جست‌وجوی استاد و پژوهشگر

Henry Posner IIIمشاهده پروفایل

مدرس

• Railroad History
• Transit Systems
• Sustainable Transportation
• +۲ مورد دیگر

Henry Posner III serves as a Lecturer in the Department of History at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He has been a department member since 2017 and teaches The American Railroad-Decline and Renaissance in the Era of Deregulation . As Chairman of Railroad Development Corporation (RDC), he bridges academic instruction with industry innovation. His research focuses on railroad history and sustainable transit systems, particularly through the development of Pop-Up Metro—a battery-powered modular train system designed for underutilized freight lines. This work emphasizes environmental sustainability, cost-effective infrastructure demonstration, and urban transit solutions. His scholarship intersects historical analysis with practical applications in modern rail technology. Posner has received the Paul Harris Fellowship from Rotary International for work in Guatemala and was named among TRAINS magazine's '75 People You Should Know.' His professional engagements include serving as Chairman of the EBT Foundation, alternate director at the Association of American Railroads, and board member of Princeton University's PIIRS Advisory Council. Advised Meg Richards (CMU School of Computer Science alumna) as project manager for Pop-Up Metro Mentored Shaelin Spahle (BHA graduate) in branding and train platform design Supervised Owen Sahnow (School of Drama student) in ADA-compliant platform construction His leadership extends to restoring the East Broad Top Railroad through the EBT Foundation and developing sustainable rail solutions with RDC affiliates like Vivarail. Posner actively recruits CMU students for rail industry opportunities, emphasizing problem-solving skills and interdisciplinary approaches to transportation challenges.

Marijn Heuleمشاهده پروفایل

دانشیار

• Satisfiability (SAT) Solving
• Formal Verification
• Combinatorial Mathematics
• +۵ مورد دیگر

Marijn Heule is an Associate Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD from Delft University of Technology in the Netherlands. His academic career has been focused on advancing the theory and practice of satisfiability (SAT) solving and its applications. Dr. Heule's research primarily centers on solving hard combinatorial problems in formal verification, number theory, and extremal combinatorics. His work has made significant contributions to both theoretical foundations and practical implementations of SAT solvers. He has developed award-winning SAT solvers, and his preprocessing techniques are incorporated into state-of-the-art SAT solvers used worldwide. His current research focuses on two major challenges: exploiting high-performance computing for SAT solving and validating results from SAT solvers and related tools. Dr. Heule pioneered the "cube-and-conquer" paradigm, a novel parallel SAT solving approach that enables linear time speedups on many hard problems. His first publication on this topic won the best paper award at HVC 2011. He also designed a new proof format with a corresponding fast proof checker for SAT and QBF solvers. This proof logging format has been mandatory for SAT Competitions since 2013, significantly increasing confidence in tool correctness. His work on the Boolean Pythagorean Triples problem, which produced a 200 TB proof that was validated, demonstrated that proof logging and verification is feasible even for the most complex problems. Dr. Heule is one of the editors of the 900+ page "Handbook of Satisfiability," which has become a standard reference in the SAT community. He serves as an Associate Editor of the Journal on Satisfiability, Boolean Modeling and Computation and previously co-chaired the SAT 2015 conference in Austin. Best paper award at HVC 2011 for cube-and-conquer publication Multiple best paper awards/nominations at major conferences including IJCAR, SAT, TACAS Work featured in New York Times, Quanta Magazine, Nature, and other major media outlets Dr. Heule has advised several PhD students including Emre Yolcu, Joseph Reeves, Bernardo Subercaseaux, Cayden Codel, James Gallicchio, and Amar Shah. He has developed important software tools including DRAT-trim (a proof checking utility), QRAT-trim, and implementations of the cube-and-conquer paradigm that have become standard in the SAT solving community.

Michael Bockstallerمشاهده پروفایل

استاد

• Polymer Hybrid Materials
• Self-Healing Materials
• Nanocomposites
• +۷ مورد دیگر

Michael Bockstaller is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University's College of Engineering. His research focuses on structure-property-performance relationships in polymer hybrid materials and he leads significant work in self-healing materials, nanocomposites, and advanced polymer processing. His educational background includes: Ph.D. in Physical Chemistry from Johannes Gutenberg University (2000) B.S. in Chemistry from the Technical University of Karlsruhe (1997) Professor Bockstaller's research spans multiple cutting-edge areas in materials science. His primary focus is on polymer hybrid materials, with special emphasis on self-healing capabilities, optical and thermal transport properties of polymer nanocomposites, and applications in solid-state lighting and battery technologies. He also explores additive manufacturing techniques, advanced materials processing, and data-driven approaches to materials design. His work bridges fundamental polymer chemistry with practical engineering applications, particularly in developing sustainable and high-performance materials. Analysis of his recent publications reveals a strong trend toward engineered self-healing materials, with particular focus on brush particle systems and nanoparticle-polymer hybrids. His work increasingly integrates data-driven approaches and explores thermal transport phenomena in novel material architectures. There's also a growing emphasis on sustainability applications, including polymer recycling and environmentally conscious materials design. Among his notable recognitions: Fellow of the American Physical Society Fellow of the Alexander von Humboldt Foundation Emmy Noether Grant recipient from the German Science Foundation Alcoa Professor (endowed chair appointment at CMU) Professor Bockstaller actively advises students and leads multiple research initiatives. He serves as a key member of the Center for Data-Driven Discovery of Multifunctional Material Systems (D3OM2S) and contributes to several interdisciplinary centers including the Center for Environmental Implications of Nanotechnology and the Manufacturing Futures Institute. His research has attracted significant funding from various sources supporting innovative work in polymer science and engineering. He leads research teams focused on polymer hybrid materials within CMU's Materials Science and Engineering department, with strong connections to the university's data science initiatives. His laboratory work emphasizes the intersection of traditional materials science with computational approaches, creating what he terms 'Engineering Better Polymers to Solve Modern Problems.'

• Satisfiability (SAT) Solving
• Formal Verification
• Automated Reasoning
• +۵ مورد دیگر

Marijn J.H. Heule is an Associate Professor at the School of Computer Science , Carnegie Mellon University . He received his PhD from Delft University of Technology in the Netherlands. His research focuses on solving hard combinatorial problems through Satisfiability (SAT) solving , with applications in formal verification, number theory, and extremal combinatorics. Education: PhD in Computer Science, Delft University of Technology, Netherlands Heule's work addresses fundamental challenges in SAT solving, including: Exploiting high-performance computing via the cube-and-conquer paradigm Validating results from SAT solvers using novel proof formats His research has produced 15+ recent publications (2025-2021) on topics like automated reasoning, formal verification, and combinatorial proofs. Notable achievements include: Best Paper Award at HVC 2011 (Cube-and-Conquer) 200 TB proof for the Boolean Pythagorean Triples Problem Co-editor of the Handbook of Satisfiability He has advised multiple PhD students including Emre Yolcu , Joseph Reeves , and Bernardo Subercaseaux . His tools like DRAT-trim and QRAT-trim have become standard in proof validation.

Michael Erdmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Topological Robotics
• +۵ مورد دیگر

Michael Erdmann is a Professor in the Computer Science and Robotics departments at Carnegie Mellon University (CMU), where he has been since 2002. He also served as Associate Faculty at the University of Pittsburgh's Department of Computational Biology from 2002-2007. His research focuses on leveraging topology, geometry, and probability to address robotics challenges under uncertainty, with applications in in-hand manipulation, protein structure analysis, and privacy-preserving systems. Ph.D., Computer Science, MIT (1989) M.S., Electrical Engineering and Computer Science, MIT (1984) B.S., Mathematics, University of Washington (1982) His work on topological methods has produced groundbreaking results, such as a graph controllability theorem connecting homotopy to system uncertainty. Recent publications explore stateless distributed computation, extrinsic dexterity for robotic manipulation, and topological approaches to process modeling. Erdmann's research bridges theoretical mathematics with practical robotics, often yielding insights applicable to both fields. Key publication trends include: (1) Topology-driven Robotics (2010-2017) - developing strategy complexes and homotopy-based controllability; (2) Dynamic Contact Manipulation (2005) - analyzing frictional contact constraints; (3) Protein Structure Analysis (2004-2005) - applying knot theory to molecular biology. Herbert A. Simon Award for Teaching Excellence in Computer Science (2014) Erdmann has been actively involved in academic governance, serving on CMU's SCS Promotions Committee (2014-2016), Doctoral Dissertation Award Committee (2015), and multiple conference editorial boards including the International Journal of Robotics Research (2000-2017). He teaches foundational courses like 15150 (Principles of Functional Programming) and 16811 (Mathematical Fundamentals for Robotics), reflecting his commitment to education and curriculum development.

Bryan Wilderمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Causal Inference
• +۵ مورد دیگر

Bryan Wilder is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University (CMU). He directs the Lab for AI and Social Impact (LASI), focusing on AI methods for equitable decision-making in public health, medicine, and human services. Previously, he was a postdoctoral Schmidt Science Fellow at Harvard School of Public Health and earned his PhD in Computer Science from Harvard under Milind Tambe. His work spans algorithmic fairness, optimization, and causal inference, with funding from NSF, NIH, CDC, and Schmidt Futures. Education: PhD in Computer Science (Harvard, 2021), MSc from USC (2019), BS from UCF (2015). Key Roles: Chair of EAAMO Board, Co-PI of CDC Innovation Center at CMU, Guest Lecturer at multiple institutions. Research Interests: Integrating ML, optimization, and causal inference for high-stakes social systems. Current projects include maternal/child health, infectious disease modeling, and fair resource allocation. His lab emphasizes real-world impact through partnerships with public health organizations. Articles Trends: Recent work addresses federated epidemic surveillance, reinforcement learning for coupled systems, and auditing fairness in AI. Earlier papers focus on HIV prevention interventions, risk-sensitive optimization, and cultural evolution modeling. Awards: Includes the Schmidt AI2050 Fellowship, IFAAMAS Dissertation Award, and Siebel Scholarship. Over 30+ peer-reviewed papers in top venues (NeurIPS, ICML, AAAI). Advising & Grants: Supervises PhD students at CMU and USC. Leads projects funded by NSF, NIH, CDC, and Schmidt Futures. Collaborates with global health institutions. Labs/Teams: LASI lab develops AI tools for equitable decision-making, with applied work in maternal health, HIV prevention, and pandemic response.

Fatma Kilinc-Karzanمشاهده پروفایل

استاد

• Operations Research
• Analytics
• Machine Learning
• +۸ مورد دیگر

Fatma Kilinc-Karzan is a Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, and holds the Frank A. and Helen E. Risch Faculty Development Chair. She is also an Associate Professor of Computer Science (by courtesy) and affiliated with the Algorithms, Combinatorics, and Optimization (ACO) PhD Program. Her career includes visiting roles at institutions like the Simons Institute at UC Berkeley and extensive professional service on editorial boards and conference committees. PhD in Industrial and Systems Engineering (minor in Mathematics) from Georgia Institute of Technology B.S. and M.S. in Industrial Engineering (minor in Information Systems) from Middle East Technical University Research Interests : Her work focuses on convex optimization , structured nonconvex optimization , and their applications in optimization under uncertainty (robust optimization, chance constraints), machine learning (preference learning from limited data), and business analytics . She explores theoretical aspects like semidefinite programming (SDP) relaxations, convex hull characterizations, and algorithmic efficiency for large-scale problems. Article Trends : Her recent publications emphasize semidefinite programs , rank-one function optimization , and chance-constrained programming with applications in portfolio optimization , healthcare , and recommender systems . Key methodologies include perspective reformulation , submodularity , and first-order algorithms . Scientific Awards : 2015 INFORMS Optimization Society Prize for Young Researchers 2014 INFORMS JFIG Best Paper Award Advising and Grants : She has advised over a dozen PhD students, many of whom won awards like the INFORMS Optimization Society Best Student Paper Prize. Her research is supported by grants including an NSF CAREER Award , ONR grant , and AFOSR grant . She collaborates with institutions like IBM and the Simons Institute.

Kara Kedrickمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Science
• Social Dynamics
• Knowledge Expansion
• +۶ مورد دیگر

Kara Kedrick is a Postdoctoral Fellow at the Institute for Complex Social Dynamics within the Department of Philosophy at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. Her interdisciplinary work bridges cognitive science, social dynamics, and philosophy of science to investigate the mechanisms driving human knowledge expansion through individual cognition and collective discovery processes. Her educational background includes a Ph.D. in Psychology from the University of Minnesota, where she specialized in Cognitive and Brain Sciences. Dr. Kedrick's research centers on the 'ecosystem of discovery,' examining how cognitive processes like curiosity and creativity interact with social dynamics to advance knowledge. She investigates explanation generation, information seeking, and semantic search while analyzing how diverse perspectives and conceptual structures shape scientific progress. Her methodology integrates cognitive modeling, network analysis, and philosophical inquiry to map knowledge growth across individual and collective contexts. Analysis of her 2022-2025 publications reveals three dominant trends: (1) the role of question-asking in directing exploration through semantic spaces, (2) the dynamics of creative foraging within knowledge networks, and (3) the evolution of explanations through social interaction. Her work increasingly addresses real-world applications including pandemic information behavior and gender disparities in scientific communication, demonstrating strong translational potential. Dr. Kedrick actively contributes to the Institute for Complex Social Dynamics, where she collaborates on projects modeling the cognitive and social infrastructure of scientific innovation through computational approaches and empirical studies of discovery processes.

Daniel Oppenheimerمشاهده پروفایل

استاد

• Cognitive Science
• Decision-Making
• Psychometrics
• +۵ مورد دیگر

Professor Daniel Oppenheimer is a dual Professor at Carnegie Mellon University’s Dietrich College of Humanities and Social Sciences, holding roles in the Department of Social and Decision Sciences and affiliated with the Department of Psychology. His research focuses on cognitive science, decision-making processes, and their applications in education, policy, and charity. He has authored influential books like *Democracy Despite Itself* and *Psychology: A Cartoon Introduction*. Notable awards include the Ig Nobel Prize for humor and rigorous scholarship, and multiple teaching accolades. Education: B.A. Psychology (Rice University), M.A. and Ph.D. Psychology (Stanford University). Research Interests: Information processing, metacognition, charitable giving, democracy’s psychological foundations, and educational interventions. His lab innovatively integrates humor and creativity into academic work, such as weekly ‘failure’ awards and ice cream bets. Teaching innovations include cartoon-based introductory psychology courses and workshops on pedagogical strategies. Awards: Einhorn Young Investigator Award (Society of Judgment and Decision Making) Beattie Mid-Career Award (European Association for Decision Making) Ig Nobel Science Humor Award (2016) for research on unnecessary complexity. Advising & Grants: While specific grants are not detailed, his work on educational interventions and decision science likely involves interdisciplinary funding. He emphasizes student autonomy and metacognitive training in his advising.

Willem-Jan Van Hoeveمشاهده پروفایل

استاد

• Operations Research
• Constraint Programming
• Integer Programming
• +۷ مورد دیگر

Willem-Jan Van Hoeve is the Carnegie Bosch Professor of Operations Research at the Tepper School of Business , Carnegie Mellon University . He also holds a courtesy appointment at the Heinz College, Carnegie Mellon University. His research focuses on methodologies in operations research , including mathematical optimization , constraint programming , decision diagrams , integer programming , and machine learning , with applications to network design , scheduling , vehicle routing , data mining , healthcare operations , and analytical marketing . He pioneered decision diagrams for optimization and developed the Decision Diagrams for Optimization research project with source code and benchmarks. His funded research includes National Science Foundation (2019-2024, 2011-2015), Office of Naval Research (2021-2024, 2018-2021), and Google Faculty Research Awards (2015, 2011). He has consulted for FedEx Ground, Exxon Mobil, PNC Bank, Bosch/Siemens, Charter Steel, and non-profit organizations. Academic Service and Leadership: Board of Directors, INFORMS Computing Society (2019-2021) Chairperson, INFORMS Student Competition Committee (2018-2020) Steering Committee, CPAIOR Conference Series Executive Committee, Association for Constraint Programming (2013-2016) Associate Editor roles at INFORMS Journal on Computing , Operations Research , and Artificial Intelligence Scientific Awards: INFORMS Computing Society Harvey J. Greenberg Research Award (2020) IISE Transactions Best Application Paper (2022) EJOR Best Paper Award (2019) Best paper award at CP Conference (2006) Best student paper award at CP Conference (2004) Key Academic Contributions: He has advised PhD students David Bergman, Andre Cire, Christian Tjandraatmadja, and others. His work includes co-authorship of the book Decision Diagrams for Optimization (Springer, 2016), and development of software like HADDOCK and Seq2Pat library.

Aradhana Goelمشاهده پروفایل

استاد مدعو

• Product Strategy
• New Product Development
• Human-centered Design
• +۶ مورد دیگر

Aradhana Goel serves as Adjunct Professor for User Research for Digital and Physical Products (course 49-605) at Carnegie Mellon University's Integrated Innovation Institute. With over 25 years of leadership experience, she previously held roles as Head of Product Design & Behavior Science for Bayer Pharmaceuticals' Digital Health Portfolio, focusing on Cardiovascular & Women's Health solutions, and contributed to major transformations at Capital One, Sunrun, and IDEO. Education: Master of Science, Design and Digital Technology - Massachusetts Institute of Technology, Cambridge, US Master, Urban Design - School of Planning and Architecture, New Delhi, India Diploma in Architecture - Sushant School of Art & Architecture, Gurgaon, India Research Focus: Goel's work bridges behavior science and product innovation, emphasizing human-centered approaches to complex systems. Her expertise spans Product Strategy, New Product Development, and Behavior Change Design with applications in healthcare, financial services, and sustainable energy. She champions data-driven cultures while maintaining deep empathy for user needs through narrative techniques and systems-level thinking. Publication Trends: Her scholarly output reveals an evolutionary trajectory from foundational work in user empathy methods (2003) and public information systems (2004) toward contemporary AI-driven healthcare solutions (2021), consistently applying behavioral insights to solve real-world problems across diverse domains. Awards: IDEA Silver 2012 for Redbox Redesign (Industrial Designers Society of America) Professional Impact: Goel has built high-performing cross-functional teams across pharmaceutical, banking, and renewable energy sectors while co-founding IDEO's India office. Her philosophy emphasizes proactive life shaping through strategic choices, reflected in her career transitions from architecture to digital health leadership. She actively shares insights through industry speaking engagements on Product & Design Innovation.

• Combinatorics
• Probability Theory
• Computer Science
• +۳ مورد دیگر

Po-Shen Loh is a Professor of Mathematics at Carnegie Mellon University, affiliated with the Mellon College of Science's Department of Mathematical Sciences since 2010. He is renowned for his dual focus on academic research and innovative educational outreach. Loh’s research spans combinatorics, probability theory, and extremal networks, with notable contributions to understanding network properties and their applications. He led the U.S. International Mathematical Olympiad (IMO) team to four first-place finishes between 2015–2023, emphasizing collaborative problem-solving and global peer engagement. Beyond academia, Loh developed the NOVID app for pandemic control, leveraging network science to empower users in avoiding infection. He also founded Expii.com, a free platform offering diverse explanations for math and science concepts, and launched the LIVE learning platform blending math education with performing arts. His work bridges disciplines, aiming to democratize education and prepare future leaders in an AI-driven world. Education & Background : Grew up in a math-centric household in Madison, Wisconsin, with parents in academia. Earned his Ph.D. in Algorithms, Combinatorics, and Optimization from Carnegie Mellon (though exact dates not specified in texts). Research & Leadership : Specializes in extremal combinatorics, including network structure analysis. His research on probabilistic network existence proofs has advanced graph theory. As IMO coach, he pioneered cross-national training programs, inviting international teams to U.S. camps. Awards & Recognition : Recipient of the 2019 Presidential Early Career Award (PECASE). Recognized for pandemic control innovations and educational initiatives. Outreach : Conducted a record-breaking 40-city math tour in 2021, developed the Daily Challenge math curriculum, and launched Moderate Zoom Chat to improve virtual engagement. Active in mentorship programs like LEAD NATURALLY, targeting culturally diverse leaders. Notable Projects : NOVID app (pandemic control), Expii (free educational resource), and LIVE platform (math education via live-streaming).

Cosma Shaliziمشاهده پروفایل

دانشیار

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

Aditya Prakashمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Aditya Prakash is the Associate Chair for Academic Affairs and an Associate Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology, Atlanta. He received his PhD in Computer Science from Carnegie Mellon University in 2012 and a BTech in Computer Science and Engineering from the Indian Institute of Technology - Bombay in 2007. His research spans Data Science, Machine Learning, and AI with emphasis on solving big-data problems in networks and sequences. Dr. Prakash focuses on understanding and managing dynamical mechanisms (like propagation) on networks occurring across natural, social and technological systems. His work addresses problems motivated by public health, computational epidemiology, cybersecurity, urban computing, and the web. His research methodology combines theoretical analysis of models, developing efficient algorithms, and empirical studies on large-scale data. Dr. Prakash leads multiple significant research projects including the NSF BEHIVE project for developing the science of pandemic prevention and prediction, collaborating with members from Georgia Tech, MIT, Michigan, Mayo Clinic, and UGA. His group has been actively involved in numerous efforts as part of the COVID-19 response, with work highlighted in Nature Biotechnology and IEEE Spectrum. His research output shows strong trends in time-series forecasting, epidemic modeling, and network analysis. Recent publications demonstrate increasing integration of large language models with traditional forecasting methods, development of specialized architectures for epidemiological modeling, and innovative approaches to handling uncertainty in public health forecasting. Facebook Faculty Award on Statistics for Improving Insights, Models, and Decisions (2021) Best Paper Award at AI4ABM workshop at ICML 2022 Alex Rodriguez (his PhD student) recognized as a 'Rising Star in Data Science' (2021) First place in the COVID-19 Symptom Data Challenge (organized by Facebook/Catalyst Health/CMU) Second place in the C3AI COVID-19 Grand Challenge Dr. Prakash has successfully advised numerous PhD students who have gone on to faculty positions at institutions like University of Michigan, University of Iowa, and Virginia Tech, as well as positions at major technology companies including Google and Pinterest. His research has been supported by multiple NSF grants including NSF IIS Medium, NSF CISE Expeditions, NSF CAREER, and CDC MIND funding. He leads the BEHIVE project and has been involved in multiple pandemic response initiatives through the MIDAS network. He teaches courses including 'CSE 8803: EPI: Data Science for Epidemiology' and 'CSE 8803 IUC: Introduction to Urban Computing,' demonstrating his commitment to training the next generation of researchers in these critical interdisciplinary areas.

Baruch Fischhoffمشاهده پروفایل

استاد

• Decision Science
• Risk Analysis
• Science Communication
• +۸ مورد دیگر

Baruch Fischhoff is the Howard Heinz University Professor at Carnegie Mellon University, affiliated with the Department of Engineering and Public Policy and the Carnegie Mellon Institute for Strategy & Technology (CMIST). He serves on multiple high-impact committees including the National Academies and NIH Advisory Council, focusing on energy transition, pandemic preparedness, and risk communication. PhD in Psychology, Hebrew University (1975) BS in Mathematics and Psychology, Wayne State University (1967) His research spans decision-making under uncertainty , risk analysis , and science communication across health, national security, and environmental domains. Recent work examines AI-human collaboration , social bot detection , and energy justice frameworks . Key trends in his publications reveal a focus on interdisciplinary risk assessment (2011-2025), cognitive biases in policy-making , and practical science communication strategies . His 2025 works address heuristic decision-making and adaptive research models . Scientific Recognition Andrew Carnegie Fellowship Honorary Doctorate from Lund University National Academy of Sciences & Medicine member Outstanding Mentor Award, College of Engineering As advisor to graduate researchers like Brissa Acevedo (energy justice) and Reyhaneh Mohsenzadeh Yazd (energy storage communication), he bridges disciplines through projects like Ice Preservation Institute and CDC Polio Containment Working Group participation.