Rising Stars

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.

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

• 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 via this form.