جست‌وجوی استاد و پژوهشگر

Gordon S. Ruleمشاهده پروفایل

استاد

• Biochemistry
• Structural Biology
• Enzymology
• +۳ مورد دیگر

Gordon S. Rule is a Professor and Interim Department Head in the Department of Biological Sciences at Carnegie Mellon University (CMU), part of the Mellon College of Science. His research focuses on understanding molecular interactions in biological systems using tools like NMR spectroscopy, X-ray crystallography, and computational methods. He holds a Ph.D. from CMU and completed postdoctoral training at Stanford University. Research interests include enzyme-substrate interactions (e.g., glutathione transferases), protein-DNA recognition via restriction endonucleases, and development of anti-infective drugs targeting thymidylate kinases. Collaborations include work with Dr. Saxena (University of Pittsburgh) and Dr. Jen-Jacobson (University of Pittsburgh). His lab also develops automated NMR assignment methods using Monte Carlo algorithms. Key publications (2017–2021) explore protein dynamics, enzyme specificity, and structural biology applications. His work bridges computational biology with experimental methods, contributing to drug discovery and molecular mechanism understanding.

• Empirical Asset Pricing
• ETF Markets
• Mutual Funds
• +۲ مورد دیگر

Linda Du is an Instructor of Finance at Carnegie Mellon's Tepper School of Business. She researches investment vehicles including ETFs and mutual funds, examining market microstructure and competition mechanisms. Her teaching focuses on valuation and financial modeling for undergraduates. Her publications explore algorithmic trading strategies, blockchain applications, and financial market interdependencies. Current work investigates liquidity transformation mechanisms in corporate bond ETFs.

• Causal Discovery
• Machine Learning
• Educational Software
• +۳ مورد دیگر

Richard Scheines is a Professor and Dean at Carnegie Mellon University, with affiliations in the Machine Learning Department and the Human-Computer Interaction Institute. His research focuses on causal discovery, particularly through the TETRAD project, which develops algorithms for inferring causal structures from statistical data. He also explores educational technologies, including intelligent tutoring systems and virtual labs for causal reasoning. Scheines’ work bridges computer science, philosophy, and education, emphasizing practical applications of causal inference in biomedical research and policy analysis. His research interests include causal modeling, machine learning, and the development of interactive educational software. Scheines has collaborated extensively on projects like the CSR online courseware and the Center for Causal Discovery. His contributions span theoretical advancements in causal inference, empirical studies on learning strategies, and interdisciplinary applications in healthcare and public policy. Publications highlight his work on measurement error, multidimensional data analysis, and the integration of AI into education. He has also engaged in policy-related research, such as evaluating environmental risk assessment frameworks. Scheines’ academic leadership and innovative approaches to causal discovery and educational technology have positioned him as a key figure in computational and applied philosophy.

Eswaran Subrahmanianمشاهده پروفایل

استاد پژوهشی

• Socio-technical systems design
• Decision support systems
• Engineering informatics
• +۵ مورد دیگر

Eswaran Subrahmanian is a Research Professor at Carnegie Mellon University (CMU), affiliated with the Engineering Research Accelerator and the Department of Engineering and Public Policy within the College of Engineering. He concurrently serves as a Visiting Honorary Professor at the International Institute of Information Technology, Bangalore and an Adjunct Faculty member at Ahmadabad University. His roles include part-year work at the National Institute of Standards and Technology (NIST), USA, focusing on smart networks and societies. Education: Ph.D. in Public Policy & Information Systems, Carnegie Mellon University (1987) M.S. in Computer Science, University of South Carolina (1979) B.E. (Hons) in Chemical Engineering, Birla Institute of Technology & Science (1976) Research Focus: Subrahmanian's work centers on socio-technical systems design, decision support systems, and design theory. He explores intersections of technology, public policy, and societal impact through projects with industry leaders like Boeing, Bosch, and the World Bank. His theoretical contributions include the PSI framework and categorical models for complex systems analysis. Publications & Awards: With over 90 peer-reviewed articles and co-edited books, he co-authored We Are Not Users: Dialogues, Diversity, and Design (MIT Press, 2020). Honors include the Steven Fenves Award (2006), AAAS Fellowship (2008), and ACM Distinguished Scientist (2013). Advising & Innovation: Co-founder of Fields of View , a nonprofit designing games/simulations for policy challenges, and involved in initiatives like the National Health Information Systems Project. His work emphasizes inclusive design methodologies and interdisciplinary collaboration. Labs & Teams: Active in the Engineering Research Accelerator and collaborates across disciplines to address urban systems, energy policy, and ICT for sustainable development.

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.

Freida Abtanمشاهده پروفایل

دانشیار

• Electronic Music Composition
• Immersive Media
• Multimedia Performance
• +۳ مورد دیگر

Abtan’s research focuses on the intersection of technology design and artistic practice, emphasizing real-time systems and inclusive audiovisual mappings. She teaches Electronic Music Composition and contributes to programs in Music & Technology at CMU. Her creative output includes solo projects and collaborative works that challenge traditional boundaries between media and performance. Her articles and installations reflect interests in inter-sensory gestures, instrument design, and gender dynamics in electronic music culture. Abtan’s work bridges experimental music traditions with cutting-edge digital innovation, fostering interdisciplinary approaches to artistic creation.

Annie Hui-Hsin Hsiehمشاهده پروفایل

عضو هیئت علمی

• Music Composition
• Electroacoustic Music
• Acoustic Music
• +۳ مورد دیگر

Annie Hui-Hsin Hsieh is an Associate Teaching Professor of electronic music and composition at Carnegie Mellon University's School of Music in Pittsburgh, USA. She holds a doctorate from the University of California, San Diego, and prior degrees from the University of Melbourne. Her work focuses on creating visceral physical experiences through acoustic and electroacoustic mediums, emphasizing spatial gestures and relational sociality in sound. Her music has been performed internationally by ensembles such as the BBC Scottish Symphony Orchestra, JACK Quartet, and ELISION Ensemble. Notable commissions include works for Symphony Services Australia, Lucerne Festival, and Wien Modern. She has received prestigious awards including the Belegura Composer Award (2022 Melbourne Prize) and the 2023 Monash Performance Art Centre Commission. Recent projects include collaborations with video artist Johannes DeYoung and roboticist Jean Oh, showcased at festivals like MaerzMusik Berlin and Digerati Emergent Media Festival. Her research explores intersections of sound, technology, and interdisciplinary art.

Richard Randallمشاهده پروفایل

دانشیار

• Music Theory
• Neuroscience of Music
• Cultural Studies
• +۵ مورد دیگر

Dr. Richard Randall is an Associate Professor of Music Theory at Carnegie Mellon University's School of Music and holds a faculty appointment at the Center for the Neural Basis of Cognition and the Neuroscience Institute. His research focuses on the cultural, technological, and psychological foundations of musical experiences, with particular emphasis on auditory perception, cognitive processes, and the intersection of music with social and political contexts. He directs the Music Experience Lab (MEL), which explores interdisciplinary questions about music's role in human life, including projects centered on Romani musicians in the Balkans and the ethical dimensions of music technology. Randall co-founded the Listening Spaces Project, examining how technology shapes contemporary musical practices, and led initiatives like the Pittonkatonk festival, advocating for music as a public good. His work bridges neuroscience, media studies, and ethnomusicology, supported by grants such as the Rothberg Research Award and NIH funding. Affiliations: School of Music, Center for the Neural Basis of Cognition, Neuroscience Institute Key Projects: MEL, Listening Spaces Project, Romani Drummers Project, Pittonkatonk Education: (Not explicitly stated in provided text) Randall's research spans neuroimaging studies of auditory perception (e.g., auditory scene analysis) to cultural critiques of digital music distribution. His lab emphasizes collaborations with artists, activists, and technologists to address systemic issues like cultural representation and labor rights in music. He co-edited 21st Century Perspectives on Music, Technology, and Culture and developed educational programs such as the Young Musicians Collaborative, fostering community engagement through music. His recent publications explore topics like predictive coding models of musicality perception and the impact of low-level auditory features on grouping strength. Awards include the NIH grant T32-MH19983. Randall also leads the experimental ensemble Bombici, merging Balkan folk traditions with electronic improvisation. Grants: Rothberg Research Award, NIH grant T32-MH19983, Fine Foundation, Sprout Fund Labs/Teams: Music Experience Lab (MEL), Listening Spaces Project, Bombici collective

• Applied Mathematics
• Data Science
• Partial Differential Equations
• +۷ مورد دیگر

Dejan Slepčev is a Professor and Associate Dean for Faculty and Graduate Affairs at the Mellon College of Science, Department of Mathematical Sciences, Carnegie Mellon University. His research focuses on applied analysis, with applications to data science, collective behavior of particle systems, and energy-driven systems. He holds a Ph.D. from the University of Texas at Austin. His research interests include partial differential equations, calculus of variations, optimal transportation, and their applications to machine learning tasks such as clustering and classification. He develops mathematical frameworks for analyzing variational and PDE-based problems on random data samples, improving algorithms for data science. He also studies systems of interacting particles, particularly those modeling collective behavior in biological and physical systems, and energy-driven systems involving pattern formation and interface evolution. His work bridges theoretical analysis and computational methods, addressing challenges in data analysis, image processing, and the dynamics of complex systems. Key contributions include studies on spectral clustering, graph-based semi-supervised learning, and the continuum limits of discrete variational problems. Education: Ph.D., University of Texas at Austin.

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.

David Offnerمشاهده پروفایل

استاد آموزشی

• Combinatorics
• Graph Theory
• Discrete Mathematics

David Offner is an Associate Teaching Professor and Director of Undergraduate Studies in the Department of Mathematical Sciences at Carnegie Mellon University's Mellon College of Science. He teaches combinatorics and mentors undergraduate researchers. His research focuses on combinatorial problems including packing/decomposition problems, polychromatic coloring problems, and pursuit-evasion games on graphs. His publications reflect a strong emphasis on discrete mathematics and graph theory applications. Offner has mentored numerous undergraduate researchers on accessible combinatorial problems. He serves as Director of Undergraduate Studies, overseeing academic programs and student advising.

Elizabeth Holmمشاهده پروفایل

استاد

• Computational Materials Science
• Microstructural Evolution
• Machine Learning in Materials
• +۳ مورد دیگر

Elizabeth Holm is Professor and Department Chair of Materials Science and Engineering at the University of Michigan. Her research employs computational methods to study microstructural evolution, with expertise spanning atomic-scale molecular dynamics to continuum-scale modeling. Education includes a PhD in Materials Science and Scientific Computing from University of Michigan. Research focuses on computational materials science with emphasis on grain boundary dynamics, machine learning applications in materials, and additive manufacturing characterization. Recent publications develop AI methods for microstructure prediction, grain growth modeling, and automated analysis of material images. Work integrates machine vision with physics-based models to understand material behavior across scales. National Academy of Engineering Member (2025) ASEE Mike Ashby Outstanding Materials Educator Award (2022) AIME Honorary Member (2022) ASM Edward DeMille Campbell Memorial Lecturer (2021) The Minerals, Metals & Materials Society Fellow (2019) Professor Holm leads research on computational design of hierarchical materials and has held leadership positions including President of The Minerals, Metals & Materials Society (2013).

• Materials Chemistry
• Biomaterials
• Sustainability
• +۳ مورد دیگر

Newell Washburn is an Associate Professor in the Departments of Chemistry and Biomedical Engineering at Carnegie Mellon University, with a courtesy appointment in Materials Science and Engineering. He is affiliated with the Mellon College of Science and leads the Washburn Group, focusing on sustainable materials chemistry and machine learning applications. His work emphasizes reducing environmental impact through innovations in biobased polymers, cement systems, and advanced manufacturing. Education: Ph.D. in Chemistry (University of California, Berkeley, 1998); B.S. in Chemistry (University of Illinois at Urbana-Champaign, 1993). Prior roles include Research Chemist at NIST (2000–2004) and Post-Doctoral Associate at the University of Minnesota (1998–2000). Research interests span materials sustainability, polymer formulation, and machine learning-driven design. Key projects include developing hierarchical machine learning algorithms for rapid material optimization, biobased polyurethanes, and low-carbon cement alternatives like LC3. His work bridges laboratory experimentation with computational modeling, preparing students for interdisciplinary challenges. Recent articles highlight advancements in cement rheology modeling, autonomous laboratory networks, and drug design via diffusion models. Awards include the Covestro Global Science Award (2020) and the Coulter Foundation Translational Research Award (2010). Labs/Teams: The Washburn Group collaborates across disciplines, integrating chemistry, engineering, and data science to address global sustainability goals.

Tom Bohmanمشاهده پروفایل

استاد

• Extremal Combinatorics
• Probabilistic Combinatorics
• Discrete Mathematics
• +۳ مورد دیگر

Tom Bohman is a Professor of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. His research focuses on extremal and probabilistic combinatorics, exploring discrete structures inspired by mathematics, information theory, statistical physics, and computer science. He holds a Ph.D. from Rutgers University and has held postdoctoral positions at MIT and the Mathematical Sciences Research Institute (MSRI). Bohman's work includes studies on random graph processes, such as the triangle-free process and Hamilton cycles in random graphs, as well as hypergraph coloring and Ramsey numbers. His publications span over three decades, addressing topics like coprime matchings, lonely runner conjectures, and dynamic concentration phenomena in combinatorial systems. He teaches advanced courses in combinatorics and discrete mathematics, including Graph Theory and Random Structures & Algorithms. His research has contributed to understanding phase transitions in random processes and the interplay between combinatorial structures and probabilistic methods. While no awards are explicitly listed, his extensive publication record and editorial roles (e.g., with Random Structures & Algorithms ) highlight his academic impact. Bohman's work often bridges theoretical foundations with algorithmic applications, influencing both pure and applied combinatorics.

Rebecca Taylorمشاهده پروفایل

استاد

• DNA nanotechnology
• Advanced manufacturing
• Biomechanics
• +۱۲ مورد دیگر

Rebecca Taylor is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, with courtesy appointments in Biomedical Engineering and Electrical and Computer Engineering. Her research spans DNA nanotechnology, advanced manufacturing, and bio-inspired micro/nanosystems, focusing on integrating self-assembly with top-down microfabrication. 2013 Ph.D. in Mechanical Engineering (minor in Bioengineering), Stanford University 2010 MS in Mechanical Engineering, Stanford University 2001 BS in Mechanical Engineering with Robotics Certificate, Princeton University Her research centers on: (1) DNA nanotechnology for molecular and cellular mechanobiology (AFOSR YIP, NIH R21), (2) bio-inspired materials using gammaPNA (NSF CAREER), and (3) biomanufacturing with DNA robotics. She also develops workforce training tools like voice assistants for skill mastery. Recent publications highlight DNA microswimmers, compliance in colloidal assemblies, and generative design for origami nanostructures. Awards include the NSF CAREER Award and Ansys Career Development Chair. She advises interdisciplinary students and leads the Microsystems and Mechanobiology Lab, collaborating with Biomedical Engineering, Chemistry, and Cardiovascular Medicine teams. NSF CAREER Award Ansys Career Development Chair Lab members include postdocs (Grace Rohaley, Sarah Weintraub), Ph.D. students (Taryn Imamura, Vismaya Walawalkar), and undergraduates (Irene Yap). She teaches courses like Nanoscale Manufacturing Using Structural DNA Nanotechnology and Modern Manufacturing in Steeltown , emphasizing design and automation.