Highly accomplished Ph.D. Candidate in Computer Science with deep expertise in Artificial Intelligence, Machine Learning, and High-Performance Computing. Proven innovator in developing cutting-edge generative AI models, GPU-accelerated simulators, and scalable data platforms for scientific discovery and critical decision-making. Seeking to leverage advanced research skills and a strong publication record to drive impactful technological advancements in a research-focused or engineering leadership role.
Ph.D. Researcher
Charlottesville, VA, US
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Summary
Engineered a GPU-accelerated HPC platform utilizing approximation algorithms to optimize large-scale epidemiological simulations.
Highlights
Designed and implemented a GPU-accelerated HPC platform, leveraging approximation algorithms for efficient large-scale, network-based epidemiological simulations.
Achieved 10x+ runtime improvements in complex population modeling and disease-spread forecasting, directly enhancing public health decision-making capabilities.
Ph.D. Researcher
Charlottesville, VA, US
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Summary
Architected the first GPU-accelerated, differentiable wildfire simulator achieving millisecond-level efficiency, extending to wildland-urban interface scenarios.
Highlights
Architected the first GPU-accelerated, differentiable wildfire simulator, achieving millisecond-level efficiency, orders of magnitude faster than existing methods.
Developed a novel firebreak placement algorithm by integrating real-time trained diffusion and Reinforcement Learning (RL) models.
Created a decision-making tool that demonstrates robust generalization across diverse climates for effective wildfire mitigation.
Ph.D. Researcher
Charlottesville, VA, US
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Summary
Pioneered generative AI models (diffusion and flow matching) to solve inverse problems in hadron spectroscopy, creating foundational models that correct detector distortions with unprecedented fidelity.
Highlights
Developed and deployed generative AI models (diffusion and flow matching) to precisely solve complex inverse problems in hadron spectroscopy.
Created foundational models that achieved unprecedented fidelity in correcting detector distortions, significantly advancing particle physics research.
Built reusable and FAIR-principled ML infrastructure for the MLCommons open-source ecosystem, facilitating scalable scientific discovery.
Graduate Teaching Assistant
Charlottesville, VA, US
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Summary
Served as a Graduate Teaching Assistant for CS 2102: Discrete Mathematics and Theory 1 at the University of Virginia.
Highlights
Provided comprehensive instructional support for CS 2102: Discrete Mathematics and Theory 1, enhancing student comprehension and academic performance.
AI/ML Researcher
Brisbane, QLD, Australia
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Summary
Built an end-to-end deep learning pipeline combining YOLOv5 and SegFormer for automated microstructural analysis, achieving state-of-the-art performance.
Highlights
Developed and deployed an end-to-end deep learning pipeline, integrating YOLOv5 and SegFormer for automated microstructural analysis.
Achieved state-of-the-art performance, outperforming existing tools and creating high-throughput capabilities for materials discovery.
Lead Data Platform Developer
Brisbane, QLD, Australia
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Summary
Led the development of ExploreDB, a centralized data platform for consolidating geological datasets with an engineered API for advanced spatial queries.
Highlights
Led the development of ExploreDB, a centralized data platform, effectively consolidating diverse geological datasets.
Engineered robust APIs for advanced spatial queries, enhancing data accessibility and analytical capabilities for geological research.
Designed the Magma platform to democratize big data analytics, integrating AI for scalable geospatial processing and insights.
Peer Reviewer
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Summary
Provided critical peer review for leading academic journals and conferences in computer science and software engineering.
Highlights
Conducted rigorous peer reviews for prestigious venues including ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), The Journal of Open Source Software, SoftwareX, and Journal of Open Research Software.
Contributed to maintaining high academic standards and advancing the quality of research in parallel programming and open-source software.
Artifact Evaluation Committee Member
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Summary
Served as an Artifact Evaluation Committee Member for PPOPP 2026, contributing to the rigorous review of research artifacts.
Highlights
Evaluated and validated research artifacts for the PPOPP 2026 conference, ensuring reproducibility and quality of published work.
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Ph.D.
Computer Science
Courses
AI4Science (Generative Methods)
High-Performance Computing (Modeling & Simulation)
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Master
Computer Science
Grade: 3.97/4.0
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Bachelor
Information Technology
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Bachelor
Software Engineering
Grade: 4.0/4.0 (WES Verified, 182 Total Credits)
Awarded By
NVIDIA
Awarded for exceptional academic potential and significant research contributions in the fields of AI and Machine Learning.
Awarded By
University of Virginia
Secured a prestigious research assistantship, supporting advanced studies and projects within the Computer Science department.
Awarded By
University of Virginia
Recognized for outstanding academic achievement and promising contributions in computer science through a competitive fellowship.
Awarded By
Queensland University of Technology
Received for consistently achieving high academic performance and excellence during studies at QUT.
Awarded By
Chinese Government
Awarded to the top 0.2% of undergraduates nationwide for superior academic merit and exceptional performance.
Awarded By
Queensland University of Technology
Granted for demonstrating outstanding academic achievement as an international student at QUT.
Awarded By
China Robotics & Artificial Intelligence Competition Committee
Secured first place in a national competition, showcasing advanced skills and innovative solutions in robotics and AI.
Published by
USPTO
Summary
Patent Pending (19/405,194). Describes an AI-driven method and system for multimodal quantitative financial risk prediction.
Published by
USPTO
Summary
Patent Pending (19/242,584). Details a GPU-accelerated cellular automaton method for real-time wildfire prediction and calibration.
Published by
Physical Chemistry Chemical Physics
Summary
Details the application of two-stage deep neural networks for precise identification and measurement of precipitate areas in chromium-based alloys.
Python, C/C++, JavaScript/TypeScript, SQL, Bash.
PyTorch, JAX, TensorFlow, NumPy, SciPy, Scikit-learn, Hugging Face, Generative AI, Diffusion Models, Flow Matching, Reinforcement Learning, Deep Learning, Computer Vision, High-Performance Computing (HPC), Approximation Algorithms, Epidemiological Modeling, Wildfire Simulation, Materials Science, Particle Physics.
Git, Docker, Slurm, Node.js, React, MySQL, Linux, API Development, Data Platforms, Big Data Analytics, Geospatial Processing.