Cambridge, MA
Erwin Deng
Aspiring AI / ML Engineer
Graduate student at MIT, focused on applying machine learning and operations research to real-world decisions, from manufacturing defect detection to wildfire resource deployment.
About
A bit about me
I'm a graduate student at the MIT Master of Business Analytics, Operations Research Center, where I work on machine learning and analytics problems that sit close to real operational decisions: assembly lines, wildfire crews, construction schedules, and financial documents.
Before MIT, I studied engineering at CentraleSupélec in France, and worked as a data scientist intern across manufacturing, luxury goods, and investment banking.
Experience
Where I've worked
Research and industry roles applying ML and analytics to real operational problems.
MIT Sloan / Ford Motor Company
Data Scientist Intern, MIT Capstone Project · Cambridge, MA
2026 – Present
- Developing AI models for early defect detection in car assembly lines using images (Python)
MIT Sloan / BMW Group
Generative AI Lab Team Member · Cambridge, MA
Spring 2026
- Built a closed-loop LLM evaluation and prompt optimization pipeline for repair-order document extraction
- Developed deterministic and LLM-as-judge evaluation framework in Python to assess structured JSON extraction quality
- Improved extraction performance by 50% over five iterations using LLM reflection and Pareto-based prompt evolution
MIT Operations Research Center
Graduate Research Assistant for Professor Jacquillat · Cambridge, MA
2025 – 2026
- Developed a double ML model to quantify crew effects on wildfires and guide resource deployment decisions (Python)
- Expanded a wildfire crew dataset by integrating satellite embeddings and weather data
MIT Sloan / Suffolk Construction
Analytics Lab Team Member · Cambridge, MA
Fall 2025
- Developed ML models to predict delays on 200+ construction projects, identifying future losses of $10M+ (Python, SQL)
Richemont
Data Scientist Intern, Research & Innovation · Buttes, Switzerland
Spring 2025
- Analyzed manually labeled datasets for watch components, identifying issues such as inconsistent labeling
- Developed universal defect detection model that works across any watch piece, enabling cost-free assessment (Python)
- Presented findings to multiple brands and delivered a production-ready solution (Python)
Societe Generale
Data Scientist Intern, Investment Banking · Paris, France
Fall 2024
- Fine-tuned AI models on domain-specific data for document structure analysis, improving accuracy for investment banking
- Developed table parsing and reading order identification models to enhance document understanding (Python)
Projects
Selected work
Each card links to a full case study with the problem, approach, and impact.
Closed-Loop LLM Evaluation & Prompt Optimization
Coming soonMIT Sloan / BMW Group
A closed-loop evaluation and prompt optimization pipeline that improved LLM extraction quality by 50% over five iterations.
Early Defect Detection on Car Assembly Lines
Coming soonMIT Sloan / Ford Motor Company
Computer vision models that catch assembly-line defects earlier, using images captured during production.
Quantifying Wildfire Crew Effects with Double ML
Coming soonMIT Operations Research Center — Research Assistant for Professor Jacquillat
A double machine learning model that quantifies how firefighting crews affect wildfire outcomes, to guide resource deployment.
Predicting Construction Project Delays
Coming soonMIT Sloan / Suffolk Construction
ML models predicting delays across 200+ construction projects, surfacing over $10M in future losses.
Universal Defect Detection for Watch Components
Coming soonRichemont — Research & Innovation
A defect detection model that generalizes across watch components, enabling cost-free quality assessment.
Document Structure Analysis for Investment Banking
Coming soonSociete Generale — Investment Banking
Fine-tuned models for table parsing and reading order identification to improve document understanding.
Storm Damage Assessment from Satellite Imagery
Coming soonEY Open Science Data Challenge 2024
A fine-tuned computer vision model for storm damage assessment that placed 2nd runner-up out of 11,000 entrants and was presented at IEEE IGARSS 2024.
Education
Academic background
Massachusetts Institute of Technology
2025 – August 2026
Candidate for Master of Business Analytics, Operations Research Center — GPA: 5.0/5.0 · Cambridge, MA
- Selected coursework: Machine Learning, Optimization, Advanced Analytics Edge, Hands-on Deep Learning, GenAI Lab, Power and Negotiation, Communication through Data
CentraleSupélec, Université Paris-Saclay
2022 – 2024
Bachelor and Master of Engineering — GPA: 4.0/4.0 · Paris, France
- Coursework: Advanced Statistics, Partial Differential Equations, Software Engineering, Economics, Climate Sciences
- Teaching Assistant: Led algorithmics tutorials (graphs, dynamic programming, etc.) for 30 first-year undergraduate students
- Leadership: Awarded the CentraleSupélec scholarship for involvement in Student Life and Associations
- Community involvement: Raised $15,000 and organized a 5-week mission in Nepal supporting children's education
Skills
Technical toolkit
Languages
ML & Data
Tools & Web
More
Additional information
Awards
- Bronze Medal, European Junior Olympiad in Informatics · 2017
- 1st Prize out of 220,000 in the Algoréa French Computer Science Competition · 2019
- 2nd runner-up out of 11,000 in the EY Open Science Data Challenge 2024 — see the storm damage assessment case study
Languages
- French — Native
- English — Fluent
- Chinese — Conversational
Interests
- Fencing (organized a national student competition in France)
- Piano
- Running