Activities

Mentorship

I’m open to mentor early career (MSc/PhD) students to guide them in their own research topics. Please contact me with your CV and brief description of the research problem (no need to write an elaborate plan) you are interested in, and I’ll get back to you. You can check out my publications page to understand my area of expertise, to evaluate where I can guide you the best.

Supervising / Mentoring

• Han Lin, Research Intern @ Meta AI, 2024
• Peter Tong, co-supervised with Mike Rabbat and Zhuang Liu, Research Intern @ Meta AI, 2024
• Benno Krojer, co-supervised with Nicolas Ballas and Mido Assran, Research Intern @ Meta AI, 2024
• Karen Chen, co-supervised with Adriana Romero Soriano and Michal Drozdal, Research Intern @ Meta AI, 2024
• Oscar Manas, co-supervised with Adriana Romero Soriano and Michal Drozdal, Research Intern @ Meta AI, 2024
• Shufan Wang, Collaboration, 2023
• Bhargavi Paranjape, Research Intern @ Meta AI, 2023
• David Wan, co-supervised with Ram Pasunuru, Research Intern @ Meta AI, 2023
• Song Jiang, co-supervised with Asli Celikyilmaz, Research Intern @ Meta AI, 2023
• Jake Bremerman, co-supervised with Mingda Chen, Research Intern @ Meta AI, 2023
• Kumar Shridhar, co-supervised with Jason Weston, Research Intern @ Meta AI, 2023
• Silin Gao, co-supervised with Tianlu Wang, Research Intern @ Meta AI, 2023
• Saeed Goodarzi, Nikhil Kagita & Dennis Minn, co-supervised with Adina Williams, Shubham Toshniwal and Jack Lanchatin, UMass Industry Mentorship Program with Meta, Summer of 2023
• Kexin (Nicole) Liang, 2021-2022
• Shanya Sharma, 2020-2022
• Manan Dey, 2020-2022

Tutorials

• Towards Reproducible Machine Learning Research in Natural Language Processing, ACL 2022 (Website, ACL Anthology)
• Towards Reproducible Machine Learning Research in Information Retrieval, SIGIR 2022 (Conference Website)

Public Talks

• Keynote Talk, Reproducibility Tutorial, MICCAI 2023
• Panelist, Reproducibility and Rigor in ML, ML Evaluation Standards Workshop at ICLR 2022, April 2022
• Evaluating Logical Generalization with Graph Neural Networks, Weights and Biases Salon, (Online) May 2020
• ML Reproducibility - From Theory to Practice
  • DL4Science Seminar, Lawrence Berkeley National Laboratory, Berkeley, (Online) August 2020
  • MICCAI Hackathon, Peru, 2020 (Online), October 2020
  • Bielefield University, Germany, hosted by Malte Schilling, October 2021 (Online)

Conference Organization

• ACL 2024, Senior Area Chair
• ICLR 2023, Journal Chair
• NeurIPS 2022, Journal Chair
• NeurIPS 2020, Reproducibility Co-Chair
• NeurIPS 2019, Reproducibility Co-Chair

Workshop Organization

• Genbench: the first workshop on (benchmarking) generalisation in NLP @ EMNLP 2023
• NILLI: Novel Ideas for Learning to Learn with Interaction @ EMNLP 2022
• NILLI: Novel Ideas for Learning to Learn with Interaction @ EMNLP 2021
• ML Retrospectives@ NeurIPS 2019

Reproducibility Challenge Organization

• 2023 ML Reproducibility Challenge
• 2022 ML Reproducibility Challenge
• 2021 ML Reproducibility Challenge
• 2020 ML Reproducibility Challenge
• 2019 NeurIPS Reproducibility Challenge
• ICLR 2019 Reproducibility Challenge
• ICLR 2018 Reproducibility Challenge

Conference Volunteering

• NeurIPS 2018, Montreal, Canada
• MAIS 2018, Montreal, Canada
• ICWSM 2017, Montreal, Canada

Teaching Assistantship

• Winter 2022: COMP 424 Artificial Intelligence
• Fall 2018: COMP 652 Machine Learning
• Winter 2018: COMP 551 Applied Machine Learning
• Fall 2017: COMP 551 Applied Machine Learning
• Winter 2017: COMP 102B Computers and Computing
• Fall 2016: COMP 189 Computers and Society