CS, Math & HCI @ Northwestern. Regeneron STS Scholar ‘25. Building AI products and exploring research in machine learning.
About Me
CS, Math & HCI @ Northwestern. Regeneron Science Talent Search Scholar '25. Founding engineer at CrowdListen, building the shared context layer that turns social conversations into structured intelligence for AI agents.
My research spans drug-drug interaction prediction with graph neural networks and mathematical modeling competitions. In my free time, I photograph sunsets by the sea and chase the Milky Way. Always happy to chat.
Skills
Product
Prototyping, Roadmapping, User Research, A/B Testing, SQL, Figma, JIRA
Programming
Python, C/C++, JavaScript, HTML/CSS, Git, React.js, Node.js, TensorFlow, PyTorch
ML & Research
Neural Networks, K-Means Clustering, Mathematical Modeling, PDEs, Optimization
Recent Posts
Beyond Prompt Engineering: Context, Harness, and the Product Architecture of AI Agents
Prompt engineering has been demoted from the whole problem to one layer of the stack. Context engineering decides what the model can think with. Harness engineering decides whether that thinking becomes durable work.
REST API + Skill Documents vs MCP: Two Strategies for Connecting AI Agents to Backend Capabilities
REST APIs are not inherently weak contracts. MCP is not inherently superior. The real comparison requires distinguishing three layers of interface quality — prose documentation, machine-readable specification, and protocol-native tool discovery.
GMPNN-CS++: A Novel Dual-Contrasting Framework for Drug-Drug Interaction Prediction
The proposed GMPNN-CS++ model introduces a dual-contrasting sampling approach and self-attention mechanism for DDI prediction, achieving 97% overall accuracy.
To view all posts, please visit the Posts section.