جست‌وجوی استاد و پژوهشگر

• Computer-Aided Process Engineering (CAPE)
• Flowsheet Optimization
• Reactor Network Synthesis
• +۸ مورد دیگر

Lorenz T. (Larry) Biegler is the Covestro University Professor in the Chemical Engineering Department at Carnegie Mellon University. He has held numerous visiting and research positions, including at Northwestern University, Argonne National Lab, and the University of Alberta, and is a leading figure in process systems engineering. Education: Ph.D. in Chemical Engineering (University of Wisconsin, 1981), M.S. in Chemical Engineering (University of Wisconsin, 1979), B.S. in Chemical Engineering (Illinois Institute of Technology, 1977) Research Interests focus on optimization and numerical methods for chemical processes, including: Advanced algorithms for flowsheet simulation and optimization Large-scale nonlinear programming (NLP) methods Dynamic modeling of chemical and energy systems Real-time nonlinear estimation and control Design optimization using PDE-based models Applications in CO2 capture and energy sustainability Awards and Honors include: Sargent Medal (2022) for contributions to computer-aided process engineering Long-Term Achievements Award by the European Federation of Chemical Engineering Fellowships from AIChE, IFAC, and SIAM Member of the US National Academy of Engineering Honorary doctorate from Technical University of Berlin Grants and Leadership : Awarded $400,000 from the U.S. Department of Energy and serves as director of Carnegie Mellon's Center for Advanced Process Decision-Making (CAPD).

Rachel Dzombakمشاهده پروفایل

استاد

• AI Ethics
• Innovation Strategy
• Digital Transformation
• +۵ مورد دیگر

Rachel Dzombak is the Distinguished Service Professor of Design and Innovation and Senior Innovation Advisor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. She also holds teaching roles in the Human-Computer Interaction Institute, Tepper School of Business, and College of Engineering. Her work focuses on AI ethics, innovation strategy, and digital transformation, with an emphasis on responsible AI adoption in public and private sectors. Dr. Dzombak earned her PhD in Civil and Environmental Engineering from UC Berkeley, followed by a postdoctoral fellowship at Berkeley’s Haas School of Business. She holds a BS in Bioengineering from Penn State University. Her research spans AI governance, circular systems, and sustainable engineering, with notable contributions to design thinking, technology policy, and social entrepreneurship. She founded Outcome x Design, a consultancy advising organizations on AI adoption and innovation processes. Previously, she led digital transformation initiatives at CMU’s Software Engineering Institute and contributed to Berkeley’s Master’s in Development Engineering program. Her articles and engagements emphasize ethical AI practices, systemic accountability, and bridging technology with societal needs. Collaborations include the DoD’s National Security Innovation Network and projects in Kenya’s healthcare sector. Dzombak’s teaching and advisory work aim to foster innovation that addresses global challenges while prioritizing human and environmental well-being.

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.

Michael P. Hendrichمشاهده پروفایل

استاد

• Spectroscopy
• Biophysical Chemistry
• Enzymatic Mechanisms
• +۶ مورد دیگر

Michael P. Hendrich is a Professor of Chemistry at Carnegie Mellon University's Mellon College of Science. He holds a Ph.D. from the University of Illinois (1988). His research focuses on metalloproteins and enzymes, particularly their catalytic mechanisms and structural dynamics. Using advanced spectroscopic techniques (EPR, ENDOR, Mössbauer), he investigates intermediates in enzymatic reactions and designs biomimetic models to understand oxygen activation, electron transfer, and redox processes. His work includes developing novel instrumentation and computational tools for studying metalloenzymes. Notably, he received the Searle Scholar Award in 1995. Education: Ph.D., University of Illinois, 1988. Research interests span bioinorganic chemistry, enzymatic mechanisms, and metalloprotein structure-function relationships. Key projects include studying metalloenzyme catalysis (e.g., cytochrome c peroxidases, diiron enzymes) and developing synthetic systems to mimic biological active sites. His interdisciplinary approach integrates experimental methods with computational modeling to elucidate catalytic pathways and reactivity. Publications highlight contributions to understanding enzyme intermediates (e.g., ferryl species), designing synthetic metalloproteins, and characterizing heterobimetallic complexes. Awards include the Searle Scholar Award. Labs/Teams: Active in the Department of Chemistry and collaborates with researchers in spectroscopy, biochemistry, and materials science. Leads projects on enzyme mechanisms and biomimetic chemistry at Carnegie Mellon's Mellon College of Science.

Tomasz Kowalewskiمشاهده پروفایل

استاد

• Physical Chemistry
• Atomic Force Microscopy
• Organic Electronics
• +۷ مورد دیگر

Tomasz Kowalewski is a Professor in the Department of Chemistry at Carnegie Mellon University, affiliated with the Mellon College of Science. He holds a Ph.D. from the Polish Academy of Sciences (1988) and has held academic positions since 1989, including roles at Washington University in St. Louis and Southern Illinois University. His research focuses on interdisciplinary polymer science, emphasizing self-assembly in organic materials, nanostructured carbons, and proximal probe techniques like AFM. Education: Ph.D., Polish Academy of Sciences, Poland (1988) Post-graduate studies, Institute of Physics, Polish Academy of Sciences (1979–1981) Research Interests: Design and synthesis of functional polymeric materials Nanostructured carbons for energy storage and electronics AFM development for nanoscale material characterization Conductive polymers and organic photovoltaics Self-healing polymer networks His work bridges molecular design, device fabrication, and nanoscale characterization, with applications in energy storage, sensors, and organic electronics. Lab & Collaborations: The Kowalewski Lab develops carbon-based materials from polymer precursors and explores directed self-assembly techniques. Collaborations include work with Krzysztof Matyjaszewski (controlled polymerization) and Anna C. Balazs (computational modeling). Recent projects include porous nanographenes for supercapacitors and nitrogen-doped carbons for fuel cells.

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.

Ananya Senمشاهده پروفایل

استادیار

• Digital Economy
• Media Platforms
• Online Misinformation
• +۳ مورد دیگر

Ananya Sen serves as Assistant Professor of Information Technology and Management at Carnegie Mellon University's Heinz College, previously holding a Post Doctoral Associate position at MIT Sloan School of Management. His research centers on empirical analysis of digital platforms, media ecosystems, and innovation dynamics using field experiments and observational data. Education: Ph.D. in Economics, Toulouse School of Economics Post Doctoral Associate, MIT Sloan School of Management Research Interests: Digital economy platform governance Online misinformation and trust mechanisms Political communication in digital spaces Data privacy economics Media innovation and content consumption His 2022-2025 publications reveal intense focus on GenAI misinformation, political messaging efficacy, book ban impacts, and platform monetization ethics. Sen consistently employs experimental methodologies to establish causal relationships across media, political, and educational contexts, demonstrating interdisciplinary rigor bridging economics, computer science, and policy studies. Scientific Awards: None documented in source materials. Advising and Grants: No information available regarding student supervision, research grants, or sponsored projects.

Larry Heimannمشاهده پروفایل

استاد آموزشی

• Organizational Decision Making
• Security Economics
• Game Theory
• +۲ مورد دیگر

Larry Heimann is a Teaching Professor at the Heinz College of Carnegie Mellon University in the Information Systems department. His research bridges organizational decision making and security economics , utilizing game theory and computer simulations to analyze cybercriminal behavior in network security contexts.

Anupam Guptaمشاهده پروفایل

استاد مدعو

• Algorithms
• Complexity
• Artificial Intelligence
• +۴ مورد دیگر

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.

Tai-Sing Leeمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Neuroscience
• Computer Vision
• +۵ مورد دیگر

Dr. Tai-Sing Lee is a Professor in the Computer Science Department at Carnegie Mellon University, with affiliations in the Center for the Neural Basis of Cognition (CNBC) and the Machine Learning Department. He holds adjunct roles at the University of Pittsburgh's Neuroscience Department. His research focuses on computational neuroscience, visual perception, and the intersection of biological and machine intelligence. Lee earned his S.B. from Harvard (1986), and dual Ph.D.s from Harvard (1993) and MIT (1993), followed by postdoctoral training at Harvard and MIT. He leads the Lee Lab for Biological and Machine Intelligence Research and directs programs such as the Peking University-CMU and Tsinghua-CMU summer programs in Computer Science. His research investigates computational principles of visual perception, leveraging neurophysiological, mathematical, and machine learning approaches. Key areas include statistical modeling of neural codes, learning/adaptation mechanisms in neural systems, and Bayesian inference frameworks. Lee has developed novel machine learning techniques for analyzing neural data, including studies on V1/V2/V4 neural coding and hierarchical perceptual inference. Lee has advised numerous Ph.D., M.S., and undergraduate students, many of whom pursued academic or tech careers. His honors include the NSF CAREER Award (2000) and ICCV Helmholtz Prize (2013). His lab’s projects include the MICrONS initiative to reverse-engineer brain algorithms and collaborations on predictive coding models in vision and music perception.

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Computer Graphics
• +۳ مورد دیگر

Yue Jiang is an incoming Assistant Professor at the University of Utah (Fall 2025) and is completing their PhD at Aalto University and the Finnish Center for Artificial Intelligence (FCAI). Their research focuses on computational user interface understanding, eye tracking, and adaptive GUI layouts. They have held roles such as Accessibility Chair for CHI2023/2024 and have organized workshops on computational UI methodologies. Education : PhD in Intelligent Systems (Aalto University & FCAI, Finland) Visiting PhD Student (CMU's BIG Lab, 2024) Master's in Computer Graphics (UMD, USA) Bachelor's in Computer Science & Mathematics (U of Toronto, Canada) Research Interests : Developing human-centered technologies for adaptive UIs, eye tracking analysis, and AI-driven HCI. Key projects include Graph4GUI, EyeFormer, and computational methods for GUI layout optimization. Awards : Meta PhD Fellowship (2023-2025) Google Europe Students with Disabilities Scholarship (2022) CHI2022 Best Paper Honorable Mention Heidelberg Laureate Forum Young Researcher (2024) Service : PC Member for VL/HCC2025, CHI2026 Associate Chair for CHI2025/2026 Organized three Computational UI Workshops at CHI Labs & Collaborations : Collaborates with Prof. Jeffrey Bigham (CMU), Prof. Wolfgang Stuerzlinger (SFU), and Prof. Christof Lutteroth (U of Bath). Former internships at Apple AIML Lab and Adobe Research.

Swarnalatha Ashokمشاهده پروفایل

استاد آموزشی

• Software Engineering
• Cloud Computing
• Platform Engineering
• +۶ مورد دیگر

Swarnalatha Ashok is an Associate Teaching Professor at the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. She is a member of the core faculty for the Master of Software Engineering (MSE) professional programs and serves as the Associate Director of MSE for Executive Education and Corporate Engagement. Previously, she held faculty and leadership roles at Singapore Management University (SMU) and the Institute of Systems Science (ISS) at National University of Singapore (NUS), where she directed and shaped software engineering and technology programs for industry professionals. Prior to her academic career, she worked in software engineering and project management roles at corporations like Tata Consultancy Services (TCS) and Motorola. Educational background includes an MSc(Tech) in Computer Science from BITS Pilani, India, and a PGDip in Business Administration from Annamalai University, India. Her research and teaching interests focus on practical software engineering topics such as API design, cloud computing, platform engineering, and agile methodologies. She emphasizes industry-relevant curriculum development and has supervised numerous industry projects, particularly in networking and systems software.

Eduardo Feo Flushingمشاهده پروفایل

استاد آموزشی

• Robotics
• Multi-Agent Systems
• Machine Learning
• +۴ مورد دیگر

Dr. Eduardo Feo Flushing is an Assistant Teaching Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science , based in Pittsburgh, PA. His role involves academic instruction and research at the intersection of software engineering, societal computing, and intelligent systems. Research Focus: His work spans robotics, distributed AI, and networked systems, with applications in critical domains. Primary research themes include: Multi-robot coordination for spatially distributed tasks Machine learning in healthcare diagnostics and renewable energy Wireless network optimization for mobile systems Human-robot collaboration under uncertainty Publication Trends: Recent articles (2021-2025) demonstrate a strong emphasis on applied machine learning (LLMs for medical ECG, deep learning for solar panel inspection) and advanced robotics (indoor mapping, task allocation in communication-constrained environments). Earlier work (2016-2020) focused on foundational aspects of multi-robot coordination, optimization, and wireless network resilience.

David Farberمشاهده پروفایل

استاد مدعو

• Computer Science
• Networking
• Internet Development
• +۳ مورد دیگر

David Farber is an Adjunct Professor of Internet Studies at the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. He played a pivotal role in early Internet development through initiatives like CSNet, NSFNET, NREN, and the NSF Gigabit Testbed. His work laid foundational infrastructure for modern Internet expansion. Key contributions to distributed systems and global networking adoption Recipient of prestigious SIGCOMM and John Scott Awards Active in Internet Society governance and national technology policy Contact: 5000 Forbes Avenue, Pittsburgh, PA 15213

Zachary Liptonمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۵ مورد دیگر

Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu