GEICO
Machine Learning Engineering Intern
Incoming Summer 2026
Jun 2026 - Aug 2026Palo Alto, CA
About
About me
I'm a Computer Science student at Cornell University pursuing both a B.S. and M.Eng. in Computer Science through Cornell's Early M.Eng. (3+1) program. My interests lie at the intersection of machine learning, applied AI, and quantitative systems.
Through internships at Pfizer, Anote AI, and GEICO, as well as projects spanning NLP, computer vision, ML infrastructure, and financial modeling, I've developed a passion for building systems that create measurable impact. I'm currently seeking opportunities in software engineering, machine learning engineering, applied AI, and quantitative technology.
Experience
Relevant Experience
Anote
Machine Learning Engineering Intern
May 2025 - Aug 2025New York, NY
- Benchmarked zero-shot object-detection pipelines using YOLOv8, DINO, and Faster R-CNN, implementing workflows in Python with PyTorch and Ultralytics to compare mAP, IoU, and other ML metrics across models
- Designed and deployed a smart orchestrator reasoning system using Python, Docker, and Ollama; engineered multi-agent workflows with LangChain and custom routing logic, reducing LLM inference latency by ∼25%
- Integrated MCP into LLM pipeline using Python, FastAPI, JSON Schema, and LangChain, expanding agent tool interoperability by 40% and enabling tool-augmented reasoning across internal and external system endpoints
PythonPyTorchUltralyticsDockerOllamaLangChainFastAPI
Pfizer Inc.
Software Engineering Intern
Sep 2023 - Feb 2024Peapack, NJ
- Queried over 50,000 internal license-request records using Splunk and SQL for downstream model training
- Developed a multimodal language sentiment analysis algorithm in Python using TensorFlow and Scikit-learn with 5 individual machine learning models to evaluate the justification behind license and permission requests
- Applied optimization techniques including L1/L2 penalties, dropout regularization, and learning rate scheduling to improve model performance, achieving 97.6% accuracy for automated request approvals and denials
PythonTensorFlowScikit-learnSplunkSQL
Featured Projects
Selected Projects
A collection of software, machine learning, and applied AI projects I’ve built.
Apr 2026
Equity Options Volatility & Risk Engine
- Built a vectorized SPY options pricing engine for cross-model comparative analysis implementing Black–Scholes, Binomial Tree, and Heston models, evaluated on 6.6M real contracts across 757 trading sessions from 2019–2021
- Conducted a large-scale out-of-sample implied volatility surface study with liquidity filtering on 548K+ SPY options; Found that binomial tree achieved best model with 44.8% of predictions within ±10% of observed prices
- Analyzed model robustness under calm vs. high volatility regimes (March 2020), finding Black–Scholes and binomial accuracy improved by ∼15–17% under high volatility while Heston declined ∼2%
PythonNumPyPandasSciPy
Apr 2026
Event-Driven Backtesting Engine
- Built a multi-asset event-driven backtesting engine that replays historical OHLCV bars through modular data, strategy, portfolio, execution, risk, analytics, and reporting components
- Implemented train/test strategy evaluation over historical market data, comparing moving-average, mean-reversion, linear-regression, and logistic-regression strategies on out-of-sample performance
- Engineered risk controls including per-symbol exposure caps, gross leverage limits, stop-loss exits, and max-drawdown portfolio de-risking to make strategy simulations more realistic
PythonPandasNumPy
Oct 2025
Timbr
- Developed a full-stack “Tinder-for-Homes” iOS app using swift, enabling users to swipe through real-estate listings, integrating real-time data and dynamic filtering to give users personalized property recommendations
- Engineered responsive UI/UX components and backend APIs (React, Flask/FastAPI, PostgreSQL) to support seamless user onboarding, secure authentication, scalable data handling, and accurate prediction models
- Built scikit-learn inference pipelines that featurized swipe directionality, dwell-time distributions, response latency, listing metadata, and user interaction sequences into calibrated ranking models for low-latency property preference prediction
SwiftReactFastAPIPostgreSQLScikit-learn
Nov 2025
GreenTab
- Built a full-stack sustainability analytics Chrome extension using JavaScript, Manifest V3, and Chrome APIs to monitor real-time browsing activity and estimate the environmental impact of spending time on the internet
- Integrated backend APIs to calculate CO₂, energy, and water waste metrics via precise calculation models
- Implemented user authentication using Google OAuth and persistent data storage with Supabase (PostgreSQL + SQL queries) to manage user profiles and browsing-impact telemetry to create personalized dashboards for users
PythonJavaScriptFirebaseChrome APIsSupabase
Sep 2024 - Jan 2025
Skin Cancer Classification App
- Evaluated multiple machine learning pipelines in Python using TensorFlow, Scikit-Learn, and OpenCV, comparing metrics across CNN, KNN, Logistic Regression, Linear Regression, and SVM models for skin cancer classification
- Developed a multiplicative-weights update ensemble algorithm to optimally combine predictions from heterogeneous classifiers, improving overall accuracy, precision, and robustness against model-specific bias by over 10%
- Built and deployed an iOS application using SwiftUI, integrating the trained ensemble model pipeline to allow real-time inference on mobile devices with a clean and accessible UI supporting seamless on-device classification
Sep 2023
LetMeCook
- Built a computer vision-powered recipe generation web app that lets users scan their fridge or cupboard and receive personalized recipes based on detected ingredients and dietary preferences.
- Integrated Google Cloud Vision API to identify food items from user-uploaded images, then connected ingredient data with Edamam and OpenAI APIs to generate recipes with nutritional information and step-by-step instructions.
- Developed responsive frontend workflows for image capture, photo editing, dietary preference input, and recipe display using React, TypeScript, Tailwind, Radix UI, and Lucide.
- Helped implement food detection and recognition features while supporting demo/video materials for a hackathon project that won Best High School Hack at MediHacks 2023.
June 2024
JigTurn
- Engineered an Arduino-based autonomous bicycle turn signal that lets riders signal turns without removing their hands from the handlebars.
- Developed embedded software using IMU sensor readings over I2C to detect turn completion and automatically deactivate blinking LED signals.
- Soldered and integrated Arduino Uno, IMU, battery pack, buttons, and LED panels into a custom bike-mounted prototype.
- Led project management, testing, documentation, and user instructions for a three-person engineering team.
- Tested with 12 riders, achieving 100% signal recognition from 50 feet and an 8.7/10 average user satisfaction rating.
ArduinoEmbedded SystemsI2CIMUHardware Prototyping
Skills
Technical Skills
Languages
PythonJavaOCamlJavaScriptTypeScriptSQL
ML / Data
PyTorchTensorFlowKerasscikit-learnPandasNumPy
Web / Backend
ReactNext.jsTailwind CSSFastAPISupabaseDocker
Tools
GitHubAWSLinuxJupyterVS Code
Contact