Rising Stars Program
ML and Systems Rising Stars
The ML and Systems Rising Stars is an initiative designed to identify a cohort of early-to-late-stage and recently graduated PhD students, as well as other researchers with a relevant background, to develop community, foster research and career growth, enable collaborations, and discuss career opportunities among the rising generation of researchers at intersections of machine learning and systems.
The 2024 MLCommons Rising Stars application is now closed!
The 2024 Rising Star cohort workshop will be held in the NVIDIA HQ in Santa Clara, CA in July, 2024.
Meet the 2024 Rising Stars
2023 Rising Stars
Key Goals
This initiative has a number of key goals including:
- Fostering academic and industry collaboration: Given the strong, collective interest in the intersection of machine learning and systems, we aim to foster collaboration across academia and industry. Providing connections, resources, and equitable access to such collaboration is a crucial step to building community.
- Enabling growth: We aim to facilitate research and career growth of rising stars at intersections of machine learning and systems. This effort includes providing opportunities for recipients to explore research opportunities with invited panelists and speakers from industry and academia, necessary skills building sessions and networking sessions, and highlighting career opportunities across academic, industry, and other settings.
- Promoting diversity: A core focus of this initiative is to identify and include a diverse range of ML and Systems researchers in the Rising Stars cohort with particular attention to historically underrepresented gender, racial, geographic, socioeconomic, and other vectors of identity in computing and technology.
2024 Rising Star Important Dates
- Applications Deadline: Applications now closed
- Award Notifications: May 8, 2024
- Workshop Dates: July 15-16, 2024
How to Apply
Eligibility
The ML and Systems Rising Stars program is open to all graduate students and post-doctoral associates (in academic and industry institutions) with research backgrounds and/or interests in the machine learning and systems area. There are no strict guidelines for what year of graduate study applicants should be. Participants interested in both academic and industry career paths are welcome to apply. We strongly encourage individuals from historically marginalized and underrepresented backgrounds, including gender, racial, geographic, socioeconomic, and other vectors of identity in computing and technology, to apply.
Research Areas of Interest
- Hardware and systems for machine learning
- Efficient algorithms, software, and frameworks for machine learning
- Machine learning for systems
- Datasets, benchmarks, tools, and methodologies for the machine learning ecosystem
- Security and privacy for machine learning
- Infrastructure support, profiling, and analyzing machine learning systems
- Robust and resilient machine learning
- Methods to enable responsible ML systems
- Systems for collecting, processing, and governing data
- Additional intersections of machine learning and systems
Application Details
Rising Star application materials include:
- CV or Resume
- Personal statement (500 words): Potential topics you can address include: your personal background and interests; your career path and future career interests; teaching and service; your personal experiences in DEI.
- Research statement (500 words): Briefly describe your research interests.
- Motivation and goals statement (300 words): What are your motivations and goals for applying to this program?
- Reference letter: Candidates are required to contact their reference letter writer to submit a letter by April 15, 2024 (AOE) via this form.
Contact Us
Email inquiries regarding the ML and Systems Rising Stars program and/or eligibility can be directed to [email protected].
Sponsors
The 2024 Rising Stars program is generously supported by MLCommons! If you are interested in supporting the ML and Systems Rising stars program, please contact the organizing team.
Program Committee
- Mark Ren (NVIDIA)
- Qirong Ho (MBZUAI)
- Eiko Yoneki (University of Cambridge)
- Tianyu Jia (Peking University)
- Francis Yan (Microsoft)
- Chris Re (Stanford University)
- Thierry Tambe (Stanford University)
- Jenny Huang (NVIDIA)
- Brandon Reagan (NYU)
- Emma Wang (Google)
- Jeff Zhang (Arizona State University)
- Shivaram Ventakaraman (University of Wisconsin-Madison)
- Ruichuan Chen (Nokia Bell Labs)
- Gilles Pokam (Intel)
- Kanak Mahadik (Adobe)
Steering Committee
- Vijay Janapa Reddi (Harvard University)
- Diana Marculescu (UT Austin)
- Joel Emer (NVIDIA/MIT)
Organizing Committee
- Udit Gupta (Assistant Professor at Cornell Tech)
- Abdulrahman Mahmoud (Postdoctoral Fellow at Harvard University)
- Lillian Pentecost (Assistant Professor of Computer Science at Amherst College)
- Akanksha Atrey (Research Scientist at Nokia Bell Labs)
- Sercan Aygun (Assistant Professor at University of Louisiana at Lafayette)
- Muhammad Husnain Mubarik (SMTS Graphics and ML at AMD)