Yingyan (Celine) Lin is an Associate Professor in the School of Computer Science at Georgia Institute of Technology, leading the Efficient and Intelligent Computing (EIC) Lab. Her work focuses on cross-layer innovations in machine learning systems, from algorithms to chip design, aiming to advance green AI and ubiquitous machine learning. She holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2017). Research interests include efficient machine learning, neural rendering (e.g., NeRF), hardware-software co-design for AI acceleration, and graph neural networks. Her lab has pioneered projects like RTML and 3DML, funded by NSF, NIH, DARPA, and industry partners (Qualcomm, Intel, Meta). Awards: NSF CAREER Award (2021), ACM SIGDA Outstanding Young Faculty (2022), Meta Faculty Research Award (2022) Grants: Multi-university projects funded by NSF, NIH, DARPA, SRC, ONR, and industry Recognition: First-place wins at DAC 2022 and TinyML Design Contest 2022, IEEE Micro Top Pick 2023 Her research bridges algorithmic innovation with hardware implementation, emphasizing energy efficiency and real-time performance for applications in AR/VR, computer vision, and neuro-symbolic AI systems.
Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.
Xupeng Miao is a Visiting Assistant Professor in the Department of Computer Science at Purdue University, holding the Kevin C. and Suzanne L. Kahn New Frontiers Assistant Professorship. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University's Catalyst Group under Professors Zhihao Jia and Tianqi Chen. He earned his Ph.D. in Computer Science from Peking University (2022) and a Bachelor’s degree from Northeastern University. His research focuses on machine learning systems, distributed computing, and data management, with notable contributions to systems like Hetu (a distributed deep learning framework) and innovations in large language model (LLM) serving (e.g., SpotServe, SpecInfer). He has received awards including the 2024 WAIC Yunfan Award and an Outstanding Paper Award at ACL 2024. Current projects emphasize efficient systems for generative AI, including optimizing LLM training and inference on heterogeneous hardware, fault-tolerant distributed training, and scalable graph-based machine learning. He teaches CS 59200-MLS: Machine Learning Systems at Purdue and actively mentors students in PhD/MS programs and internships. Key achievements include NVIDIA Academic Awards, IEEE Micro Top Picks recognition, and leadership in international conference roles (e.g., Artifact Evaluation Co-Chair for KDD 2025 and MLSys 2025). His work bridges system design, algorithmic innovation, and hardware-aware optimizations to advance scalable AI infrastructure.
Alaina Lemon is a Professor of Anthropology at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). She serves as Director of Undergraduate Studies in Anthropology and holds affiliations with the Center for Russian, East European, and Eurasian Studies (CREES), the Weiser Center for Europe and Eurasia, and the Interdepartmental Program in Anthropology and History. Her research focuses on socio-cultural anthropology, performance studies, and visual culture, with expertise in Russia, the Romani diaspora, and post-Socialist states. Education: BA in Anthropology (University of Wisconsin, 1987), MA (University of Chicago, 1988), and PhD (University of Chicago, 1996). Notable awards include the Wayne S. Vucinich Book Prize (2001), Heldt Book Prize (2001), and a 2023 visiting scholar position at the École des Hautes Études en Sciences Sociales (Paris). Her work bridges historical and contemporary issues, exploring themes like phatic labor, media infrastructures, and affective politics. Recent projects include editing the documentary Tremors (2023) and researching Soviet-era moral projects through studies of Moscow’s Metro and canine-human interactions. Lemon’s interdisciplinary approach integrates performance studies, linguistic anthropology, and critical race theory to analyze cultural dynamics in transitional societies. Grants include a 2019-2024 University of Michigan Office of Research grant for film collaboration and a 2002-2003 International Research and Exchanges Board fellowship. She has authored influential books such as Between Two Fires (2000) and Technologies for Intuition (2017), and her films document Romani experiences and urban cultural phenomena.
Yang Zhou is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on big data algorithms, machine learning, data mining, and distributed computing. He has contributed to advancements in federated learning frameworks, graph mining tools, and spatial machine learning for environmental applications like flood mapping. Education includes a Ph.D. in Computer Science from Georgia Tech (2021), M.E. in Computer Application Technology from Chongqing University (2016), and B.E. in Engineering from Jiangnan University (2014). His work emphasizes scalable algorithms for large-scale systems, with tools like DirDense for dense subgraph mining and FedASMU for federated learning optimization. Recent publications explore adversarial robustness, blockchain strategies in IoT, and curriculum-based learning for large language models. He advises on interdisciplinary projects at the intersection of AI and environmental science.
Ksana Blank is a Senior Lecturer at Princeton University’s Department of Slavic Languages and Literatures. She coordinates the Russian Language Program and directs the Princeton-in-Petersburg Program, fostering academic exchange and interdisciplinary research. With a PhD in Russian Literature (Columbia University, 1997) and a BA in French Philology (St. Petersburg State University, 1979), her academic career spans over two decades at Princeton, preceded by teaching roles at Hunter College and Middlebury College Russian Summer School. Education: PhD, Russian Literature, Columbia University (1997) BA, French Philology, St. Petersburg State University (1979) Her research centers on Russian literature, emphasizing stylistic analysis and interdisciplinary connections with music, visual arts, folklore, and religious philosophy. Notable works include studies on Dostoevsky’s dialectics, Gogol’s The Nose , and the interplay between Russian modernism and Western art movements. She has authored multiple books and articles, many translated into Russian, and has published in academic journals across Russia, Europe, and China. Recent publications explore Dostoevsky’s paradoxes, Gogol’s linguistic innovation, and the intersection of Russian literature with music (Shostakovich), art (Malevich), and philosophy. Her 2023 book Парадоксы классики expands traditional interpretations of classic works through interdisciplinary lenses, while her 2021 The Nose guide deciphers Gogol’s wordplay and its narrative function. Earlier works address Tolstoy’s spiritual crisis and Bakhtinian dialogism in Russian literary theory. While no scientific awards are listed, her contributions to pedagogy include advanced Russian language courses and graduate-level Tolstoy studies. She currently works on Leo Tolstoy’s The Prisoner of the Caucasus and teaches courses on Russian literature, spirituality, and translation.
Elaine Shi is a Professor with a joint appointment in the Department of Computer Science (CSD) and the Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University (CMU). She is also an Adjunct Professor of Computer Science at the University of Maryland. Her research focuses on cryptography, security, mechanism design, algorithms, blockchains, and programming languages. Shi co-founded Oblivious Labs, Inc., and her work on Oblivious RAM and differentially private algorithms has been adopted by major companies like Signal, Meta, and Google. Affiliations: CMU’s Crypto Group CMU’s Cylab Crypto Seminar Series (co-founder) Research Interests: Cryptography and Security: Focused on oblivious computation, private information retrieval, and secure protocols. Blockchain Technology: Including consensus mechanisms, transaction fee design, and decentralized systems. Privacy-Preserving Algorithms: Such as differential privacy and secure multi-party computation. Algorithm Design: With applications to parallel computing and efficient data structures. Awards: Packard Fellow Sloan Fellow ACM Fellow IACR Fellow Advising & Grants: Current advisees include PhD students like Benjamin Chan, Yanyi Liu, Mingxun Zhou, and Nikhil Vanjani. Past students have taken academic roles at institutions like UVA, University of Michigan, and Duke University. Grants supported research in secure computation, blockchain mechanisms, and privacy-preserving technologies. Labs & Teams: Co-founder of Oblivious Labs, Inc. Leading CMU’s Crypto Group and Cylab Crypto Seminar Series.
Fred B. Schneider is the Samuel B. Eckert Professor of Computer Science at Cornell University , where he has been a faculty member since 1978. His career spans foundational work in trustworthy systems , fault-tolerant distributed systems , and system security . He served as department chair from 2014–2018 and previously earned a B.S. in Engineering from Cornell (1975) and a Ph.D. in Computer Science from Stony Brook University (1978).
Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
Jason Eisner is a Professor in the Department of Computer Science at Johns Hopkins University's Whiting School of Engineering, with a secondary joint appointment in Cognitive Science. He is affiliated with the Center for Language and Speech Processing (CLSP), the Human Language Technology Center of Excellence, and leads JHU's cross-departmental machine learning group. His research focuses on developing probabilistic modeling, inference, and learning techniques for linguistic structure. Eisner has authored over 100 papers in computational linguistics, particularly in parsing, grammar induction, machine translation, computational phonology, computational morphology, and weighted finite-state methods. He is the lead designer of Dyna, a declarative programming language for AI research that allows concise programs backed by efficiency tricks. Eisner's work centers on novel methods in NLP and machine learning, with emphasis on probabilistic modeling and inference in complex, structured settings. His research program combines computer science with statistics and linguistics to create statistical models that capture linguistic structure and develop efficient algorithms for applying these models to data with minimal supervision or through large pre-trained models. As an ACL Fellow, Eisner has made significant contributions to the field. His recent work (2023-2025) shows a strong focus on large language models, semantic parsing, controlled text generation, model interpretability, and privacy-preserving techniques, continuing his long-standing interest in the intersection of probabilistic modeling and linguistic structure. ACL Fellow He teaches courses including Natural Language Processing (601.465/665), Machine Learning: Linguistic and Sequence Modeling (601.765), Declarative Methods (601.325/425/625), and Selected Topics in Natural Language Processing (601.865). His advising focuses on research students through the Argo research group, with emphasis on fundamental research questions in NLP rather than immediate applied engineering.
Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He leads the Alternative Computing Technologies (ACT) Laboratory and serves as Associate Director of the Center for Machine Integrated Computing and Security (MICS) . Previously, he was an Assistant Professor at Georgia Institute of Technology. Ph.D., Computer Science and Engineering, University of Washington (2013) Research focuses on computer architecture , machine learning acceleration , and approximate computing His work has produced 15+ publications spanning IEEE Micro Top Picks , CACM Research Highlights , and ISCA . Key projects include: Tabla : Cross-stack ML acceleration framework DnnWeaver : Open-source DNN acceleration platform Major honors include: IEEE TCCA Young Computer Architect Award ISCA Hall of Fame Qualcomm Innovation Fellowship Georgia Tech PURA Award Teaching roles: CSE 141: Introduction to Computer Architecture CSE 240D: Accelerator Design for Deep Learning CSE 240A: Principles of Computer Architecture
Virginia Smith is the Leonardo Associate Professor of Machine Learning at Carnegie Mellon University's School of Computer Science, with a courtesy appointment in the Electrical and Computer Engineering Department. She leads research addressing critical challenges in machine learning systems, particularly focusing on safety and efficiency. Her research interests span federated and collaborative learning, efficient training methods, data privacy, and AI safety. Recent work has explored topics such as model unlearning, LLM security, and resource-efficient distributed learning systems. She has made significant contributions to understanding how to make machine learning systems more robust, private, and efficient while maintaining performance. Professor Smith's publication record demonstrates a clear progression toward addressing practical challenges in deploying machine learning systems at scale. Her recent work shows strong emphasis on large language model safety, privacy-preserving techniques, and efficient distributed learning approaches. The research spans theoretical foundations to practical implementations, with numerous papers appearing in top-tier venues including NeurIPS, ICML, ICLR, and MLSys. AFOSR Young Investigator Award Sloan Research Fellowship 2023 Samsung AI Researcher of the Year Best Paper Award at ICML 2025 Exploration in AI Workshop Outstanding Paper Award at MLSys 2023 As an educator, Professor Smith mentors numerous PhD students and postdocs while teaching advanced machine learning courses at CMU. She serves as Program Chair for ICML 2025 and co-organizes a semester program on Federated and Collaborative Learning at the Simons Institute. Her research group maintains strong collaborations with industry partners including Amazon, where she has received research awards.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.
