Curriculum vitae
Data scientist with extensive experience in probabilistic modeling, operations research, simulation, and agentic AI systems. Additional research expertise in astrophysics and Bayesian inference through large-scale scientific collaboration, supported by a proven publication record.
Resume
Downloadable PDF (Industry): PDF
Downloadable PDF (Academia): PDF
Education
- M.Sc. in Computer Science, Georgia Institute of Technology, 2029 (Expected)
- Part-time program, started Fall 2026.
- M.Sc. in Data Science, The University of Texas at Austin, 2026
- Advanced Diploma in Financial Technology, HKU School of Professional and Continuing Education, 2023
- Classification of Award: Distinction
- GPA: 3.58 / 4.3 (Transcript)
- Reference Letter: PDF
- B.Sc. in Physics, The Chinese University of Hong Kong, 2022
- Classification of Award: Second Class Honours, Division I
- Enrichment Stream in Theoretical Physics
- Astrophysics and Particle Physics
- Minor in Japanese Language
- GPA: 3.4 / 4.0 (Transcript)
- Special Auditing Student, Tohoku University, Fall 2021
- Course: Intermediate Japanese for JLPT N2
- Grade: A (Transcript)
- Virtual Exchange Student, Nagoya University, Spring 2021
- Course: Japanese Grammar and Communication
- Grade: A (Certificate)
Research experience
- Aug 2021 - Present: Research Assistant (Part-time), Universidad de Santiago de Compostela
- Supervisor: Professor Juan Calderón Bustillo
- Research Collaboration: LIGO Scientific Collaboration
- Develop and maintain archeo, an open-source Python package for rapid Bayesian inference of black-hole progenitor properties and hierarchical-formation scenarios.
- Researched parental parameter estimates of black holes in GW190521:
- Developed a Bayesian framework to infer posterior probability distributions of ancestral black-hole masses, spins, and birth recoils under hierarchical formation scenarios.
- Investigated hierarchical formation of black holes within the pair-instability supernova mass gap.
- Evaluated environmental retention scenarios and hierarchical-merger viability for GW190521-like systems.
- Optimized and revamped the research group’s numerical simulation codebase, achieving up to 100× runtime performance improvement.
- Reduced selected inference workflows from hour-scale to minute-scale execution through algorithmic and software optimization.
- Researched Hubble Constant measurement with intermediate-mass black-hole mergers:
- Explored the feasibility of measuring the Hubble Constant using gravitational-wave signals detectable by LISA.
- Simulated gravitational-wave waveforms of intermediate-mass black-hole mergers and analyzed conditions for breaking the mass-redshift degeneracy.
- Constructed redshifted waveform pairs and computed signal-to-noise ratios to identify redshift drift in year-long signals.
- Selected publications:
- Co-authored “Kicking Time Back in Black Hole Mergers: Ancestral Masses, Spins, Birth Recoils, and Hierarchical-formation Viability of GW190521” (Carlos Araújo-Álvarez et al. 2024, ApJ 977, 220).
- Contributed as an analysis team member and co-authored “GW231123: a Binary Black Hole Merger with Total Mass 190-265 (M_{\odot})“ (A. G. Abac et al. 2025, ApJL 993, L25).
- Contributed as an analysis team member and co-authored “GW241011 and GW241110: Exploring Binary Formation and Fundamental Physics with Asymmetric, High-spin Black Hole Coalescences” (A. G. Abac et al. 2025, ApJL 993, L21).
- Feb 2025 - Aug 2025: Research Assistant (Part-time), Johns Hopkins University
- Supervisor: Professor Emanuele Berti
- Researched deep-learning-based parameter estimation of post-merger gravitational-wave signals.
- Developed neural posterior estimation methods for simulation-based inference.
- Built and evaluated machine-learning workflows for probabilistic parameter inference from simulated gravitational-wave data.
- Sep 2020 - Dec 2021: Student Researcher (Part-time), The Chinese University of Hong Kong
- Supervisor: Professor Tjonnie G. F. Li
- Research Collaboration: LIGO Scientific Collaboration
- Researched searches for strongly lensed gravitational-wave images:
- Conducted gravitational-wave data analysis and parameter estimation using
bilby and PyCBC. - Simulated 200 lensed signal pairs under different signal-to-noise-ratio settings and generated sky-localization probability maps.
- Developed overlap statistics based on two-dimensional posterior distributions of gravitational-wave signals.
- Achieved >99% filtering efficiency for non-lensed pairs at a false-positive rate of 10^{-2}.
- Co-authored “Using overlap of sky localization probability maps for filtering potentially lensed pairs of gravitational-wave signals” (arXiv:2112.05932).
- Jun 2021 - Aug 2021: Research Intern, The Chinese University of Hong Kong
- Supervisor: Professor Kenny C. Y. Ng
- Researched semi-analytical modeling of solar atmospheric gamma rays with the Potential-Field Source-Surface model:
- Developed semi-analytical methods and numerical simulations to reproduce solar atmospheric gamma-ray flux.
- Used HMI synoptic maps to compute the global coronal magnetic field and simulate trajectories of relativistic cosmic-ray protons.
- Reduced simulation time from weeks to hours, enabling efficient calculation of emission probabilities and total gamma-ray flux.
- May 2020 - Aug 2020: Research Intern, The Chinese University of Hong Kong
- Supervisor: Professor Tjonnie G. F. Li
- Researched searches for strongly lensed gravitational-wave images; this work later continued during the student researcher appointment above.
Work experience
- Sep 2025 - Present: Data Scientist, IBM Hong Kong
- Develop stochastic optimization models for aircraft schedule disruption recovery, reducing propagated delay by up to 30% across fleets of 100+ aircraft.
- Build Monte Carlo simulation frameworks to stress-test aircraft schedules, model disruptions, identify breakpoints, and inform recovery strategies under uncertainty.
- Coordinate with cross-functional teams to gather requirements, design data pipelines and optimization workflows, and deploy models into production environments.
- Jun 2024 - Sep 2025: Associate Data Scientist, Orient Overseas Container Line (OOCL)
- Developed robust optimization engine for bunker procurement planning, generating up to US$5 million/year in estimated savings across 70+ vessels based on pilot-scope backtesting.
- Designed and implemented stochastic and linear optimization models for bunker procurement, replacing the production model and improving planning quality.
- Applied clean architecture to revamp an optimization engine for bunker procurement planning, achieving up to 2× runtime performance improvement.
- Developed a common optimization library to:
- support rapid switching across CPLEX, PuLP, Pyomo, and Gurobi,
- standardize linear-programming model development across projects,
- support sensitivity analysis and parallel solving of optimization problems.
- Conducted sensitivity analysis and backtesting to evaluate optimization-engine performance.
- Developed Spark workflows for dataset preparation, scheduled optimization, and production operations.
- Designed APIs and data schemas for the bunker procurement optimization engine.
- Developed data patching, missing-data imputation, and validation algorithms, improving data integrity from below 50% to 97%.
- Researched program-aided reasoning for advanced table understanding with large language models.
- Designed multi-agent LLM workflows to extract embedded tables and email content into standardized tabular data, achieving 93% accuracy on an internal evaluation set.
- Temporarily led a data science project for 3 months:
- planned tasks for 4 data science team members,
- supervised a data scientist trainee,
- handled production issues and troubleshooting,
- coordinated with developer teams on data-pipeline development and deployment schedules.
- Developed Jenkins deployment pipelines for RESTful API services and a web-based chatbot application.
- Standardized the team’s development workflow by unifying linting and coding standards, introducing pre-commit hooks, and migrating dependency management to Poetry and
uv. - Set up GitLab CI for automated testing, Docker image building, and structured package-release processes.
- Researched bunker price forecasting using machine-learning models and time-series analysis:
- Conducted literature review on bunker procurement strategies and machine-learning models for handling prediction delays.
- Trained and fine-tuned models for bunker price prediction, achieving 2% MAPE for short-term forecasts and 6% MAPE for long-term forecasts.
- Experimented with feature engineering and model selection to improve generalization across bunker types and regions.
- Aug 2023 - Jun 2024: Assistant R&D Engineer, ATAL Engineering Group
- Reference Letter: PDF
- Developed setpoint optimization engine for HVAC systems, reducing electricity consumption by 20% in buildings and malls.
- Applied survival analysis techniques and designed a physics-guided model to predict equipment failure and enhance system reliability.
- Revamped HVAC energy optimization system with an event-driven structure and reorganized optimization strategies.
- Developed a web-based interface for model fitting and cooling-system performance visualization.
- Implemented RESTful APIs for facility management and equipment monitoring, integrating them with AWS cloud services and a data lakehouse.
- Designed database schemas and contributed to MLOps pipelines for dataset preparation, model training, evaluation, and versioning.
- Conducted a proof of concept for automatic SQL generation with an LLM agent:
- built a knowledge base using data schemas, query templates, and SQL examples,
- developed a self-correction loop using error feedback from database engines such as Amazon Athena,
- evaluated LLMs and prompt-engineering strategies for SQL generation,
- developed a web-based interface for SQL generation and chatbot interaction.
- Jun 2022 - Aug 2023: Software Engineer, Sebit Company Limited
- Reference Letter: PDF
- Researched sensor-data analysis for elevator abnormalities:
- Developed fault-detection algorithms for traction motor, brake, lift-door, and lift-car vibration anomalies using frequency-domain, spectral, and statistical analysis.
- Implemented data pipelines for sensor-data preprocessing and analysis.
- Researched generative adversarial networks for abnormality simulation and training-data augmentation.
- Presented key findings at the 14th Symposium on Lift & Escalator Technologies in the United Kingdom.
- Co-authored “Condition-based and Predictive Maintenance Strategy for Lift Installations using Big Data Analytics” (Proceedings of the 14th Symposium on Lift & Escalator Technologies).
- Developed deep-learning and computer-vision applications:
- Developed deep-learning models for real-time object detection, classification, and Cantonese voice recognition.
- Fine-tuned computer-vision neural networks for object classification, improving accuracy by 5 percentage points and achieving 97% accuracy.
- Implemented tracking algorithms for video analytics and optimized system performance for real-time processing.
- Designed critical detection mechanisms for a lift-door monitoring system, which was presented at the 48th International Exhibition of Inventions Geneva and won a silver medal.
- Developed model training and deployment workflows using Ansible and Jenkins.
- Dec 2021 - Jan 2022: AI Developer Intern, Flying Milk Tea Limited
- Developed programs for data scraping, data annotation, and preprocessing with Blender.
- Supported preparation of structured datasets for downstream AI and graphics workflows.
Skills
- Programming
- Python, SQL, R, C++, C, Go, Bash, JavaScript, LaTeX
- Machine Learning / AI
- PyTorch, PyTorch Lightning, Keras, scikit-learn
- Deep learning, neural posterior estimation, simulation-based inference, time-series forecasting, survival analysis, computer vision, large language models, agentic AI workflows
- Optimization / Simulation
- Gurobi, PuLP, CPLEX, Pyomo
- Stochastic optimization, robust optimization, linear programming, Monte Carlo simulation, sensitivity analysis, backtesting
- Data / Cloud
- PySpark, AWS Athena, Redshift, SageMaker, VPC, EMR, Lightsail, Bedrock
- Software Engineering / DevOps
- Docker, Podman, Git, GitLab CI, GitHub Actions, Jenkins, Poetry,
uv, pre-commit, Ansible, Helm - RESTful API design, package development, CI/CD, automated testing, MLOps, data pipeline design
- Languages
- English, Cantonese, Mandarin, Japanese
- English Proficiency Test: IELTS Academic 7.5
- Japanese-Language Proficiency Test: JLPT N1
Publications
LIGO Scientific Collaboration, Virgo Collaboration, & KAGRA Collaboration. (2025). GW241011 and GW241110: Exploring Binary Formation and Fundamental Physics with Asymmetric, High-spin Black Hole Coalescences. Astrophysical Journal Letters, 993(1), L21.
Al-Shammari, S. et al. (2025). GW231123: A binary black hole merger with total mass 190–265 M⊙. The Astrophysical Journal Letters, 993(1).
Araújo-Álvarez, C., Wong, H. W., Liu, A., & Bustillo, J. C. (2024). Kicking Time Back in Black Hole Mergers: Ancestral Masses, Spins, Birth Recoils, and Hierarchical-formation Viability of GW190521. The Astrophysical Journal, 977(2), 220.
Chan, J. K., Leung, C. K., Wong, W. T., Kwok, S. C., & Wong, H. W. (2023, September). Condition-based and Predictive Maintenance Strategy for Lift Installations using Big Data Analytics. In 14th Symposium on Lift & Escalator Technologies (Vol. 14, No. 1, pp. 33-45).
Wong, H. W., Chan, L. W., Wong, I. C., Lo, R. K., & Li, T. G. (2021). Using overlap of sky localization probability maps for filtering potentially lensed pairs of gravitational-wave signals. arXiv preprint arXiv:2112.05932.
Talks
April 11, 2026
AgentCon Hong Kong 2026 at Hong Kong Institute of Information Technology, Hong Kong, Hong Kong
March 07, 2026
vLLM Hong Kong Meetup at The Hong Kong Polytechnic University, Hong Kong, Hong Kong
June 28, 2025
Hong Kong Python User Group Workshop at Auki Labs, Hong Kong, Hong Kong
November 16, 2024
PyCon Hong Kong 2024 at City University of Hong Kong, Hong Kong
October 22, 2024
Hong Kong Python User Group Meetup at Oursky Limited, Hong Kong
April 04, 2024
2024 American Physical Society April Meeting at SAFE Credit Union Convention Center, United States
April 26, 2023
48th International Exhibition of Inventions Geneva at Palexpo, Switzerland
September 26, 2020
Annual Physics Student Conference 2020 at The Chinese University of Hong Kong, Hong Kong
Certifications
- Google Project Management Professional Certificate, Google Career Certificates, Jan 2025
- Generative AI for Data Scientists Specialization, IBM, Jun 2024
- Generative AI for Software Developers Specialization, IBM, Jun 2024
- Reinforcement Learning Specialization, University of Alberta & Alberta Machine Intelligence Institute, Mar 2024
- Data Science for Investment Professionals Specialization, CFA Institute, Sep 2023
- Accelerated Computer Science Fundamentals, University of Illinois at Urbana-Champaign, Dec 2022
- TensorFlow Developer Professional Certificate, DeepLearning.AI, Nov 2022
- Google Data Analytics Professional Certificate, Google Career Certificates, Nov 2022
- Japanese-Language Proficiency Test, JLPT N1, The Japan Foundation, Jul 2022
Service
- Jan 2021 - Present: Undergraduate Member, LIGO Scientific Collaboration
- Aug 2023 - Present: Associate Financial Technologist, Institute of Financial Technologists of Asia
- Dec 2024 - Jan 2025: Meetup Host, Open Source Hong Kong and Hong Kong Python User Group
- Jun 2024 - Nov 2024: Proposal Reviewer and Sprint Technical Support, PyCon Hong Kong
