Portfolio
- Implemented a deep reinforcement-learning agent based on DQN that surpasses human benchmark on the atari suite.
- Implemented algorithmic optimizations to reduce compute requirements by 200x compared to its predecessor Agent57.
- Achieved state-of-the-art performance across all 57 Atari games, demonstrating robust generalization and sample efficiency.
- Architected and implemented core Transformer architectures—including Transformer, Transformer-XL, Longformer, and Block-Recurrent Transformer—mirroring foundations of large-scale language models.
- Bootstrapped the codebase and personally wrote 80 % of the implementation, ensuring modularity for easy extension and experimentation.
- Validated model correctness through benchmarked language modeling tasks and attention‐visualization tools.
- Built a web platform that automatically aggregates and ranks the week’s newest AI research papers by author prominence and citation count.
- Implemented real-time features: “Like” button, personalized saving of papers, and dynamic category filtering via interactive JavaScript charts.
- Integrated Google OAuth for secure login and user session management, facilitating personalized reading lists and alerts.
- Designed and developed a 2D multiplayer strategy game in Unity inspired by Clash Royale, featuring three unique character classes.
- Programmed core gameplay mechanics (unit spawning, resource management, combat resolution) in C#, ensuring smooth networked play.
- Implemented UI/UX elements (health bars, cooldown timers) and balanced character abilities through iterative playtesting.
