Fang‐Chieh Chou

About

Fang‐Chieh Chou is a researcher whose work spans the frontiers of autonomous systems and computational biology. His most impactful contribution lies in autonomous driving, where he pioneered the use of deep convolutional networks for multimodal trajectory prediction. His 2019 paper on this topic, which has garnered over 670 citations, addresses a critical challenge in self-driving vehicle (SDV) safety: anticipating the diverse, plausible future paths of other road users. By modeling multiple possible trajectories simultaneously, Chou’s approach helps SDVs make safer, more robust decisions, directly advancing the goal of preventing road accidents. Beyond autonomous driving, Chou has demonstrated remarkable versatility in computational biology, co-authoring a highly cited 2015 paper on modeling complex RNA tertiary folds using the Rosetta framework. This work showcases his ability to apply computational methods to fundamental molecular problems. Together, his research highlights a unique interdisciplinary talent—bridging the gap between high-stakes robotics and the intricate world of biomolecular structure. Chou’s work continues to inspire new approaches in both fields, making him a notable figure for students interested in the intersection of AI, robotics, and the life sciences.

Research Focus

Key Achievements

Top Papers

1. 1
   Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

   671 citations · 2019
2. 2
   Modeling Complex RNA Tertiary Folds with Rosetta

   109 citations · 2015
3. 3
   Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

   27 citations · 2018

Key Collaborators

Contact & Links