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Explainable AI: Object Recognition With Help From Background

Published in The International Conference on Learning Representations (ICLR), CSS Workshop, 2022

This paper explores how and when backgrounds help a model with object classification task

Recommended citation: Raza Hashmi, Qiang Li. (2022). "Object Recognition With Help From Background." ICLR CSS Workshop.(1) https://sites.google.com/view/cssiclr2022/home

Adversarial Label Learning: Training a Predictive Network with a Label-based Adversary

Published: November 04, 2020

The idea was born out of my fascination with Generative Adversarial Networks (GANs). The concept of two networks locked in a competitive dance—a Generator creating fakes and a Discriminator spotting them—was revolutionary. It transformed what we could do with AI-generated art, music, and more. But I wondered: could this adversarial dynamic be used for something other than generating data?

I Gave an LLM a Holographic Brain — Here’s What Actually Worked (and What Didn’t)

Published: October 03, 2025

We have a memory problem in AI. If you’ve built applications with Large Language Models, you’ve hit the wall: the context window. The moment your conversation exceeds the token limit, the model forgets. The beginning of the chat falls off a cliff, and the AI loses who you are. The industry’s standard fix is RAG (Retrieval Augmented Generation). We chop up data, embed it, and stuff it into a vector database like Pinecone or Chroma. It works. But RAG has a hidden cost: linear growth. To store 1 million facts, you need 1 million vectors. Your database grows with every interaction.

Work Experience

For nearly a decade, I was deeply involved in building innovative, user-centric products.