جست‌وجوی استاد و پژوهشگر

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.

Anastasia Ailamakiمشاهده پروفایل

دانشیار مدعو

• Database Systems
• Computer Architecture
• Scientific Data Management
• +۴ مورد دیگر

Anastasia Ailamaki is an Adjunct Associate Professor at Carnegie Mellon University's School of Computer Science, Department of Computer Science. Since 2008, she leads the Data-Intensive Applications and Systems (DIAS) laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL). Her research focuses on database systems, computer architecture, and scientific data management. She is a co-founder of CMU's Database Group and a member of the Parallel Data Lab. Research Interests: Staged Database Systems Architecture-Conscious Query Processing Automated Database Design for Scientific Data Performance Optimization for Deep Memory Hierarchies Workflow Management and I/O Traffic Modeling Professional Activities: Program Chair, Workshop on Data Management on New Hardware (2006) General Chair, Workshop on Data Management on New Hardware (2005) Editorial roles for IEEE Data Engineering and ACM SIGMOD Teaching: Taught courses like Database Applications, Database Management Systems, and Hot Topics in Database Systems at CMU from 2001-2006. Students: Advised over a dozen PhD and Master’s students, including graduates at Intel, Google, and MIT.

Prasad Tetaliمشاهده پروفایل

استاد

• Discrete Mathematics
• Probability Theory
• Theory of Computing
• +۶ مورد دیگر

Prasad Tetali is the Alexander M. Knaster Professor and Department Head of the Department of Mathematical Sciences at Carnegie Mellon University (CMU), located in Pittsburgh, PA. He also holds adjunct professorships at Emory University (Math/CS) and the Georgia Institute of Technology (Math/CoC). His academic journey includes a Ph.D. from the Courant Institute of Mathematical Sciences, NYU, and postdoctoral research at AT&T Bell Labs. Education: Ph.D. (1991), Courant Institute of Mathematical Sciences, NYU M.S. (1987), Indian Institute of Science, Bangalore, India Postdoctoral Appointments: Mathematical Sciences Research Center, AT&T Bell Labs Research Interests: Dr. Tetali's work focuses on Discrete Mathematics, Probability Theory, and Theoretical Computing. Key areas include Markov chains, isoperimetry, combinatorics, computational number theory, and algorithm design. His contributions span foundational theory and applications in optimization, statistical physics, and network analysis. Publications & Trends: His research has led to influential papers on topics like mixing times of Markov chains, entropy inequalities, and combinatorial optimization. Notable works include foundational studies on the Potts model, the Swendsen-Wang algorithm, and sharp threshold phenomena in number theory. Awards & Recognition: AAAS Fellow SIAM Fellow (Inaugural Class, 2009) Fellow of the American Mathematical Society AMS Fellow (Inaugural Class, 2012) Georgia Tech’s Regents Professor Advising & Grants: Dr. Tetali has mentored 11 PhD students and numerous postdocs. His grants include leadership roles in initiatives such as the SIAM Activity Group on Discrete Mathematics and the ACO PhD Program at Georgia Tech. He has also held editorial roles at top journals like SIAM Journal on Discrete Mathematics and Random Structures & Algorithms. Labs & Collaborations: His work often intersects with interdisciplinary teams, including contributions to distributed algorithms, network science, and computational mathematics. He actively collaborates with researchers in computer science, physics, and applied mathematics.

Jason Howellمشاهده پروفایل

استاد آموزشی

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۵ مورد دیگر

Jason Howell is a Teaching Professor and Associate Department Head in the Department of Mathematical Sciences at Carnegie Mellon University. He holds a Ph.D. in Mathematical Sciences from Clemson University. His research focuses on numerical analysis and computational mathematics, particularly finite element methods for fluid and structural dynamics, fast linear solvers, and applications in natural and social sciences. He actively mentors undergraduate research projects in fluid-structure interaction, numerical linear algebra, and differential equations. His work spans theoretical foundations of finite element methods, including compatibility conditions and mixed formulations, as well as applied projects with chemists and physicists on topics like solubility parameters and nanostructures. Current research activities include computational fluid dynamics, iterative solvers for large systems, and mathematical modeling in biomedical and engineering contexts. Notable contributions include studies on cantilever dynamics, sparse matrix preconditioning, and viscoelastic flow modeling. He collaborates across disciplines, integrating numerical methods with real-world applications in materials science and engineering systems. His teaching and research emphasize bridging mathematical theory with practical scientific challenges.

Boris Bukhمشاهده پروفایل

استاد

• Combinatorics
• Discrete Geometry
• Extremal Graph Theory
• +۷ مورد دیگر

Boris Bukh is a Professor of Mathematics at Carnegie Mellon University (CMU), affiliated with the Department of Mathematical Sciences within the Mellon College of Science. He holds a Ph.D. from Princeton University and has held postdoctoral positions at the University of Cambridge and Churchill College. His research focuses on combinatorics, discrete geometry, extremal graph theory, and geometric selection theorems. He has received prestigious awards such as the Sloan Research Fellowship and the NSF CAREER Award. His work spans topics like Turán problems, geometric configurations, and algebraic methods in combinatorics. Recent publications explore extremal graph structures, convex polytopes in restricted point sets, and applications of random algebraic constructions to computational complexity. Bukh organizes events like the Math Kangaroo competition, fostering mathematics engagement among students. Key contributions include advancements in Ramsey theory, coding theory, and the intersection of combinatorics with geometry. His research often bridges theoretical insights with computational techniques, addressing problems in graph density, geometric incidences, and discrete optimization.

Soummya Karمشاهده پروفایل

استاد

• Decision-Making in Large-Scale Networked Systems
• Stochastic Systems
• Multi-Agent Systems
• +۷ مورد دیگر

Soummya Kar is the Buhl Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He holds a Ph.D. (2010) and B.Tech (2005) in Electrical Engineering from CMU and IIT Kharagpur, respectively, and has been a Postdoctoral Research Associate at Princeton University (2010-2011). Research Interests: Decision-making in large-scale networked systems, stochastic systems, multi-agent control, and data science with applications in cyber-physical systems and smart energy grids. Scientific Awards: IEEE Fellow (2022) Dean’s Early Career Fellowship (2016) Wilton E. Scott Institute for Energy Innovation - Energy Fellow Recent Article Trends: His work focuses on distributed optimization, secure inference in sensor networks, smart grid resilience, and coded computing for fault tolerance, with keywords spanning Computer Science , Control Theory , and Energy Systems . Students & Team: He advises current and former Ph.D. students including Brian Swenson (Penn State), Javad Mohammadi (UT Austin), and Sergio Pequito (TU Delft), alongside postdoctoral researchers like Panayiotis Moutis. His research involves collaborations with labs such as CyLab Security and Privacy Institute and the Wilton E. Scott Institute for Energy Innovation.

Peter Zhangمشاهده پروفایل

استادیار

• Optimization Theory
• Supply Chain Management
• Transportation Systems
• +۳ مورد دیگر

Peter Zhang is an Assistant Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, with a courtesy appointment in Civil and Environmental Engineering. He holds a PhD in Engineering Systems from MIT (2019), an MASc in Mechanical and Industrial Engineering from the University of Toronto (2013), and a BASc in Engineering Science from the same university (2011). His research focuses on optimization theory, applied to supply chains, transportation systems, health, and socio-technical systems. He emphasizes robust optimization, dynamic and bilevel problems, and their applications in policy design and high-stakes decision-making. Education PhD in Engineering Systems, MIT (2019) MASc in Mechanical and Industrial Engineering, University of Toronto (2013) BASc in Engineering Science, University of Toronto (2011) Research Interests Robust optimization and its applications Transportation safety and last-mile delivery systems Supply chain resilience and policy design Machine learning integration with optimization Awards & Recognition INFORMS Koopman Prize (2020) First Place, INFORMS Junior Faculty Forum (2022) Ford Engineering Excellence Award (2015) INFORMS Daniel H. Wagner Prize (2014) Advising & Mentorship Supervised PhD students including Hao Hao and Guanting Wu, with postdoctoral mentorship for Ningji Wei and Alberto Japón Sáez. Research teams have participated in datasets like the U.S. Household Commuting Dataset and NASA’s Blue Skies Competition. Labs & Collaborations Collaborates on transportation fairness, hydrogen aviation decarbonization, and school bus optimization through interdisciplinary projects involving data science and policy analysis.

Maria Kurnikovaمشاهده پروفایل

استاد

• Computational Chemistry
• Biophysical Chemistry
• Molecular Modeling
• +۳ مورد دیگر

Maria Kurnikova is a Professor of Chemistry at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. She holds concurrent roles as Co-Director of the Molecular Biophysics & Structural Biology (MBSB) Graduate Program and affiliated faculty in the Joint CMU-Pittsburgh Computational Biology Program. Her research focuses on computational chemistry, molecular modeling, and theoretical biophysics, particularly in understanding protein structure-function relationships through high-performance computing and multi-scale modeling approaches. Education: M.Sc., Moscow Institute of Physics and Technology, 1984–1990 Ph.D., University of Pittsburgh, 1998 Postdoctoral Fellowships: University of Tel Aviv (1998–1999) and University of Pittsburgh/NIST (1999–2001) Research Interests: Dr. Kurnikova's work spans computational modeling of membrane proteins, ion channels, and drug design. Her group develops hierarchical models to bridge atomistic and macroscale processes, with applications in understanding ligand binding, channel gating, and ion selectivity. Current projects include: Membrane protein receptors and ion channel dynamics In silico drug design using machine learning and free energy simulations Development of accurate force fields for molecular interactions Awards: 2024 Department of Chemistry Distinguished Alumni Award (University of Pittsburgh) 2002 Research Corporation Innovation Award Collaborations & Resources: Active user of NSF/NIH supercomputing facilities (e.g., Pittsburgh Supercomputing Center) Collaborations with Olexandr Isayev’s group on drug design methodologies Labs & Teams: The Kurnikova Research Group specializes in theoretical and computational approaches to biological macromolecules, with ongoing projects in TRPM7 channel regulation, AMPA receptor allostery, and heme transfer mechanisms.

William Kuszmaulمشاهده پروفایل

استادیار

• Theory
• Algorithms and Complexity
• Randomized Algorithms
• +۳ مورد دیگر

William Kuszmaul is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on designing and analyzing randomized algorithms and data structures, with a particular emphasis on hashing techniques, algorithm complexity, and data structure optimization. He holds a PhD from MIT, advised by Charles E. Leiserson, and previously served as the Rabin Postdoc of Theoretical Computer Science at Harvard University. His research interests include theory and algorithms and complexity , with recent work addressing optimal bounds for open addressing, efficient cuckoo hashing, and tight analyses of linear probing. Notable contributions include studies on list labeling, minimal perfect hashing, and distributed load balancing under dependency constraints. Kuszmaul has advised students Jingxun Liang and Renfei Zhou in his current role. His publications span 2023–2025, reflecting a focus on foundational algorithmic problems with applications in memory management, parallel computing, and data structure efficiency. Key themes include optimizing hash table performance, analyzing algorithmic phase transitions, and establishing theoretical limits for dynamic retrieval systems. No scientific awards are explicitly listed in the provided materials. His academic trajectory includes postdoctoral work at Harvard and doctoral research at MIT, supported by the John and Fannie Hertz Fellowship. Current courses listed include 15-151 (Fall 2025) and 15-756 (Fall 2024) , suggesting involvement in both introductory and advanced computer science education.

Jonathan Caganمشاهده پروفایل

استاد

• Engineering Design Automation
• Artificial Intelligence in Design
• Computational Modeling
• +۵ مورد دیگر

Jonathan Cagan is the George Tallman and Florence Barrett Ladd Professor in Engineering at Carnegie Mellon University's College of Engineering. His work bridges AI, machine learning, and cognitive science to enhance engineering design and decision-making. He co-founded CMU's Integrated Innovation Institute and held leadership roles including Associate Dean and Interim Dean. Research focuses on computational modeling of designer processes, biomechanical systems, and human-AI collaboration. Collaborations span psychology, neuroscience, computer science, and architecture. Recent publications highlight AI integration in design automation, additive manufacturing, and mixed reality systems. His work explores trust dynamics, confidence modeling, and optimization algorithms in human-AI teams. Scientific awards include the Robert A. Doherty Award for Excellence in Education and the ASME Design Theory and Methodology Award. He is a Fellow of ASME.

Robert Harperمشاهده پروفایل

استاد

• Programming Languages
• Type Theory
• Cost Semantics
• +۳ مورد دیگر

Robert Harper is a Professor in the Computer Science Department at Carnegie Mellon University, Pittsburgh, PA. His research focuses on the intersection of type theory and programming language design, with emphasis on formal verification, cost semantics, and concurrency models. University: Carnegie Mellon University Department: Computer Science Academic Rank: Professor Email: rwh@cs.cmu.edu Harper's research program integrates cost analysis with behavioral properties in type theory, including Synthetic Tait Computability for logical frameworks and Amortized Analysis via Coalgebraic Methods . He explores foundational issues in Higher-Order Recursion and resource-sensitive programming through Decalf , a directed effectful cost-aware framework. His work combines theoretical rigor with practical implementation in systems like Twelf and Red* . Recent publications demonstrate his contributions to Computational Adequacy , Session-Typed Concurrency , and Compiler Verification . Collaborations with institutions like the Max Planck Institute for Software Systems and researchers such as Stephanie Balzer and Jan Hoffmann extend his impact to concurrent systems and cost-sensitive programming. Scientific Distinctions ACM SIGPLAN Programming Languages Achievement Award (2021) ACM Fellow (2005) Allen Newell Award for Research Excellence (2001) He advises students on projects like Cost Verification in Calf and Parallel Scan Verification , while teaching advanced courses including 15-413: Advanced Topics in Principles of Programming Languages (Spring 2025) and 15-312: Principles of Programming Languages (Fall 2025). Research funded by AFOSR grants including MURI A210038S002.

Weina Wangمشاهده پروفایل

استادیار

• Applied Probability
• Stochastic Systems
• Reinforcement Learning
• +۷ مورد دیگر

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .