Introducing the 2026 MLCommons Rising Stars

2026-05-19 · Source: MLCommons · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

The 2026 MLCommons Rising Stars program has announced its 4th annual cohort, comprising 39 outstanding junior researchers from 26 institutions worldwide. Selected from over 175 applicants, these individuals are primarily advanced PhD students (3rd-6th years) and postdoctoral researchers. Their work spans critical areas including large language models, ML systems efficiency, hardware-software co-design, trustworthy AI, and multimodal learning, with a strong emphasis on scalability and real-world deployment. The cohort reflects a global and interdisciplinary community, with participants from North America, Europe, Asia, and Australia, and 28% identifying as women or gender-diverse researchers. A workshop will be held at AMD headquarters in Santa Clara, California, on July 30-31, offering research presentations, career development, and networking opportunities. This initiative underscores the growing importance of ML systems research in advancing AI engineering.

Key takeaway

For AI Scientists and Research Scientists focused on ML systems, this cohort highlights key research directions and potential collaborators. You should explore the specific research areas of these Rising Stars, particularly in hardware-software co-design, trustworthy AI, and efficient LLM inference, to identify emerging trends and partnership opportunities. Consider attending the Rising Stars Workshop to connect with these future leaders and gain insights into practical, systems-driven ML innovation.

Key insights

The MLCommons Rising Stars program identifies and supports top early-career researchers in ML systems, fostering innovation and community.

Principles

• ML systems research is crucial for AI's future.
• Scalability, efficiency, and deployment drive ML innovation.
• Interdisciplinary collaboration accelerates ML advancements.

Method

The program selects early-career researchers via a competitive application process, then provides a workshop for research presentation, career development, and networking with leaders and peers.

In practice

• Engage with MLCommons for early-career researcher support.
• Focus research on ML systems efficiency and deployment.
• Seek interdisciplinary collaborations in ML.

Topics

• MLCommons Rising Stars
• Machine Learning Systems
• Hardware-Software Co-design
• Large Language Models
• Trustworthy AI
• AI Engineering

Best for: AI Scientist, AI Student, Research Scientist

Related on AIssential

• 60 of our biggest AI announcements in 2025 — Google's 2025 AI strategy focused on integrating advanced models and features across its diverse product portfolio.
• STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming — STAR-Teaming uses a multiplex network and multi-agent system for efficient, interpretable LLM red teaming.
• 12 AI Co-Scientists of 2026 — AI co-scientists are rapidly accelerating scientific discovery across diverse fields through autonomous hypothesis generation, experimentation, and analysis.
• "Something Big is Happening." This quarter has a start-up vibe: LLM experiments, great projects, and new optimization material — Rapid AI advancements necessitate immediate curriculum retooling to provide students with practical, hands-on LLM experience.
• Announcing the ICLR 2026 keynotes — ICLR 2026 keynotes highlight diverse AI applications, from human-robot interaction to open science and cosmic imaging.
• Paper Digest: CIKM 2025 Papers & Highlights — CIKM 2025 research highlights advancements in LLMs, graph learning, and multimodal data, addressing challenges in recommendation, forecasting, and privacy.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by MLCommons.