WSDM Conference Glad to share that I received the NSF Student Travel Grant Award to present my work "Graph Anomaly Detection via Neighborhood Reconstruction" at the WSDM 2024 WSDM Conference. Again, big thanks to Prof. Pan Li, collaborator Juan Shu and Purdue Computer Science for their valuable support.

Interesting application of diffusion and invariance.

Very interested in how transformer performs graph reasoning...

Can we prompt over videos as well? #NLQA4Videos #VideoEditingviaPrompts #MultimodalRetrieval #MultiModalEditing

a senior engineer at google just dropped a 400-page free book on docs for review: agentic design patterns. the table of contents looks like everything you need to know about agents + code: > advanced prompt techniques > multi-agent patterns > tool use and MCP > you name it

I finally understand how large language models actually work After reading the 2025 textbook “Foundations of LLMs” It blew my mind and cleared up years of confusion Here’s everything i learned (in plain english):

I finally understand the difference between LLMs, RAG, and AI Agents. After two years of building production AI systems, I realized most people are treating them like competing tools when they’re actually three layers of the same intelligence stack. 1. The LLM is the brain.

🚨 Before you build your first AI agent, learn these 3 fundamentals: LLMs, RAG, and Tool Use. Because if you don’t know how they connect you’re just building chaos with a fancy wrapper. Let’s break it down: 1. LLM (Large Language Model) This is the brain. It understands

This Aviral Kumar lecture is AMAZING.

Can LMs learn to faithfully describe their internal features and mechanisms? In our new paper led by Research Fellow Belinda Li, we find that they can—and that models explain themselves better than other models do.

What is equivalent to Next-Token Prediction in Multimodal Setting? Next-Frame Prediction? Next event Prediction?