Asim Kadav

Research Interests: Content understanding and generation and efficient ML systems

News: Watch my lecture on Video understanding at Yale University, Topics in Deep Learning, Spring 2020.

I currently work at SRA leading the coreml research leading a team working on research for computer vision, machine learning, and large language models.

Previously, I worked at Freenome and Tonal on machine learning and data science engineering leading teams for improving people's health. I've worked on ML models for early cancer detection, LLMs for clinical and biomedical tasks and also built Tonal's first camera and computer vision product, Tonal Smart View.

Before that, I was at NEC Labs leading the eigen team. My team and I built extremely efficient image classification, video classification, video retrieval and pose tracking methods. I have also worked on several multi-modal and text learning methods.

I have built and deployed several cloud and edge based video AI products that process large volumes and diversity of data. Apart from machine learning, I also have a background in distributed and operating systems, and have built several real world data center solutions, distributed file systems, and OS reliability tools and patches. I have published in ICLR, CVPR, ECCV, SOSP, OSDI and ASPLOS with 5000+ citations and have filed 50+ patents.

Email: firstlastname@gmail.com

Recent Publications | All, By area | Students

  • COMPOSER: Compositional Reasoning of Group Activity in Videos.
    Honglu Zhou, Asim Kadav, Aviv Shamsian, Shijie Geng, Farley Lai, Long Zhao, Ting Liu, Mubbasir Kapadia, Hans Peter Graf.
    In ECCV 2022. PDF.
  • Hopper: Multi-hop Transformer for Spatiotemporal Reasoning.
    Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf.
    In ICLR 2021. PDF. Poster.
  • 15 Keypoints is All You Need. Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf. In CVPR 2020.PDF. Ranked #1 in PoseTrack.
  • S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation. Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf.
    In CVPR 2020. PDF.
  • Tripping Through Time: Efficient Localization of Activities in Videos. (Spotlight) Meera Hahn, Asim Kadav, James M. Rehg, Hans Peter Graf.
    In CVPR Workship on Language and Vision, 2019. Also, appears in BMVC'20. PDF.
  • Visual Entailment: A Novel Task for Fine-Grained Image Understanding. Ning Xie, Farley Lai, Derek Doran, Asim Kadav.
    In NeurIPS Workshop on Visually-Grounded Interaction and Language, 2018 (ViGIL’18). PDF.Dataset. Check out the leaderboard!
  • Teaching Syntax using Adverserial Distraction. Juho Kim, Christopher Malon, and Asim Kadav. In FEVER-EMNLP, 2018. PDF.
  • Attend and Interact: Higher-Order Object Interactions for Video Understanding. Chih-Yao Ma, Asim Kadav, Iain Melvin, Zsolt Kira, Ghassan AlRegib, and Hans Peter Graf. In CVPR, 2018. PDF. Video NEC Video. Live Demo.
  • DeepConfig: Automating Data Center Network Topologies Management with Machine Learning. Christopher Streiffer, Huan Chen, Theophilus Benson, Asim Kadav. SIGCOMM Net AI Workshop, 2018. PDF.
  • ASAP: Asynchronous Approximate Data-Parallel Computation.
    Asim Kadav and Erik Kruus. arXiv Preprint. Dec 2016. PDF. Code.

Open-source software/datasets

  • CATER-H: A more difficult CATER dataset with longer reasoning chains, ICLR 2021. Github.
  • SNLI-VE: A novel visual entailment task using image/text pairs. Github.
  • MALT-v2: A federated learning framework over RDMA and TCP. Github. Website. PDF.
  • Numerous patches to the Linux and android kernel to improve the software/hardware layer.[1][2][3][4].

Press

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