Alex
I am a first-generation college student studying computer science and mathematics at Cornell, specializing in machine learning and software engineering.
In my free time, I enjoy working on personal projects, working out, and reading.
Welcome to my website!
Education
Bachelor of Arts, Honors - Cornell University
Double Major: Computer Science & Mathematics
High School Diploma - Central Bucks High School South
STEM FocusExperience
Salesforce- Software Engineer Intern
- Develop custom virtual formula field functionality in Salesforce Data Cloud, empowering customers to create dynamic data transformations without code and enabling self-service analytics capabilities.
- Build scalable backend services using Java and Apache Spark for formula computations on distributed metadata services, enabling processing that scales automatically with customer data volume.
- Implement REST API endpoints with robust error handling and validation, providing developers with reliable programmatic access to formula field operations and reducing integration complexity.
- Create intuitive React and TypeScript frontend interface that simplifies virtual formula field creation and data exploration, dramatically improving user experience and accelerating customer onboarding.
Salesforce- Software Engineer Intern
Jun 2025 - Aug 2025- Develop custom virtual formula field functionality in Salesforce Data Cloud, empowering customers to create dynamic data transformations without code and enabling self-service analytics capabilities.
- Build scalable backend services using Java and Apache Spark for formula computations on distributed metadata services, enabling processing that scales automatically with customer data volume.
- Implement REST API endpoints with robust error handling and validation, providing developers with reliable programmatic access to formula field operations and reducing integration complexity.
- Create intuitive React and TypeScript frontend interface that simplifies virtual formula field creation and data exploration, dramatically improving user experience and accelerating customer onboarding.
Cornell Course Management System- Software Engineer
- Maintain Cornell's official CS course management system, used by 10,000+ students, ensuring efficiency and reliability.
- Oversee operations on the team, including project management, architecture decisions, team coordination, recruitment, and onboarding
- Received several A+s of academic research credit through intensive technical work on the team
- Lead a team of developers in rewriting our JSP website in React, modernizing the platform and enhancing responsiveness.
- Mentored junior developers and conducted code reviews
- Lead the recruitment process
- Migrated CMSX from Redux to a component-level state system, creating a scalable foundation for future development.
- Designed 10+ APIs and serializable data structures, allowing secure data transfer between the backend and frontend.
- Implemented the student-facing frontend using MUI component library, offering phenomenal user experience and reliability.
- Worked on both frontend and backend components using React, TypeScript, and Java
- Implemented a parser for CSVs in Java and UI in JSP, enabling professors to grant assignment extensions via file upload.
Cornell Course Management System- Software Engineer
- Maintain Cornell's official CS course management system, used by 10,000+ students, ensuring efficiency and reliability.
- Oversee operations on the team, including project management, architecture decisions, team coordination, recruitment, and onboarding
- Received several A+s of academic research credit through intensive technical work on the team
- Lead a team of developers in rewriting our JSP website in React, modernizing the platform and enhancing responsiveness.
- Mentored junior developers and conducted code reviews
- Lead the recruitment process
- Migrated CMSX from Redux to a component-level state system, creating a scalable foundation for future development.
- Designed 10+ APIs and serializable data structures, allowing secure data transfer between the backend and frontend.
- Implemented the student-facing frontend using MUI component library, offering phenomenal user experience and reliability.
- Worked on both frontend and backend components using React, TypeScript, and Java
- Implemented a parser for CSVs in Java and UI in JSP, enabling professors to grant assignment extensions via file upload.
Cornell Generative AI- Founding Member and Machine Learning Engineer
- Developed AI agent for QuickFi, a commercial lending technology company, automating insurance certificate validation and compliance verification processes, reducing operational costs.
- Built frontend and vector database for ClassGPT, enabling professors to create AI tutors for classes.
Cornell Generative AI- Founding Member and Machine Learning Engineer
Jan 2025 - Present- Developed AI agent for QuickFi, a commercial lending technology company, automating insurance certificate validation and compliance verification processes, reducing operational costs.
- Built frontend and vector database for ClassGPT, enabling professors to create AI tutors for classes.
Carnegie Mellon University- Software Engineer and Research Intern
- Worked on the interpreter for SASyLF, a language for writing proofs, implementing core features in Java.
- Implemented a polymorphic type system for SASyLF, similar to Java generics, enabling type-safe code reuse and significantly reducing duplication in proofs by allowing abstraction over multiple data types.
- Created a modular testing framework using Python to automate integration tests, ensuring reliability.
Carnegie Mellon University- Software Engineer and Research Intern
May 2024 - Aug 2024- Worked on the interpreter for SASyLF, a language for writing proofs, implementing core features in Java.
- Implemented a polymorphic type system for SASyLF, similar to Java generics, enabling type-safe code reuse and significantly reducing duplication in proofs by allowing abstraction over multiple data types.
- Created a modular testing framework using Python to automate integration tests, ensuring reliability.
Cornell Bowers CIS- Teaching Assistant
- Topics Covered: Clustering algorithms, PCA, MLE/MAP, regression, optimization algorithms, SVM, kernelization, ERM, decision trees, ensemble methods, neural networks, CNNs, generative AI, and AI ethics.
- Held office hours helping students with homework assignments and programming projects, graded assignments and exams, and proctored exams.
- Topics Covered: Greedy algorithms, dynamic programming, network flow, divide and conquer algorithms, NP hardness, approximation algorithms, computability, and cryptography.
- Held office hours helping students with homework assignments and programming projects, graded assignments and exams, and proctored exams.
- Topics Covered: OCaml programming, polymorphism, models of evaluation, functors, mutability, logic, concurrency, data structures, lambda calculus.
- Awarded the CS course staff award for outstanding service to students.
- Held office hours helping students with homework assignments and programming projects, lead weekly recitations, graded assignments and exams, and proctored exams.
Cornell Bowers CIS- Teaching Assistant
- Topics Covered: Clustering algorithms, PCA, MLE/MAP, regression, optimization algorithms, SVM, kernelization, ERM, decision trees, ensemble methods, neural networks, CNNs, generative AI, and AI ethics.
- Held office hours helping students with homework assignments and programming projects, graded assignments and exams, and proctored exams.
- Topics Covered: Greedy algorithms, dynamic programming, network flow, divide and conquer algorithms, NP hardness, approximation algorithms, computability, and cryptography.
- Held office hours helping students with homework assignments and programming projects, graded assignments and exams, and proctored exams.
- Topics Covered: OCaml programming, polymorphism, models of evaluation, functors, mutability, logic, concurrency, data structures, lambda calculus.
- Awarded the CS course staff award for outstanding service to students.
- Held office hours helping students with homework assignments and programming projects, lead weekly recitations, graded assignments and exams, and proctored exams.
Awards & Honors
Recognition for academic excellence, research, and teaching.
Phi Beta Kappa
2026The Phi Beta Kappa Society
Inducted into the oldest and most prestigious academic honor society in the United States. About 1% of college seniors in the U.S. are invited.
Summa Cum Laude in Computer Science
2025Cornell University
Awarded for excelling in research, undertaking graduate-level coursework in addition to the standard CS major requirements, and graduating with above a 4.0 GPA.
Cum Laude in Mathematics
2025Cornell University
Earned by demonstrating exceptional performance in the mathematics curriculum, attaining a math major GPA of 4.16/4.3.
NSF Research Experience for Undergraduates (REU)
2024Carnegie Mellon University
Competitively selected for CMU's NSF-funded REUSE program to conduct summer research in programming languages and software engineering. Received a full research stipend.
CS Course Staff Award
2024Cornell University
Awarded to the top 10% of teaching assistants for exceptional teaching performance; received while TAing CS 3110 (Data Structures and Functional Programming).
Dean's List
2022 - 2025Cornell University
Recognizes students with GPA of 3.6 or higher; received every eligible semester throughout undergraduate career.
AP Scholar with Distinction
2022College Board
Took 8 AP courses and received high scores on exams.
Boomerang Youth Recognition Award
2022Central Bucks School District
Recognized for outstanding volunteer work promoting equality and social justice through community organizations.
Papers
Research papers, technical documents, and academic writings.
Voting-Based Multi-Agent Bandits
A research paper exploring multi-armed bandit algorithms in multi-agent systems using voting mechanisms. My final project for CS 6783 - Machine Learning Theory.
Voting-Based Multi-Agent Bandits
A research paper exploring multi-armed bandit algorithms in multi-agent systems using voting mechanisms. My final project for CS 6783 - Machine Learning Theory.
From Redux to Component-level State: Modernizing CMSX Architecture
A comprehensive technical document detailing the migration of Cornell's Course Management System (CMSX) from Redux-facilitated global state to component-level state architecture. I worked on this project from Jan 2024 to Dec 2024, receiving academic research credit through CS 4999. This migration yielded a massive improvement in both developer experience and user experience.
From Redux to Component-level State: Modernizing CMSX Architecture
A comprehensive technical document detailing the migration of Cornell's Course Management System (CMSX) from Redux-facilitated global state to component-level state architecture. I worked on this project from Jan 2024 to Dec 2024, receiving academic research credit through CS 4999. This migration yielded a massive improvement in both developer experience and user experience.
Low-Level Learning
A technical document on low-level learning approaches and methodologies. My project for CS 4701 - Practicum in Artificial Intelligence. We implemented the C-Torch library in C++, a high-performance machine learning library supporting numerical methods, calculus, linear algebra, and 15+ ML models.
Low-Level Learning
A technical document on low-level learning approaches and methodologies. My project for CS 4701 - Practicum in Artificial Intelligence. We implemented the C-Torch library in C++, a high-performance machine learning library supporting numerical methods, calculus, linear algebra, and 15+ ML models.
Show, Attend, Tell: Visual Attention for Image Captioning
Technical report on implementing the Show, Attend, Tell architecture for generating descriptive captions for images using visual attention mechanisms. Final project for CS 4782 - Deep Learning.
Show, Attend, Tell: Visual Attention for Image Captioning
Technical report on implementing the Show, Attend, Tell architecture for generating descriptive captions for images using visual attention mechanisms. Final project for CS 4782 - Deep Learning.
Projects
Primarily independent work, done in my own time.
Featured Projects
My most substantial projects.
Custom functional programming language with a performant interpreter, type inference, and core language features.
LambdaScript - Custom Programming Language
OCaml, OUnit, TypeScript, Jest
Custom functional programming language with a performant interpreter, type inference, and core language features.
Browser-based interactive sandbox for applied mathematics and machine learning. Features labs for stochastic processes (SDEs, Markov chains), probability theory (LLN, CLT), reinforcement learning (bandits, MDPs), supervised learning (regression, classification, decision trees, ensembles, SVM), clustering (K-means, DBSCAN, PCA), optimization (simplex, gradient descent), linear algebra (eigenvalues, matrix factorizations), and physics simulations (pendulum, heat equation in 1D/2D/3D). All numerical methods and visualizations run entirely client-side with interactive parameter controls.
dX - Math & ML Lab
Math, TypeScript, React
Browser-based interactive sandbox for applied mathematics and machine learning. Features labs for stochastic processes (SDEs, Markov chains), probability theory (LLN, CLT), reinforcement learning (bandits, MDPs), supervised learning (regression, classification, decision trees, ensembles, SVM), clustering (K-means, DBSCAN, PCA), optimization (simplex, gradient descent), linear algebra (eigenvalues, matrix factorizations), and physics simulations (pendulum, heat equation in 1D/2D/3D). All numerical methods and visualizations run entirely client-side with interactive parameter controls.
ML/AI Projects
Deep learning, reinforcement learning, and AI-powered applications.
A Deep Q-Network (DQN) reinforcement learning implementation for the classic Snake game. Features an AI agent that learns optimal strategies through 29-dimensional state representation, intelligent reward functions, and anti-looping mechanisms. The agent achieves scores of 20-40+ points through sophisticated spatial reasoning and strategic planning.
Snake DQN - AI Snake Game Agent
Python, PyTorch, Reinforcement Learning
A Deep Q-Network (DQN) reinforcement learning implementation for the classic Snake game. Features an AI agent that learns optimal strategies through 29-dimensional state representation, intelligent reward functions, and anti-looping mechanisms. The agent achieves scores of 20-40+ points through sophisticated spatial reasoning and strategic planning.
Visual Attention Image Captioning - Show, Attend, Tell Implementation
PyTorch, Python, LSTM, CNN
A deep learning model that automatically generates descriptive captions for images using visual attention. Implements the Show, Attend, Tell architecture and achieves superior METEOR scores. Final project for CS 4782 - Deep Learning.
Visual Attention Image Captioning - Show, Attend, Tell Implementation
PyTorch, Python, LSTM, CNN
A deep learning model that automatically generates descriptive captions for images using visual attention. Implements the Show, Attend, Tell architecture and achieves superior METEOR scores. Final project for CS 4782 - Deep Learning.
A transformer that translates natural English into propositional logic. Synthetic data pipeline (formula generator + English realizer) trains without human-labeled data. ~88% exact-match accuracy on nested formulas; ~30 min on an A100.
English2Logic
PyTorch, Python, Transformer
A transformer that translates natural English into propositional logic. Synthetic data pipeline (formula generator + English realizer) trains without human-labeled data. ~88% exact-match accuracy on nested formulas; ~30 min on an A100.
MCTSLab is a small Python framework for Monte Carlo Tree Search. It includes UCT and a Flat UCB variant, playable games (tic-tac-toe, Connect Four, 2048), tree visualization, and a benchmark script that measures MCTS win rate against a random player.
MCTSLab - Monte Carlo Tree Search Framework
Python
MCTSLab is a small Python framework for Monte Carlo Tree Search. It includes UCT and a Flat UCB variant, playable games (tic-tac-toe, Connect Four, 2048), tree visualization, and a benchmark script that measures MCTS win rate against a random player.
A reinforcement learning agent that navigates a 2D world using Q-learning and Deep Q-Networks (DQN). CoinBot learns optimal movement strategies through trial and error, demonstrating core RL concepts with real-time learning visualizations.
CoinBot - Reinforcement Learning 2D Navigation Bot
Python, PyTorch, NumPy, Matplotlib
A reinforcement learning agent that navigates a 2D world using Q-learning and Deep Q-Networks (DQN). CoinBot learns optimal movement strategies through trial and error, demonstrating core RL concepts with real-time learning visualizations.
A comprehensive library for simulating and benchmarking Multi-Armed Bandit (MAB) algorithms, including Epsilon-Greedy, UCB, Thompson Sampling, neural network-based contextual bandits, and more. Designed for research, teaching, and practical experimentation.
BanditBot - Multi-Armed Bandit Algorithm Library
Python, NumPy, Matplotlib
A comprehensive library for simulating and benchmarking Multi-Armed Bandit (MAB) algorithms, including Epsilon-Greedy, UCB, Thompson Sampling, neural network-based contextual bandits, and more. Designed for research, teaching, and practical experimentation.
Transformer models for boolean logic: evaluation, generation, and simplification. Built in PyTorch with encoder-only, decoder-only, and encoder-decoder architectures. Simplifier reaches ~92% test accuracy on synthetic data.
Bool Transformer
PyTorch, Python, Transformer
Transformer models for boolean logic: evaluation, generation, and simplification. Built in PyTorch with encoder-only, decoder-only, and encoder-decoder architectures. Simplifier reaches ~92% test accuracy on synthetic data.
CourseSphere - LLM-powered course recommendation system
TypeScript, Python, Jupyter Notebook, SCSS, Pinecone
AI-powered assistant for Cornell students that matches users to courses and professors based on their academic interests, backgrounds, and goals. Aggregates course and professor data for quick, informed decisions.
CourseSphere - LLM-powered course recommendation system
TypeScript, Python, Jupyter Notebook, SCSS, Pinecone
AI-powered assistant for Cornell students that matches users to courses and professors based on their academic interests, backgrounds, and goals. Aggregates course and professor data for quick, informed decisions.
Software Engineering Projects
Full-stack applications and more.
TriageForce - AI-Powered Disaster Relief Platform
Salesforce Agentforce, Salesforce Data Cloud, Salesforce Service Cloud
Automated disaster relief resource allocation via Agentic AI and Salesforce Agentforce, drastically cutting waste and achieving real-time response.
TriageForce - AI-Powered Disaster Relief Platform
Salesforce Agentforce, Salesforce Data Cloud, Salesforce Service Cloud
Automated disaster relief resource allocation via Agentic AI and Salesforce Agentforce, drastically cutting waste and achieving real-time response.
SQL database engine in C++ built from scratch—custom lexer, parser, and in-memory execution. No external SQL libraries.
ObsidianDB - SQL Database Engine
C++
SQL database engine in C++ built from scratch—custom lexer, parser, and in-memory execution. No external SQL libraries.
HabitStack - Habit Tracking Application
React, TypeScript, JavaScript, SASS, Express.js, Firebase, Vite
Full-stack habit tracking application with OAuth2, monthly calendar interface, and real-time goal management.
HabitStack - Habit Tracking Application
React, TypeScript, JavaScript, SASS, Express.js, Firebase, Vite
Full-stack habit tracking application with OAuth2, monthly calendar interface, and real-time goal management.
CritterWorld - Evolving Artificial Life Simulator
Java, JavaFX, SceneBuilder, Gradle, JUnit
Evolving artificial life simulator with JavaFX GUI and custom programming language for organism behavior.
CritterWorld - Evolving Artificial Life Simulator
Java, JavaFX, SceneBuilder, Gradle, JUnit
Evolving artificial life simulator with JavaFX GUI and custom programming language for organism behavior.
AlgoSandbox - Interactive Algorithm Visualizer
React, TypeScript, Vite, CSS, JavaScript, Netlify
Interactive platform for visualizing and experimenting with classic algorithms and data structures. Designed for students, educators, and developers to learn and teach algorithms in an engaging way.
AlgoSandbox - Interactive Algorithm Visualizer
React, TypeScript, Vite, CSS, JavaScript, Netlify
Interactive platform for visualizing and experimenting with classic algorithms and data structures. Designed for students, educators, and developers to learn and teach algorithms in an engaging way.
Interactive particle sandbox — C++, SDL2, OpenGL. A real-time 2D physics sandbox where you place particles and gravity wells, then watch them move, collide, and leave trails. Built in C++ with SDL2 for windowing and input and OpenGL for rendering.
Orb - Particle Simulator
C++, OpenGL
Interactive particle sandbox — C++, SDL2, OpenGL. A real-time 2D physics sandbox where you place particles and gravity wells, then watch them move, collide, and leave trails. Built in C++ with SDL2 for windowing and input and OpenGL for rendering.
