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Jiankai Sun

Email:  jksun [at] stanford [dot] edu

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Current Research Interests

Hi there! I am a fourth-year Ph.D. candidate and Interdisciplinary Graduate Fellow at Stanford University, advised by Mac Schwager. I am also affiliated with the Stanford Center for AI Safety. I have spent time at Meta Superintelligence Lab and Microsoft Research.

Research Interests:

News

Publications (Google Scholar)

(* and † indicate equal contribution or alphabetical order)

Experience

Talks

  • [2026/01] Talk @ NVIDIA, Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
  • [2025/12] Talk @ Cohere Labs, Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
  • [2025/10] Talk @ Ling Team, Ant Group, Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
  • [2024/03] Talk @ Meta, Egocentric Human Trajectory Forecasting With a Wearable Camera and Multi-Modal Fusion
  • [2021/10] Talk @ Max Planck Institute for Intelligent Systems, Localization and recognition of human action in 3D using transformers

Selected Honors

  • AAAI Doctoral Consortium
  • Ant InTech Technology Scholarship
  • Meshy Fellowship
  • NVIDIA Graduate Fellowship Finalist
  • Honorable Mention for the HRI Pioneers
  • Stanford Interdisciplinary Graduate Fellowship (SIGF)
  • ICRA 2024 Best Paper Award
  • ICRA 2024 Best Student Paper Finalist, Best Manipulation Paper Finalist Awards
  • CoRL 2023 Best Paper/Best Student Paper Awards Finalist, and Best Systems Paper Award Finalist
  • CoRL 2022 Best Paper Nomination
  • The World Artificial Intelligence Conference (WAIC) Yunfun Award
  • Stanford Min Zhu and Susan Xu Engineering Fellowship
  • NeurIPS Top 10% of High-scoring Reviewer
  • National Scholarship (highest honor) × 3 years
  • Tang Lixin Scholarship
  • SenseTime Scholarship
  • UCLA CSST Scholarship
  • Outstanding Winner, MCM/ICM
  • Vilfredo Pareto Award, MCM/ICM

Professional Service

Press

  • Emerging Cyber Attack Risks of Medical AI Agents was covered by: South China Morning Post.
  • Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation was covered by: Microsoft.

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