I work at the intersection of computer science, neuroscience, and cognitive science. I am interested in the computational underpinnings of intelligent behavior, particularly in the use of memory and attention.

I have recently left my research scientist role at Intel Labs to pursue other projects, both in machine learning and cognitive neuroscience.

Previously I spent 5 years working in AI/ML in the Brain-Inspired Computing Lab at Intel Labs, most recently on vector similarity search, RAG, and LLM code generation.

• 12/2024: Scientific Data, published. The 'Naturalistic Free Recall' Dataset: four stories, hundreds of participants, and high-fidelity transcriptions.
• 10/2024: arXiv, preprinted. Report on our Sequence Order Recall Task (SORT), a benchmark to evaluate long-term memory behavior in LLMs. We release data and code.
• 09/2024: IEEE High Performance Extreme Computing, presented. Outstanding paper award for Monocoder: Domain-specific code language model for HPC Codes and Tasks.
• 09/2023: NeurIPS, accepted. Paper with UT Austin and Emergent AI at Intel Labs on multimodal representation transfer with fMRI encoding model.

• 11/2024: Workshop on Attributing Model Behavior at Scale, NeurIPS. Poster on optimizing vector similarity search and retrieval for retrieval augmented generation (RAG) systems
• 11/2024: Intel tech demo. Video demonstrating custom-built RAG application relying on the open-source Intel Scalable Vector Search library for fast data retrieval
• 08/2023: KDD (Knowledge Discovery & Data Mining). Served as an invited discussion panelist at LLM Day [website]