جست‌وجوی استاد و پژوهشگر

Neal Sullivanمشاهده پروفایل

استاد

• Electrochemical Engineering
• Energy Storage
• Renewable Energy Technologies
• +۳ مورد دیگر

Neal Sullivan is a Professor of Mechanical Engineering at the Colorado School of Mines (CSM), leading experimental research at the Colorado Fuel Cell Center as its director. His expertise lies in electrochemical ceramics, with a focus on fuel cells, electrolyzers, and membrane reactors for energy conversion and storage. Sullivan’s work spans from materials development to large-scale system integration, addressing applications such as hydrogen production, CO₂-to-fuels processes, and geothermic fuel cell systems for unconventional oil recovery. His research is supported by grants from the U.S. Department of Energy (DOE), NASA, and industry partners, totaling over $15M. Notable projects include the development of proton-conducting ceramic electrolyzers for water splitting, high-efficiency hybrid SOFC-IC engine systems, and Mars-based CO₂ methanation. Sullivan has led collaborative efforts with global leaders in electrochemistry, emphasizing scalability and durability in energy systems. Key contributions include innovations in protonic ceramic fabrication, catalyst integration, and multi-stack system design. His lab focuses on bridging early-stage materials research with full-scale demonstrations, achieving power outputs up to 100 kW. Sullivan’s work has been published in top journals like Nature Energy and International Journal of Hydrogen Energy , with a strong emphasis on practical applications and renewable energy solutions. Labs/Teams: Director of the Colorado Fuel Cell Center. Grants/Advising: PI/co-PI on multiple DOE and NASA grants, including $5M for hybrid SOFC systems and $1.5M for geothermic fuel cells. Advises on advanced materials and system integration for energy storage and conversion.

Ben Bloem-Reddyمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Bayesian Methods
• +۳ مورد دیگر

Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.

• Lean construction
• Takt planning
• Industrialized Construction
• +۱۲ مورد دیگر

Iris D. Tommelein serves as the Roy W. Carlson Distinguished Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley's College of Engineering, where she directs the Project Production Systems Laboratory (P2SL). A globally recognized pioneer in Lean Construction, she has revolutionized architecture-engineering-construction (AEC) practices through research, industry workshops, and leadership since co-founding the Lean Construction Institute in 1997. Her educational foundation spans multiple disciplines: Ph.D. in Civil Engineering (Construction Engineering and Management), Stanford University, 1989 M.S. in Computer Science (Artificial Intelligence), Stanford University, 1989 M.S. in Civil Engineering (Construction Engineering and Management), Stanford University, 1985 B.S. (5-year degree) in Civil Engineer-Architect, Vrije Universiteit Brussel, Belgium, 1984 Professor Tommelein's research centers on transforming construction processes through Lean principles and digital innovation . Her work pioneers takt planning for workflow reliability, industrialized construction for labor and sustainability challenges, and mistakeproofing to eliminate errors. She integrates digital twins , AI , and optimization to develop practical decision-support systems for supply chains, logistics, and production management. Recent focus includes modular offsite construction and Industry 4.0 applications. Analysis of her 2023-2025 publications reveals intensifying research on takt planning maturity models and industrialized construction feasibility , with growing emphasis on mass timber automation and visual management systems. Her work consistently bridges lean theory with practical implementation across megaprojects, subcontracting networks, and heavy civil engineering. Her exceptional contributions have earned: Lean Pioneer Award (Lean Construction Institute, 2015) National Academy of Construction induction (2019) PPI Technical Achievement Award (2022) Robert B. Harris Award (University of Michigan, 2024) ASCE Construction Management Award (2024) - first woman recipient in 51 years Through the P2SL, she leads industry-collaborative research on production system design, mistakeproofing frameworks, and digital transformation. Her grant-funded projects develop assessment tools for industrialized construction adoption and takt planning methods adaptable to diverse project types. She actively mentors graduate students and drives knowledge transfer via workshops and the annual Construction Innovation Day. The Project Production Systems Laboratory (P2SL) operates as a global hub for construction innovation, partnering with owners, contractors, and suppliers to implement lean production systems. Current initiatives include developing serious games for mistakeproofing training, optimizing work density methods for heavy civil projects, and creating digital twins for real-time construction management.

David Simmons-Duffinمشاهده پروفایل

استاد

• Theoretical Physics
• Conformal Field Theory
• Bootstrap Methods
• +۵ مورد دیگر

David Simmons-Duffin is a Professor of Theoretical Physics at the California Institute of Technology (Caltech), where he has held positions since 2016. He is part of the Division of Physics, Mathematics and Astronomy, contributing to the Physics Department. His career progression includes roles as Visiting Associate (2016–17), Assistant Professor (2017–20), and Associate Professor (2020–21) before becoming full Professor in 2021. Education: A.B. and A.M. from Harvard University (2006), CASM from the University of Cambridge (2007), and Ph.D. from Harvard University (2012). His research focuses on conformal field theory (CFT), bootstrap methods, quantum field theory, and AdS/CFT correspondence. Key areas include precision computations in strongly coupled systems, critical phenomena, and applications to holography and quantum gravity. Research highlights include advancing the conformal bootstrap program, analyzing CFT data in 3D Ising models, and exploring connections between CFTs and gravitational theories. His work often bridges theoretical frameworks with numerical methods, yielding insights into operator product expansions (OPE), spectral gaps, and causality constraints. Affiliations include the Institute for Quantum Information and Matter (IQIM) and other Caltech research centers. His contributions have shaped modern approaches to understanding universality in critical systems and the geometric aspects of quantum field theories. Notable collaborations involve high-precision calculations, bootstrap island techniques, and studies of thermal QFT and light-ray operators. His work emphasizes interdisciplinary methods, combining analytic tools with computational advancements to tackle complex theoretical problems.

Kyojin Chooمشاهده پروفایل

استادیار

• Charge-domain circuits
• Sensor interfaces
• Energy converters
• +۴ مورد دیگر

Kyojin Choo is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Engineering , affiliated with the Mixed-Signal Integrated Circuits Lab (MSIC-LAB). He also holds teaching roles in Microengineering and Electrical and Electronics Engineering at EPFL. B.S. and M.S. in Electrical Engineering from Seoul National University (2007, 2009) Ph.D. in Electrical Engineering from the University of Michigan (2018) His research focuses on charge-domain analog/mixed-signal circuits , low-power sensor interfaces , and compact ADCs for IoT, wearables, and millimeter-scale systems. He has pioneered charge-injection cell techniques for energy-efficient circuits in energy management, sensor front-ends, and communication. His work emphasizes reducing power consumption to nanowatt levels while enabling ultra-compact designs. His recent publications highlight advancements in compact SAR ADCs , low-power MEMS accelerometers , millimeter-scale imaging systems , and ultra-low-power timing generators . His research integrates charge-domain circuit design with sensor interface optimization , energy harvesting , and high-speed link architectures . He holds over 20 US patents and has taught courses in Microengineering and Electrical Engineering at EPFL. His group (MSIC-LAB) addresses challenges in battery-free sensor design, power-constrained system scaling, and commercialization of wearables with unconventional form factors.

• Power Systems
• Smart Grids
• Energy Infrastructure
• +۱ مورد دیگر

Antonello Monti is a Professor and Director of the Institute for Automation of Complex Power Systems at RWTH Aachen University. His research focuses on modern power systems, including smart grid technologies, hybrid AC-DC grids, and quantum computing applications in energy systems. Recent publications demonstrate innovations in grid resilience, EV charging optimization, quantum-assisted power system planning, and advanced simulation techniques. His team develops open-source tools like JuliaGrid for power system analysis and validates concepts through real-time testing platforms. Research addresses energy transition challenges including renewable integration, grid modernization, cyber-physical security, and next-generation optimization methods combining quantum computing with traditional power engineering approaches.

Kavan Modiمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Mechanics
• Quantum Physics
• +۶ مورد دیگر

Kavan Modi is a Professor at the School of Physics and Astronomy, Monash University. His research focuses on quantum information theory applied to dynamics, metrology, computation, thermodynamics, and relativity. He leads the Monash Quantum Information Science (MonQIS) group and serves as Director of the Centre for Quantum Technology at Transport for NSW (2022–2024). Education: B.Sc. Engineering Physics (Embry-Riddle Aeronautical University, 2001), M.A. Physics (University of Texas at Austin, 2004), Ph.D. Physics (University of Texas at Austin, 2008). Postdoctoral positions included the Centre for Quantum Technologies (Singapore, 2008–2011) and Clarendon Lab, Oxford (2011–2013). Joined Monash in 2014. Research interests center on quantum dynamics, non-Markovian processes, and their applications in quantum computing and information science. Projects include developing error correction codes, quantum algorithms for network analysis, and mitigating correlated noise in quantum systems. He has authored over 111 publications, with recent work emphasizing non-Markovian characterization, quantum process tomography, and topology-based quantum algorithms. Awards and grants include leadership in multiple Australian Research Council projects. Advising/Grants: Primary Chief Investigator in projects like 'Quantum Software Platform' (2023–2026) and 'Mitigating Correlated Noise in Quantum Machines' (2020–2021). Supervises graduate students and collaborates globally on quantum information science. Labs/Teams: MonQIS group focuses on foundational and applied quantum research, integrating theory and experimental collaborations.

Konstantinos Pelechrinisمشاهده پروفایل

دانشیار

• Network Science
• Urban Informatics
• Sports Analytics
• +۳ مورد دیگر

Konstantinos Pelechrinis is an Associate Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. He holds a Ph.D. in Computer Science from the University of California, Riverside. His research focuses on network science, urban informatics, and sports analytics. He has been recognized with the Army Research Office Young Investigator Award for his contributions. Education: Ph.D. in Computer Science, University of California, Riverside Research Interests: Urban mobility patterns and infrastructure analysis Sports performance quantification and strategy Data-driven decision-making in transportation systems Network science applications in social and urban systems His recent work explores topics such as implicit biases in sports refereeing, anomaly detection in NFT markets, and optimizing bike-sharing systems using predictive models. He also investigates urban infrastructure resilience through projects like the Epui platform for experimental urban informatics. Awards: Army Research Office Young Investigator Award He contributes to academic outreach through courses like TELCOM2125 (Network Science and Analysis) and collaborates on initiatives like the Healthy Ride Pittsburgh bike-sharing study. His lab focuses on bridging theoretical models with real-world urban and sports datasets.

Douglas C. Hopkinsمشاهده پروفایل

استاد پژوهشی

• Power Electronics
• Advanced Packaging
• Wide Band Gap Semiconductors
• +۴ مورد دیگر

Douglas C. Hopkins is a Research Professor and Director of the Laboratory for Packaging Research in Electronic Energy Systems (PREES) at North Carolina State University's Department of Electrical and Computer Engineering. He joined the ECE faculty in 2011 and is affiliated with the FREEDM Systems Center and the Center for Additive Manufacturing and Logistics (CAMAL). He holds a Ph.D. in Electrical Engineering from Virginia Tech (1989). His research focuses on Very High-Frequency power electronics, Wide Band Gap (WBG) devices (GaN/SiC), advanced packaging, and solid-state protection systems . He has pioneered work in integrated power electronics, harsh-environment systems, and 3D power electronics integration. His leadership includes founding conferences such as the International Symposium on 3D Power Electronics Integration and Manufacturing (3D-PEIM) and the International Symposium on Advanced Power Electronics Packaging (APEPS). Prof. Hopkins has authored over 200 publications and co-founded DensePower, LLC as CEO/CTO. He serves as Associate Editor for the IEEE Journal of Emerging and Selected Topics in Power Electronics and holds editorial roles in multiple journals. His awards include the IMAPS Outstanding Educator Award (2013) and IMAPS Fellow (2007). He has held visiting appointments at the Army Research Lab, NASA, and Lawrence Livermore National Lab, and served as a National Academy of Sciences reviewer. His consulting firm, DCHopkins & Associates, LLC, provides engineering expertise in power electronics and packaging. Key Contributions: Director of PREES Lab and FREEDM Center member Co-founder of 3D-PEIM and APEPS symposiums IEEE PELS Technical Committee member (TC2, TC-6) Recipient of IMAPS Fellow and multiple conference recognitions

• Power cycles and turbomachinery
• CO2 capture
• Process modeling and simulation
• +۳ مورد دیگر

Lars O. Nord is a Professor in the Department of Energy and Process Engineering at NTNU, specializing in thermal energy systems, CO2 capture technologies, and dynamic process modeling. He holds a PhD from NTNU (2010) and a Master's from Virginia Tech (2001). His research focuses on power cycles, turbomachinery optimization, and decarbonization strategies for energy systems. Nord has led projects such as DEXPAND and InnCapPlant, addressing CO2 capture under variable loads and expander efficiency in renewable systems. Current roles: Head of the Thermal Energy research group and teaches courses like Engineering Thermodynamics. Research highlights include thermal energy storage integration, moving bed adsorption processes, and offshore hybrid energy systems. His work spans over 80 publications, emphasizing CO2 capture dynamics, turbine design, and control strategies for flexible power plants. Notable collaborations include SINTEF and Aker Solutions. Nord advises multiple PhD candidates and has mentored alumni now leading roles in industry and academia.

Tamon Stephenمشاهده پروفایل

استاد

• Operations Research
• Combinatorial Optimization
• Algorithms
• +۲ مورد دیگر

Tamon Stephen is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on operations research, with an emphasis on combinatorial optimization, algorithms, discrete geometry, and computational biology. He holds a Ph.D. in Mathematics from the University of Michigan (2002). His work often bridges theoretical and computational aspects, addressing interdisciplinary applications. Stephen is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) and has contributed to software tools for hypergraph transversals and colorful linear programming. He has taught courses such as Math 208W (Introduction to Operations Research) and has advised projects in metabolic network analysis and scheduling optimization. His office is located at the Surrey campus (SRYC 2886). Key research collaborations include studies on firefighter scheduling, nurse rostering, and metabolic pathway analysis. His methodologies often leverage algorithm design, polytope theory, and discrete mathematics. Stephen actively participates in academic service, organizing seminars and contributing to conferences such as the West Coast Optimization Meeting. His work emphasizes practical applications of theoretical results, with a focus on solving real-world optimization challenges.

Lyle H. Ungarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Affiliations & Roles Professor of Computer and Information Science at University of Pennsylvania Faculty in Graduate Groups: Bioengineering (School of Engineering) Genomics & Computational Biology (School of Medicine) Operations, Information & Decisions (Wharton School) Psychology (School of Arts & Sciences) Research Affiliations: Annenberg Public Policy Center (Distinguished Fellow) Center for Cognitive Neuroscience Institute for Translational Medicine Research Interests Focuses on explainable AI, natural language processing (NLP), and machine learning applications in psychology and medicine. Key areas include: Language analysis for well-being and mental health Spectral methods for NLP (e.g., Eigenwords) Forecasting and decision-making models Bioinformatics and genomics Teaching Teaches advanced courses in Machine Learning, Deep Learning, and AI ethics, including: CIS 5200: Machine Learning CIS 5220: Deep Learning CIS 6200: Advanced Topics in Deep Learning Key Collaborations Works with interdisciplinary teams on projects like the Good Judgment Project (forecasting) and WWBP (Well-Being and Language). Collaborators include Martin Seligman (positive psychology), Dean Foster (statistics), and Michael Collins (NLP).

Oliver Crönertzمشاهده پروفایل

استاد

• Digital Transformation
• Business Analytics
• Industry 4.0 Technology Potentials
• +۴ مورد دیگر

Prof. Dr. Oliver Crönertz serves as Dean and Professor of Business Administration and IT-Supported Business Processes at the Faculty of Digital Transformation , HTWK Leipzig. His work bridges digital transformation with business management, focusing on interdisciplinary project management, digital business models, and sustainability. Current roles: Dean, supervising professor for HTWK Robots/nao Team Research areas: Digital Transformation, Business Analytics, Industry 4.0, Stoffstrommanagement Research Trends : Recent publications emphasize digital solutions for environmental management, business process optimization, and smart urban logistics. His work connects Industry 4.0 with financial sustainability and societal impacts of digitalization. Projects : Leads Mittelstand-Digital Zentrum Leipzig-Halle, involved in EXIST-Potentiale, Future Parking, and FAssMII (Feedback-based E-Assessment in STEM).

Stefanie Jegelkaمشاهده پروفایل

دانشیار

• Algorithmic machine learning
• Discrete optimization
• Submodularity
• +۴ مورد دیگر

Stefanie Jegelka is an Associate Professor (currently on leave) at the Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich. At MIT, she is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), the Center for Statistics and Machine Learning, and is affiliated with the Operations Research Center. Her educational background includes a PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems, followed by postdoctoral research at UC Berkeley's AMPlab and computer vision group. Her research program focuses on algorithmic machine learning, with particular emphasis on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness in learning systems, and developing methods for scaling machine learning algorithms to large datasets. She has made significant theoretical contributions to submodular optimization and its applications in machine learning. Jegelka's publication record demonstrates a consistent focus on the intersection of discrete mathematics and machine learning. Her work spans theoretical foundations of optimization with discrete structures, applications in computer vision, and practical algorithms for submodular function optimization. Her research has evolved from foundational work on submodular functions to broader applications in deep learning and robust machine learning systems, showing increasing impact through numerous workshop best paper awards and high-impact conference publications. NSF CAREER Award Google Research Award German Pattern Recognition Award (Mustererkennngspreis) ICML Best Paper Award Sloan Research Fellowship DARPA Young Faculty Award NSF BIGDATA Award ONR MURI NSF AI Institute for Optimization Professor Jegelka has advised several successful students including Keyulu (recipient of MIT's George M. Sprowls Ph.D. Thesis Award), Derek (NSF Fellowship recipient), Ching-Yao (IBM Fellowship recipient), and Nisha (now Assistant Professor at Georgia Tech). Her research has been generously supported by multiple NSF grants, DARPA awards, and industry funding from Google, Two Sigma, and Adobe. She has also organized multiple workshops and tutorials on discrete optimization and submodularity in machine learning. At MIT, Jegelka is affiliated with the Center for Statistics and Machine Learning and collaborates with researchers across CSAIL. Her work bridges theoretical computer science, optimization, and practical machine learning applications, with recent focus on high-dimensional learning dynamics and in-context learning as evidenced by her group's multiple papers at leading conferences like ICLR.

• Marketing Analytics
• Electronic Commerce
• Online Advertising
• +۸ مورد دیگر

Prof. Dr. Bernd Skiera is a leading Marketing Professor at Goethe University Frankfurt since 1999 and a member of the managing board of the efl - The Data Science Institute. His work bridges information systems and marketing, with a focus on data-driven decision making and digital transformation.