUs.
This course is designed to provide instruction in the design and implementation of GPU-based solutions to computationally intensive problems from a variety of disciplines. NVIDIA’s CUDA and OpenCL will both be used as the programming language, and inter-operate with the open standard graphics language, OpenGL, for massive data visualization.
CPSC 4770/6770: Distributed and Cluster Computing
Spring 2019, Fall 2019, Fall 2020, Fall 2021
Course Description: This course will investigate issues in modern distributed platforms by examining a number of important technologies in the areas of parallel and distributed computing in computational and data-intensive problems. At the completion of the course, students should be able to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices, analyze a problem and identify and define the computing requirements appropriate to its solution, apply design and development principles in the construction of large-scale computing systems, and function effectively on teams to accomplish a common goal.
CPSC 8810/ECE 8930: High-Performance Computing for Power System Modeling and Simulation
Spring 2018, Spring 2020, Spring 2023
Course Description: With the increasing demand, scale, and data information of power systems, fast real-time modeling and simulation tools are becoming more important in power system operation and control. Improving the computational efficiency of power system applications requires parallel computing implementation of the solution methods with high-performance-computing (HPC)) capabilities.
This course is designed to provide instruction in the design and implementation of HPC technologies in power system modeling and simulation through extensive examples, studies, and project demonstrations, so that the students can obtain hands-on parallel programming experiences to resolve large-scale real-world complex power grid problems.
Extensive case studies on the intersection of HPC with energy systems will be provided in this class, spanning from the parallel development of standalone power system applications (e.g., power flow, dynamic simulation, contingency analysis, state estimation, etc.), scalable power grid frameworks (e.g., GridPACK, GridLAB-D), to co-simulation infrastructure, an analysis technique that allows simulators from different domains to interact with each other by exchanging values through the course of the simulation (e.g., HELICS, an open-source cyber-physical-energy co-simulation framework for energy systems, with a strong tie to the electric power system.)
CPSC 8810/ECE 8930: High-Performance Computing for Computational Science and Engineering
Course Description: With the increasing demand, scale, and data in computational science and engineering, fast modeling and simulation plays a significant role in providing real-time monitoring, analytics, and decision making. This course is designed to provide instruction in the formulation, design, analysis, implementation, and application of HPC approaches in science and engineering.
Multithreading, Open Multi-Processing (OpenMP), Massive Programming Interface (MPI), and CUDA/OpenCL/OpenACC programming on a variety of shared, distributed, or hybrid memory architectures will be introduced as the parallel programming tools. Popular parallel scientific libraires and advanced computational frameworks (e.g., SuperLU, PETSc, Julia, RAPIDS, etc.) will also be introduced to facilitate fast and efficient parallel application development to tackle large-scale complex real-world problems.
The transformative HPC techniques and hands-on parallel programming experiences learned from this class can easily be applied to solve large-scale real-world computational-critical problems in another discipline for in-depth research and development purpose.
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.
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.
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.
[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] Congrats to Liwei Wang for starting a graduate student internship at Pacific Northwest National Laboratory in Summer 2022!
[June 2022] Congrats 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] Congrats to Cayden Wagner on his graduation with a Masters 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] Congrats 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] Congrats to Cong Wong for being selected as a student volunteer for Supercomputing 2020!
[August 2020] Congrats 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] Give 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 School of Computing.