جست‌وجوی استاد و پژوهشگر

Isa Verdinelliمشاهده پروفایل

استاد

• Statistics
• Bayesian Statistics
• Machine Learning
• +۵ مورد دیگر

Isa Verdinelli is a Professor in Residence in the Department of Statistics and Data Science at Carnegie Mellon University. She maintains an office in Baker Hall 232 H in Pittsburgh, PA, and can be reached at isabella@stat.cmu.edu. She has co-authored a book titled "All of Regression" with L. Wasserman. Dr. Verdinelli's research interests span several areas of statistics and data science: Bayesian Statistics and Inference Nonparametric Statistics and Estimation Machine Learning and Statistical Learning Experimental Design and Optimization Manifold Learning and Geometric Statistics Feature Selection and Variable Importance Her recent work demonstrates a strong focus on developing novel statistical methodologies with applications in machine learning, particularly in the areas of feature importance, manifold learning, and nonparametric estimation. She has made significant contributions to the understanding of Bayesian experimental design, with publications spanning several decades. Her research often bridges theoretical statistical developments with practical applications across various domains. Dr. Verdinelli has received recognition for her contributions to statistical methodology, though specific awards are not mentioned in the available information. She has co-authored numerous influential papers in top statistical journals and has contributed to the advancement of statistical science through her research on Bayesian methods, nonparametric techniques, and statistical learning theory. Her work on the book "All of Regression" suggests she is also committed to statistical education and the dissemination of statistical knowledge.

• Artificial Intelligence
• Optimization
• Game Theory
• +۷ مورد دیگر

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University (CMU), holding appointments in the Computer Science Department, Machine Learning Department, Algorithms, Combinatorics, and Optimization (ACO) Ph.D. Program, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He co-directs CMU AI and leads the Electronic Marketplaces Laboratory. His research focuses on artificial intelligence, economics, and operations research, with notable contributions to game theory, optimization, and kidney exchange systems. Sandholm has pioneered algorithms for electronic marketplaces, including the first superhuman AI in poker (Libratus and Pluribus) and kidney exchange optimization. He has founded multiple companies, including CombineNet, Strategic Machine, and Strategy Robot, and has over 500 peer-reviewed publications and 29 patents. His awards include the Vannevar Bush Faculty Fellowship, AAAI Award for AI for Humanity, and honorary doctorate from the University of Zurich. Education: Ph.D. and M.S. in Computer Science Dipl. Eng. (M.S. with B.S. included) in Industrial Engineering and Management Science Research Interests: Artificial Intelligence, Optimization, Game Theory, Kidney Exchange, Electronic Commerce, Multiagent Systems, Market Design, and Healthcare Applications. His work bridges theoretical advancements with real-world applications, particularly in strategic reasoning, organ transplantation, and defense/intelligence systems. Recent Articles: Focus on solving large-scale games, kidney exchange optimization, and AI-driven decision-making. Key areas include imperfect-information game solving (e.g., poker AIs), algorithmic fairness in healthcare markets, and combinatorial auction design. Awards & Honors: Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) Fellowships: ACM, AAAI, INFORMS, AAAS Edelman Laureate (2005) Over 100+ awards, including honorary doctorates and industry recognitions Advising & Grants: Advised PhD students now in top positions at MIT, Stanford, and CMU. Secured grants for AI game-solving and healthcare policy optimization. Current openings for PhD students in game-solving and kidney exchange policy. Labs & Teams: Electronic Marketplaces Laboratory (EML) and CMU AI Initiative. Collaborates with industry partners (e.g., Baidu, Google) and government agencies on strategic AI applications.

• Machine Learning
• Statistics
• Neuroscience
• +۴ مورد دیگر

Josue Orellana is a Research Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU) and serves as Managing Editor of the Computational Neuroscience Navigator initiative. He holds a PhD in Machine Learning and Neural Computation from CMU (2019) and a B.S. in Electrical Engineering from Washington State University. Previously, he worked as a Research Scientist at the National University of Singapore and Johns Hopkins University. His research integrates Statistics , Machine Learning , and Computational Neuroscience to model brain network interactions during cognitive tasks. He also investigates statistics education methodologies through the CMU Teach Stat research group. His work emphasizes graphical network analysis, phase coupling in neural circuits, and educational cognitive science. Publications span neuroscience, machine learning, and statistics education, with recent articles exploring neural oscillations, educational assessment tools, and multivariate phase coupling. His scholarly output demonstrates consistent focus on statistical methodology in neural systems and innovations in STEM pedagogy . Awards & Grants: CMU Simon Seed Grant (2020) GuSH cross-walk statistics education grant (2019) CMU Presidential Fellowship (2016-2018) President’s Honor Roll, WSU (2008-2012) He leads the Computational Neuroscience Navigator project at CMU’s Open Learning Initiative, developing concept-driven video resources. No advising relationships or lab affiliations are detailed beyond his educational research group.

Arun Kumar Kuchibhotlaمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Machine Learning
• High-Dimensional Statistics
• +۵ مورد دیگر

Arun Kumar Kuchibhotla is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), within the Dietrich College of Humanities and Social Sciences. His research focuses on foundational aspects of statistical inference, machine learning, and high-dimensional data analysis. Education : While specific details are not listed here, his academic trajectory aligns with roles in top-tier institutions. His research interests span Causal Inference , Statistical Machine Learning , High-Dimensional Statistics , and Nonparametric Methods . He explores theoretical guarantees for algorithms, fair machine learning applications, and algorithmic forecasting of extreme events. Notable areas include: Development of robust statistical methods for online algorithms and M-estimation. Innovations in conformal prediction, confidence regions, and self-normalization techniques. Advancements in fair risk assessments in criminal justice through optimal transport and conformal prediction. Recent work emphasizes dimension-agnostic adaptive inference and censored time-to-event analysis , with applications to climate science and policy. His publications in 2025 highlight breakthroughs in concentration inequalities, extreme heat wave forecasting, and doubly robust calibration methods. Advising & Grants : No specific advisees or grants are listed here, but his research aligns with CMU’s emphasis on interdisciplinary projects. He collaborates with groups like the CSAFE and DELPHI labs, focusing on applied statistics and societal impact. Labs/Teams : Active contributor to CMU’s CSAFE Lab Group (statistical forensics) and DELPHI Lab Group (epidemiological forecasting).

Gonzalo Menaمشاهده پروفایل

استادیار

• Biostatistics & Epidemiology
• Computational Neuroscience
• Statistical Optimal Transport
• +۴ مورد دیگر

Gonzalo Mena is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His research focuses on applying statistical and machine learning methods to life sciences problems, particularly in neuroscience and epidemiology. He has prior experience as a Florence Nightingale Bicentennial Fellow and Tutor at the University of Oxford, a Postdoctoral Fellow at Harvard University, and holds a Ph.D. in Statistics from Columbia University and a B.Sc. in Mathematical Engineering from Universidad de Chile. His work bridges cutting-edge neurotechnology data analysis with large-scale epidemiological surveillance, emphasizing valid inference from biased datasets. He is a leader in statistical optimal transport, developing methodologies to measure distributional distances and inform scientific discovery. His research spans the DELPHI Lab Group and intersects with computational neuroscience, genomics, and public health policy. Key research areas include Alzheimer's disease modeling via multimodal cell atlases, Bayesian methods for neurodegenerative dynamics, and pandemic impact analysis (e.g., socioeconomic disparities in Covid-19 outcomes). His recent publications highlight interdisciplinary applications of optimal transport theory and permutation inference in neuroscience data analysis. Mena has contributed to foundational statistical frameworks for population size estimation and neuronal identification in C. elegans. His work combines methodological innovation with real-world applications in healthcare and public health surveillance.

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

• Statistical Machine Learning
• High-Dimensional Statistics
• Nonparametric Methods
• +۷ مورد دیگر

Ann Lee is a Professor and Co-Director of the Ph.D. Program in the Department of Statistics & Data Science at Carnegie Mellon University, with a joint appointment in the Machine Learning Department. She has been at CMU since 2005, after serving as the J.W. Gibbs Assistant Professor of Applied Mathematics at Yale University. Her academic journey began with a PhD in Physics from Brown University in 2002, preceded by BSc and MSc degrees in Engineering Physics from Chalmers University of Technology in Sweden. Dr. Lee's educational background includes: PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on developing statistical methodology for complex data and problems in the physical sciences, with particular emphasis on trust-worthy scientific inference and reliable uncertainty quantification. She works at the intersection of classical statistics and machine learning, developing methods for simulation-based inference and experimental design. Dr. Lee is especially interested in likelihood-free inference, calibrated probabilistic forecasting, and interpretable diagnostics of generative models, with applications in astronomy and hurricane intensity guidance involving satellite imagery and large surveys. Dr. Lee co-founded the STAMPS (STAtistical Methods for the Physical Sciences) research group with Mikael Kuusela in 2018, which is transitioning to a CMU Research Center in Fall 2024. The group hosts public colloquia-style webinars and weekly research meetings for students and faculty at CMU and UPitt. Her notable scientific contributions have been recognized through: Her paper "Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery" being selected for The Best of AOAS session at the 2023 Joint Statistical Meeting Her student Luca Masserano winning the ASA Best Student Paper Award at the 2023 Joint Statistical Meeting Her student Alex Shen winning a Poster Award at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023 Dr. Lee has advised numerous PhD students to completion, with research spanning statistical machine learning, high-dimensional statistics, and applications in physical sciences. She has secured research funding supporting her work on statistical methods for physical sciences and has collaborated extensively with researchers across disciplines. Her current research projects focus on advancing likelihood-free inference methods and applying them to challenging problems in climate science and astronomy. She leads the STAMPS research group, which is becoming a CMU Research Center in Fall 2024, and organizes workshops including the PHYSTAT-SBI workshop on "Simulation Based Inference in Fundamental Physics" and the Hammers & Nails workshop on "Frontiers in Machine Learning in Cosmology, Astro & Particle Physics."

Peter Freemanمشاهده پروفایل

عضو هیئت علمی

• Astrostatistics
• Astronomical Data Analysis
• Machine Learning
• +۳ مورد دیگر

Peter Freeman is an Associate Teaching Professor and Director of the Undergraduate Program in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He holds a B.A. in Physics (1989, University of California) and a Ph.D. in Astronomy and Astrophysics (1997, University of Chicago). Before joining CMU in 2004, he worked as a scientific programmer for the Chandra X-ray Telescope mission, specializing in data analysis methodologies. His research focuses on astrostatistics, developing advanced statistical techniques for analyzing complex astronomical datasets. Key areas include source detection algorithms, cosmic microwave background mapping, photometric redshift estimation, and galaxy morphology analysis. He has collaborated with faculty and students from CMU and the University of Pittsburgh on interdisciplinary projects, integrating machine learning and computational methods into astrophysical research. Freeman is actively involved in educational initiatives such as the STAMPS (Statistical Pedagogy & Educational Research) group and the Carnegie Mellon Sports Analytics Camp (CMSAC). His work emphasizes authentic, community-engaged learning experiences, particularly in undergraduate laboratory courses and data science curricula. His recent publications span astrostatistics, ecological studies (e.g., Coqui frog acoustic analysis), and pedagogical innovations in STEM education. He has contributed to major projects like the Rubin Observatory Legacy Survey of Space and Time (LSST) and the CANDELS galaxy structure classification effort.

• Uncertainty Quantification
• Causal Inference
• Conformal Prediction
• +۳ مورد دیگر

Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her PhD in Statistics from Stanford University in 2024, advised by Emmanuel Candès and Dominik Rothenhäusler, and holds a B.S. in Mathematics and B.A. in Economics from Tsinghua University. Current faculty at UPenn Wharton Former postdoctoral fellow at Harvard Data Science Initiative Her research focuses on Uncertainty Quantification and Generalizability in AI models, particularly through conformal prediction , causal inference , and multiple testing . Recent work explores distribution shifts in large-scale replication studies and methods for trustworthy AI in drug discovery and medical applications. Key article trends show expertise in: Conformal prediction methods Causal inference under distribution shifts LLM-driven scientific discovery Replicability analysis AI uncertainty quantification High-stakes AI validation Scientific awards include: 2025 IMS Lawrence D. Brown PhD Student Award 2024 Jack Youden Prize for best expository paper in Technometrics She organizes the Online Causal Inference Seminar and contributes to open science through the awesome-replicability-data GitHub repository containing curated replication datasets.

Alexandre Pougetمشاهده پروفایل

استاد

• Computational Neuroscience
• Probabilistic Inference
• Neural Circuits
• +۵ مورد دیگر

Alexandre Pouget is a Full Professor in the Department of Basic Neuroscience at the University of Geneva, where he leads the Computational Cognitive Neuroscience Laboratory. His research establishes that the brain represents knowledge as probability distributions and performs computations via probabilistic inference, enabling robust neural processing under uncertainty. This foundational framework applies across diverse domains including olfactory processing, spatial representations, sensory-motor transformations, multisensory integration, perceptual learning, attention control, decision making, causal reasoning, and simple arithmetic. The approach reveals how biologically plausible neural circuits implement probabilistic population codes for efficient computation. Major recognition includes: Andrew Carnegie Prize in Mind and Brain Sciences (2016) from Carnegie Mellon University His laboratory develops theoretical models demonstrating how probabilistic inference mechanisms may operate universally across species, including insects, suggesting a fundamental principle of neural computation. Current work explores applications to visual search, arithmetic processing, and causal reasoning through experimentally testable neural circuit implementations.

Samuli Reijulaمشاهده پروفایل

دانشیار

• Philosophy of Science
• Cognitive Science
• Social Epistemology
• +۵ مورد دیگر

Samuli Reijula is a permanent university lecturer (equivalent to US Associate Professor) in Theoretical Philosophy at the University of Helsinki, with additional affiliation as a docent (habilitation) in Practical Philosophy. He is also associated with the TINT Centre for Philosophy of Social Science and leads his personal research section 'Reijulab.' His research focuses on scientific problem-solving as a distributed cognitive system, examining science at multiple levels from individual scientists to entire communities. Key interests include social epistemology, AI ethics, epistemic humility, diversity in science, and institutional epistemology. Reijula's work critically examines the relationship between scientific communities and society, emphasizing the importance of academic autonomy and trust. His recent publications analyze AI's philosophical implications, diversity policies in research, academic recruitment policies, and the enduring value of the Bildung university model. Reijula has contributed to debates on scientific uncertainty, open science, and the philosophical foundations of collective problem-solving in scientific communities. Reijula currently holds an Academy of Finland research project (2020-2025) titled 'Modeling the republic of science: Collaborative problem solving and collective rationality in scientific inquiry.' He has collaborated with prominent scholars including Jaakko Kuorikoski, Kristina Rolin, and Petri Ylikoski on topics ranging from institutional epistemology to agent-based modeling in social epistemology. As an active public intellectual, Reijula contributes to Finnish media outlets including Helsingin Sanomat, Suomen Kuvalehti, and Tietessä Tapahtuu, where he analyzes science policy issues and philosophical dimensions of contemporary scientific challenges.

• AI Ethics
• Philosophy of Computing
• AI Safety
• +۳ مورد دیگر

Seth Lazar is a Professor of Philosophy at the Australian National University (ANU) and holds multiple distinguished roles: Distinguished Research Fellow at the University of Oxford Institute for Ethics in AI, Fellow of the Carnegie Endowment for International Peace, and Senior AI Advisor to the Knight First Amendment Institute at Columbia University. He founded the Machine Intelligence and Normative Theory (MINT) Lab, focusing on philosophy of computing and AI safety. His research bridges ethics, technology, and governance, addressing societal impacts of AI, algorithmic governance, and communicative justice. Recent work explores AI's role in democracy, attention distribution, and economic systems. Lazar regularly contributes to public discourse through media interviews and op-eds, such as his Guardian article questioning AI's role in public policymaking. Lazar leads interdisciplinary projects via the MINT Lab and collaborates with institutions like Yale Law School on normative philosophy of computing. He delivers high-profile lectures (e.g., Scholl Lecture at Purdue) and organizes workshops on AI ethics and democratic freedoms. His work emphasizes proactive evaluation of emerging AI technologies to anticipate risks and align them with ethical goals.

Benjamin Moseleyمشاهده پروفایل

دانشیار

• Operations Research
• Theoretical Computer Science
• Machine Learning
• +۵ مورد دیگر

Benjamin Moseley is the Carnegie Bosch Associate Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University (CMU), with a courtesy appointment in the School of Computer Science's Machine Learning Department. He is also a consulting professor at the start-up Relational AI. Education: PhD in Computer Science from the University of Illinois, advised by Chandra Chekuri. Prior roles include Assistant Professor at Washington University in St. Louis (2014–2017), Research Assistant Professor at Toyota Technological Institute at Chicago (2012–2014), and visiting positions at Sandia National Laboratories and Yahoo Labs. Notable affiliations include the NSF-funded Algorithms, Combinatorics, and Optimization (ACO) PhD program. Research focuses on algorithm design, analysis, and evaluation, with emphasis on operations research, theoretical computer science, and machine learning. Current projects include algorithmic foundations of machine learning, distributed algorithm design, and scheduling/logistics optimization. His work bridges theoretical rigor and practical applications, addressing challenges in big data analysis, streaming algorithms, and approximation techniques. Recognition includes multiple best paper awards (IPDPS 2015, SPAA 2013, SODA 2010), top-tier NeurIPS presentations, and prestigious grants from NSF, ONR, Google, Bosch, and others. His research has been supported by a NSF CAREER Award, Google Faculty Research Awards, and the Carnegie-Bosch Chair. Teaching contributions include developing the Business Analytics and Optimization undergraduate minor at Tepper, courses for UBA/MBA/MSBA programs, and被评为“Top 50 Undergraduate Business School Professor” by Poets and Quants. He emphasizes pedagogy integrating advanced algorithms with business applications. Labs/Teams: Active in Relational AI collaborations and affiliated with CMU's machine learning and operations research research groups. His work frequently intersects with industrial partners like Google, Yahoo, and Bosch.

• Multi-Agent Systems
• Human-Robot Interaction
• Game Theory
• +۷ مورد دیگر

Katia Sycara is a Research Professor at Carnegie Mellon University's Robotics Institute, part of the School of Computer Science. She also holds the Sixth Century Chair (part-time) in Computing Science at the University of Aberdeen, UK. Her research focuses on multi-agent systems, human-robot interaction, game theory, and adversarial reasoning. She leads the Advanced Agent-Robotics Technology Lab and has developed frameworks like the RETSINA multiagent infrastructure. Education: B.S. in Applied Mathematics (Brown University), M.S. Electrical Engineering (University of Wisconsin), Ph.D. Computer Science (Georgia Tech), Honorary Doctorate (University of the Aegean, 2004). Research interests include semantic web services, agent interoperability, crisis response systems, and negotiation support. She has pioneered work on the DAML-S language for semantic web services and contributed to the OASIS UDDI standard. Notable awards include the 2002 ACM SIGART Agents Research Award and AAAI Fellowship. She has led multimillion-dollar projects funded by DARPA, NASA, AFOSR, and ONR, focusing on applications like autonomous robotics, urban search and rescue, and enterprise integration. Awards: ACM SIGART Autonomous Agents Research Award (2002) AAAI Fellow (2002) IEEE Fellow (2013) Outstanding Alumnus (University of Wisconsin, 2005) Grants and Projects: Command and Control of Multi-Robot Teams (ONR) Games on Graphs (MURI/ONR) Urban Search and Rescue (NASA) Labs/Teams: Director of Advanced Agent-Robotics Technology Lab and contributor to the Semantic Web Science Association.

Ryan Tibshiraniمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Estimation
• Distribution-free Inference
• +۵ مورد دیگر

Ryan Tibshirani is a Professor jointly appointed in the Departments of Statistics and Machine Learning at Carnegie Mellon University (CMU). He is also a Principal Investigator in the Delphi group at CMU and an Amazon Scholar in AWS AI Labs under Alex Smola's group. Prior to joining CMU in 2011, he earned his B.S. in Mathematics (2003-2007) and Ph.D. in Statistics (2007-2011) from Stanford University, advised by Jonathan Taylor. Education: B.S. in Mathematics, Stanford University (2003-2007) Ph.D. in Statistics, Stanford University (2007-2011), advised by Jonathan Taylor Research Interests: Ryan's work bridges theoretical and applied statistics, with a focus on high-dimensional statistics, nonparametric methods, distribution-free inference, convex optimization, and numerical algorithms. His applied research emphasizes epidemiological tracking and forecasting. He also explores machine learning and computational statistics. Advising & Collaborations: Current advisees include Addison Hu and Pratik Patil (Ph.D. candidates in Statistics and Machine Learning). Past advisees have transitioned to roles at institutions like Stanford, Cambridge, Argo AI, and Google. Collaborations span academic institutions and industry (e.g., AWS AI Labs). Labs & Groups: Ryan leads research in the Delphi group (epidemiological modeling) and contributes to AWS AI Labs. His work integrates interdisciplinary teams across CMU and external partners.