جست‌وجوی استاد و پژوهشگر

M. Granger Morganمشاهده پروفایل

استاد

• Energy Systems
• Climate Change Policy
• Risk Analysis
• +۵ مورد دیگر

M. Granger Morgan is the Hamerschlag University Professor of Engineering at Carnegie Mellon University , with appointments in the Department of Engineering and Public Policy , Department of Electrical and Computer Engineering , and H. John Heinz III College . He co-directs the NSF Center for Climate and Energy Decision Making and the Electricity Industry Center at CMU. Education: Ph.D., Applied Physics and Information Science, University of California, San Diego (1969) M.S., Astronomy and Space Science, Cornell University (1965) B.A., Physics, Harvard College (1963) His research spans science, technology, and public policy with focus areas in energy systems , climate change mitigation , electric grid resilience , and uncertainty characterization in policy analysis . Recent publications analyze hydrogen market barriers , carbon sequestration timelines , and interdependent energy infrastructure risks . Scientific leadership includes: Member, National Academy of Sciences Member, American Academy of Arts and Sciences Co-chair, NAS Report Review Committee Board member, International Risk Governance Council Foundation Advisory Board, E.ON Energy Research Center, RWTH Aachen DOE Electricity Advisory Committee member Former EPA Science Advisory Board Chair Fellow of AAAS, IEEE, and Society for Risk Analysis Contact: Office 5220 Wean Hall, Phone 412-268-2672, Email granger.morgan@andrew.cmu.edu

• Blockchain Technology
• Cryptocurrency
• Smart Contracts
• +۳ مورد دیگر

Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor at Cornell Tech (Cornell University) and a faculty member in the Department of Computer Science. He co-founded and co-directs the Initiative for CryptoCurrencies and Contracts (IC³) and serves as Chief Scientist at Chainlink Labs. His research focuses on blockchain technology, cryptocurrency, smart contracts, applied cryptography, authentication, and privacy. Juels holds a Ph.D. in Computer Science from UC Berkeley and previously held roles as Chief Scientist and Director of the RSA Laboratories at EMC/Dell EMC until 2013. His work spans foundational contributions to decentralized systems, including Chainlink’s oracle networks and Ekiden’s privacy-preserving smart contracts. Juels has authored influential books like *The Oracle* (2024) and *Tetraktys* (2009), blending cryptography with thriller narratives. He advises numerous students and alumni, many of whom have joined prominent blockchain firms like Chainlink Labs, Mysten Labs, and Espresso Systems. Recent publications explore topics such as liquidity in blockchain assets, oracle network security, and economic analysis of smart contracts. His research emphasizes practical applications of cryptographic principles to real-world challenges in digital systems and decentralized finance. Juels’ lab collaborates on projects like HyDRA (automated bug bounty systems) and Lanturn (economic security metrics). He actively participates in academic and industry partnerships, bridging theoretical computer science with applied blockchain innovation.

Seth Lewis Gilbertمشاهده پروفایل

دانشیار

• Distributed Systems
• Algorithms & Theory
• Systems & Networking
• +۶ مورد دیگر

Seth Lewis Gilbert is a Professor and Head of the Department of Computer Science at the National University of Singapore (NUS), within the School of Computing . He holds the Dean's Chair Associate Professor title and focuses on algorithms for large-scale distributed systems , emphasizing scalability and fault-tolerance . His work spans wireless networks , contention resolution , dynamic networks , and blockchain protocols . Ph.D. in Computer Science, MIT (2007) M.S. in Computer Science, MIT (2003) B.S. in Electrical Engineering & Mathematics, Yale University (1999) His research explores trusted coordination in systems with untrusted and unreliable participants, addressing challenges like Byzantine agreement , load balancing , and contention resolution . He has pioneered formal frameworks for accountability in distributed protocols and developed novel algorithms for asynchronous task allocation . Recent publications include work on consensus protocols , leader election , and contention resolution , reflecting his focus on dynamic network environments . His research has earned recognition at venues like DISC, ICDCS, and CCS. Scientific Awards & Honors : Young Researcher Award (NUS, 2014) Faculty Teaching Excellence Award (2013/14, 2014/15, 2015/16) School of Computing Teaching Excellence Honour Roll (2016-2021) Best Paper Awards at DISC (2023, 2022), ICDCS (2022), IPDPS (2022) Best Student Paper Award at DISC (2022) He serves on the Steering Committee for DISC as Treasurer and has chaired program committees for DISC, OPODIS, and SPAA. His work bridges foundational algorithmic theory with practical applications in blockchains and dynamic networked systems .

Catherine H. Hausmanمشاهده پروفایل

دانشیار

• Environmental Economics
• Energy Economics
• Electricity Transmission Policy
• +۷ مورد دیگر

Catherine H. Hausman is an Associate Professor at the Gerald R. Ford School of Public Policy, University of Michigan, and a Research Associate at the National Bureau of Economic Research (NBER). Her work bridges environmental economics, energy policy, and climate change mitigation. BA in Economics from University of Minnesota PhD in Agricultural and Resource Economics from University of California, Berkeley (2013) Her research explores: Electricity transmission barriers and corporate profit alignment Methane emissions and carbon intensity in fossil fuel production Climate change impacts on grid reliability and pricing Pollution inequality and nuclear power economics Recent publications examine decarbonization strategies, ancillary service market spillovers, and methane leak quantification. She has received a Fulbright grant for pre-graduate work in Peru and contributes policy insights through the Hamilton Project and NBER working papers.

Simon Peterمشاهده پروفایل

دانشیار

• Operating Systems
• Computer Networking
• Data Center System Design
• +۳ مورد دیگر

Simon Peter is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington since January 2022, specializing in Software & Hardware Systems. His research focuses on low-latency, scalable, and energy-efficient data center infrastructure. His research expertise spans: Operating Systems & Runtime Design (tiered memory management, zero-copy IO, network stack virtualization) Hardware/Software Co-design (RDMA/TCP offload, reconfigurable topologies) Power Management (datacenter power resiliency, disaggregated storage) His work bridges theoretical computer science with practical datacenter implementation challenges, emphasizing sustainability and performance optimization. Notable honors include: Sloan Research Fellowship NSF CAREER Award SIGOPS Hall of Fame Award Multiple best paper awards Peter actively mentors graduate students and leads projects through the UW Center for the Future of Cloud Infrastructure (FOCI) and Computer Systems Lab. His teaching includes advanced courses on Data Center Systems and Operating Systems Capstone, with a curriculum spanning both UW and previous appointments at UT Austin.

Joy Arulrajمشاهده پروفایل

دانشیار

• Data Systems
• Machine Learning
• Database Systems
• +۲ مورد دیگر

Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.

Rashmi Vinayakمشاهده پروفایل

دانشیار

• Computer Systems
• Networked Systems
• Information Theory
• +۵ مورد دیگر

Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.

Amilcare Porporatoمشاهده پروفایل

استاد

• Ecohydrology
• Soil-Atmosphere Interaction
• Hydrologic Time Series
• +۲ مورد دیگر

Amilcare Porporato is the Thomas J. Wu '94 Professor of Civil and Environmental Engineering at Princeton University, with joint appointment at High Meadows Environmental Institute. His research integrates hydrology, ecology, and thermodynamics to study soil-water interactions and sustainable resource management. Education includes: Ph.D. Polytechnic of Milan (1996) M.S. Civil Engineering, Polytechnic of Milan (1992) Research explores nonlinear hydrologic systems, soil moisture dynamics, ecohydrology, and environmental complexity using theoretical and experimental approaches. His work advances understanding of water-biota interactions across scales from pore-level processes to landscape evolution. Honors and Awards: Hydrologic Sciences Award, AGU Borland Lecturer American Geophysical Union Fellow Inaugural Landolt Chair, EPFL Teaches courses on Environmental Thermodynamics (ENV 385/CEE 385) and Ecohydrology (CEE 587/ENV 587).

Thomas Hackerمشاهده پروفایل

استاد

• Cloud Computing
• High-Performance Computing
• Operating Systems
• +۳ مورد دیگر

Thomas Hacker is a Professor in the Department of Computer and Information Technology at Purdue Polytechnic Institute, Purdue University. His research focuses on cloud computing, high-performance computing, operating systems, computer networking, and cyber infrastructure . He holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Michigan, along with dual B.S. degrees in Computer Science and Physics from Oakland University. Education: PhD (Computer Science & Engineering), University of Michigan (2004) MS (Computer Science & Engineering), University of Michigan (1993) BS (Computer Science, Mathematics Minor), Oakland University (1989) BS (Physics), Oakland University (1989) Dr. Hacker's research spans cloud and grid computing, operating systems, and distributed systems , with applications in earthquake engineering data systems and AI-driven infrastructure analysis. His recent work explores extended layer 2 networking for bare-metal provisioning ( 2023 IEEE Cloud Summit ) and machine-supported bridge inspection using artificial intelligence ( Transportation Research Record, 2023 ). Notable scientific contributions include 15+ publications on topics like cyberinfrastructure for earthquake engineering, container-based virtualization, and data-intensive systems. His work has been recognized with awards such as the NSF CAREER Award (2010) and multiple Purdue Seed for Success Awards . Key Scientific Awards: NSF CAREER Award (2010) Purdue Seed for Success Awards (2008-2013) ASEE Information Systems Division Best Paper Award (2012) College of Technology Outstanding Faculty in Discovery Award (2010) He has held leadership roles at Purdue, including Department Head (2018-2021) and Interim Department Head (2011-2016) . His career spans academic positions at Indiana University, University of Michigan, and industry roles at Storage Technology Corporation.

• Multiphase Flow in Porous Media
• Phase-Field Modeling
• Reactive Transport
• +۶ مورد دیگر

Xiaojing (Ruby) Fu is an Assistant Professor of Mechanical and Civil Engineering at the California Institute of Technology and a William H. Hurt Scholar (2024-present). Her research focuses on multiphase fluid mechanics in porous media, integrating theory, computation, experiments, and field observations to address geoscience and engineering challenges. Her educational background includes: B.S. in Engineering from Clarkson University (2011) M.S. from Massachusetts Institute of Technology (2015) Ph.D. from Massachusetts Institute of Technology (2017) Professor Fu's research centers on cryosphere hydrology, subsurface engineering, and phase transitions in porous media. She investigates multiphase flow dynamics in contexts like permafrost thaw, snow metamorphism, and carbon sequestration using phase-field modeling and experimental techniques. Her work bridges fundamental physics with applications in environmental resilience and energy systems, emphasizing predictive capabilities for large-scale phenomena through simplified multiscale theories. Analysis of her 15 most recent publications reveals intense focus on cryosphere processes (snow, permafrost) using advanced phase-field modeling and fiber-optic sensing. Key trends include freezing infiltration patterns, meltwater transport in layered snow, and seismic monitoring of soil moisture. Her work increasingly integrates field validation with computational models for environmental applications like drought monitoring and carbon sequestration. Her scientific recognition includes: William H. Hurt Scholar (2024) Professor Fu actively mentors graduate students, as evidenced by qualified students in her research group. She teaches core courses including Thermal Science (ME 11 abc) and Computational Methods for Flow in Porous Media (ME/CE/Ge/ESE 146), training students in both theoretical foundations and applied techniques for subsurface flow problems. She leads the Fu Research Group on Mechanics and Physics of Porous Media Flow, which develops multiscale theories to predict large-scale environmental and energy system behaviors. The group combines mathematical modeling, laboratory experiments, and field observations to address problems in geologic carbon storage, cryosphere dynamics, and subsurface resource management, with recent emphasis on climate change impacts and monitoring technologies.

Qing Cindy Changمشاهده پروفایل

استاد

• Cyber-Physical Systems for Smart Manufacturing
• Real-time Production Control
• Knowledge-guided Machine Learning based Control
• +۲ مورد دیگر

Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia, where she directs the Intelligent Systems Lab. She joined UVA in 2019 after serving as an associate professor at Stony Brook University. Prior to academia, she spent a decade at General Motors R&D, receiving their highest innovation awards. Education: M.S. from University of Wisconsin-Madison Ph.D. in Manufacturing from University of Michigan Research Focus: Chang's work integrates math-based modeling and data-driven methods to optimize manufacturing systems. Key areas include: Adaptive control and machine learning for production efficiency Human-robot collaboration frameworks Sustainable manufacturing through energy management Real-time control of cyber-physical production systems Reinforcement learning applications in industrial automation Research Trends: Her recent publications (2024-2025) demonstrate strong focus on AI-driven manufacturing optimization, with 80% leveraging reinforcement learning/LLMs for robotic control. Key themes include multi-agent coordination (67% of papers), energy efficiency (53%), and flexible production systems (47%). Awards & Recognition: Inducted as SME Scholar (2024) 20 Most Influential Professors in Smart Manufacturing - SME (2020) NSF CAREER Award (2014) Three-time GM Boss Kettering Award winner (2005,2006,2008) ASME and SME Fellow Leadership & Funding: Serves on NAMRI/SME Board of Directors with editorial roles across ASME/IEEE/SME journals. Research supported by NSF (including CAREER), Department of Energy, and multiple industry partners. Leads projects on human-robot collaboration and sustainable manufacturing. Lab & Collaboration: Directs the Intelligent Systems Lab at UVA, focusing on industrial AI applications. Collaborates with automotive and energy sectors to translate research into practical solutions for smart factories.

• Materials Science
• Energy Storage
• Battery Technology
• +۳ مورد دیگر

Jun Liu is a distinguished scientist and academic, serving as a Battelle Fellow at Pacific Northwest National Laboratory (PNNL) and holding the position of Campbell Chair Professor at the University of Washington. His career spans over three decades in materials science and energy storage research, with significant leadership roles including Director of the Battery500 Consortium, a major DOE initiative focused on developing next-generation battery technologies. Dr. Liu earned his Bachelor's degree in Chemical Engineering from Hunan University, followed by a Master's degree in Ceramic Engineering and a Ph.D. in Materials Science and Engineering, both from the University of Washington. His educational background provided the foundation for his extensive career in advanced materials development. Dr. Liu's research focuses on the development, synthesis, and characterization of new materials for energy applications, with particular emphasis on battery technologies. His work spans lithium-ion batteries, lithium-sulfur systems, redox flow batteries, and magnesium-based energy storage solutions. He has pioneered approaches to improve energy density, cycle life, and safety of battery systems through innovative materials design and interface engineering. Analysis of Dr. Liu's recent publications reveals a strong focus on practical battery applications, with particular attention to lithium metal anodes, solid electrolyte interphases, and high-energy battery systems. His research increasingly addresses the challenges of translating laboratory discoveries into commercially viable battery technologies, with growing emphasis on pouch cell development and real-world performance metrics. Distinguished Inventor of Battelle (2007) PNNL's Inventor of the Year (2012, 2016) Electrochemical Society Battery Division Technology Award DOE EERE Exceptional Achievement Award PNNL Lifetime Achievement Award Fellow of the American Association for the Advancement of Science Fellow of the Materials Research Society Member of the Washington State Academy of Science Dr. Liu has secured substantial research funding through his leadership of the Battery500 Consortium and other DOE initiatives. He has mentored numerous researchers and students throughout his career, contributing to the development of the next generation of energy storage scientists. His research group at PNNL collaborates extensively with academic institutions, national laboratories, and industry partners to advance battery technology. Dr. Liu leads the Battery500 Consortium, a major collaborative effort involving multiple national laboratories, universities, and industry partners focused on developing lithium-metal batteries with significantly higher energy density than current technologies. His research group at PNNL maintains state-of-the-art facilities for materials synthesis, characterization, and battery testing, enabling comprehensive investigation of next-generation energy storage systems.

Taner Akbayمشاهده پروفایل

استاد

• Materials Engineering
• Metallurgical Engineering
• Physical Metallurgy
• +۶ مورد دیگر

Prof. Dr. Taner Akbay is a faculty member at Yeditepe University, Faculty of Engineering , Department of Materials Science and Nanotechnology Engineering. He has held academic positions at institutions including Kyushu University, Oita University, and Imperial College London. Education: PhD in Materials Engineering (1993, Imperial College London); Master’s (1989) and Bachelor’s (1986) degrees from Middle East Technical University. His research spans Materials Engineering , Metallurgy , and Solid Oxide Fuel Cells (SOFCs) , with a focus on oxide ion conductivity, laser surface treatment, and phase transformations. Recent work explores photocatalysis , anion intercalation , and CO2 reduction using computational and experimental approaches. Key article trends include SOFC optimization (2004–2009), strain effects on catalysts (2015–2020), and dual-carbon battery technology (2016–2020). His work bridges fundamental metallurgy and advanced energy materials . Scientific Awards: Postdoctoral Research Sponsorship Award (EPSRC, UK) JSPS Fellowship (Japan) Daiwa Adrian Prize (2016, UK) PhD Studentship at Imperial College (European Commission) He has supervised multiple PhD and Master’s theses, including projects on dual-carbon batteries , microwave absorption nanocomposites , and rare earth recovery . Administrative roles include Head of Department (2020–2021). Non-University Experience: Worked with Mitsubishi Materials Corporation (2001), Çolakoğlu Metalurji (2010), and National Research Council Canada (2009).

Ben Fischمشاهده پروفایل

استادیار

• Cryptography
• Distributed Systems
• Blockchain Technology
• +۵ مورد دیگر

Ben Fisch is an Assistant Professor of Computer Science at Yale University's School of Engineering & Applied Science. He is also the co-founder of Espresso Systems, a company focused on blockchain infrastructure. His research focuses on privacy and verifiability in decentralized systems like Bitcoin and Ethereum, with applications in digital finance and healthcare. Dr. Fisch received his B.A. from the University of Pennsylvania and completed his Ph.D. at Stanford University, where he worked with Dan Boneh in the applied cryptography research group. His educational background provided the foundation for his work at the intersection of cryptography, distributed systems, and economics. His research centers on leveraging cryptographic tools such as succinct non-interactive zero-knowledge proofs (zk-SNARKs), private information retrieval, and homomorphic encryption to address challenges in verifiable computation, verifiable storage, and verifiable fairness. He has made significant contributions to verifiable delay functions (VDFs) and proofs of replication, which have been adopted by major blockchain projects including Ethereum 2.0, Chia, and Filecoin. His work on Filecoin's Proofs of Replication has helped the network reach over 1.5 exabytes of storage capacity. His publication record shows a clear trend toward increasingly sophisticated cryptographic protocols for blockchain applications, with recent work focusing on data availability for Bitcoin rollups, efficient folding schemes for pairing-based arguments, and privacy pools with proof-carrying disclosures. His research bridges theoretical cryptography with practical implementations that have real-world impact in decentralized systems. His notable recognition includes: Best Paper Finalist at ACM CCS 2017 for 'Iron: Functional Encryption using Intel SGX' Dr. Fisch's research has led to significant technology transfer, most notably with his work on Verifiable Delay Functions (VDFs) sparking a multimillion dollar industry initiative through the VDF Alliance. His research on Proofs of Replication forms the basis of Filecoin's incentive layer and consensus protocol. His newer SNARK system Basefold is being used by several commercial products. He maintains active collaborations across academia and industry, with publications spanning top conferences in cryptography and security. As co-founder of Espresso Systems, Dr. Fisch leads a team developing next-generation blockchain infrastructure, particularly focusing on sequencing layers for rollups. His work bridges academic research with practical implementation, ensuring that theoretical advances in cryptography find real-world applications in decentralized systems.

• Lithium-Ion Battery Production
• Energy Storage Systems
• Production Engineering
• +۳ مورد دیگر

Prof. Dr.-Ing. Rüdiger Daub serves as Professor and Chair of Production Engineering and Energy Storage Systems at the Technical University of Munich (TUM), operating within the Department of Mechanical Engineering. His leadership encompasses research direction, academic supervision, and strategic development of battery production technologies at TUM's Garching campus (Boltzmannstr. 15), with active industry collaborations driving innovation in sustainable manufacturing. Daub's research program pioneers advanced production methodologies for lithium-ion and solid-state batteries, focusing on electrode manufacturing, electrolyte filling, and cell assembly processes. His work investigates critical parameter interdependencies affecting battery safety and performance, developing inline monitoring systems and digital twin technologies for real-time process optimization. Key contributions include moisture control in electrode production, electrochemo-mechanical characterization of solid-state systems, and robotics solutions for deformable object assembly, all integrated with machine learning for quality assurance in industrial settings. Analysis of his 2023-2025 publications reveals a dominant research trajectory toward solving production bottlenecks in next-generation energy storage. The work demonstrates increasing integration of computational modeling with empirical validation, particularly in solid-state battery manufacturing and high-voltage electrolyte systems. A notable trend is the cross-pollination of robotics, computer vision, and uncertainty quantification techniques to address complex assembly challenges and distribution shifts in quality monitoring, reflecting industry's urgent need for adaptable, data-driven production systems. Leading TUM's specialized laboratories for battery cell production, Daub's team maintains comprehensive facilities for electrode calendering, electrolyte filling, and cell assembly with integrated tracking and tracing capabilities. The research infrastructure supports collaborative projects with automotive OEMs and battery manufacturers to develop scalable production processes, emphasizing environmental sustainability through water-based electrode production and footprint optimization. Current initiatives focus on digital factory modeling and prelithiation technologies for next-generation battery systems.