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

System Engineer

LG CNS America

• Designed and implemented network infrastructure to enhance system performance and security, collaborating with cross-functional teams to troubleshoot and resolve complex networking issues.
• Implemented automated network monitoring and reporting systems to ensure optimal uptime and reliability using Python, Netmiko, and PRTG API.
• Created comprehensive network documentation, including diagrams, operational procedures, and troubleshooting guides, to facilitate knowledge sharing and system maintenance.

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

• Implemented and fine-tuned a BERT model to classify semantic relationships between entities using PyTorch.
• Designed data annotation protocols and ML pipelines from data, training, to deployment; deployed the models to AWS for scalable 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.

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