Zeynep Temel is an Assistant Professor at Carnegie Mellon University's College of Engineering, jointly appointed in the Biomedical Engineering and Robotics Institute. She leads the Zoom Lab, focusing on bio-inspired compliant mechanisms for robotic systems. Current research emphasizes adaptable robots for complex environments through mechanical intelligence and embedded control . Key application areas include surgical robotics , search-and-rescue , and micromanipulation . Her work spans bio-inspired design, compliant robotics, and human-centered applications. Recent publications highlight advancements in: Swarm robotics for collaborative exploration Soft actuators using bioplastics and gelatin Dexterous manipulation via delta robot frameworks The Zoom Lab trains students in robotic fabrication and biological modeling, with members transitioning to roles in academia and industry.
Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.
Robert E. (Rob) Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, holding joint appointments in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His research spans Bayesian statistics, neural data analysis, and computational neuroscience. Kass earned a B.A. in Mathematics from Antioch College, a Ph.D. in Statistics from the University of Chicago, and has been at CMU since 1981. He has served as Department Head of Statistics (1995–2004) and Interim Co-Director of the CNBC (2015–2018). His work focuses on statistical methods for neuroscience, particularly analyzing spike train data and identifying cross-brain interactions. Notable contributions include co-authoring Analysis of Neural Data and foundational articles on Bayesian inference. Kass has received prestigious awards such as the National Academy of Sciences membership and COPSS Distinguished Achievement Award. Research interests include computational neuroscience, statistical modeling of neural systems, and interdisciplinary education. He has advised numerous students and co-organized major workshops like the Statistical Analysis of Neuronal Data series. Kass’s work emphasizes the interplay between statistical rigor and scientific insight, bridging theoretical and applied domains. Education: B.A. in Mathematics, Antioch College Ph.D. in Statistics, University of Chicago Postdoctoral Fellow, Princeton University Scientific contributions include advancements in spike train analysis, Bayesian model assessment, and statistical methods for brain connectivity. His work on neural synchrony and population coding has influenced both theoretical and applied neuroscience.
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.
Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.
Vyas Sekar is the Tan Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Computer Science Department. He is affiliated with CyLab and co-directs the Future of Enterprise Security initiative. His research focuses on networking, cybersecurity, distributed systems, and IoT security, with an emphasis on data-driven approaches and network verification. Education: Ph.D. in Computer Science (2010) from CMU; B.Tech. from IIT Madras (President of India Gold Medal recipient). Professional roles include Chief Scientist at Conviva and co-founder of Rockfish Data. Research Interests: Cybersecurity, network security, software-defined networking (SDN), IoT security, DDoS defense, privacy-preserving data sharing, and network performance optimization. Recent work includes developing tools like Pigasus (FPGA-accelerated intrusion detection), Nomad (cloud side-channel mitigation), and frameworks for anomaly detection in IoT networks. Articles Trends: Recent publications address advanced threats like LLM-driven network attacks, stealthy automotive network exploits (CANDid), and optical-layer DDoS defenses. Emphasis on practical solutions (e.g., SketchPlan for telemetry, Pryde for firewall evasion detection). Awards: ACM SIGCOMM Test of Time Award (2022), IIT Madras Young Alumni Achiever Award (2022), Intel Outstanding Researcher Award (2021), and NSF CAREER Award (2016). Recognized for contributions to intrusion prevention, network security, and IoT resilience. Grants & Projects: Led NSF-funded ONSET project (optical-layer DDoS defense), CyLab's Secure IoT Initiative, and collaborations with industry partners like Intel, Facebook, and Nokia Bell Labs. Advises graduate students in cybersecurity and networking. Labs & Teams: Active contributor to CyLab, co-developer of frameworks like Lumos (hidden IoT device detection) and KalKi (IoT security platform). Engages in interdisciplinary research across CMU’s Robotics Institute and Software Engineering Institute.
Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .
Tim Derdenger is an Associate Professor of Marketing and Strategy at Carnegie Mellon University’s Tepper School of Business. He holds a Ph.D. in Economics from the University of Southern California and a B.B.A. from George Washington University. His research focuses on technology markets and sports marketing, including platform dynamics, bundling strategies, celebrity endorsements, and dynamic demand modeling. He coordinates the Technology Strategy and Product Management Track for MBA students. As an Associate Editor for Management Science and editorial board member of Marketing Science , he contributes to academic publishing. His work spans empirical studies on golf equipment endorsements, EV subsidies, and AI regulation. He advises on topics like NIL in college football and generative AI bias. His affiliations include roles in executive education and industry panels on golf and tech strategy.
Dr. Min Xu is a Courtesy Professor in the Computational Biology Department within the School of Computer Science at Carnegie Mellon University. His research focuses on advancing computer vision and machine learning for biomedical image analysis, particularly cellular cryo-electron tomography (Cryo-ET) and automated science video analysis. He leads a lab developing cutting-edge computational tools for structural biology and medical imaging. Key research directions include: High-resolution 3D Cryo-ET image analysis AI-driven medical image segmentation Few-shot learning for cryo-EM analysis Video analysis frameworks for laboratory automation Notable contributions include the AITom toolkit for Cryo-ET analysis and pioneering work in adapting foundation models for medical imaging tasks. His work has been published in top venues like CVPR, MICCAI, and Nature-associated journals. No academic awards or grants are explicitly listed in the provided text. He maintains an active lab focused on translating computational methods into impactful biomedical research tools.
Mahadev Satyanarayanan is the Jaime Carbonell University Professor of Computer Science at Carnegie Mellon University. His multi-decade research focuses on performance, scalability, availability, and trust in distributed systems spanning cloud to mobile edge computing. He pioneered foundational concepts in mobile computing and Edge Computing through his seminal work on VM-based cloudlets. His current research explores cloudlet-based Edge Computing for latency-sensitive applications, wearable cognitive assistance systems integrating augmented reality, and edge-based machine learning frameworks for efficient training data discovery. He collaborates with Dan Siewiorek, Martial Hebert, and Bobby Klatzky on transformative applications. Dr. Satyanarayanan received his PhD from Carnegie Mellon University after completing Bachelor's and Master's degrees at the Indian Institute of Technology, Madras. His honors include ACM and IEEE Fellowships recognizing his contributions to distributed systems and mobile computing. ACM Fellow IEEE Fellow
Aniket 'Niki' Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how human and machine intelligence can collaborate to enhance creativity, decision-making, and innovation. He leads projects like the Semantic Reader and Skeema browser extension, aiming to reduce cognitive overload through intelligent systems. Education: BA in Psychology & Computer Science from Princeton University; PhD in Cognitive Psychology from UCLA. His work bridges HCI, crowdsourcing, and cognitive science, with 100+ publications and 17 best paper awards. He advises industry partners including Google, Microsoft, and Toyota while maintaining a lab focused on real-world impact. Research interests center on accelerating knowledge acquisition via systems that scaffold sensemaking (e.g., Selenite for web exploration) and fostering analogical innovation through crowdsourced/AI hybrid approaches. Notable contributions include CrowdForge (human-machine workflows) and Kinetica (touch-based data visualization). Awards include NSF CAREER Award, Allen Newell Award, and CHI Academy membership. His lab's Skeema tool has achieved 79% 30-day retention in beta, reflecting impactful user-centered design principles. Current projects emphasize LLM integration for composite cognition, aiming to create systems where 'LLMs + Humans > Either Alone.' Funding来自NSF, NIH, ONR, and industry partners like Bosch and Wikimedia. Teaching includes PhD bootcamps and user-centered research courses. Over 100 students have contributed to his projects, many advancing to tech leadership roles.
Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.
Yan Huang is an Associate Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University. She holds a Ph.D. in Information Systems and Management from Carnegie Mellon University (2013) and a B.Sc. (with honors) in Information Systems and Management from Tsinghua University, Beijing, China (2009). Prior to joining Carnegie Mellon University, she served as an Assistant Professor of Technology and Operations at the University of Michigan–Ann Arbor, Ross School of Business (2013-2018). Her educational background includes: B.Sc. (with honors) in Information Systems and Management, Tsinghua University, Beijing, China (2009) Ph.D. in Information Systems and Management, Carnegie Mellon University, Pittsburgh, United States (2013) Dr. Huang's research examines the economic and social impacts of technologies and identifies effective designs and policies for technology-enabled markets and platforms. She employs economic theories, structural modeling, statistical modeling, machine learning methods, and an understanding of the underlying technologies in her research. Her recent work focuses on the economics of artificial intelligence (AI) and machine learning (ML), with particular attention to algorithmic fairness, transparency, and collusion. She is among the first to bring economic and social perspectives to research on fair ML. Additionally, she studies digital platforms and online markets, examining how firms can leverage data-driven strategies to optimize pricing, personalization, and user engagement. Her recent publications demonstrate a strong focus on the intersection of AI/ML with economic principles, particularly in areas like algorithmic bias, pricing strategies, and platform regulation. A significant portion of her work examines how machine learning algorithms impact financial lending decisions, housing markets, and content creation platforms. Her research methodology frequently combines structural econometric modeling with empirical analysis of real-world data, providing both theoretical insights and practical implications for platform design and policy. Dr. Huang has received several prestigious awards for her scholarly contributions: AIS Senior Scholar Best Publication of 2023 Award for "Algorithmic Transparency with Strategic Users" Runner Up, Best Paper Published in Information Systems Research for 2021 for "Crowds, Lending, Machine, and Bias" INFORMS Information Systems Society Sandy Slaughter Early Career Award Finalist, Best Student Paper Award, CIST 2021 for "Human-Algorithmic Bias: Source, Evolution, and Impact" Pounds Fellowship As an active member of the academic community, Dr. Huang serves on various committees at CMU including the MSBA Curriculum Review Committee and the Tepper School Strategic Plan Task Force. She has also held editorial positions for Management Science, Information Systems Research, and the International Conference on Information Systems. Her teaching portfolio includes courses on Human and Algorithmic Bias, Modern Data Management, and PhD-level instruction at the Tepper School.
