Program

The program follows the following color scheme: talks, breakout sessions, program breaks, sponsor round table, and panel discussion. The schedule is in local time zone (HST). The program book is available at Program Book 2023.

09:15 - 09.30   [Introduction & Opening Remarks - Priyadarshini Kumari (Sony AI) and Giulia Luise (Microsoft) - Hall 316C ]

09:30 - 10.00   [Invited Talk - Joelle Pineau (Meta AI and McGill University, Canada)] A culture of open and reproducible research in the era of large AI generative models - Hall 316C]

We have seen in the last year an incredible pace of progress in large AI models, with increasing abilities to generate high-quality images, videos, text, sound, and more. The best of these models display signs of creativity, reasoning, generalization, and plasticity beyond what we could imagine just a few years ago. Yet many challenges and open questions remain, both on the technological aspects and the societal impact of these models. Further progress, especially in mitigating the social risks of these models, is hampered by a lack of transparency and reproducibility. In this talk, Joelle will describe ongoing efforts to increase best practices towards the responsible training and deployment of AI research systems, drawing on her experience with the ML reproducibility program and the recent release of several state-of-the-art large models. 

10.00 - 10.30 [Coffee Break and Networking] 

10:30 - 11.00  [Invited Talk - Jennifer Doudna (UC Berkeley, USA)] Science and Snorkeling: My Journey with CRISPR -  Hall - 316C]

In this talk, Jennifer will discuss her professional and personal journey working on CRISPR technology, from its genesis to its applications today, and focus on ethical challenges that mirror challenges with AI/ML.

11:00 - 12:00 [Breakout session #2 (Three parallel sessions)]

1. 1) Leveraging Large Scale Models for Identifying and Fixing Deep Neural Networks Biases. [Hall 316C]

• Leader: Polina Kirichenko, Co-leads: Reyhane Askari Hemmat, Megan Richards.

• Facilitators: Vitória Barin Pacela, Mohammad Pezeshki 

1. 2)  The Role of Mentorship and Building Long-term Professional Relationships. [Hall 326A]

• Leader:  Arushi Jain. Co-leads: Sangnie Bhardwaj

• Facilitators: Motahareh Sohrabi, Padideh Nouri 

1. 3) Robustness in Machine Learning. [Hall 326B]

• Leader: Yao Qin. Co-lead: Qi Lei

• Facilitators: Christina Baek

12:00 - 13:30 [Lunch and Sponsor Round Table  Hall 316C]

Round Table A: Apple -- Finding Mentors and Being a Mentor

• Rishika Agarwal (Engineer)

• Ivy Zhang (Engineer)

Round Table B: D. E. Shaw Research -- Machine Learning at D. E. Shaw Research

• Jocelyn Sunseri (Machine Learning Research Engineer)

Round Table C: Google DeepMind -- Keeping Up With the Pace of Change in Industry

• Kate Baumli (Research Engineer)

• Kavya Kopparupu (Research Engineer)

Round Table D: Google Research -- Life and Work at Google

• Alicia Parrish (Research Scientist, Responsible AI)

Round Table E: Microsoft -- Exploring Pathways: Career Opportunities, Growth, and Work-Life Balance at Microsoft Research

• Lili Wu (Data and Applied Scientist, Microsoft Research)

• Cyril Zhang (Senior Researcher, Microsoft Research)

Round Table F: Two Sigma -- Your Next Big ML Move: Innovation in Finance

• Brittany Clarke (Diversity Recruiting Program Manager)

• Alyssa Lees (Engineering Manager, News Engineering: a NLP Technology Team)

13:30 - 14:00 [Invited Talk - Rihab Gorsane (Instadeep, Tunisia)]  My journey at an African AI startup -  Hall 316C]

In the talk, Rihab will share her personal journey as a mid-career woman coming from Africa in the field of Artificial Intelligence (AI) and highlight the remarkable experiences she has gained working at an African AI startup. With a focus on both technical accomplishments and driving forces that have propelled her forward, I aim to inspire the audience while providing valuable insights into her professional growth -  particularly to women who aspire to build their careers in AI. 

14:00 - 15:00  [Breakout session #3 (Three parallel sessions)]

2. 1) Key Challenges for Applicable Reinforcement Learning. [Hall 316C]

• Leader: Fengdi Che. Co-leads: Arushi Jain

• Facilitators: Yueying  Tian

2. 2) Data Diversity and Downstream Impact. [Hall 326B]

• Leader: Judy Shen. Co-lead:  Paula Gradu

• Facilitators: Kristina Ulicna

2. 3) Deploying Research and Making Real-world Impact [Hall 326A]

• Leader: Fei Fang. Co-leads: Diyi Yang

• Facilitators: Bingbin Liu

15.00 - 15.30 [Coffee Break and Networking]

15:30 - 16:30 [Panel Discussion: Fostering Women's Leadership in the Realm of Emerging Trends and Technologies -  Hall 316C]

Panelists: Joelle Pineau (Meta, McGill University), Pascale Fung (HKUST), Yao Qin (UC Santa Barbara, Google Research), Rihab Gorsane (Instadeep)

Moderator: Natasa Tagasovska (Prescient Design, Genentech) 

The panel session will comprise 45 minutes of moderated discussion and a 15-minute Q&A with the audience. The session aims to bring together two significant themes: advancing women's leadership in AI and the future of machine learning with its emerging trends and technologies. The discussion will focus on empowering women in AI leadership positions to navigate these emerging trends effectively and reshape the landscape of AI.

16:30 - 16:45  [President Remarks: Sarah Tan (Cambia Health, Cornell University) -  Hall 316C]