Conference

ICLR 2025 Mentoring Chats

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April 23 2025 ICLR 2025 Mentoring Chats Yisong Yue ICLR 2025 Part of the ICLR experience is meeting people and talking with them about their research interests and experiences. To facilitate these conversations, we are thrilled to announce the third iteration of Mentoring Chats at ICLR (previously called Office Hours). The Mentoring Chats will be 45-minute round-table sessions, held during lunch ( 12:30-1:15 pm and 1:15-2:00 pm ) in the Topaz Concourse every day of the main conference ( April 24-26 ). A mentor will lead each session, and participants can bring forward relevant topics they’d like to discuss or simply engage in conversation with questions about job experience, research challenges, or general advice. There will be a bell ring approximately 22 minutes in, urging participants to switch tables, or switch topics while staying at the same table. List of mentors: Aditi Ragunathan, Amy Zhang, Bo Han, Claire Vernade, Danqi Chen, David Abel, Erin Grant, Fei Liu, Furong Huang, Huazhe Xu, Junxian He, Kyunghyun Cho, Masashi Sugiyama, Nouha Dziri, Rene Vidal, Samy Bengio, Tatsunori Hashimoto, Taylor W. Killian, Xuezhi Wang The detailed schedule is available here . Following ICLR 2024, we have a list of topics and questions that you may wish to ask mentors. We hope to see you at the Mentorship Hours! Research agenda Where should I start if I want to do research in ML? What kind of mathematical/programming skills are required for ML research? What are good courses to take? How should I use different modes of learning, such as classroom courses, video lectures, and reading a book? How to keep track of all the research literature? How to balance breadth vs depth? What are some broader goals of academic machine learning research in the era of LLMs? How can one set themselves apart in this crowded research space? What is ethical research? How to decide on a research area? How to decide on a research project? How to adapt my research according to the current trends/community interests? How to cope with the pressure of publishing while working on riskier/harder projects? Should I be worried about other groups scooping my research and how to deal with such situations? Should I establish myself as an expert in one area/technique or explore a breadth of topics? Should I master a technique and apply it to different problems, or should I master a subfield by finding all useful techniques (hammer vs nails)? ML+X: Multidisciplinary research What are good strategies for starting an interdisciplinary project? When working across disciplines, should I have one of them as my “home” community or try to be equally visible in both? What are the most efficient ways to help establish my ML+X area as a more active area? Should I organize workshops, teach tutorials, ..? How to deal with different incentive structures in interdisciplinary collaborations (e.g., journals vs conferences)? Advisor and collaborators Should I follow my advisor’s agenda or define my own? What are the pros and cons of being co-advised? When is it appropriate to change advisors and how to go about it? How to navigate conflicts with an advisor? How to get a good balance between collaborating with other researchers while also distinguishing my own research? Will too much collaboration hurt my job prospects? What to look for in a collaborator? How do I convey the level of commitment I am willing to have in a project without it being awkward? How to say no to collaborations? How to navigate different conventions wrt author ordering? Alphabetical vs contributional ordering? Should my advisor always be a coauthor because they are funding me? What do I do if my collaborator is not responsive? Communicating research and networking How to find mentors and allies beyond my advisor? What is the best way to communicate my research? Blogs, videos, presentations? How to write a good research statement? How to apply for fellowships? Should I present my work in poster sessions and workshops? Should I be scared of getting scooped? What are the pros of presenting my work early? Beyond your institution: Internships and research visits Should I do a research internship on a topic different from my dissertation? Does it make sense to do a software engineering/development internship if it is not research-related? When is a good time to look for internships? Should I apply online or email people? Should I do research visits to other universities? Does it make sense to go to semester-long programs as a junior student? How to get the most out of my internship? What should be the main goal of doing an internship? Planning after grad school: academia vs industry What should I consider when planning for the next step? How should I decide whether to go to academia or industry? How to select a postdoc advisor? Should I apply to different departments than my core department? How can I prepare for that, and how early? Is it ok to quit your PhD? How can I plan my next steps if so? Work ethics, open research discussion, personal challenges How to balance work-life? How much work is too much work? How to take care of mental and physical health? How to learn about the ethical implications around the topics of my research? How to foster inclusion in research and teaching?

Executive Summary

The ICLR 2025 Mentoring Chats provide a platform for researchers to engage in conversations with experienced mentors on various topics related to machine learning (ML) research. The sessions, led by a mentor, will be 45-minute round-table discussions held during lunch hours every day of the main conference. Participants can bring forward relevant topics or engage in conversation with questions about job experience, research challenges, or general advice. The list of mentors includes experts in the field of ML, and a detailed schedule is available for participants to plan their sessions.

Key Points

  • ICLR 2025 Mentoring Chats provide a unique opportunity for researchers to seek guidance from experienced mentors
  • The sessions will be held during lunch hours every day of the main conference
  • Participants can bring forward relevant topics or engage in conversation with questions about job experience, research challenges, or general advice
  • The list of mentors includes experts in the field of ML, such as Aditi Ragunathan, Amy Zhang, and Kyunghyun Cho

Merits

Strength of Community Building

The Mentoring Chats foster a sense of community among researchers, facilitating connections and collaborations

Access to Expertise

Participants have the opportunity to seek guidance from experienced mentors, providing valuable insights and advice

Flexibility and Accessibility

The sessions are held during lunch hours, allowing participants to attend without having to take time off from the conference

Demerits

Time Constraints

The 45-minute sessions may be insufficient for in-depth discussions, particularly for complex research topics

Limited Availability

The sessions are held during lunch hours, which may not be convenient for participants with conflicting schedules or commitments

Dependence on Mentor Availability

The quality of the sessions depends on the availability and engagement of the mentors

Expert Commentary

The ICLR 2025 Mentoring Chats represent a significant step forward in providing support and guidance to researchers in the field of machine learning. By leveraging the expertise of experienced mentors, the sessions can help researchers navigate the complexities of ML research, from research opportunities and publishing to interdisciplinary collaborations and broader goals. While there are limitations to the sessions, such as time constraints and limited availability, the benefits of the Mentoring Chats far outweigh the drawbacks, providing a valuable resource for researchers at all stages of their careers.

Recommendations

  • Researchers should take advantage of the Mentoring Chats to seek guidance and advice from experienced mentors
  • Conference organizers should consider extending the duration of the sessions or increasing the number of mentors to accommodate more participants

Sources

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