About
My journey in tech started at Columbia University, where I discovered a passion for solving complex problems with elegant code. Since then, my experience has taken me from architecting cloud infrastructure at Microsoft to developing machine learning models that drive business strategy at both Accenture and DirectBooks.
I thrive at the intersection of deep technical challenges and user-centric design, always striving to build software that is both powerful and intuitive. My work is guided by a commitment to quality, scalability, and creating real-world impact. In my free time, I bartend and enjoy the art of mixology.
Experience
Jun 2025 - Aug 2025
Data Operations Intern · DirectBooks
Project: Interactive Syndicate Bank Network Graph Visualization
• Developed a modular and configurable Python backend for a full-stack web application, engineering a data pipeline to process ~9.7k deal records, solve data integrity issues, and calculate network statistics (node-degree centrality, co-occurrence frequency) across 170+ banks.
• Empowered non-technical stakeholders by designing and building a dynamic D3.js frontend that translated complex data into a self-service analytics tool with a sophisticated UI, featuring 5+ interactive filters and multi-node selection to reveal key strategic partnership opportunities.
Project: Time Series Forecasting of Median Order Volume using ETS Models
• Architected a reusable and scalable object-oriented forecasting framework in Python which fully automates the systematic evaluation of 18 ETS (Error, Trend, Seasonality) model configurations via Maximum Likelihood Estimation and uses Akaike Information Criterion for optimal model selection.
• Delivered an 18-month forecast with 90% confidence intervals that achieved 10.8% Mean Absolute Percentage Error on a hold-out set (a 70% error reduction), validating model integrity using a comprehensive hypothesis testing, including regression analysis and automated diagnostic reporting.
Python
D3.js
networkx
scikit-learn
statsmodels
numpy
Sep 2021 - Sep 2024
Senior Software Engineer and Engineering Manager · Columbia Daily Spectator
• Led a 4-person engineering team through agile sprints and pair programming to deliver React-based special editions for Columbia's independent student newspaper, the second-oldest college daily in the nation, serving ~350k monthly users and 26.1K+ social media followers.
• Created a front-end carousel feature for top news stories, bookmark functionality using async storage and integrated iPhone notifications using React Native, Expo API and OneSignal.
• Built full-stack web app using React.js, Node.js, and MongoDB that imports and deletes Columbia Spectator sources and migrated app to ArcPublishing.
JavaScript
React
React Native
Node.js
MongoDB
May 2024 - Aug 2024
Software Engineer Intern · Microsoft
• Architected end-to-end Azure deployment automation platform using C#/.NET and ARM templates, eliminating manual deployment processes and enabling seamless orchestration of complex cloud infrastructures across multiple Azure services.
• Engineered secure, production-ready REST API client with Azure Key Vault integration and comprehensive error handling, empowering development teams to programmatically manage large-scale cloud deployments with enterprise-grade security.
• Delivered robust testing framework with comprehensive unit test coverage for deployment components, ensuring reliability and reducing deployment failures in Microsoft's cloud infrastructure pipeline.
C#
.NET
Azure
REST API
DevOps
Cloud Infrastructure
Aug 2023 - Dec 2023
Machine Learning Project Fellow · Accenture
• Collaborated with 4 interns to help Accenture's business client with product strategy, marketing and location recommendation for opening a series of 5 coffee shops in NYC.
• Developed a robust data pipeline in Python using pandas and json to ingest, clean, and filter Yelp datasets, which informed a regression model to identify optimal locations based on Yelp metrics.
• Built a sentiment analysis Natural Language Processing (NLP) model with nltk to analyze Yelp reviews and identify key customer preferences, using n-gram analysis to inform specialty menu item selection.
• Utilized matplotlib to create data visualizations, effectively communicating data-driven findings on customer sentiment and behavior for strategic decision-making.
Python
pandas
numpy
nltk
tensorflow
scikit-learn
NLP
Jun 2023 - Aug 2023
Software Engineer and Product Management Intern · Microsoft x Cyborg Mobile
• Collaborated with 4 interns to investigate a problem of interest to Microsoft, progressing through the full SDLC, from ideation to prototype implementation.
• Conducted user research to define problem space and created a detailed product management specification document.
• Built full-stack web application using React, integrating OpenAI's ChatGPT and DALL-E 2 APIs.
React
OpenAI API
Product Management
May 2023 - May 2024
Machine Learning / Artificial Intelligence Fellow · Cornell Tech
• Completed rigorous 12-month Machine Learning and Artificial Intelligence fellowship under direct instruction of Cornell faculty, mastering industry-standard tools and frameworks through hands-on implementation on real-world datasets.
• Implemented and optimized key ML/AI algorithms including linear/logistic regression, decision trees, K-means clustering, convolutional neural networks (CNNs), and Q-learning, achieving measurable performance improvements through systematic hyperparameter tuning.
PyTorch
scikit-learn
Machine Learning
Sep 2022 - Dec 2022
Columbia Machine Learning Safety Scholar · Center for AI Safety
• Completed intensive program on AI alignment and existential risk mitigation under instruction from UC Berkeley PhD researchers, covering adversarial robustness, anomaly detection, model interpretability, and cooperative AI.
• Implemented adversarial robustness techniques and anomaly detection algorithms through hands-on coding assignments, developing expertise in identifying and mitigating failure modes in neural networks.
• Mastered interpretable uncertainty quantification, model monitoring, and machine ethics frameworks, researching approaches to align AI behavior with human values and prevent deceptive model outputs.
• Engaged with cutting-edge research on emergent behavior detection, trojan attacks, and cooperative AI, contributing to discussions on long-term Al safety challenges and governance.
Python
AI Safety
Neural Networks