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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

  1. Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takáč. “AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.” Preprint & Submitted, 2022. (Download PDF)
  2. 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. (Download PDF)
  3. 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)
  4. 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. (Download PDF)
  5. 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. (SpringerLink)
  6. 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. (IEEE Xplore)
  7. 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. (IEEE Xplore)

Working Paper

  1. “Stochastic Second-Order Methods for Deep Learning.” collaboration w/ Martin Takáč, 2021.
  2. “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. (Lehigh Preserve).
  • “Forecasting the Number and Locations of Machine Installs Serviced by IBM in the United States.” Theses and Dissertations, 2013. (Lehigh Preserve)

Conference & Invited Talk

  1. “AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.” 7th International Conferences on Continuous Optimization (ICCOPT) 2022.
  2. “Improved Training of Graph-Embedding Based Neural Network Energy Functions for Catalysis.”  American Institute of Chemical Engineers (AlChE) Annual Meeting 2020. (AIChE; Video)
  3. “Finite Difference Neural Networks.” 19th IEEE International Conference on Machine Learning and Applications, USA. December 2020. (ICMLA; Video)
  4. “Deep Learning on Solving Partial Differential Equations.“ INFORMS Annual Meeting, USA. November 2020.

Seminar & Workshop Talk

  1. “Network Pruning.” OptML, Lehigh University, PA, USA. December 2020.
  2. “Finite Difference Neural Networks.” COR@L Lab Seminar Presentations, Lehigh University, PA, USA. October, 2020.
  3. “Residual Learning, Attention Mechanism and Multi-tasks Learning Networks.“ OptML, Lehigh University, PA, USA. April 2020.
  4. “Deep Learning in Molecular Quantum Mechanics.” COR@L Lab Seminar Presentations, Lehigh University, PA, USA. October 2019.
  5. “A Markov Decision Process Model for Inventory Planning.“ COR@L Lab Seminar Presentations, Lehigh University, PA, USA. April 2018.
  6. “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.

Collaborator:

  1. Martin Takáč (Ph.D. Advisor) – Mohamed bin Zayed University of Artificial Intelligence
  2. Peter Richtárik – KAUST
  3. Michael W. Mahoney – UC Berkeley
  4. Marilyn A Brown – Georgia Institute of Technology
  5. Srinivas Rangarajan – Lehigh University
  6. Shamim Pakzad – Lehigh University
  7. Albert S Berahas – University of Michigan
  8. Nicolas Loizou – Mila – Quebec Artificial Intelligence Institute
  9. Huibin Du – Tianjin University
  10. Nur Sila Gulgec – Thornton Tomasetti
  11. Majid Jahani – Target AI
  12. Sergey Rusakov – Lehigh University