Introducing the 2026 MLCommons Rising Stars

The MLCommons Rising Stars program is designed to support and connect early-career researchers working at the intersection of machine learning (ML) and systems. Through this initiative, participants engage with a vibrant global community, connect with leaders across academia and industry, and further develop their technical and professional skills.

We are excited to announce the 4th annual MLCommons Rising Stars cohort, featuring 39 outstanding junior researchers from 26 institutions worldwide. Selected from a highly competitive pool of over 175 applicants, these individuals have demonstrated exceptional promise in ML, systems, and data systems research. They stand out not only for their current achievements but also for their potential to shape the future of the field.

This year’s cohort reflects both the depth and breadth of emerging talent in the field. A strong majority of Rising Stars are advanced PhD students (primarily in their 3rd-6th years) alongside a select group of postdoctoral researchers. Their research spans a wide range of topics, including large language models, ML systems efficiency, hardware-software co-design, trustworthy AI, multimodal learning, and applications in domains such as healthcare, cybersecurity, and scientific computing. Notably, many projects emphasize scalability, efficiency, and real-world deployment, supporting a growing shift toward practical, systems-driven ML innovation.

The cohort also reflects the increasingly global and interdisciplinary nature of the ML research community. Participants represent leading institutions across North America, Europe, Asia, and Australia, with a meaningful international presence that contributes to the exchange of ideas among regions. At the same time, the program continues to make progress toward broadening participation: this year’s cohort includes researchers from a range of gender identities and backgrounds, with women and gender-diverse researchers comprising 28% of participants.

As part of the program, we will host the Rising Stars Workshop at AMD headquarters in Santa Clara, California, on July 30-31. During the workshop, participants will present their research, explore emerging opportunities, participate in career development sessions, and build lasting connections with peers and mentors across sectors.

Collectively, this year’s cohort reflects the growing importance of research on machine learning systems in shaping the future of AI.

“MLCommons Rising Stars highlights the researchers helping shape the future of AI engineering across the machine learning systems stack, spanning algorithms, models, systems, hardware, and scalable infrastructure. The rapid evolution of AI increasingly depends on advances across these interconnected layers,” said Vijay Janapa Reddi, Vice President of MLCommons and professor at Harvard University.

We warmly congratulate this year’s Rising Stars and thank all who applied for their interest and enthusiasm.

We also extend our sincere appreciation to the Rising Stars organizers – Abdulrahman Mahmoud (MBZUAI), Akanksha Atrey (Nokia Bell Labs), Muhammad Husnain Mubarik (AMD), Sercan Aygun (University of Louisiana at Lafayette), and Udit Gupta (Cornell Tech) – along with the broader organizing and program committee for their dedication in assembling this exceptional cohort. Finally, we thank Dave Graham (MLCommons) and Ralph Witting (AMD) for their invaluable support in bringing the program and workshop to life.

ML & Systems Rising Stars 2026

Recognizing exceptional early-career researchers in machine learning and systems

Simeon Adebola

University of California Berkeley

Simeon Adebola is a fifth-year PhD student in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, advised by Ken Goldberg and a member of AUTOLab and Berkeley Artificial Intelligence Research (BAIR). His research lies at the intersection of machine learning, robotics, and systems, with a focus on scalable infrastructure, datasets, and representations for real-world embodied AI. He previously earned a master’s degree from Middle Tennessee State University, where he worked with Lei Miao on robotics for fall detection and received the Outstanding Master’s Research Award from the College of Basic and Applied Sciences. He has also conducted research at UCLA and Vanderbilt University.

Yuetao Chen

The Chinese University of Hong Kong

Yuetao Chen is a Ph.D. student in Computer Science and Engineering at The Chinese University of Hong Kong, advised by Prof. Hong Xu. His research lies at the intersection of machine learning and computer systems, focusing on efficient and scalable systems for modern ML workloads, including large language model inference, speculative decoding, distributed training, and high-performance computing. His work has appeared in top-tier venues such as MLSys, ASPLOS, PPoPP, and FAST. His paper on Tensor Core-based stencil computation received the Best Paper Award at PPoPP 2024. He has also conducted research at Microsoft Research Asia and contributed to academic services such as EuroSys artifact evaluation.

Valerie Chen

Carnegie Mellon University

Valerie is a Machine Learning PhD student at CMU. Her work bridges machine learning, natural language processing, and human-computer interaction to advance the design of collaborative AI systems. Her research has fostered close collaborations with major engineering and financial companies, with findings cited by leading model providers and deployed in industry products. Valerie has been recognized with the Rising Stars in Data Science award, CMU Presidential Fellowship, and the NSF Graduate Research Fellowship. Her research has also received various awards, including Best Paper at a NeurIPS workshop and Oral Presentations at ICLR and AAAI.

Yuzong Chen

Cornell University

Yuzong Chen is a final-year PhD student in the School of Electrical and Computer Engineering at Cornell Tech, advised by Prof. Mohamed Abdelfattah. His research focuses on Algorithm-Hardware Co-Design for Machine Learning Acceleration, with a special focus on quantization numerics, FPGA architectures, and processing in-memory. He was named a ML and Systems Rising Star by MLCommons in 2026 and was a finalist for the 2024 Qualcomm Innovation Fellowship.

Jae-Won Chung

University of Michigan

Jae-Won Chung is a fifth-year PhD candidate in Computer Science and Engineering at the University of Michigan, advised by Professor Mosharaf Chowdhury. He builds efficient software systems for machine learning, with a focus on treating energy as a first-class systems resource to be carefully measured, optimized, and allocated alongside time. He created and leads the ML.ENERGY initiative, a cross-institutional effort, and his research and open-source work, including the Zeus library, have been recognized and adopted by NVIDIA, Google, Microsoft, the PyTorch Foundation, and GitHub.

Stefany Cruz

University of Washington

Stefany Cruz is a Washington Research Foundation Postdoctoral Fellow at the University of Washington’s Paul G. Allen School of Computer Science and Engineering, where she works with Dr. Vikram Iyer. In her research, she builds on-device agentic AI for urban safety and sustainability, focusing on real-time, privacy-preserving sensing and decision-making. She received her PhD from Northwestern University’s Department of Electrical and Computer Engineering, where she was supported by the Ada Lovelace Microsoft Research PhD Fellowship and received the Best Dissertation Award in Computer Engineering.

Vasisht Duddu

University of Waterloo

Vasisht Duddu is a final-year Ph.D. candidate at the University of Waterloo, advised by Prof. N. Asokan. His research is on trustworthy machine learning, covering the design of novel attacks and defenses to mitigate them, studying deployment trade-offs in ML systems, and designing technical mechanisms to support governance. His work has appeared in various top-tier security, privacy, and machine learning venues (e.g., IEEE S&P, ACL, ACM CCS, TMLR, ICML, and PETS). He is a recipient of the IBM Ph.D. Fellowship (2024), a Distinguished Paper Award at IEEE S&P (2024), and a Best Paper Award at ACM CODASPY (2025).

In Gim

Yale University

In Gim is a fourth-year Ph.D. student at Yale University, advised by Prof. Lin Zhong. His research focuses on systems for machine learning, with an emphasis on scalable and programmable abstractions that bind accelerators, models, and application logic together. He currently leads the development of Pie, a programmable LLM serving system that enables dynamic application logic to run natively within the inference engine. His first-author works have appeared at venues including SOSP, MLSys, MobiSys, HotOS, EMNLP, and AAAI.

Alicia Golden

Harvard University

Alicia is a fourth-year PhD candidate at Harvard University, advised by David Brooks and Gu-Yeon Wei. Her research sits at the intersection of computer architecture, machine learning, and systems, with a focus on designing efficient hardware systems for large-scale AI. Prior to Harvard, she completed her undergrad at Cornell University, where she received a B.S. in Electrical and Computer Engineering.

Yongjun He

ETH Zurich

Yongjun He is a fifth-year PhD student in the Systems Group of the Department of Computer Science at ETH Zurich, supervised by Prof. Dr. Gustavo Alonso and Prof. Dr. Ana Klimović. Before 2024, he was supervised by Prof. Ce Zhang and Dr. Theodoros Rekatsinas. Prior to that, he earned his M.Sc. from Simon Fraser University and his B.Eng. from Nanjing University. His research focuses on building systems that democratize the customization and deployment of generative AI models across diverse computing environments, from personal devices to large-scale clusters. In parallel, he has also been working on specialized hardware accelerators such as FPGAs for machine learning pipelines.

Muyan Hu

University of Illinois Urbana-Champaign

Muyan Hu is a 3rd-year PhD student at UIUC, advised by Prof. Charith Mendis and Prof. Vikram Adve. His research interests focus on MLSys and compilers, especially mid-end compiler optimizations for AI models, including operator fusion and data movement optimization at the graph and kernel levels. His work has been published at top compiler and system venues, including OSDI and ASPLOS. He is also currently a High-Performance AI Intern at NVIDIA, where he works on agents for compilers and kernels targeting next-generation NVIDIA GPUs. He obtained his B.S. degree in Computer Science from Tsinghua University.

Lanxiang Hu

University of California, San Diego

Lanxiang Hu is a third-year Computer Engineering PhD student at UCSD, advised by Prof. Hao Zhang and Prof. Tajana Šimunić Rosing. He is also a research intern at NVIDIA. Lanxiang’s research focuses on efficient AI, particularly on developing efficient algorithms and systems for training and serving parallel-decoding transformers. His work also involves evaluations of multimodal agentic workloads.

Yafan Huang

University of Iowa

Yafan Huang received his Ph.D. in May 2026 from the Department of Computer Science at the University of Iowa, advised by Prof. Guanpeng Li. He has been a visiting graduate student at Argonne National Laboratory since 2021, where he works with Dr. Sheng Di and Dr. Franck Cappello. His research focuses on high-performance computing (HPC), with particular interests in data compression, fault tolerance, parallel computing, and compiler optimizations for machine learning systems and scientific applications. Yafan is the recipient of the 2025 ACM–IEEE CS George Michael Memorial HPC Fellowship and has received multiple best-paper finalist and award recognitions at systems conferences, including SC’22, SC’24, ICS’25, and LDAV’25.

Yiqiao Jin

Georgia Institute of Technology

Yiqiao Jin is a Ph.D. candidate in Computer Science at the Georgia Institute of Technology, advised by Prof. Srijan Kumar. His research focuses on reliable and efficient intelligent systems, spanning multi-agent systems, multimodal large language models, and efficient AI. His work studies how large language models can reason, collaborate, and adapt under limited supervision and computational resources. His research has led to over 20 publications in leading AI, ML, NLP, and data mining venues, including ICLR, ICML, ACL, EMNLP, The Web Conference, KDD, AAAI, and ICWSM.

Hyungyo Kim

University of Illinois at Urbana-Champaign

Hyungyo Kim is a 5th-year Ph.D. student in the electrical and computer engineering department at the University of Illinois at Urbana-Champaign and has worked at IBM Research, Intel, and Samsung as a research intern. He earned his B.S. degree in electrical and computer engineering from Seoul National University. His research focuses on system architectures for AI.

Hyunji Lee

University of North Carolina at Chapel Hill

Hyunji Lee is a postdoctoral researcher at the University of North Carolina at Chapel Hill, where she works with Mohit Bansal. She earned her Ph.D. in AI from KAIST under the supervision of Minjoon Seo. Her research focuses on developing robust semi-parametric models that integrate external knowledge modules, combining the strengths of both parametric and nonparametric representations. She is currently focusing on developing a memory system as a form of nonparametric knowledge across diverse domains.

Wei Li

Carnegie Mellon University

Wei Li (Neway) is a Ph.D. candidate in the Department of Electrical and Computer Engineering at Carnegie Mellon University, advised by Prof. Shawn Blanton and Prof. José Moura. His research is driven by a vision of End-to-End Autonomous EDA, aiming to shift the human role from active “”in-the-loop” intervention to strategic “”on-the-loop” supervision. To realize this, his work integrates multi-modal LLMs to perceive design context, differentiable optimization for high-performance sub-tasks, and hardware testing to anchor AI-driven synthesis in physical silicon reality. Wei is a recipient of the Croucher Fellowship (2026), the Apple PhD Fellowship in Integrated Systems (2022, 2024), and the Qualcomm Innovation Fellowship (2024). His research has been recognized with Best Paper Awards at ASP-DAC (2021), ISSTA (2019), and ICTAI (2019), as well as a Best Paper Honorable Mention at ICLAD (2025). His contributions deliver proven industrial impact, with algorithms integrated into Apple’s industrial physical design flows and a joint patent filing with NVIDIA for differentiable global routing. Looking forward, Wei is committed to establishing the iDAT Lab to pioneer the next generation of intelligent design automation and testing.

Payal Mohapatra

Northwestern University

Payal Mohapatra recently completed her PhD in Computer Engineering at Northwestern University, advised by Prof. Qi Zhu, and is an incoming Staff Research Scientist at Arm. Her research builds machine learning systems for time-series sensing under real-world deployment constraints: robustness to distribution shifts, efficient multimodal fusion across heterogeneous modalities, and graceful handling of missing or noisy signals. Her work on MAESTRO (NeurIPS Spotlight 2025) replaces expensive pairwise cross-attention with sparse, scalable multimodal fusion, and her phase-anchored representation learning framework (TMLR 2025; Spotlight at TS4H @ NeurIPS 2025) generalizes across cross-domain nonstationary time series. She has applied these methods to industrial fatigue monitoring with Boeing and John Deere (PNAS Nexus 2024), silent speech understanding from surface EMG (ACL 2025), and on-device wearables (through internships at Meta Reality Labs and Mitsubishi Electric Research Labs). She has won grand challenges at IEEE ICASSP (2022) and ACM Multimedia (2023), and was a DAC Young Fellow (2021). Before her PhD, she was an IC Design Engineer at Analog Devices and earned her Master’s from IIT Madras.

Rui Pan

Princeton University

Rui Pan is a PhD candidate at Princeton, advised by Ravi Netravali. His research lies at the intersection of systems and machine learning, with a recent focus on software infrastructure and algorithms for efficient large-language-model inference. In particular, he studies how to co-design novel models (e.g., hybrid LLMs, reasoning LLMs, diffusion LLMs) and the underlying runtimes that serve them, ensuring they scale and perform efficiently for emerging generations of AI innovations.

Vaidehi Patil

University of North Carolina at Chapel Hill

Vaidehi Patil is a PhD candidate in the Computer Science Department at UNC Chapel Hill and a Google PhD Fellow. Her research focuses on privacy, safety, and security in LLMs, VLMs, and agentic systems, with an emphasis on unlearning sensitive information, adversarial attack–defense evaluation in unimodal and multimodal settings, and privacy leakage and belief steering in multi-agent LLM systems. Her work has been recognized with a spotlight presentation at ICLR. Vaidehi was also the lead organizer of the ICML 2025 Workshop on Machine Unlearning and Generative AI.

Shvetank Prakash

Harvard University

Shvetank Prakash is a Ph.D. candidate in computer science at Harvard University. His research focuses on hardware-software co-design for ultra-low-power machine learning systems at the Edge and on developing AI agents for computer architecture design problems. His work has appeared in ASPLOS, ICML, and Nature, and he is a 2026–2027 NVIDIA Graduate Fellow. Prior to Harvard, he received his B.S. in computer engineering from Columbia University.

Wenjie Qu

National University of Singapore

Wenjie Qu is a PhD candidate in Computer Science at the National University of Singapore, advised by Prof. Jiaheng Zhang, and working closely with Prof. Dawn Song. His research lies at the intersection of machine learning, security, and cryptography, with a focus on making large language models trustworthy and verifiable. He develops systems for auditing and proving the correctness of AI services, including zero-knowledge proofs for model inference and training. His work has appeared in top security venues such as IEEE S&P, USENIX Security, ACM CCS, and NDSS, and has influenced both academia and industry.

Derrick Quinn

Cornell University

Derrick Quinn is a second-year Ph.D. student in the Computer Systems Laboratory (CSL) at Cornell University, where he is advised by Professor Mohammad Alian. His research philosophy is driven by the belief that today’s increasingly complex systems require holistic co-design in order to realize their full potential. Currently, he’s focused on co-designing novel algorithms and near-data processing architectures to accelerate dense retrieval and long-context inference. Derrick’s long-term goal is to develop generalized, scalable architectures for Neural Memory Systems, enhancing their capability, adaptability, and sustainability across diverse computing environments.

Md Mostafijur Rahman

The University of Texas at Austin

Md Mostafijur Rahman is a Ph.D. candidate at The University of Texas at Austin, advised by Radu Marculescu. His research sits at the intersection of AI, biomedical imaging, and computer vision, with a focus on building efficient, reliable, and scalable AI systems for deployment in healthcare under real-world constraints. His work has been translated into practice through research internships at GE Healthcare, the National Institutes of Health (NIH), and Bosch Research. He has published over 20 peer-reviewed papers in venues including CVPR, NeurIPS, MICCAI, and ICCV, with several works selected for Spotlight and Oral presentations. His research contributions have been recognized by the NIH Summer IRTA Fellowship, the Texas Health Catalyst Award, and the Discovery to Impact Award.

Akshat Ramachandran

Georgia Institute of Technology

Akshat Ramachandran is a third-year Ph.D. student at the Georgia Institute of Technology, advised by Prof. Tushar Krishna. His research lies at the intersection of computer architecture, systems, numerical formats, and AI/ML algorithms, with a focus on designing efficient hardware–software co-design techniques for emerging AI workloads. His work spans three key areas: (1) next-generation arithmetic and numerical representations for lower-overhead and adaptive computation, (2) post-training model compression methods that assign layer-wise quantization and sparsity attributes, and (3) flexible mixed-precision hardware architectures for efficient AI processing. Toward this research vision, he actively collaborates with both academic and industrial research teams, including NVIDIA Research, Intel Labs, and Samsung Research. His research has resulted in multiple patent filings, a growing publication record in premier computer architecture and AI venues, and several best paper and excellence-in-research awards.

Jie Ren

Massachusetts Institute of Technology

Jie Ren is a Postdoctoral Associate at MIT CSAIL. He received his Ph.D. from Michigan State University and his bachelor’s degree from Tsinghua University. His research focuses on trustworthy, interpretable, and scalable AI, with particular interests in data and model protection in generative AI and efficient test-time scaling. His work has been published in top-tier venues including NeurIPS, ICLR, ICML, ACL, CVPR, and WWW. His recent research explores how to build AI systems that are both powerful and accountable, especially in the context of large foundation models and generative AI.

Yeonju Ro

The University of Texas at Austin

Yeonju Ro is a Ph.D. student at UT Austin, co-advised by Aditya Akella and Atlas Wang. Her research sits at the intersection of computer systems and machine learning, with a focus on algorithm-system co-design for next-generation AI systems. She is also a contributor to the Learning-directed Operating System expedition and the Infra AI Center at UT. She is a 2024 IBM Ph.D. Fellow and a 2024 Qualcomm Fellowship Finalist.

Amit Samanta

University of Utah

Amit Samanta is a Ph.D. student in Computer Science at the University of Utah, advised by Prof. Ryan Stutsman and Prof. Rohan Basu Roy. His research focuses on building ML and HPC infrastructure that is fast, fair, and sustainable, treating carbon and water footprint as first-class metrics alongside performance. He has interned at Lawrence Livermore and Argonne National Laboratories. He is honored to be a 2026 MLCommons ML and Systems Rising Star, an Internet Society Pulse Research Fellow, and a Young Researcher at the Heidelberg Laureate Forum.

Efe Sencan

Boston University

Efe Sencan is a Ph.D. candidate in Computer Engineering at Boston University, advised by Prof. Ayse K. Coskun. His research lies at the intersection of machine learning and computer systems, with a focus on building practical ML methods for performance anomaly detection, bottleneck diagnosis, and telemetry-driven analytics in high-performance computing systems. His work aims to make large-scale scientific computing systems more reliable, efficient, and easier to diagnose. He has collaborated with national laboratories and supercomputing centers to apply ML techniques in production HPC environments.

Erfan Shayegani

University of California, Riverside

Erfan is a 4th-year Computer Science Ph.D. student at the University of California, Riverside. His research focuses on Multimodal Language Models (LLMs/MLLMs) and AI Agents, such as Computer-Use Agents (CUAs), with an emphasis on Alignment, Robustness, Safety, Ethics, Fairness, Bias, and Security/Privacy. He has also completed two research internships at Microsoft Research, and I’m currently interning at Apple, looking at misalignment issues in multimodal models.

Michael Shen

Cornell Tech

Michael is an Electrical and Computer Engineering Ph.D. student at Cornell Tech and a researcher in the Cornell Computer Systems Laboratory, co-advised by Professor Udit Gupta and Professor G. Edward Suh. His current research interests are in computer architecture and computer systems for efficient machine learning. Recently, he has been focused on improving inference pipelines for Retrieval-Augmented Generation (RAG) and Agentic AI systems.

Sudipta Saha Shubha

University of Virginia

Sudipta Saha Shubha is a PhD candidate at the University of Virginia. His research sits at the intersection of distributed systems and artificial intelligence (AI). In particular, his research has focused on developing distributed systems for cost-efficient AI inference serving at scale, including generative and agentic AI. His work involves networked infrastructure, operating systems, GPU architectures and kernels, and AI models. He has published his research at top-tier AI systems conferences, including OSDI, SIGCOMM, EuroSys, SoCC, and IPDPS, and his work has been shipped to production in industry through research internships at Microsoft and HPE Labs.

Saranya Vijayakumar

Carnegie Mellon University

Saranya Vijayakumar is a Ph.D. candidate in Computer Science at Carnegie Mellon University, advised by Christos Faloutsos and Matt Fredrikson. Her research focuses on AI security and safety with a particular interest in red teaming and multi-agentic alignment. Before CMU, she did her undergraduate studies at Harvard with a joint concentration in Computer Science and Government, working with Cynthia Dwork and Jim Waldo on algorithmic fairness, and spent three years as a data scientist in electronic trading at Goldman Sachs. She has held research positions at Anthropic, IBM Research, Inria, and Fujitsu Research, and is supported by the DoD NDSEG Fellowship. She founded Women in CSD at CMU.

Ryan Wong

University of Illinois Urbana-Champaign

Ryan Wong is a fifth-year Ph.D. student in computer science at the University of Illinois Urbana-Champaign, working with Prof. Saugata Ghose. His research interests are in the broad area of computer architecture, with particular emphasis on memory and storage systems, as well as accelerators for machine learning, scientific computing, and database systems. He is a Mavis Future Faculty Fellow and has won the UIUC CS Outstanding TA Award. He received his B.S. in Computer Science, B.A. in Chemistry, and M.S. in Electrical Engineering, all from the University of Rochester. For more information, please visit his website at https://rwong.cs.illinois.edu/

Sanjali Yadav

University of Maryland, College Park

Sanjali Yadav is a third-year PhD student at the University of Maryland, College Park, where she also earned her Bachelor’s degree in Computer Science. Advised by Dr. Bahar Asgari, her research operates at the intersection of computer architecture and machine learning, focusing on developing self-adaptive systems. By leveraging machine learning as a foundational architectural mechanism rather than just a target workload, she pioneers the design of systems that autonomously learn and adapt to changing demands throughout their lifecycle. Her work transcends rigid, pre-defined design heuristics in favor of autonomous systems that proactively self-optimize for latency, throughput, and energy efficiency, ensuring architectural agility against the high-variance demands of modern computing. This research philosophy is embodied in her work on Misam, an adaptive framework that earned Sanjali First Place at the ACM Student Research Competition at MICRO 2024. Her contributions, including both Misam and the Boötes framework, have been featured at top-tier venues such as MICRO, establishing a new standard for treating intelligence as a first-class citizen within the hardware stack. By integrating adaptability directly into the architectural fabric, her research continues to push the boundaries of how intelligent computing infrastructure can be effectively deployed in increasingly resource-constrained and dynamic environments.

Ruokai Yin