Autonomous Vehicles Research

Nvidia

Jose M. Alvarez

Jose M. Alvarez

I lead the Autonomous Vechicle Applied Research Group at Nvidia, CA, USA. The group focuses on scaling up deep learning for AV, spaning efficient and data-centric deep learning, 3D computer vision, and Self-Supervised Learning.

    Scholar

News

2025

  • 4 Papers accepted to CVPR 2025!
    • MDP: Multidimensional Vision Model Pruning with Latency Constraint
    • PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models
    • Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video
    • OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counter Factual Reasoning
  • We are organizing 2 tutorials at CVPR 2025. Stay tunned!
    • Full-Stack, GPU-based Acceleration of Deep Learning tutorial.
    • Continuous Data Cycle via Foundation Models.
  • Our paper Advancing Weight and Channel Sparsification with Enhanced Saliency has been accepted to WACV 2025!
  • We are organizing three workshops at CVPR 2025:

2024

2023

  • April: We will be organizing a tutorial at ICCV, Paris, France 2023. Stay tunned.
    • Learning with Noisy and Unlabeled Data for Large Models beyond Categorization.
  • March: 2 new papers accepted at IV 2023
    • Shen et al. Hardware-Aware Latency Pruning for Real-Time 3D Object Detection.
    • Clemons et al. Leaf: Legacy Networks for Flexible Inference.

2022

2021 - Older News

Interns (2024)

  • Zhenxin Li – MSc, Fudan Univ.
  • Shihao Wang – MSc, Beijing Institute of Technology
  • Zi Wang – MSc, Carnegie Mellon Univ.
  • Sihan Liu – MSc, Fudan Univ.
  • Shan Wang – PhD, Australian National Univ.
  • Nikita Durasov – PhD, EPFL
  • Jenny Schmalfuss – PhD, University of Stuttgart
  • Chonghao Sima – PhD, Hong Kong Univ.
  • Jihao Liu – PhD, The Chinese University of Hong Kong.
  • Feiyang Kang – PhD, Virginia Tech Univ.
  • Vibashan VS – PhD, John Hopkins University

Interns (2023)

  • Zhiqi Li – PhD, Nanjing Univ.
  • Yiming Li – PhD, NYU

Interns (2022)

  • David Wang – PhD, Tsinghua Univ.
  • Shixing Yu – PhD, Univ. Texas Austin
  • Yanwei Li – PhD, CUHK
  • Zhiqi Li – PhD, Nanjing Univ.
  • Zetong Yang – PhD, CUHK
  • Tzofi Klinghoffer – PhD, MIT
  • Bingyin Zhao – PhD, Clemson Univ.
  • Annamarie Bair – PhD, CMU
  • Yilun Chen – PhD, CUHK
  • Alex Sun – MS, Stanford
  • Justin Hsu – BS, Stanford
  • Arnav Joshi – BS, Stanford
  • Shubhranshu Sigh – MS, CMU

Interns (2021)

  • Jiayu Yang – PhD, Australian National University
  • Marc Finzi – PhD, NYU
  • Enze Xie – PhD, HKU
  • Ryan Humble – PhD, Stanford
  • Vlad Sobal – PhD, NYU
  • Jianna Liu – BS, MIT
  • Xinlong Wang – PhD, Univ. Adelaide
  • Daquan Zhou – PhD, NUS
  • Faith Johnson – PhD, Rutgers
  • Javier Sagastuy Brena – PhD, Stanford
  • Joshua Chen – MS, CMU (now Nvidia)
  • Nithya Attaluri – BS, MIT
  • Jessica Lee – MS, CMU

Interns (2020)

  • Jiwoong Choi PhD, Seoul Nat. University (now NVIDIA)
  • Ismail Elezi PhD TUM (now PostDoc TUM)
  • Shuxuan Guo - PhD. EPFL
  • Marvin Kim – MS CMU (now Waymo)
  • Lilian Luong – BS MIT
  • Cynthia Liu – MS, MIT (now Cerebras)
  • Nadine Chang – PhD CMU
  • Yerlan Idelbayav – PhD UC Merced (now Amazon)
  • Xinnan Du – MS CMU (now Goldman Sachs)
  • Hoang Vu Nguyen – PhD Stony Brooks Univ.

Interns (2019)

  • Maying Shen – MS CMU (now Nvidia)
  • Michael Zhang – BS Hardvard (now PhD Stanford)
  • Akshay Chawla – MS CMU (now Vicarious)
  • Kashyap Chitta – MS CMU (now PhD student MPI)
  • Tony Wang – BS MIT
  • Wenbo Guo – PhD Penn. State Univ.
  • Nikhil Murthy – BS MIT

Interns (2018)

  • Jiaming Zeng – PhD Stanford (now IBM Research)
  • Yousef Hindy – MS Stanford (now NewCo)
  • Kashyap Chitta – MS CMU (now PhD student MPI)

Invited Talks

  • From Research to Product: Transforming AV Technology with AI, GTC 2025, San Jose, California, USA
  • Towards Robust and Reliable Autonomous Vehicles with Foundation Models, NYU Seminar, Oct 2024, New York, USA
  • Towards Safe and Reliable Autonomous Vehicles, CMU, Pittsburgh, USA
  • Towards an Effective use of Foundation Models in Autonomous Driving, Geen Foundation Model workshop @ECCV 2024, Milan, Italy
  • Towards Robust and Reliable AV with Foundational Models, 2nd Workshop on Vision-Centric Autonomous Driving @ ECCV 2024, Milan, Italy
  • Camera-based perception for AV: From Data Collection to network Robustness, E2E Autonomous Driving Workshop @ CVPR 2023
  • Optimizing large deep models for real-time inference, Embedded Vision Workshop @ CVPR 2023
  • Achieving Better Accuracy in 3D Occupancy Prediction for Autonomous Driving, GTC 2024, San Jose, California, USA
  • Efficient Deep Learning at Scale, UvA Seminar, October 2022, Amsterdam, The Netherlands
  • Towards Robust Perception Systems in Open World, GTC 2022, San Jose, California, USA
  • Deep Learning at Scale, UvA Seminar, October 2021, Amsterdam, The Netherlands
  • Scaling-up Deep Learning for Autonomous Driving, GTC 2019, San Jose, California, USA
  • Scaling-up Deep Learning for Autonomous Driving, MVA'19 Invited Tutorial, Tokyo, Japan
  • Scaling up Deep Learning for Autonomous Driving, ECCV 2018 workshop, Munich, Germany
  • Efficient ConvNets for Perception in Autonomous Driving, ICML'2017, Sydney, Australia
  • Efficient Deep Model Selection. GTC'17, San Jose, CA, US
  • Efficient Networks for Real Time Scene Understanding. GTCx Australia'16. Melbourne, Australia
  • Large-scale scene understanding in constrained platforms. NVIDIA RoadShow. Canberra, Australia
  • Compacting ConvNets for end to end learning. Deep Learning workshop ACRA 2015

Academic Service

  • I served as an Area Chair for IV 2018-2025, IEEE ICRA 2015, IEEE ITSC 2018, MM 2017, IEEE WACV 2016-2018, IJCAI 2022, KDD 2024, NeurIPS 2024, ECCV 2024, IJCAI 2025
  • I am Associate Editor for IEEE TPAMI
  • I am Associate Editor for IEEE T-ITS
  • I was a Chair IEEE-ACT Comp. Science Chapter

Awards and Recognitions

Relevant Workshop Organization