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Nihar B. ShahView profile

Associate Professor

• Machine Learning
• Peer Review Systems
• Algorithmic Fairness
• +4 more

Nihar B. Shah is an Associate Professor at Carnegie Mellon University with joint appointments in the Machine Learning and Computer Science departments within the School of Computer Science. His research focuses on developing theoretically grounded algorithms for evaluating scientific work, with applications in peer review, fairness, and human-AI collaboration. His work has impacted over 100,000 research papers and grant evaluations. Education: Ph.D. in EECS, UC Berkeley M.E. in Telecommunications, Indian Institute of Science B.Tech. in Electronics, NIT Karnataka Research Interests: Shah's group investigates the science of evaluation through machine learning, optimization, and large-scale experiments. Key areas include peer review systems, algorithmic fairness, LLM applications in science, and human-AI collaboration frameworks. Research addresses fundamental questions about research validity, funding allocation, and equitable assessment. Publication Trends: Recent work focuses on improving peer review through randomized controlled trials, security against collusion, LLM-based review systems, and bias mitigation. Publications consistently appear in premier venues (NeurIPS, PLOS ONE, AAAI) with growing emphasis on real-world deployments. Awards & Honors: Young Alumnus Medal (IISc 2024) NSF CAREER Award (2020-2025) Google Research Scholar Award (2021) Multiple best paper awards (HCOMP, ICLR) Research Group & Funding: Leads a focused research team with NSF, Google, and JP Morgan support. Alumni hold positions in academia and industry. Current projects involve large-scale evaluations of scientific work and algorithmic fairness.

Pedro FerreiraView profile

Professor

• Information Systems
• Education Technology
• Media Analytics
• +5 more

Pedro Ferreira is a Full Professor at Carnegie Mellon University (CMU), holding a joint appointment in the School of Information Systems & Management at the Heinz College and the Department of Engineering and Public Policy within the College of Engineering. His research focuses on how technology influences education, media consumption, and peer effects, leveraging large datasets from randomized experiments. Ferreira has been recognized with the 2018 INFORMS Early Career Award and Top 17th worldwide research scholar ranking (2020–2022). He co-founded CMU's Initiative for Teaching and Education Analytics (iTEA) and advises numerous students in areas like AI/ML in education and media analytics. Education: BSc in Computer Science (IST), MSc in Electrical Engineering and Computer Science & Technology Policy (MIT), PhD in Telecommunications Policy (CMU). He has taught at MIT, IST, and invited roles at Católica-Lisbon and the University of Cambridge. Research Interests: Impact of digital technologies on education outcomes (e.g., smartphones in classrooms, video analytics), peer influence in media industries (e.g., binge-watching, recommender systems), and empirical methods using randomized experiments. Current projects include AI-driven education improvement and policy implications of AI/ML technologies. Notable Achievements: Over 15 peer-reviewed articles in top journals like Management Science and MIS Quarterly. Key grants include Gates Foundation funding for video-based education research and Koch Foundation support for online certification studies. He serves as Associate Editor for Management Science and previously for MIS Quarterly. Advising & Grants: Advised 23 PhD students, many now in academia and industry. Current students research facial recognition in education and hybrid recommender systems. Ferreira has led grants totaling millions, including studies on GDPR's impact on piracy tracking and worldwide VoD availability. Professional Service: Organized conferences like the Symposium on Statistical Challenges in eCommerce Research (SCECR). Served on NSF review panels and CMU’s Portugal PhD program steering committee.曾参与葡萄牙知识社会局(UMIC)的国家级政策制定，推动宽带学校项目。

• Statistics
• Optimization
• Machine Learning
• +3 more

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.

• Marketing
• Applied Economics
• Econometrics
• +10 more

Alan Montgomery is a Professor of Marketing at Carnegie Mellon University's Tepper School of Business, where he has held a tenured position since 2018 (previously as Associate Professor from 2005-2017). He also maintains an affiliation with the Machine Learning Department at CMU's School of Computer Science, demonstrating his interdisciplinary research approach at the intersection of marketing, economics, and computational methods. Dr. Montgomery earned his educational credentials from prestigious institutions: Ph.D. in Marketing/Economics, University of Chicago (1994) MBA, University of Chicago (1994) BS in Economics, University of Illinois at Chicago (1989) His research focuses on applying advanced quantitative methods to marketing problems, with particular expertise in consumer behavior modeling, clickstream data analysis, pricing strategies, and micro-marketing. Dr. Montgomery's work bridges traditional marketing theory with computational approaches, making significant contributions to both academic literature and practical business applications. His research often involves large-scale data analysis to uncover patterns in consumer decision-making processes, with recent work exploring mental accounting, bandit algorithms, and the impact of digital phenomena like movie piracy on traditional markets. Dr. Montgomery has received notable recognition including the 1999 Mitchell Prize from the American Statistical Association for his paper "Estimating Price Elasticities with Theory-based Priors." His work has been published in top-tier journals across marketing, economics, and computer science disciplines, demonstrating the interdisciplinary impact of his research. As an educator and mentor, Dr. Montgomery has advised numerous PhD students and collaborated extensively with researchers across multiple institutions. His interdisciplinary approach has led to collaborations with computer scientists studying web browsing behavior and economists examining consumer decision frameworks. His research has been supported by various grants throughout his career, enabling extensive data collection and analysis projects. Dr. Montgomery's work spans multiple research environments, including collaborations with the Machine Learning Department at CMU's School of Computer Science. His research group likely focuses on applying computational methods to marketing problems, particularly in the areas of consumer behavior modeling, clickstream analysis, and data-driven marketing strategies. His recent work shows increasing integration of machine learning techniques with traditional marketing research methodologies.

Michael D. SmithView profile

Professor

• Information Systems
• Digital Economics
• Consumer Behavior in Online Markets
• +4 more

Michael D. Smith is a Professor of Information Technology and Public Policy at Carnegie Mellon University, with joint appointments at Heinz College and Tepper School of Business. His research employs economic and statistical methods to analyze digital markets, focusing on firm and consumer behavior in online environments. Education: B.Sc. in Electrical Engineering (Summa Cum Laude), University of Maryland M.Sc. in Telecommunications Science, University of Maryland Ph.D. in Management Science and Information Technology, MIT Research Interests: Professor Smith investigates the economics of digital information markets, consumer behavior in online platforms, and policy implications of technological disruption. His work spans copyright enforcement, digital advertising, and the impact of piracy on legal media consumption. Publications: His recent studies examine AI’s role in copyright policy, effectiveness of anti-piracy measures, and market dynamics in digital streaming. Articles often bridge economics, computer science, and public policy. Awards: National Science Foundation CAREER Award Multiple Best Teacher Awards at CMU Recognized as a Top 100 Emerging Engineering Leader (NAE, 2020) Best Paper Runner-Up (Information Systems Research, 2006) Editorial and Industry Roles: Served as Senior Editor at Information Systems Research and Associate Editor at Management Science . Prior to academia, he worked in telecommunications at GTE and Booz Allen Hamilton, earning a patent for AI applications in network design. Contact: mds@cmu.edu | Office: 4800 Forbes Avenue, Hamburg Hall 2204, Pittsburgh PA 15213

Shuangning LiView profile

Assistant Professor

• Econometrics
• Statistics
• Machine Learning
• +5 more

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

• Data Science
• Business Analytics
• Human-AI Interaction
• +3 more

Zoey Jiang is an Assistant Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University since 2020. She holds a PhD from the University of Michigan–Ann Arbor (2020) and dual bachelor’s degrees in BA/BS from Peking University (2014). Her research bridges data science and operations management through innovative applications in AI collaboration, pricing optimization, and healthcare analytics. Education : PhD (University of Michigan–Ann Arbor, 2020), BA/BS (Peking University, 2014) Research Interests focus on: Human-AI collaboration frameworks High-dimensional choice modeling for retail analytics Competitive pricing mechanisms Crowdsourcing contest optimization Healthcare operations through behavioral data Key Publications demonstrate expertise in: Machine learning for substitution patterns in online retail Real-time AI assistance in healthcare Dynamic feedback policies in crowdsourcing Competitive pricing at scale Scientific Recognition : Recipient of Ross T&O Research Productivity Award (2019) Professional Contributions include: INFORMS TIMES Vice President (2025) Editorial reviewing for Management Science , Information Systems Research , and Manufacturing & Service Operations Management Teaching covers data science applications in business through courses like: Data Exploration and Visualization Data Mining & Business Analytics Seminar in Business Tech: Human-AI Interactions

Bruce McLarenView profile

Researcher

• Artificial Intelligence
• Educational Technology
• Digital Learning Games
• +3 more

Bruce McLaren is a Researcher at Carnegie Mellon University ’s Human-Computer Interaction Institute and head of the McLearn Lab . His work bridges Artificial Intelligence and Educational Technology , focusing on Digital Learning Games , Intelligent Tutoring Systems , and Collaborative Learning . He co-founded the free math tutoring platform Mathtutor and has authored over 200 academic articles, holding five patents. Education : B.S. in Computer Science (1981), M.S. in Computer Science (1984), M.S. in Intelligent Systems (1994), Ph.D. in Intelligent Systems (1999) from the University of Pittsburgh. McLaren's research explores how digital learning games like Decimal Point enhance math education, emphasizing student agency , gender effects , and feedback mechanisms using AI. His studies on erroneous examples and collaborative argumentation tools (e.g., ARGUNAUT, LASAD) have transformed pedagogical strategies. Recent work integrates Large Language Models (ChatGPT) into educational games for automated feedback. His publications span AI in Education , STEM Learning , and Machine Ethics , with a focus on learning analytics , knowledge tracing , and adaptive tutoring . McLaren’s studies often involve partnerships with institutions globally, including the German Research Center for Artificial Intelligence and Saarland University. Scientific Awards : Best Paper Award at ECTEL 2023; top 0.5% scholar in AI (ScholarGPS). McLaren actively pursues grants from the NSF , U.S. Department of Education , and Accenture , totaling over $2 million in recent projects. His lab, McLearn, collaborates with educators and technologists to refine teacher orchestration tools and student modeling frameworks.

David S. ChoiView profile

Associate Professor

• Statistics
• Machine Learning
• Network Analysis
• +5 more

David S. Choi is an Associate Professor at Heinz College, Carnegie Mellon University, which houses the Schools of Public Policy and Information Systems. He maintains a courtesy appointment in the Department of Statistics at the same institution. His academic credentials include a Ph.D. in Electrical Engineering from Stanford University under Benjamin Van Roy. Professional experience encompasses roles as a technical staff member at MIT Lincoln Laboratory, postdoctoral research at Harvard University's School of Engineering, and a visiting postdoc at UC Berkeley's Department of Statistics. Choi's research centers on statistical and machine learning methodologies for network-structured data. His work pioneers community detection techniques, latent variable network modeling, unsupervised learning frameworks, and causal inference methods that account for interference in social networks. These innovations address fundamental challenges in analyzing interconnected systems across public policy, healthcare, and social sciences. Publication analysis reveals consistent contributions to causal inference in networked experiments, particularly in characterizing interference and spillover effects. His scholarship spans network clustering algorithms, biological network modeling, and policy evaluation methodologies, with impactful work published in Journal of the American Statistical Association, PNAS, and Annals of Statistics. No scientific awards were documented in the source material. The available information does not reference doctoral students supervised or research grants managed by Professor Choi. Specific research laboratories or collaborative teams were not mentioned in the provided documentation.

• Experimental Mathematics
• Artificial Intelligence
• Legal aspects of technology
• +4 more

Michael Ian Shamos is a Distinguished Career Professor at Carnegie Mellon University's School of Computer Science, with appointments in the Language Technologies Institute and Software and Societal Systems Department. His career spans academia, law, and technology entrepreneurship, with expertise in experimental mathematics, artificial intelligence, and legal aspects of technology. Dr. Shamos earned his educational credentials through an impressive multidisciplinary path: A.B. in Physics from Princeton University (1968) under John Wheeler M.A. in Physics from Vassar College (1970) M.S. in Technology of Management from American University (1972) M.S. and M.Phil. in Computer Science from Yale University (1973-1974) Ph.D. in Computer Science from Yale University (1978) J.D. from Duquesne University (1981) His research interests bridge multiple domains with exceptional depth. In experimental mathematics, he develops computational systems that automatically generate and prove novel mathematical theorems, particularly in number theory, having contributed hundreds of new results to the field. As a leading expert in electronic voting security, he has examined over 120 voting systems for seven states and testified before Congress multiple times. His work uniquely integrates legal expertise with technical knowledge, especially regarding intellectual property in the digital age. He directs the M.S. in Artificial Intelligence and Innovation program at CMU, advising 74 students in this cutting-edge field. His publication trajectory reveals an evolution from foundational computational geometry (co-authoring the seminal "Computational Geometry: An Introduction") to contemporary issues in voting security and experimental mathematics. The consistent thread is applying computational methods to solve real-world problems with careful attention to legal and societal implications. His notable recognitions include: Industry Service Award of the Billiard and Bowling Institute of America (1996) Black and White Scotch Achiever's Award for contributions to bagpipe musicography (1991) As an educator and mentor, Dr. Shamos directs the M.S. in Artificial Intelligence and Innovation program and teaches courses including "AI & Future Markets" and "The Law of Computer Technology." He has served as an expert witness in over 360 legal cases involving computer technology and has been a statutory examiner of computerized voting systems for Pennsylvania since 1980. His extensive industry experience includes founding technology companies and serving as General Counsel for an AI company. Dr. Shamos is actively involved with the Universal Library project, which has scanned over 1.5 million books. He also serves as faculty advisor to the Carnegie Mellon Pool Team and is Curator of The Billiard Archive, reflecting his deep commitment to preserving billiards history alongside his academic pursuits.

Isabella VerdinelliView profile

Professor

• Nonparametric Methods
• High-Dimensional Statistics
• Bayesian Experimental Design
• +5 more

Isabella Verdinelli is a dual-affiliated professor serving as Professor in Residence at Carnegie Mellon University's Department of Statistics (Dietrich College of Humanities and Social Sciences) and as Full Professor at Sapienza University of Rome's Department of Statistical Sciences. She splits her academic year between Pittsburgh (fall) and Rome (spring), maintaining active research collaborations at both institutions. Her education includes a Master's degree from University College London and a PhD from Carnegie Mellon University. Her career spans postdoctoral work, assistant/associate positions, and professorship roles since her student days in Rome. Verdinelli's research focuses on: Nonparametric and high-dimensional methods for uncovering latent structures in complex datasets Bayesian experimental design with applications in medicine and engineering Manifold/filament estimation and minimax convergence theory Monte Carlo Markov Chains and hypothesis testing using Bayes factors Multiple testing procedures (FDR control) Her publications demonstrate sustained focus on Bayesian methodologies, nonparametric inference, and optimization techniques. Recent work (2007-2010) emphasizes high-dimensional data structures and theoretical statistics, while earlier contributions center on experimental design and Bayesian model selection.

Yandi ShenView profile

Assistant Professor

• Nonparametric Statistics
• Semiparametric Statistics
• High-Dimensional Inference
• +6 more

Yandi Shen is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from the University of Washington (2021), advised by Fang Han and Daniela Witten. Prior to joining CMU, he was a Kruskal Instructor in Statistics at the University of Chicago (2021–2023) and a postdoctoral researcher at Yale University under Zhou Fan. His research focuses on nonparametric and semiparametric statistics, high-dimensional inference, applied probability, and optimization algorithms. Education: Ph.D. in Statistics (University of Washington, 2021); Postdoctoral work at University of Chicago and Yale University. Research Interests: Nonparametric and semiparametric methods High-dimensional statistical inference Applied probability and stochastic processes Empirical Bayes methods Optimization and algorithm design Statistical machine learning Teaching Experience: University of Washington: Teaching Assistant for advanced inference courses (STAT 581-583), probability (STAT 394-395), and applied statistics (STAT 528). University of Chicago: Instructor for courses like STAT 22400 (Applied Regression Analysis) and STAT 33611 (Gaussian Processes). Recent Research Trends: His publications emphasize theoretical advancements in high-dimensional statistics, including empirical Bayes estimation, gradient flows, and optimization in overparameterized models. Key areas include analyzing phase transitions in spline regression, universality in regularized estimators, and nonparametric mixture models under optimal transport distances. Labs/Teams: While no specific lab is named, his collaborative work spans statistical theory and applications, often involving interdisciplinary teams in data science and machine learning.

Rahul TelangView profile

Professor

• Digital Media Industry
• Economics of Information Security and Privacy
• Copyright and Intellectual Property Policy
• +7 more

Rahul Telang is a Professor of Information Systems at Heinz College, Carnegie Mellon University, with a courtesy appointment at the Tepper School of Business. He has been at Heinz College since 2002 and is currently the director of the PhD program at the Heinz College. Professor Telang is also co-director of the Initiative for Digital Entertainment Analytics (IDEA) and part of Cylab and the Institute for Infrastructure Protection (I3P). Professor Telang's research broadly focuses on how Information and Communication Technologies (ICTs) and digitization impact consumers, businesses, and policies. His work spans two major domains: Digital Media Industry and Economics of Information Security and Privacy. In the Digital Media domain, he examines how digitization and piracy affect content providers, distributors, and users, with the aim of shaping optimal copyright and intellectual property policy. In the security domain, he investigates the incentives of various parties (users, firms, and hackers), market failures, and policy frameworks for information security. His recent research shows a strong focus on bug bounty programs, vulnerability disclosure economics, data breach impacts on consumer behavior, digital media consumption patterns, and the economic implications of AI on copyright policy. His work combines empirical analysis with economic modeling to provide actionable insights for both industry and policymakers, often using randomized field experiments and large-scale data analysis. Sloan Foundation Industry Study fellowship Multiple Google Faculty awards NSF CAREER award for work on economics of information security Senior editor positions at Management Science, Information Systems Research, and MIS Quarterly Professor Telang has secured extensive external funding from the National Science Foundation, National Security Agency, and industry partners for his research. He has worked extensively with industry and policymakers on media digitization issues, consulting with major entertainment companies like Disney and Warner Brothers on streaming strategies. As deputy director of the "living analytics" (LARC) project, he directs research on digital and social media analytics in collaboration with Singapore Management University. Professor Telang co-directs the IDEA center for digital entertainment analytics and contributes to Cylab's research on cybersecurity economics. His work on the Security Behavior Observatory has created infrastructure for long-term monitoring of client machines to understand security behavior, providing valuable insights into user security practices and vulnerabilities.

Beibei LiView profile

Professor

• Information Systems
• Applied Economics
• Business and Management
• +7 more

Beibei Li is a Professor of IT and Management at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. She holds joint appointments at the Tepper School of Business and a courtesy appointment at the School of Computer Science's Human-Computer Interaction Institute. Ph.D. with distinction from NYU Stern School of Business BS and MS in Computer Science with a minor in Fashion Design Her research explores the intersection of social and technical aspects of technology, focusing on human decisions in technological disruptions, digitization of behavior, AI ethics, data privacy, and economic welfare. She employs interdisciplinary approaches combining econometrics, machine learning, and randomized experiments. Beibei has received prestigious awards including the INFORMS ISS Sandy Slaughter Early Career Award (2019), three NSF Awards exceeding $3M, and multiple Best Paper Awards at top conferences like ICIS and WWW. Her work appears in Management Science , Marketing Science , and leading IS, Economics, and CS venues. NSF Civic Innovation Challenge Award (2021) Junior Marketing Researcher Award (2015) INFORMS ISS Nunamaker-Chen Dissertation Award (2013) She has chaired or co-chaired Ph.D. committees for students now at institutions like University of Maryland, Arizona State University, and UC San Diego, with several advisees winning academic awards. Her teaching includes graduate courses in AI strategy, business analytics, and data mining.

Christina FongView profile

Researcher

• Behavioral Economics
• Public Economics
• Income Redistribution
• +2 more

Christina Fong is a Research Scientist in the Department of Social and Decision Sciences at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She holds a Ph.D. from the University of Massachusetts and a B.A. from the University of Michigan. Her research examines behavioral and public economics, focusing on social preferences, distributive justice, and decision-making processes. Key areas include generosity determinants, fairness perceptions in resource allocation, racial dynamics in charitable behavior, and redistributive politics. Her experimental approach investigates how information framing and target characteristics influence giving behaviors. Recent work explores hiring discrimination through online platforms and attitudes toward income redistribution. Methodologically, she employs randomized experiments and nationally representative surveys to test theories of social preferences.