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Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.

• Machine Learning
• Deep Learning Systems
• Compiler Optimization
• +6 more

Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.

• Computer Systems
• Networked Systems
• Information Theory
• +5 more

George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.

• Trustworthy Machine Learning
• Time-to-Event Prediction
• Survival Analysis
• +3 more

Jeffrey P. Bigham is an Associate Professor at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University . His research spans human-computer interaction , human-AI interaction , accessibility , dialog systems , NLP , and crowdsourcing . Current PhD Students: Hamza El Alaoui, Jessica Yin Huynh, Sara Kingsley, Peya Mowar, Yi-Hao Peng, Atieh Taheri PhD Graduates: Erin Brady, Yu Zhong, Ting-Hao Huang, Anhong Guo, Cole Gleason, Prakhar Gupta, Stephanie Valencia, Kundan Krishna, Jason Wu His work is funded by Apple , Bosch , DARPA , Google , Microsoft , the National Institute of Disability Rehabilitation Research , the National Science Foundation , and Yahoo! He also holds a CMU HCII Career Development Fellowship . Selected Awards: NSF CAREER Award 2019 Best Paper at ASSETS 2021 Best Paper Nomination at CHI 2024 Best Paper Nomination at CHI 2021 Best Paper Nomination at DIS 2021

• Human-Computer Interaction
• Human-AI Interaction
• Accessibility
• +6 more

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.

• Parallel Computing
• Distributed Systems
• Databases
• +4 more

Tom Mitchell is the Fredkin Professor of AI and Learning and Director of the Center for Automated Learning and Discovery (CALD) at Carnegie Mellon University's School of Computer Science. His research focuses on machine learning, computational neuroscience, and their applications in neuroimaging and natural language processing. He is renowned for pioneering work in developing algorithms to decode brain activity and for contributions to foundational machine learning theory, including co-training and explanation-based learning. Mitchell authored the seminal textbook *Machine Learning* (McGraw Hill, 1997) and has led projects like Never-Ending Learning (NELL), an AI system that autonomously learns from web content. His work bridges computer science and cognitive science, exploring how machines can learn from data and human interaction. Notable research interests include brain-computer interfaces, automated knowledge extraction, and ethical AI. Mitchell's publications span influential journals like *Science* and *Nature*, and he has been recognized for advancing interdisciplinary research in AI and neuroscience. He has advised numerous students and contributed to initiatives like the AAAI Presidential Address on AI and brain sciences. Mitchell's current projects include studying the neural basis of language and developing AI tools for education and healthcare.

• Machine Learning
• Artificial Intelligence
• Neuroimaging
• +4 more

Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.

• Machine Learning
• Natural Language Processing
• Quantitative Trading
• +5 more

Jamie Callan is a Professor at Carnegie Mellon University's Language Technologies Institute (School of Computer Science), where he leads research in Information Retrieval and Neural Search Architectures . He teaches advanced courses on search engine design and mentors students in multiple programs. Research Focus: Federated retrieval, knowledge graph integration in search, ClueWeb dataset development, and neural approaches to document ranking Leadership: Past SIGIR Treasurer/Chair, Co-founding Editor of Foundations and Trends in IR, former TOIS Editor-in-Chief His recent work explores: Neural Retrieval: Latent vocabulary for sparse systems, hypothetical documents for dense vector retrieval Dataset Innovation: Maintenance and distribution of ClueWeb09, ClueWeb12, and ClueWeb22 datasets Search Efficiency: Selective search architectures with 90% reduced computational costs Scientific Recognition: International ACM SIGIR Conference Leadership Co-founding Editor-in-Chief, Foundations and Trends in IR Former Editor-in-Chief of ACM TOIS Dr. Callan's Lemur Project has produced Indri/Galago search engines and supported TREC evaluations through dataset contributions.

• Information Retrieval
• Neural Search Engine Architectures
• Knowledge Graphs
• +5 more

Bruce M. McLaren is a Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he heads the McLearn Lab. He previously served as President of the International Artificial Intelligence in Education Society (2017-2019) and has been elected to the Executive Committee of the AI-ED Executive Committee for two separate terms (2012-2017 and 2022-2027). Dr. McLaren's research focuses on how technology can support education through digital learning games , intelligent tutoring systems , and collaborative learning environments. His work has demonstrated that students can learn decimals more effectively through Internet-based learning games compared to conventional methods. He has also conducted significant research on erroneous examples in learning, showing how students benefit from working with incorrect solutions alongside intelligent tutors. His publications reveal a strong trend toward integrating artificial intelligence with educational technologies, particularly in analyzing student learning patterns, developing adaptive educational games, and creating tools that support both students and teachers in classroom settings. Recent work increasingly focuses on the application of generative AI like ChatGPT in educational contexts. Best Paper Award at ECTEL 2023 for research on ChatGPT in decimal learning Fulbright Scholarship recipient for educational technology work in Chile (2019) Multiple best paper awards at major AI in Education conferences Erskine Fellowship for teaching Intelligent Tutoring Systems in New Zealand As an advisor, Professor McLaren has mentored numerous students in the HCII program and has secured significant research funding, including NSF grants for projects like AdaptErrEx and LASAD. His lab, the McLearn Lab, serves as a hub for interdisciplinary research at the intersection of learning sciences and artificial intelligence. Beyond academia, he brings over 20 years of commercial sector experience, having served as Director of eCommerce Technologies at OpenWebs Corporation and leading expert system projects at Carnegie Group, Inc.

• Learning Sciences
• Educational Technologies
• Human-Centered AI
• +7 more

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +7 more

Lisa Lee is a Research Scientist at Google DeepMind, focusing on creating AI agents that emulate biological learning and adaptability. She previously taught at Princeton University and received TA awards for Deep Reinforcement Learning and Probabilistic Graphical Models. Education: PhD in Machine Learning from Carnegie Mellon University (advised by Ruslan Salakhutdinov and Eric Xing); A.B. in Mathematics from Princeton University (advised by Sanjeev Arora). Her research centers on AI embodiment, intrinsic motivation, and hierarchical planning. She explores how evolutionary-inspired inductive biases and memory mechanisms can enable agents to generalize across physical and conceptual domains, as demonstrated in her work on robotic agility benchmarks and multimodal transformers. Notable scientific contributions include the Barkour quadruped robot benchmark, Gemini multimodal models, and theoretical work on causal language models. She co-organized key AI workshops at NeurIPS and ICML, and her awards include Princeton's TA of the Year for technical courses. Leadership: ICML Workflow Chair (2019), NeurIPS workshop co-organizer (2019, 2021), peer reviewer for top AI conferences.

• Artificial Intelligence
• Reinforcement Learning
• Continual Learning
• +5 more

Dominik Moritz is an Assistant Professor at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University and Machine Learning researcher at Apple. He leads the Data Interaction Group and develops systems for interactive data visualization. Ph.D. in Computer Science from University of Washington (2019) B.S. in Computer Science from Hasso Plattner Institute (2013) His research focuses on: Human-Centered Machine Learning Data Visualization Toolkits Interactive Systems Design Accessibility in Visual Analytics Scalable Web-Based Visualization Recent publications address visualization design knowledge formalization (Draco), cross-filtering frameworks (Mosaic), and accessibility evaluation (Chartability). He has received Best Paper Honors at VIS and EuroVis conferences. Professional Collaborations: Co-founder of Vega-Lite visualization grammar Collaborations with Jeff Heer, Bill Howe, Jock MacKinlay Projects with Microsoft Research and Google Research

• Human-Centered Machine Learning
• Data Science
• Interactive Systems
• +6 more

Shihong Huang is a Teaching Professor in the Information Systems Program at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. Her research focuses on human-system co-adaptation, integrating software engineering with human-computer interaction to optimize symbiotic collaboration between humans and autonomous systems. Education: Ph.D. and M.Sc. in Computer Science from University of California, Riverside Her work explores how mutual adaptation between humans and systems—using tools like EEG monitoring—can enhance decision-making in AI-driven environments. Current research includes brain-computer interaction (BCI), health informatics, and clinical applications of computer systems. She received FAU's Award for Excellence and Innovation in Undergraduate Teaching twice (2008, 2015) and the Engineer’s Council Merit Award in 2022. Her teaching interests include web application development, software engineering, and information visualization.

• Software Engineering
• Human-Computer Interaction
• Brain-Computer Interfaces
• +3 more

William Martin is a PhD Researcher and Teaching Fellow at the Carnegie Mellon University School of Design, focusing on transdisciplinary strategies to create inclusive, equitable, and resilient futures. He works across physical and digital environments, utilizing tools like web applications, geospatial mapping, and documentary films to evolve communities. B.A. in International Studies in Economics from Middlebury College M.Arch and M.S. in Conservation from the University of Michigan Co-founding principal of Studiobvio (Denver-based architecture and urban research firm) Founder of the Architecture Lobby advocating for equitable labor practices His research spans urban design, data science, and community empowerment, with projects presented at institutions like MoMA, Yale School of Architecture, and the Architectural League of New York. He bridges economics, geography, and design to develop cooperative modes of practice. William’s publications demonstrate expertise in robotics, control systems, and facial recognition. His work includes algorithm development for bipedal locomotion on challenging terrain, optimal policy learning for compliant gaits, and component-based SVM approaches for biometric systems. He collaborates internationally through the Architecture Lobby and has worked at award-winning firms such as WXY (New York) and RNL (Denver). His career integrates design, activism, and technology to reimagine built environments.

• Urban Design
• Data Science for Social Impact
• Transdisciplinary Design
• +3 more

Peter Brusilovsky is a Professor of Information Science and Intelligent Systems at the University of Pittsburgh's School of Computing and Information. He directs the Personalized Adaptive Web Systems (PAWS) Lab and the Learning Technologies Lab (LTL), focusing on adaptive educational systems, user modeling, and intelligent interfaces. His roles include Associate Editor-in-Chief of IEEE Transactions on Learning Technologies and board memberships in journals like User Modeling and User-Adapted Interaction. Brusilovsky earned his PhD from Moscow State University and has held visiting positions at institutions worldwide, including Carnegie Mellon University. He is a recipient of prestigious awards, including the Alexander von Humboldt Fellowship and NSF CAREER Award, and holds an honorary doctorate from the Slovak University of Technology. His research spans adaptive web systems, social computing, and e-learning, with contributions to intelligent tutoring systems and student modeling. Over 20 years, he has authored numerous papers and books on adaptive hypermedia and the adaptive web. Professional service includes roles with ACM SIGWEB and IEEE, and leadership in User Modeling Inc. Brusilovsky's grants include NSF-funded projects, and his work emphasizes learner control, explainable AI, and the integration of educational technologies. His labs develop tools like QuizGuide and Progressor, advancing personalized learning and open social student modeling.

• Adaptive Web Systems
• Social Web
• Adaptive Hypermedia
• +5 more