Ping-Jung(Lawrence) Lu

Ping-Jung(Lawrence) Lu

Research Assistant

New York University

Research Interests

Self-Supervised & Foundation Models for Neural Signals
Neural Representation Learning & Decoding
Neuro-genomics for Medicine & Drug Discovery
Computational Neuroscience
Coffee Science

About

I am a Research Assistant at the Neuroinformatics Lab (NYU VIDA), advised by Prof. Erdem Varol. I completed my M.S. in Electrical and Computer Engineering at New York University in May 2026. I am also affiliated with the Flinker Lab at NYU Langone Health, where I am advised by Prof. Adeen Flinker and Dr. Amir Khalilian, using intracranial (ECoG) recordings to uncover the latent neural representations that underlie speech across different tasks and stimuli.

My research focuses on multimodal foundation modeling for neural signals, with an emphasis on neural decoding and representation learning across recording modalities, species, and cognitive domains. I am particularly interested in learning shared and transferable neural representations from complex brain data.

Building on this, I am working to connect electrophysiology with molecular and cellular information, recovering signatures of tissue, gene expression, and cell types from neural recordings, toward the longer-term goal of linking cells, genes, and neural activity to inform medicine and therapeutic discovery.

Outside of research, I spend my free time playing piano and guitar, and brewing coffee. I am an avid amateur tennis player and a big dog lover, and I am a huge fan of Novak Djokovic and Bayern Munich FC.

Selected Publications

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Decoding Anatomical Structure from Neural Population Activity using Self-supervised Learning

Tianxiao He, Malhar Patel, Chenyi Li, Anna Maslarova, Mihály Vöröslakos, Subhrajit Dey, Ping-Jung(Lawrence) Lu, Sirish Parupudi, Thiago Viegas, Nalini Ramanathan, Wei-Lun Hung, Eden Wu, Saurabh Vyas, György Buzsáki, Erdem Varol

Computational and Systems Neuroscience (Cosyne)

Poster presented at Cosyne 2026, Lisbon. A self-supervised framework that decodes anatomical identity from raw single-channel LFP, enabling zero-shot brain-region decoding across subjects, labs, and species.

News

Jul 2026

Started as a Research Assistant at the Neuroinformatics Lab (NYU VIDA).

May 2026

Graduated with my M.S. in Electrical & Computer Engineering from NYU 🎓

Mar 2026

Our work on decoding anatomical structure from neural activity was presented at Cosyne 2026 (March 12–15) in Lisbon 🇵🇹

Jan 2026

Began serving as a Teaching Assistant for Neuroinformatics (CS-GY 9223) at NYU Tandon.