About Me
About me
My name is Xupeng Chen, born in CHINA in 1995.
I’m currently a Research Scientist at TikTok in Seattle, working on LLM / multimodal AI for e-commerce — SFT and reinforcement learning for multimodal LLMs, grounding and alignment strategies (verifiable rewards, retrieval-augmented reasoning, semantic consistency), and multimarket multimodal models for large-scale governance and content moderation.
I completed my Ph.D. in Electrical Engineering at NYU Tandon in May 2025, advised by Prof. Yao Wang in NYU Video Lab, and co-advised by Prof. Adeen Flinker on neural speech decoding. Before that I studied biology (XueTang program) and minored in statistics at Tsinghua University.
Research interests: multimodal large language models, LLM reasoning / alignment, neural speech decoding from ECoG/sEEG, and healthcare & brain–computer interface applications.
Resume
Contact
Work
TikTok — Seattle, WA Research Scientist, LLM / Multimodal AI for E-commerce | Jun 2025 – Present
- Trained multimodal LLMs with SFT + RL (RLHF / reward modeling variants), focusing on reasoning quality and uncertainty calibration.
- Researched grounding & alignment methods including verifiable reward design, retrieval-augmented reasoning (RAG), and semantic consistency regularization.
- Built production-grade multimodal moderation systems for e-commerce, supporting multi-market deployment with improved precision/recall trade-offs and reduced labeling cost.
Education
New York University, Tandon School of Engineering — Brooklyn, NY Ph.D. in Electrical Engineering | Sep 2019 – May 2025
- Advisor: Prof. Yao Wang (Video Lab)
- Multimodal LLMs for medical imaging and LLM reasoning / alignment. Neural speech decoding with applications in healthcare and brain–computer interfaces.
Tsinghua University, School of Life Science — Beijing, China B.Sc. in Life Science (XueTang Program), Minor in Statistics | Sep 2014 – Jun 2019
- XueTang program for top research-track students.
Publications
- Chen, X., Wang, R., Khalilian-Gourtani, A., Yu, L., Dugan, P., Friedman, D., Doyle, W., Devinsky, O., Wang, Y., Flinker, A. A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis. Nature Machine Intelligence (2024).
- Wang, R., Chen, X., Khalilian-Gourtani, A., Yu, L., Dugan, P., Friedman, D., Doyle, W., Devinsky, O., Wang, Y., Flinker, A. Distributed feedforward and feedback cortical processing supports human speech production. PNAS (2023).
- Chen, X., Lai, Z., Ruan, K., Chen, S., Liu, J., Liu, Z. R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest. IJCNN (2025).
- Li, X., Chen, X., Fan, J., Jiang, E.H., Gao, M. ENCORE: Entropy-guided Reward Composition for Multi-head Safety Reward Models. AAAI (2026).
- Li, X., Yu, Z., Zhang, Z., Chen, X., Zhang, Z., Zhuang, Y., Sadagopan, N., Beniwal, A. When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs. NeurIPS (2026).
- Chen, X., Chen, J., Wang, R., Le, C., Khalilian-Gourtani, A., Jensen, E., Dugan, P., Doyle, W., Devinsky, O., Friedman, D., Flinker, A., Wang, Y. Transformer-based neural speech decoding from surface and depth electrode signals. Journal of Neural Engineering (2025).
- Wang, R., Chen, X., Khalilian-Gourtani, A., Chen, Z., Yu, L., Flinker, A., Wang, Y. Stimulus speech decoding from human cortex with generative adversarial network transfer learning. ISBI (Best Paper Finalist) (2020).
- Christou, P., Chen, S., Chen, X., Dube, P. Test Time Learning for Time Series Forecasting. NeurIPS 2024 Workshop on Time Series in the Age of Large Models (2024).
- Lin, Z., Wei, D., Jang, W.D., Zhou, S., Chen, X., Wang, X., Schalek, R., Berger, D., Matejek, B., Kamentsky, L., Peleg, A. Two stream active query suggestion for active learning in connectomics. ECCV (2020).
- Khalilian-Gourtani, A., Wang, R., Chen, X., Yu, L., Dugan, P., Friedman, D., Doyle, W., Devinsky, O., Wang, Y., Flinker, A. A corollary discharge circuit in human speech. PNAS (2024).
- Ni, H., Meng, S., Chen, X., Zhao, Z., Chen, A., Li, P., Zhang, S., Yin, Q., Wang, Y., Chan, Y. Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach. arXiv (2024).
- Meng, S., Chen, A., Wang, C., Zheng, M., Wu, F., Chen, X., Ni, H., Li, P. Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models. arXiv (2024).
- Le, C., Gong, Z., Wang, C., Ni, H., Li, P., Chen, X. Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs. ICAHN (2025).
- Le, C., Zhao, Y., Emami, N., Yadav, K., Liu, T., Chen, X. VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI. arXiv (2025).
- Emami, N., Khalilian-Gourtani, A., Qian, J., Ratouchniak, A., Chen, X., Wang, Y., Flinker, A. Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping. arXiv (2025).
- Yang, K., Chen, X., He, J. Multimodal Fusion of Skeleton Dynamics and Clinical Gait Features for Video-Based Cerebral Palsy Severity Assessment. arXiv (2026).
- Meng, S., Chen, X. Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets. arXiv (2026).
- Chen, X. Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption. arXiv (2026).
- Chen, X., Meng, S. When AI Levels the Playing Field: Skill Homogenization, Asset Concentration, and Two Regimes of Inequality. arXiv (2026).
- Chen, Y., Chen, X., Wang, F., Jiao, N., Liu, J. VANGUARD: Vehicle-Anchored Ground Sample Distance Estimation for UAVs in GPS-Denied Environments. arXiv (2026).
See also Google Scholar.
Research and Work Experience
Large Language-and-Vision Assistant for BioMedicine with Vision Prompts — New York University
- Developed a multimodal LLM for disease diagnosis from medical images, focusing on regions of interest (ROI). Pretrained on a large-scale medical image question-answering dataset, then instruction fine-tuned using ROI-labeled medical images and QA pairs.
- Outperformed LLaVA-Med on four evaluation datasets in medical question answering and ROI localization with 3–8% accuracy and recall improvement.
Harnessing Earnings Reports for Stock Predictions with LLMs — New York University
- Developed a QLoRA-enhanced LLM for stock predictions by integrating financial metrics, earnings transcripts, and other factors. Outperformed GPT-4 with a 19% accuracy increase and 7% weighted F1 improvement.
- Implemented instruction-based fine-tuning for precise next-day stock movement predictions following earnings reports.
Radar and LiDAR Point Cloud Classification and Object Detection — NXP Semiconductors, Autonomous Driving Group | Summer 2022
- Developed Radar and LiDAR point cloud classification and object detection models using PointNet++, PointPillar, and Point Transformer.
- Implemented a semantic-information-guided sampling strategy; achieved 1–3% mAP improvement on KITTI and private datasets while reducing inference time by 20%.
Neural Speech Decoding and Synthesizing from sEEG/ECoG signals — Video Lab, New York University | 2020–2025 Supervisors: Yao Wang and Adeen Flinker · Grants: NSF IIS-2309057, NSF IIS-1912286
- Developed differentiable and quantized speech re-synthesis frameworks for overt and imagined speech decoding, achieving 17–30% higher accuracy and fidelity than baselines.
- Designed multi-subject neural decoding models with spatial–temporal attention for robust cross-participant generalization and improved intelligibility.
- Proposed a disentangled contrastive learning approach to capture semantic and instance-level features from neural signals.
Efficient Instance Annotation for Connectomics — Lichtman Lab, Harvard University | Summer 2018 Supervisors: Hanspeter Pfister and Jeff Lichtman
- Built a 3D U-Net for synapse detection on CREMI dataset — ranked 1st in CREMI contest.
- Built 3D U-Net and 3D-CNN for synaptic connections and intracellular structures (e.g. mitochondria); constructed an active-learning annotation framework for proofreading.
Activities & Awards
- 2025 — Dr. Li Annual Publication Award in Electrical Engineering — New York University
- 2024 — Dante Youla Award for Graduate Research Excellence in Electrical Engineering — New York University
- 2018–2022 — Teaching Assistant for Image and Video Processing, Medical Imaging, and Bioinformatics — NYU / Tsinghua
- 2015–2019 — XueTang Scholarship ($10,000 for research) — Tsinghua University
- 2017 — First Prize, The First National College Students’ Brain Computation and Application Competition — International
- 2017 — First Prize, eMaize Challenge: Machine learning in genetic variation prediction — National
- 2018 — Meritorious Winner, Mathematical Contest in Modeling (MCM) — International
- 2016–2018 — Initiative Scientific Research Program ($8,000 for biomedical image analysis) — Tsinghua University