Academic Profile

Profile

Education, technical skills, service, open-source work, and collaboration network.

LinkedIn Google Scholar GitHub Request CV

Education

PhD Candidate, Computer Science

Kent State University, Kent, OH

Advisor: Prof. Qiang Guan

Expected Completion Fall 2026

MS, Computer Science

University of Waterloo, Waterloo, ON, Canada

MS, Applied Mathematics

Toronto Metropolitan University, Toronto, ON, Canada

BS, Mathematics (minor in Economics)

Memorial University of Newfoundland, St. John's, NL, Canada

News & Talks

PhD Milestones

  • Passed PhD preliminary exam, April 2023.
  • Passed PhD candidacy exam, July 2024.
  • Passed PhD thesis proposal exam, May 2026.
  • Planning thesis defense for Fall 2026.

Invited Research Presentations

  • Case Western Reserve University, Department of Computer Science, master's and PhD student seminar course, December 2023.
  • Case Western Reserve University, Department of Computer Science, master's and PhD student seminar course, December 2024.

Recent News

  • Paper "Uncertainty Quantification Driven Benchmarking and Characterization of Noisy Quantum Backends: A VQE Case Study" accepted at GLSVLSI 2026.
  • Co-authored submission "System-Level Energy and Cost Characterization of Hybrid Quantum–Classical Cloud Workloads" to ICPP 2026 (with Waylon Luo as lead author); decision pending.

Technical Skills

Quantum Software

Qiskit, PennyLane, quantum circuit simulation, transpilation reuse, backend benchmarking, and noise-aware experiment design.

Programming

Python, C++, Java, shell scripting, MATLAB, MAPLE, and Racket.

Scientific Computing

SciPy, scikit-learn, TensorFlow, PyTorch-Geometric, graph neural networks, numerical workflows, and reproducible research pipelines.

Research Methods

Uncertainty quantification, Bayesian posterior estimation, sensitivity analysis, resource estimation, compilation-aware benchmarking, Markovian QCVV-informed evaluation, and visualization.

Professional Service

Program Committee Reviewer

IEEE International Conference on Quantum Computing and Engineering (QCE), 2025

ACM Transactions in Quantum Computing · IEEE QCE Conference

Open Source

Developed tutorials on quantum algorithms and quantum error mitigation techniques as part of the Quantum Computing at KSU educational resource.

Collaborations

Research collaborations span national laboratories, universities, and industry partners across quantum computing, uncertainty quantification, visualization, reproducibility, and network-science applications.

National Laboratories

Pacific Northwest National Laboratory (PNNL)

Themes: QSP/QSVT, molecular spectroscopy, open quantum dynamics, uncertainty quantification, quantum machine learning

  • Bo Peng - PNNL mentor; co-author on QSP/QSVT for molecular core spectra and UQ-VarQA
  • Ang Li - co-author on PQML for NISQ-machine reproducibility in quantum ML
  • Vibin Abraham - co-author on real-time many-body methods for molecular core spectra

Oak Ridge National Laboratory (ORNL)

Themes: scientific visualization for quantum computing, reproducibility on NISQ devices

  • Tushar M. Athawale - co-author on quantum noise visualization, PQML, and visualization of quantum application outputs
  • David Pugmire - co-author on quantum noise visualization and visualization of quantum application outputs
  • Travis S. Humble - co-author on redefining reproducibility in quantum computing on NISQ devices

RIKEN (Japan)

Theme: real-time many-body methods for molecular core spectra

  • Himadri Pathak - co-author on real-time many-body methods for molecular core spectra

Universities

Kent State University

Home institution. Themes: compilation-aware benchmarking, transpilation reuse, UQ for variational quantum algorithms, QML code translation

  • Qiang Guan - PhD advisor; co-author across the majority of publications
  • Cheng Chang Lu - co-author on quantum noise visualization, PQML, and variational circuit compilation
  • Waylon Luo - research collaborator at Kent State

Case Western Reserve University

Themes: variational quantum circuit compilation, reproducibility on NISQ devices

  • Shuai Xu - co-author on variational circuit compilation and reproducibility on NISQ devices
  • Vipin Chaudhary - co-author on variational circuit compilation
  • Vinooth Rao Kulkarni - co-author on variational quantum circuit compilation

George Mason University

Themes: quantum machine learning, reproducibility on NISQ devices

  • Weiwen Jiang - co-author on PQML and reproducibility on NISQ devices

George Washington University

Theme: uncertainty quantification for variational quantum algorithms

  • Shengye Zhu - co-author on UQ-VarQA

Rochester Institute of Technology (RIT)

Theme: LLM-assisted code translation for quantum machine learning

  • Runjia Zeng - co-author on Q-Bridge (under review)
  • Dongfang Liu - co-author on Q-Bridge (under review)

Rutgers University

Theme: LLM-assisted code translation for quantum machine learning

  • Ruixiang Tang - co-author on Q-Bridge (under review)

University of California, Berkeley

Theme: contact-network bottlenecks and pandemic containment modeling

  • Shenghao Yang - co-author on targeted pandemic containment via local contact-network bottlenecks (collaboration began while at Waterloo; now at UC Berkeley)

University of Texas at Arlington

Themes: UQ for variational quantum algorithms, reproducibility on NISQ devices, quantum machine learning on NISQ

  • Bo Fang - co-author on UQ-VarQA, PQML, and reproducibility on NISQ devices (collaboration began while at PNNL; now at UT Arlington)

University of Waterloo

Themes: contact-network bottlenecks, pandemic containment modeling

  • Kimon Fountoulakis - co-author on targeted pandemic containment via local contact-network bottlenecks
  • Chris T. Bauch - co-author on targeted pandemic containment via local contact-network bottlenecks

Industry

Amazon Science

Theme: reproducibility on NISQ devices

  • Zhepeng Wang - co-author on redefining reproducibility in quantum computing on NISQ devices (collaboration began while at George Mason; now at Amazon Science)

Google Research

Theme: contact-network bottlenecks and pandemic containment modeling

  • Di Wang - co-author on targeted pandemic containment via local contact-network bottlenecks

Wells Fargo

Theme: quantum machine learning on NISQ machines

  • Samuel Yen-Chi Chen - co-author on PQML for predictive reproducibility of quantum ML applications on NISQ machines