Brief Bio

I am a KAIST Endowed Chair Professor in the Kim Jaechul School of Artificial Intelligence and School of Computing at KAIST. Prior to working at KAIST, I was an assistant professor in the School of Electric and Computer Engineering at UNIST, and before that I was a postdoctoral research associate at Disney Research, working under the supervision of Professor Leonid Sigal. I did my Ph.D. in computer science at University of Texas at Austin, under the supervision of Professor Kristen Grauman. During my Ph.D. I also closely collaborated with Professor Fei Sha at University of Southern California.

I am also a co-founder and Head of AI Division, of AITRICS, an AI startup company located in Seoul.

Here is my CV.
 

Vacancies

To prospective master students
If you want to join our research group, please contact me as early as possible, so that you can have a short-term internship before applying to the master program. Also, please be aware that I will only hire students planning to pursue Ph.D. study under my supervision (since most applicants want to do Ph.D. anyways, I will consider their applications with higher priority).

This year, I am planning to recruit students who want to develop machine learning models and algorithms for video/speech synthesis, neural rendering and online continual learning.

To prospective Ph.D. students
If you want to do Ph.D. study under my supervision, then I recommend you to apply to the master’s program first (The master program at KAIST is an academic degree program and you will be fully supported). Please note that I can take only a limited number of Ph.D. students each year as advisees (currently three), and will put higher priorities on students who have done master’s study under my supervision. However I will still consider few qualified applicants outside my group as my advisees (e.g. applicants with more than one top-tier ML/AI papers).

To prospective postdocs
I am looking for postdocs with strong background in deep learning, preferably with multiple top-tier ML/AI papers, and experience on drug discovery, ML systems, deep generative models (for video/speech generation), real-time computer vision (for egocentric videos), or neural rendering. You will get involved in multiple ongoing projects and will have a chance to directly advise some of my students. Please send me your CV if you are interested.

 

News

5/15/2022: Four papers accepted to ICML 2022. I have 17 papers this year (7 ICLR, 4 ICML, 2 AAAI, 1 CVPR, 1 ACL, 1 NAACL and 1 CVIU)

4/8/2022: One paper accepted to NAACL 2022.

3/31/2022: I will serve as an area chair for NeurIPS 2022. I am also currently serving as an AC for ICML 2022 and served as an AC for AAAI 2022 and ICLR 2022.

3/2/2022: I am appointed as a KAIST Endowed Chair Professor.

3/1/2022: One paper accepted to CVPR 2022.

2/28/2022: One paper accepted to ACL 2022 (short paper).

1/21/2022: Seven papers accepted to ICLR 2022 (1 oral, 1 spotlight).

12/1/2021: Two papers accepted to AAAI 2022.

9/30/2021: Five papers accepted to NeurIPS 2021 (two spotlights). I have 23 papers this year (5 NeurIPS, 5 ICML, 7 ICLR, 1 ACL, 1 ICCV, 2 AAAI, 1 IJCAI, and 1 Interspeech)

9/1/2021: I am officially tenured.

7/23/2021: One paper accepted to ICCV 2021.

5/8/2021: Five papers accepted to ICML 2021.

5/6/2021: One paper accepted to ACL 2021 (long paper).

4/29/2021: One paper accepted to IJCAI 2021.

1/13/2021: Seven papers accepted to ICLR 2021 (one spotlight).

12/02/2020: Two papers accepted to AAAI 2021. I am serving as an area chair for ICLR 2021, IJCAI 2021, ICML 2021, and NeurIPS 2021.

7/25/2020: Nine papers accepted to NeurIPS 2020 (one spotlight). I have 22 papers this year (9 NeurIPS, 4 ICML, 4 ICLR, 1 ACL, 1 EMNLP, 1 AAAI, 1 ICRA, and 1 Interspeech).

7/18/2020: Our paper “Federated Semi-Supervised Learning with Inter-Client Consistency” received the Best student paper award at the ICML 2020 Workshop on Federated Learning for User Privacy and Data Confidentiality.

 

Research Interests

My research interest is mainly on developing novel models and algorithms for tackling new challenges in deploying artificial intelligence systems to various real-world application domains. Regarding machine learning problems, I am mostly interested in the following topics:

  • Low-resource learning: meta-learning, network pruning & quantization, deep generative models, self- & semi-supervised learning
  • On-device learning: network compression (pruning, quantization, and knowledge distillation), continual learning, federated learning
  • Safe and secure learning: uncertainty modeling & quantification, robustness to distributional shifts, defense against adversarial attacks
  • Large-scale learning: meta-learning, neural architecture search, distributed & federated learning

    • The application domains of interests include but are not limited to, visual recognition, natural language understanding, speech recognition, automatic drug/material discovery, healthcare and finance.

     

    Education

    The University of Texas at Austin, TX, USA
    Ph.D., Computer Science (Aug 2013)
    Thesis: ‘Discriminative Object Categorization with External Semantic Knowledge’

    The University of Texas at Austin, TX, USA
    M.A., Computer Science (May 2010)
    Thesis: ‘Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags.’

    Seoul National University, Seoul, Korea
    B.S., Computer Science and Engineering (Feb 2008)

     

    Work

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
    Associate Professor, Graduate School of AI / School of Computing (Mar 2020 ~ )
    Assistant Professor, School of Computing (Jan 2018 ~ Feb 2020)

    Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
    Assistant Professor, School of Electric and Computer Engineering (Aug 2014 ~ Dec 2017)

    Disney Research, Pittsburgh, PA, USA
    Postdoctoral Research Associate, Computer Vision Group (Sep 2013 ~ Aug 2014)

    Microsoft Research, Redmond, WA, USA
    Research Intern, Interactive Media Group (June 2011 ~ Sep 2011)
    Mentors: Sing Bing Kang, Ashish Kapoor

    SK Communications (Formerly EMPAS), Seoul, Korea
    Lead Staff, Search Department (May 2005 ~ Jan 2007)

     

    Professional Service

    Area Chair

    ICML 2020, ACML 2020, ICLR 2021, IJCAI 2021, ICML 2021, NeurIPS 2021, ICLR 2022, AAAI 2022, ICML 2022, NeurIPS 2022

    Senior Program Committee

    AAAI 2020, IJCAI 2020, AAAI 2021, IJCAI 2022

    Reviewer & Program Committee

    I am a regular program committee member (or a reviewer) for the following journals and conferences:
    IEEE Transactions on Pattern Analysis and Machine Intelligence, NeurIPS, ICML, ICLR, IJCAI, AAAI, CVPR, ICCV, ECCV, ACL, EMNLP, UAI, AISTATS.

    I also (less frequently) review for the following journals/conferences:
    IEEE Transactions on Multimedia, Neural Networks, SIGGRAPH, SIGGRAPH ASIA, ACCV, EG

     

    Contact

    Office: E3-1 1427
    KAIST School of Computing
    291 Daehak-ro, Yuseong-gu, Daejeon, Korea 34141
    Email: sjhwang82 at kaist dot ac dot kr