جستجوی استاد راهنمای دانشگاهی

Larry Pileggi is the Coraluppi Head and Tanoto Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He is also the Department Head of CMU’s ECE Department and has held prior roles at Westinghouse Research and Development and the University of Texas at Austin. His work bridges academic research and industry innovation, with co-founding ventures like Fabbrix Inc., Extreme DA, and Pearl Street Technologies. Dr. Pileggi earned his Ph.D. in Electrical and Computer Engineering from CMU (1989), following an M.S. (1984) and B.S. (1983) in Electrical Engineering from the University of Pittsburgh. His research focuses on three core areas: secure integrated circuit hardware (mitigating supply-chain threats), integrated circuits design methodologies (supporting sub-20nm CMOS and heterogeneous technologies), and power systems simulation (developing robust grid analysis tools like SUGAR). He has been recognized with numerous awards, including the prestigious 2023 Phil Kaufman Award for contributions to electronic system design, and is an IEEE Fellow. His academic leadership includes fostering maker initiatives and interdisciplinary research programs to address future challenges in energy and computing systems. Pileggi’s advising record includes over 47 Ph.D. students, many of whom collaborate with him in industry ventures. Current and past grants support his work on resilient power grids and novel memory technologies, reflecting a commitment to both theoretical and applied research. His lab, the Pileggi Lab, develops cutting-edge solutions for energy and integrated systems, emphasizing scalable simulation, secure hardware design, and next-generation memory architectures. Collaborations span institutions like ETH Zurich and industry partners in EDA and semiconductor sectors.

• Secure Integrated Circuit Hardware
• Integrated Circuits Design Methodologies
• Power Systems Simulation and Optimization
• +۳ مورد دیگر

Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.

• Statistical Genetics
• Genomics
• Biostatistics
• +۵ مورد دیگر

Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.

• Scientific Machine Learning
• Trust-Worthy UQ
• Likelihood-Free Inference
• +۱ مورد دیگر

Qing Li is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.E. in Electronics Engineering from Tsinghua University (2006) and a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2013). Prior to CMU, he worked as a postdoctoral researcher at the National Institute of Standards and Technology (NIST), where he developed quantum frequency conversion and microresonator-based optical systems. His research focuses on light-matter interactions in integrated photonics, emphasizing nonlinear optics and quantum information processing. He has pioneered silicon carbide and aluminum nitride platforms for chip-scale quantum technologies and optical metrology. Dr. Li has been recognized with prestigious awards including the Darpa Young Faculty Award (2019), OSA Paul F. Forman Team Engineering Excellence Award (2020), and Sigma Xi Best Ph.D. Thesis Award (Georgia Tech). His work bridges classical and quantum information systems, with applications in secure communication, atomic systems interrogation, and high-precision frequency synthesis. He actively contributes to the Pittsburgh Quantum Institute (PQI), advancing regional quantum engineering initiatives. His research group’s key projects include developing compact optical frequency synthesizers, soliton microcombs for communication grids, and entangled photon pair sources for quantum networks. Grants and collaborations support his exploration of novel photonic materials and devices, targeting advancements in both fundamental science and applied technologies.

• Photonics
• Nanofabrication
• Quantum Information
• +۳ مورد دیگر

Zeynep Temel is an Assistant Professor at Carnegie Mellon University's College of Engineering, jointly appointed in the Biomedical Engineering and Robotics Institute. She leads the Zoom Lab, focusing on bio-inspired compliant mechanisms for robotic systems. Current research emphasizes adaptable robots for complex environments through mechanical intelligence and embedded control . Key application areas include surgical robotics , search-and-rescue , and micromanipulation . Her work spans bio-inspired design, compliant robotics, and human-centered applications. Recent publications highlight advancements in: Swarm robotics for collaborative exploration Soft actuators using bioplastics and gelatin Dexterous manipulation via delta robot frameworks The Zoom Lab trains students in robotic fabrication and biological modeling, with members transitioning to roles in academia and industry.

• Bio-Inspired Robotics
• Compliant Mechanisms
• Robotic Manipulation
• +۳ مورد دیگر

Tathagata Srimani is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. He previously served as a Postdoctoral Scholar in Electrical Engineering at Stanford University. His academic journey includes a Ph.D. and S.M. in EECS from MIT (2022 and 2018 respectively) and a B.Tech. in E&ECE from IIT Kharagpur (2016). Research Focus: Srimani’s work centers on nanoelectronics and transformative NanoSystems. Key areas include: Carbon nanotube field-effect transistors (CNFETs) and their monolithic 3D integration with silicon Ultra-dense 3D integration of logic and memory to address the 'memory wall' in AI/ML Technology-architecture co-design frameworks for energy-efficient computing Key Achievements: Developed first silicon fab-compatible CNFET processes (TNANO ’18, Nature ’19) Enabled CNFET RISC-V microprocessor and monolithic 3D integration with Analog Devices/SkyWater Recipient of MIT Presidential Fellowship (2016) and Morris Joseph Levin Award (2018) Teaching & Outreach: Teaches semiconductor devices and hardware design, including hands-on 'Hacker Fab' courses. Leads the NEXUS Research Group exploring heterogeneous nanomaterials (e.g., magnetic and oxide semiconductors) and thermal/power management in 3D systems. Future Directions: Expanding into probabilistic computing hardware, co-design frameworks for application-specific systems, and scaling 3D NanoSystem technologies for industrial adoption.

• Nanotechnology
• Beyond-silicon CMOS technologies
• 3D Integration
• +۷ مورد دیگر

Anna Mayo is the Anna Loomis McCandless Assistant Professor of Organizational Behavior at Carnegie Mellon University’s Heinz College. Her research focuses on dynamic teamwork in modern organizations, particularly in healthcare and cross-sector settings. She investigates how teams adapt to fluid participation, technology integration, and volatile environments to enhance productivity and collaboration. Education: Ph.D. & M.S. in Organizational Behavior & Theory, Carnegie Mellon Tepper School of Business B.A. in Psychology, Denison University Research Interests: Mayo explores team coordination, cognitive versatility, and the impact of organizational structures on teamwork efficacy. She combines lab and field studies (e.g., healthcare, sales teams) to address challenges like rapid team formation/dissolution and distributed member roles. Her work emphasizes agility while mitigating risks to team outcomes. Publications: Her research appears in top journals such as Administrative Science Quarterly, Academy of Management Annals, and BMJ Leader. Recent studies address pandemic teamwork dynamics, nursing-physician collaboration, and the role of coordinated attention in group performance. Prior Experience: Before joining CMU, Mayo held roles at Johns Hopkins Carey Business School and worked in nonprofit human services. She teaches Organizational Design & Implementation (Course 94-700).

• Organizational Behavior
• Team Dynamics
• Dynamic Teams
• +۳ مورد دیگر

Xu Zhang is an Assistant Professor in Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He directs the Zhang Lab, focusing on atomically precise 2D materials (e.g., graphene, tellurium) for nanoelectronic/photonic devices in computing, sensing, energy, and healthcare. His research bridges metamaterials, neuromorphic systems, and scalable nanofabrication. Zhang holds a PhD from MIT and BS from USTC, with honors including MIT Technology Review's Innovators Under 35 (2022), NSF CAREER Award (2023), and multiple MIT fellowships. His group's recent work demonstrates programmable mid-infrared metasurfaces, high-mobility tellurium photodiodes, and kirigami-actuated optical systems for biomedical imaging and AR/VR. Advisees include Kevin St. Luce, Yibai Zhong, and Tianyi Huang. Zhang has secured research funding from NSF and industry partners.

• Nanoelectronics
• Nanophotonics
• 2D Materials
• +۳ مورد دیگر

Will Townes is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He joined CMU in 2022 after completing a postdoctoral fellowship in computer science at Princeton University with Barbara Engelhardt. His academic journey includes a Ph.D. in biostatistics from Harvard University under Rafael Irizarry's supervision, an M.S. in math and statistics from Georgetown, and earlier work in tropical ecology fieldwork in the Philippines. Dr. Townes specializes in applied statistics with primary focus areas in biomedical and public health domains. His research centers on wastewater-based epidemiology, wearable devices, and auxiliary signals for infectious disease tracking and forecasting as part of the Delphi research group. He has developed normalization, feature selection, and dimension reduction methods for single cell RNA-Seq and spatial transcriptomics data analysis. His broader research interests span biostatistics, epidemiology, genomics, time series forecasting, and theoretical aspects of Tweedie distributions. His recent publications reveal a strong emphasis on wastewater surveillance methodologies, single-cell data analysis techniques, and infectious disease forecasting models. The research demonstrates a consistent focus on computational scalability and efficiency through approximate inference techniques. Dr. Townes approaches statistical problems with a pragmatic perspective, comfortable with probabilistic (Bayesian) models while drawing inspiration from diverse statistical perspectives. Member of DELPHI Lab Group at CMU Active contributor to genomics and biostatistics research Focus on computational efficiency in statistical methods Dr. Townes mentors several students including Gabrielle Thivierge (PhD candidate working on infectious disease forecasting methods), Julia Elrod, and Anna Rosengart. He teaches data science courses at CMU, including a field course in Costa Rica where students work with community partners on real-world data projects involving water quality indicators, spring flow rates, and ecological monitoring.

• Biostatistics
• Epidemiology
• Infectious Disease Forecasting
• +۷ مورد دیگر

Greg Ganger is the Jatras Professor of Electrical and Computer Engineering at Carnegie Mellon University and Director of the Parallel Data Lab (PDL). His research focuses on computer systems, including cloud computing, storage systems, distributed systems, and machine learning infrastructure. He holds a Ph.D. in Computer Science and Engineering from the University of Michigan and completed postdoctoral work at MIT. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Michigan (1991–1995). Research Interests: Ganger leads projects in cloud computing, storage/file systems, operating systems, and systems for big data and large-scale machine learning. Recent work includes optimizing cloud resource scheduling, developing sustainable storage solutions, and improving ML cluster efficiency. The PDL explores storage system architecture, file systems, and leveraging new storage technologies like non-volatile memory (NVM). Awards: 2021 OSDI Best Paper, 2021 SOSP Best Paper, 2021 SoCC Test of Time Award, and 2021 R&D 100 Award. His team's work on Kangaroo caching and MACARON cloud caching exemplifies cutting-edge contributions. Advising & Grants: Advises graduate students in ECE and Computer Science. Active in grants related to distributed storage, cloud systems, and ML infrastructure. Collaborates with industry partners like Los Alamos National Lab on storage systems. Labs/Teams: Directs the Parallel Data Lab (PDL), a leading research group in storage and distributed systems. Collaborates with CMU’s CyLab on security aspects of storage systems and ML infrastructure.

• Cloud Computing
• Storage Systems
• Operating Systems
• +۳ مورد دیگر

Phillip Gibbons is a Professor in the Department of Computer Science at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on Systems , particularly in robotics computing, heterogeneous hardware, and architectural optimization. Recent research trends include hybrid convolution-transformer models for AR/VR systems, robotics performance benchmarking, and neural architecture search for NPU-CIM heterogeneous devices. His work bridges machine learning and computer architecture for modern hardware platforms. Publications (2024-2025) highlight collaborations in robotics computing and hybrid AI-hardware systems. Notable contributions include the RobotPerf benchmarking suite and studies on autonomous systems hardware integration. Students : Siyuan Chen, Valerie Choung, Hyoungjoo Kim.

• Systems

Evan Spotte-Smith is a computational scientist focusing on sustainable materials and energy systems. In Fall 2025, he will join Carnegie Mellon University as an Assistant Professor in Chemical Engineering. Previously, he completed his Ph.D. in Materials Science and Engineering at UC Berkeley under Kristin Aslaug Persson, specializing in computational simulations, machine learning, and reaction networks. His research addresses climate change through decarbonizing chemical manufacturing, improving battery technologies, and establishing ethical standards for AI in chemistry. He is a CBI Post-Doctoral Fellow, leveraging this role to advance machine learning in catalysis and electrochemistry. Education: Ph.D., Materials Science & Engineering, UC Berkeley (Advisor: Kristin A. Persson); Postdoctoral Research at CBI, CMU. Research Interests: Computational chemistry, data-driven materials discovery, sustainability in chemical processes, electrochemical systems, and ethical AI frameworks for chemical sciences. He explores how machine learning can accelerate the design of energy storage materials while addressing environmental impacts. Recent articles focus on machine learning ethics, electrochemical reaction cascades, battery degradation mechanisms, and sustainable electrolyte design. His work bridges fundamental chemistry with industrial applications, aiming to translate lab discoveries into scalable environmental solutions. Awards: CBI Post-Doctoral Fellowship, which provides mentorship and resources for early-career researchers in computational and data science. Grants/Advising: His upcoming faculty position will expand collaborations with CBI and CMU’s energy initiatives. Current focus includes decarbonizing feedstock chemicals and improving plastic recycling via electrochemistry. Labs/Teams: Active in CMU’s Center for Behavioral and Integrated Data Science (CBI), collaborating with Bosch and other industry partners on applied electrochemical challenges.

• Computational Chemistry
• Data Science
• Sustainability
• +۴ مورد دیگر

Lauren Herckis is an anthropologist affiliated with Carnegie Mellon University's Simon Initiative, School of Computer Science (Human-Computer Interaction Institute), and University Libraries. Her research focuses on implementation science, faculty culture, educational technology, and sociotechnical systems in higher education. She explores how technology adoption impacts teaching practices and organizational culture, with a particular emphasis on issues like digital literacy, VR pedagogy, and AI-Augmented Learning. Her work is funded by organizations including the National Science Foundation, the Chan Zuckerberg Initiative, and the Hillman Family Foundations. She has held a Fulbright Fellowship and collaborated with institutions such as the U.S. Department of Veterans Affairs and the University of Pittsburgh's Center for Teaching and Learning. Her research methodologies combine ethnography, mixed-methods approaches, and design-based research. Key projects include developing the Digital Robotics Archive and Multimodal Archive Toolkit, analyzing barriers to technology adoption in academia, and designing interventions for workforce readiness in community colleges. She advises students like Rotem Guttman and contributes to initiatives like the CyberSim Lab and AI-literacy programs in academic libraries. Key Awards: Fulbright Fellowship Funding Sources: NSF, CZI, Carnegie Corporation, Hillman Foundations Major Projects: Simon Initiative collaborations, Digital Robotics Archive, AI-Augmented Learning in Community Colleges Her interdisciplinary approach bridges anthropology, education science, and computer science to address challenges in postsecondary education transformation and sociotechnical systems.

• Implementation Science
• Educational Technology
• Anthropology of Technology
• +۷ مورد دیگر

Nick Pretnar serves as an Adjunct Professor of Economics at Carnegie Mellon University's Tepper School of Business while concurrently holding a postdoctoral position at the Laboratory for Aggregate Economics and Finance (LAEF) at the University of California, Santa Barbara. He teaches Macroeconomics for the Master of Computational Finance program and Principles of Macroeconomics for undergraduates, leveraging his expertise in household finance and structural economic change. His academic credentials include: Ph.D. in Economics, Carnegie Mellon University (2020) M.S. in Economics, Carnegie Mellon University (2018) B.A. in English, University of Missouri (2015) B.S. in Mathematics, University of Missouri (2015) B.S. in Economics, University of Missouri (2015) Pretnar's research program integrates micro-foundational behavioral phenomena with macroeconomic modeling, focusing on how time allocation and consumption decisions shape growth, inequality, and structural transformation. His work examines spatial and sectoral distributions of economic activity, market power dynamics, and policy impacts on consumer behavior across health, environmental, and labor domains. This interdisciplinary approach bridges household finance with aggregate economic outcomes through rigorous quantitative methods. Analysis of his 15 most recent publications (2019-2024) reveals concentrated research trajectories in health sector structural change, renewable energy subsidy effectiveness, and time-use economics. These works demonstrate consistent application of structural modeling to investigate how micro-level behavioral mechanisms—including mental accounting, liquidity constraints, and time allocation—generate macroeconomic phenomena like market concentration, inequality patterns, and sectoral shifts, with particular emphasis on policy-relevant questions in environmental and health economics. No scientific awards or fellowships were explicitly documented in the source materials. Research funding has been secured from the National Science Foundation (NSF), the PNC Center for Financial Services at Carnegie Mellon University, and LAEF at UCSB. While actively mentoring through teaching roles, no formal PhD or Master's advisees were listed in the provided documentation. His experimental work occurs within UCSB's Laboratory for Aggregate Economics and Finance, which specializes in macroeconomic modeling using large-scale datasets and structural estimation techniques to analyze business cycles, growth dynamics, and policy impacts.

• Macroeconomics
• Household Finance
• Time Use
• +۱۱ مورد دیگر

Evan Walter Clark Spotte-Smith is an Adjunct Professor in the Department of Chemical Engineering at Carnegie Mellon University (CMU) and an incoming Ad Astra Fellow and Assistant Professor of Digital Chemistry at University College Dublin (UCD). Their research focuses on computational simulations, data science, and electrochemistry, particularly in battery materials, catalysis, and sustainability. They hold a Ph.D. and M.S. from UC Berkeley and a B.S. from Columbia University. Education: Ph.D. (2023), M.S. (2021) in Materials Science & Engineering from UC Berkeley; B.S. (2019) in Materials Science & Engineering from Columbia University. Research Interests: Leveraging machine learning and reaction networks to study electrochemical systems, battery degradation mechanisms, and sustainable technologies. Specific areas include Li-ion/Mg-ion batteries, electrolyte decomposition, and ethics in AI for science. Key Contributions: Developed tools like RNMC for reaction network simulations, contributed to the Materials Project database, and co-founded the CoReACTER research group. Leads efforts on ethical AI in chemical sciences. Awards: Carnegie Bosch Institute Fellowship (2024), Winton-Kavli Research Pitch Competition Prize (2023), NSF Honorable Mentions (2020, 2019). Advising & Grants: Mentored multiple students in computational chemistry and materials science. Secured funding including a $500k Toyota grant and high-performance computing allocations. Collaborates with Rachel Kurchin (CMU) and Samuel Blau (LBNL). Labs/Teams: CoReACTER (anti-oppressive research group), ACME group (UC Berkeley), and the Materials Project.

• computational chemistry
• electrochemistry
• materials science
• +۵ مورد دیگر