This website is scheduled to be sunset on 10/01/2022, please look for my new site on LinkedIn.
- Research Interest:
- Deep Learning,
- Reinforcement Learning,
- Optimization for Machine Learning.
- Current Advisor:
- Past Advisor:
Coursework (GPA – 4.0)
- Linear Optimization, Nonlinear Optimization, Discrete Optimization
- Dynamic Programming and Reinforcement Learning, Optimization Methods for Machine Learning, Computational Methods in Optimization, Apache Spark and Data Science
- Real Analysis, Stochastic Models, Stochastic Calculus, Probability, Statistics
- Applied Operations Research, Logistics & Supply Chain Management, Financial Optimization, Game Theory, Simulation Based Optimization
- Quantum Computing for Optimization
Publication and Preprint
- Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takáč. “AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.” Preprint & Submitted, 2022.
- Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takáč. “Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information.” (Accepted) ICLR 2022.
- Zheng Shi, Nur Sila Gulgec, Albert S Berahas, Shamim Pakzad, Martin Takáč. “Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations.” 19th IEEE International Conference on Machine Learning and Applications (ICMLA) 2020.
(Download PDF;IEEE Xplore)
- Nur Sila Gulgec, Zheng Shi, Neil Deshmukh, Shamim Pakzad, Martin Takáč. “FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods.” Beyond First Order Methods in ML Workshop @ NeurIPS 2019.
- Huibin Du, Linxue Wei, Marilyn A Brown, Yangyang Wang, Zheng Shi. “A bibliometric analysis of recent energy efficiency literatures: an expanding and shifting focus.” Energy Efficiency 2013.
- Huibin Du, Zheng Shi, Yi Jia. “Change of total factor efficiency of financial service industry in China.” 16th International Conference on Industrial Engineering and Engineering Management 2009.
- Huibin Du, Hui Xu, Zheng Shi, Lihe Chai, Yi Jia. “Evaluation on competitiveness of service industry with non-equilibrium statistical mechanics.“ 6th International Conference on Service Systems and Service Management 2009.
- “Stochastic Second-Order Methods for Deep Learning.” collaboration w/ Martin Takáč, 2021.
- “Optimization Methods for Deep Learning in Quantum Chemistry.” collaboration w/ Srinivas Rangarajan, Martin Takáč, 2021.
Technical Report & Thesis
- “Advanced Algorithms and Applications in Machine Learning.” Theses and Dissertations, 2022.
- “Forecasting the Number and Locations of Machine Installs Serviced by IBM in the United States.” Theses and Dissertations, 2013.
Conference & Invited Talk
- “AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.” 7th International Conferences on Continuous Optimization (ICCOPT) 2022.
- “Improved Training of Graph-Embedding Based Neural Network Energy Functions for Catalysis.” American Institute of Chemical Engineers (AlChE) Annual Meeting 2020.
- “Finite Difference Neural Networks.” 19th IEEE International Conference on Machine Learning and Applications, USA. December 2020.
- “Deep Learning on Solving Partial Differential Equations.“ INFORMS Annual Meeting, USA. November 2020.
Seminar & Workshop Talk
- “Network Pruning.” OptML, Lehigh University, PA, USA. December 2020.
- “Finite Difference Neural Networks.” COR@L Lab Seminar Presentations, Lehigh University, PA, USA. October, 2020.
- “Residual Learning, Attention Mechanism and Multi-tasks Learning Networks.“ OptML, Lehigh University, PA, USA. April 2020.
- “Deep Learning in Molecular Quantum Mechanics.” COR@L Lab Seminar Presentations, Lehigh University, PA, USA. October 2019.
- “A Markov Decision Process Model for Inventory Planning.“ COR@L Lab Seminar Presentations, Lehigh University, PA, USA. April 2018.
- “Forecasting the Number and Locations of Machine Installs Serviced by IBM in the United States.“ COR@L Lab Seminar Presentations, Lehigh University, PA, USA. March 2017.
- Martin Takáč (Ph.D. Advisor) – Mohamed bin Zayed University of Artificial Intelligence
- Peter Richtárik – KAUST
- Michael W. Mahoney – UC Berkeley
- Marilyn A Brown – Georgia Institute of Technology
- Srinivas Rangarajan – Lehigh University
- Shamim Pakzad – Lehigh University
- Albert S Berahas – University of Michigan
- Nicolas Loizou – Mila – Quebec Artificial Intelligence Institute
- Huibin Du – Tianjin University
- Nur Sila Gulgec – Thornton Tomasetti
- Majid Jahani – Target AI
- Sergey Rusakov – Lehigh University