We have three types of mentorship roundtables: Research roundtables, Career and Life roundtables, and Sponsor roundtables. The mentorship session is at two time slots on Fri Dec 10, 11:00 AM - 12:45 PM and 20:15 PM - 21:55 PM (UTC time in 24-hour format) in the Roundtable Rooms in WiML Gather.Town.

To allow WiML attendees to gain exposure to a wide range of topics, as well as to allow mentors and mentees to connect with a variety of people, attendees (but not mentors) at the mentorship session will rotate between the discussion tables throughout the event. Specifically, there will be 3 timed opportunities to rotate between the Research, Career and Life, and Sponsorship tables. Each discussion period will last for approximately 15 minutes (during which the participants will be asked to remain at their seats for the ongoing conversation). After each 15-minute session, the WiML organizers will announce that it is time for the participants to move to a different roundtable, and all participants will have 10 minutes to explore the different rooms and decide on their discussion topic of interest. All mentors and mentees will be free to use the last 10 minutes of the roundtables session as they wish, either remaining at their discussion tables or moving between tables to meet the other WiML participants.

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Mentorship Roundtable I: Friday, December 10: 11:00 - 12:45 (UTC time in 24-hour format)

Table 1 (Research):

Reinforcement learning I (Feryal Behbahani)

Table 2 (Research):

Reinforcement learning II (Minhae Kwon)

Table 3 (Research):

Control and online learning (Katja Hofmann)

Table 4 (Research):

Probabilistic graphical models and Bayesian methods (Isabel Valera)

Table 5 (Research):

Statistical inference and estimation (Emtiyaz Khan)

Table 6 (Research):

Learning theory (Arthur Gretton)

Table 7 (Research):

AutoML (Katharina Eggensperer)

Table 8 (Research):

Computer Vision (Enzo Ferrante)

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Table 9 (Research):

Robotics (Daniela Pamplona)

Table 10 (Research):

Fairness, accountability, and ethics in machine learning (Jessica Schrouff)

Table 11 (Research):

Social science applications (Alice Oh)

Table 12 (Career and Life):

Navigating the job search (industry) and doing research in the industry (Lavanya Tekumalla)

Table 13 (Career and Life):

Developing a long-term research plan (Ferenc Huszar)

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Table 14 (Career and Life):

Navigating academia (job search and tenure application process) (Razvan Pascanu)

Table 15 (Career and Life):

Choosing between academia and industry (Alessandra Tosi)

Table 16 (Career and Life):

Seeking funding (academia edition): PhD fellowships / professorship grants (Ioana Bica)

Table 17 (Career and Life):

Establishing collaborations (Yarin Gal)

Table 18 (Career and Life):

Work-life balance (academia) (Nando de Freitas)

Table 19 (Career and Life):

Surviving graduate school (Truyen Tran)

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Table 20 (Career and Life):

Work-life balance (industry) (Shane Legg)

Table 21 (Career and Life):

Scientific communication (Shakir Mohamed)

Table 22 (Career and Life):

Taking on the leadership roles (academic + industry) (Po-Ling Loh)

Table A (Sponsor):

Entering AI Research from other STEM fields [Deepmind] (Andy Brock, Michela Paganini)

Table B (Sponsor):

Careers at G-Research [G-Research] (Clara Dolfen, Jamie Watson, Olivia Bateman)

Table C (Sponsor):

Machine Learning at Microsoft Research [Microsoft] (Rianne van den Berg)

Table D (Sponsor):

Careers at NVIDIA [NVIDIA] (Monica Spehar)

Mentorship Roundtable II: Friday, December 10: 20:15 - 21:55 PM (UTC time in 24-hour format)

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Table 1 (Research):

Deep learning (Ian Goodfellow)

Table 2 (Research):

Reinforcement learning I (Sham Kakade) [Canceled]

Table 3 (Research):

Reinforcement learning II (Amy Zhang)

Table 4 (Research):

Optimization (Tatjana Chavdarova)

Table 5 (Research):

Learning theory (Karan Singh)

Table 6 (Research):

Natural language processing (Layla El Asri)

Table 7 (Research):

Data-efficient machine learning (Nicolas Le Roux)

Table 8 (Research):

Interpretability and explainability in machine learning (Jennifer Wortman Vaughan)

Table 9 (Research):

Causal inference and counterfactuals (Sarah Tan)

Table 10 (Research):

Robotics (Eugene Vinitsky)

Table 11 (Research):

Computer vision (Jennifer Hobbs)

Table 12 (Research):

Music applications (Pablo Samuel Castro)

Table 13 (Research):

Machine learning for healthcare (Adriana Romero-Soriano)

Table 14 (Research):

AI 4 Science (Animashree Anandkumar)

Table 15 (Career and Life):

Navigating the job search (industry) and doing research in industry (Timnit Gebru)

Table 16 (Career and Life):

Finding mentors throughout your career (Yisong Yue)

Table 17 (Career and Life):

Navigating academia (job search and tenure application process) (Sinead Williamson)

Table 18 (Career and Life):

Choosing between academia and industry (Negar Rostamzadeh)

Table 19 (Career and Life):

Seeking funding: negotiating compensation in industry (Samy Bengio)

Table 20 (Career and Life):

Establishing collaborations (Anitha Vijayakumar)

Table 21 (Career and Life):

Surviving graduate school (Katherine Niehaus)

Table 22 (Career and Life):

Building your professional brand (Chelsea Finn)

Table 23 (Career and Life):

Work-life balance (industry) (Wonmin Byeon)

Table 24 (Career and Life):

Life with kids (Sarah Poole)

Table 25 (Career and Life):

Scientific communication (Been Kim)

Table 26 (Career and Life):

Non-traditional paths to machine learning (Jennifer Wei)

Table 27 (Career and Life):

Doing a postdoc I (Hyeji Kim)

Table 28 (Career and Life):

Networking (Bethany Edmunds)

Table 29 (Career and Life):

Democratizing ML research: Non-traditional research methods (Jade Abbott)

Table E (Sponsor):

Apple Internships [Apple] (Lauren Araujo, Lauren Hannah)

Table F (Sponsor):

Task Oriented Dialog Research @ ASAPP [ASAPP] (Ramya Ramakrishnan, Ryan McDonald, Sravana Reddy)

Table G (Sponsor):

Careers in AI and ML at Capital One [Capital One] (Helen Lee-Righter, Vannia Gonzalez Macias)

Table H (Sponsor):

Machine Learning at D. E. Shaw Research [D. E. Shaw Research] (Jocelyn Sunseri)

Table I (Sponsor):

Present and Future of AI Research at Intel [Intel] (Huma Abidi, Lama Nachman)

Table J (Sponsor):

AI Careers at Meta (Beliz Gokkaya, Kavya Srinet, Sahar Karimi)

Table K (Sponsor):

Machine Learning at Microsoft Research [Microsoft] (John Langford, Nicolas Le Roux)

Table L (Sponsor):

From Academia to Quantitative Finance – Careers at PDT Partners [PDT Partners] (Kurt Miller, Winnie Yang)

Table M (Sponsor):

Meet & Greet Qualcomm AI Research [Qualcomm] (Dipika Khullar, Sangeetha Siddegowda, Shreya Kadambi)

Table N (Sponsor):

QuantumBlack Careers Roundtable [QuantumBlack] (Huilin Zeng, Marta Lopez, Xilin Cecilia Shi)

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Table O (Sponsor):

Early Career Advice for Industry [Salesforce] (Shelby Heinecke, Vena Li)

Table P (Sponsor):

Software Story for Accelerators and Engineer Experience in a Startup World [SambaNova Systems] (Mary Jo Doherty, Weiwei Chen)

Table Q (Sponsor):

Self-Driven Women: Careers at Waymo [Waymo] (Drago Anguelov, Wei Chai, Chen Wu, Congcong Li, Kevin Peterson)