cv

MS

New York University

Computer Science

• Computer Vision
• Natural Language Processing
• Responsible AI
• Data Science for Healthcare
• Advanced Database Systems
• Big Data

BSc

University of Manchester

Computer Science and Mathematics

• Machine Learning
• Convex Optimization
• Linear Algebra
• Partial Differential Equations
• Complex Analysis
• Image Processing
• Cryptography
• Algebraic Structures

Network Engineer

LG CNS America

• Lead customer-facing technical workstreams for U.S. financial institutions, coordinating clients, vendors, and internal engineers from discovery and solution design through technical approval, implementation, acceptance, and operational handoff.
• Translate ambiguous requirements into architectures, project plans, technical presentations, runbooks, acceptance criteria, and executive-ready validation reports for systems-integration and managed-service engagements.
• Design and validate regulated banking integrations across data-center, disaster-recovery, and branch environments, including Fiserv/Zelle connectivity, VPNs, DMZ segmentation, firewall ACL/NAT, routing, MPLS/DIA, failover, and production changes.
• Build Python/API automation with Netmiko, Flask, Cisco FMC REST API, and PRTG API for configuration backup, firewall analytics, monitoring evidence, audit-support reporting, and troubleshooting knowledge workflows.

Software Engineer

RND4Impact

• Translated stakeholder needs into backend/data-processing prototypes, validation scripts, and reusable documentation workflows supporting research-to-product handoff and structured evidence review.

Co-founder / Software Engineer

Stealth Project (EPLIA)

• Co-founded a healthcare startup aimed at improving accessibility by addressing language barriers in telemedicine.
• Led the design and development of a web application using Next.js, AWS cloud infrastructure, and WebRTC for real-time communication.
• Managed cross-functional collaboration to deliver a scalable, reliable platform tailored to the unique needs of diverse users.

Graduate Research Assistant

Center for Responsible AI, NYU

• Conducted research under Professor Julia Stoyanovich on evaluating differentially private (DP) synthetic data generation methods.
• Developed “Epistemic Parity,” an evaluation metric based on the likelihood of reproducibility of quantitative claims in social science research.
• Created SynRD, an open-source DP synthetic data benchmarking Python package that organizes the Epistemic Parity workflow, existing papers, and datasets.

Data Scientist Intern

Pricewaterhouse Coopers

• Developed a BERT-based NLP relation-extraction POC with PyTorch and AWS SageMaker, achieving 94% F1 on the target task.
• Built preprocessing, training, evaluation, deployment, and monitoring workflows, including data annotation protocols for production use.

Graduate Research Assistant

McDevitt Lab, NYU

• Performed diagnostic prediction modeling research for the Colgate Project under Professor John T. McDevitt, utilizing machine learning and statistical methods for data analysis.
• Preprocessed and visualized complex unstructured biomarker data from microfluidic sensors using SQL, R, Pandas, and Seaborn.
• Conducted a meta-analysis to combine and analyze data from multiple sources by extracting semantics.

Undergraduate Research Assistant

Spiking Neural Network Simulation, University of Manchester

• Designed and implemented a Spiking Neural Network simulator using Python, QtPy5, Brian2, and neurodynex to investigate neuromorphic computing paradigms inspired by biological neural systems.
• Simulated and analyzed dynamical behaviors and synchronization patterns in neuron populations influenced by network topology and external stimuli, leveraging Complex Systems methodologies.
• Conducted research under the supervision of Dr. Eva Navarro Lopez, culminating in the thesis “Models of Neurons and Neuronal Networks,” which received the Best Paper award.

Undergraduate Research Intern

Wireless Intelligence at Network Edge Lab, Korea University

• Worked on an IoT Drone project under the supervision of Professor Hwangnam Kim as a Summer Undergraduate Research Intern.
• Developed and implemented new functionalities in MATLAB to optimize real-time simulation of networked drone fleets.

ChangeSafe — AI Infrastructure Change Airlock

A v0.1 safety boundary that converts AI-generated infrastructure recommendations into typed declarative patches, validates them against strict schemas and permitted state paths, and applies them transactionally to a cloned infrastructure model.

• Built deterministic policy checks for management reachability, protected resources, blast radius, rollback completeness, and required verification.
• Enforced fail-closed domain states, tested inverse rollback, and generated hash-bound decision receipts; production execution remains intentionally disabled in v0.1.

SynRD

Open-source reproducibility framework for privacy-preserving synthetic-data research, translating research questions into reusable Python experiments, evaluation workflows, and documented limitations.

Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy

ACM SIGMOD Record 2024

Rosenblatt, L., Herman, B., Holovenko, A., Lee, W., Loftus, J., McKinnie, E., ... & Stoyanovich, J. (2024). Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy. ACM SIGMOD Record, 53(1), 65-74.

Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy

Proc. VLDB Endow. 2023

Rosenblatt, L., Herman, B., Holovenko, A., Lee, W., Loftus, J., McKinnie, E., ... & Stoyanovich, J. (2023). Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy. Proceedings of the VLDB Endowment, 16(11), 3178-3191.

Out of Distribution Performance of State-of-the-Art Vision Models

arXiv

Rahman, S., & Lee, W. (2023). Out of distribution performance of state of art vision model. arXiv preprint arXiv:2301.10750.

Models of Neurons and Neuronal Networks

University of Manchester

Lee, W. (2018). Models of Neurons and Neuronal Networks. Department of Computer Science, University of Manchester.

SIGMOD Research Highlight Awards

ACM SIGMOD

Best Experiment, Analysis & Benchmark Paper Runner-up

VLDB 2023

Wasserman Center Career Grant

NYU

KMA Landslide Prediction Big Data Contest

Korea Meteorological Administration

Computer Science Final-Year Project Award

University of Manchester

International Mathematical Excellence Scholarship

University of Manchester

2015, 2016, 2017