جستجوی استاد راهنمای دانشگاهی

Tengfei Maمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Biomedical Informatics
• +۶ مورد دیگر

Tengfei Ma is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliations to Computer Science and Applied Mathematics & Statistics. He holds a Ph.D. from The University of Tokyo, M.S. from Peking University, and B.E. from Tsinghua University. Previously, he was a Research Scientist at IBM T.J. Watson Research Center. His research focuses on machine learning, natural language processing (NLP), and biomedical informatics, particularly deep graph learning, scalable graph methods, and healthcare applications. He has contributed to frameworks like EvolveGCN for dynamic graphs and IGB datasets for graph benchmarks. Key awards include ISWC 2021 Best Paper (Research Track) and IBM Outstanding Research Accomplishments (2019, 2022). His work bridges theory and practice, addressing challenges like over-dilution in GNNs and interpretable time series analysis. Collaborations span interdisciplinary areas, such as AI for wound monitoring and code summarization. He teaches BMI530: Software Development for Biomedical Informatics and is open to graduate students from CS, BMI, and AMS departments. Research highlights include: Deep Graph Learning: Scalability (FastGCN, IGB), dynamic graphs (EvolveGCN), and topology-enhanced GNNs. Healthcare: Models for EHR analysis, medication recommendation (GAMENet), and wearable wound monitoring. NLP: Document summarization, code summarization (CP-BCS), and commonsense generation via knowledge graph compression. Recent projects include AI tools like Influencer for promotional content creation and neural-symbolic models for interpretable time series analysis. His lab explores foundational AI for healthcare, code analysis, and graph systems.

Mahsa Baktashmotlaghمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Domain Adaptation
• +۳ مورد دیگر

Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.

Torsten Berningمشاهده پروفایل

دانشیار

• Fuel Cell Engineering
• Electrolyzer Technology
• Computational Fluid Dynamics
• +۳ مورد دیگر

Torsten Berning serves as Associate Professor at AAU Energy within The Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on thermal engineering systems, hydrogen production technologies, and electro-fuels development, with significant contributions to fuel cell and electrolyzer innovation. His primary research domains include fuel cell engineering, electrolyzer technology, and computational fluid dynamics applied to energy systems. He investigates water management in proton exchange membrane fuel cells, heat and mass transfer in electrolysis cells, and efficiency optimization of hydrogen production systems. His methodology combines experimental validation with advanced CFD modeling to address durability and performance challenges in electrochemical energy conversion. Recent publications (2024-2025) demonstrate concentrated research on alkaline electrolysis systems and thermal management solutions. Key advancements include reducing gas crossover in electrolyzers, optimizing indirect evaporative coolers, and designing proton exchange membrane electrolyzers. His work leverages computational modeling to enhance efficiency and scalability of hydrogen production technologies while addressing multiphase flow and heat transfer complexities. Professor Berning actively supervises PhD candidates: H. D. Miller on Degradation Modeling and Lifetime Prediction of Electrolyzers D. L. Martinho on Computational Fluid Dynamics of Alkaline Electrolysis Cells W. Liu on Water Transport in Proton Exchange Membrane Fuel Cells His research is funded by multiple grants including EUDP's 'Boosting Economic Electrolyzer Stack Technology 2' (2022-2025) and the Danish Energy Agency's 'Degradation Modeling and Lifetime Prediction of Electrolyzers' project (2024-2027). He collaborates within AAU Energy's research ecosystem on hydrogen production systems and thermal management technologies, contributing to projects like the Adiabatic Cooling Systems for Decentralised Ventilation and advancing electrolyzer stack technology through industry partnerships.

Fengqi Youمشاهده پروفایل

استاد

• Sustainable Energy Systems
• Artificial Intelligence
• Complex Systems
• +۹ مورد دیگر

Fengqi You is the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at Cornell University. He holds affiliations with multiple departments and graduate fields, including Chemical Engineering, Computer Science, and Sustainability. His research focuses on systems engineering, artificial intelligence, and their applications in energy systems, digital agriculture, and sustainability. You serves as Chair of Ph.D. Studies in Systems Engineering and leads initiatives like the Cornell AI for Sustainability Initiative (CAISI) and the Cornell Institute for Digital Agriculture (CIDA). Education: B.Eng. from Tsinghua University (2005), Ph.D. from Carnegie Mellon University (2009). Before Cornell, he worked at Argonne National Laboratory and Northwestern University. Research Interests Decarbonization of energy systems (e.g., photovoltaics, battery tech) Material informatics and computer-aided molecular design AI-driven solutions for agriculture and climate resilience Supply chain optimization and smart manufacturing Awards & Recognition Fellow of AAAS, AIChE, and Royal Society of Chemistry Over 25 major awards including NSF CAREER (2016), AIChE Research Excellence (2017), and the 2024 Cecil Award Grants & Leadership Recipient of NSF NRT Award for AI in Sustainability Sciences Amazon Research Award for LCA transparency using LLMs Labs & Initiatives Cornell AI for Science Institute (CUAISci) Cornell Institute for Digital Agriculture (CIDA)

Lei Zhouمشاهده پروفایل

استاد

• Precision Mechatronics
• Control Engineering
• Electric Machine Design
• +۴ مورد دیگر

Professor Lei Zhou is a faculty member in the Department of Mechanical Engineering at the University of Wisconsin-Madison, with an affiliate appointment in Electrical & Computer Engineering. His research focuses on Precision Mechatronics, integrating precision mechanical design, electromagnetics, and control engineering to address challenges in electric machines, motion control, and precision systems. He holds a PhD from MIT (2019), an MS from MIT (2014), and a BE from Tsinghua University (2012). Research interests include: High-performance motion systems and electric motor design Control solutions for precision positioning and robotic actuation Magnetic levitation technologies for manufacturing and medical applications Recent work emphasizes magnetic levitation systems (e.g., LevCube nanopositioning stage) and over-actuated precision stages that break traditional performance trade-offs. His publications span 2020-2025, reflecting advancements in motor design, control algorithms, and mechatronics integration. Notable achievements include the 2023 ASPE Early Career Award and Meta Research Award. Teaching includes courses on mechatronics (ME 376), automatic controls (ME 577/ECE 577), and advanced research supervision (ECE 790/ME 890). He leads the FlexLab and LevLab, focusing on portable mechatronics education and precision motion systems. Current research explores lightweight stages for semiconductor manufacturing and magnetic catheter systems for medical treatments.

• Computer Science
• Social Sciences
• Geography and Planning
• +۳ مورد دیگر

Dr. Jeffrey Morgan is a Researcher at Cardiff University's School of Social Sciences, specializing in multidisciplinary research at the intersection of computer science, social science, and geography. His work emphasizes human-computer interaction, visualization, and big data analytics. He holds a Research Software Engineer role, combining technical expertise with academic inquiry. Key research interests include AI-driven patent analysis, IoT applications in rural citizen science, and geospatial Twitter demographics. He has contributed to studies on Hadoop infrastructure optimization and social media conflict detection, often collaborating with institutions like Xiamen University and the University of Bremen. His publications span topics like energy-efficient big data processing, digital geography of Welsh identity, and scalable social media analysis frameworks. Notable projects include COSMOS (a cloud-based social media analysis platform) and studies on post-devolution cultural narratives in Wales. Award-winning work includes computational Twitter analysis for detecting online community tensions and geotagging behavior patterns. His research often bridges technical innovation with societal impact, addressing challenges in rural technology deployment and digital sociology.

Theodora Varvarigouمشاهده پروفایل

استاد

• Cloud Computing
• Multimedia Content Processing
• Social Networking Technologies
• +۳ مورد دیگر

Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.

Joe Patonمشاهده پروفایل

استاد

• Neural Mechanisms of Time
• Interval Timing
• Rodent Neurophysiology
• +۵ مورد دیگر

Joe Paton is a Professor and Principal Investigator at the Champalimaud Neuroscience Programme, Champalimaud Foundation in Lisbon, Portugal. He leads the Paton Lab which focuses on understanding how animals determine which environmental cues are predictive of behaviorally relevant events, known as the credit assignment problem. His research combines behavioral experiments with neurophysiological recordings in rodents to investigate neural mechanisms of time perception and decision making. Dr. Paton's research interests center on interval timing, temporal processing in the brain, and the neural basis of learning. His work particularly examines how the striatum and dopamine systems contribute to time perception and how animals solve the credit assignment problem through statistical inference in the time domain. His lab employs advanced techniques including optogenetics, neural recordings, and computational modeling to address these questions. Analysis of Dr. Paton's recent publications reveals a strong focus on striatal function in timing processes, with particular attention to how neural populations encode temporal information. His work bridges behavioral neuroscience with computational approaches, demonstrating how timing mechanisms influence decision making and learning processes. The research spans multiple levels from cellular mechanisms to behavioral outputs. Midbrain dopamine neurons control judgment of time (2016) Striatal dynamics explain duration judgments (2015) A Scalable Population Code for Time in the Striatum (2015) The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions (2018) Dr. Paton has mentored numerous PhD students and postdoctoral researchers through the INDP (International Neuroscience Doctoral Program) and supervises a diverse team including research technicians, postdocs, and students. His lab has contributed significantly to understanding the neural basis of time perception and its role in learning and decision making. The Paton Lab also develops experimental tools and frameworks like Bonsai for behavioral neuroscience research.

Ryan Giordanoمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Variational Methods
• +۴ مورد دیگر

Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.

Viktor Leisمشاهده پروفایل

استاد

• Database Systems
• Cloud Computing
• Data Management
• +۳ مورد دیگر

Prof. Dr. Viktor Leis is a Professor in the Department of Computer Science at the Technical University of Munich (TUM), leading the Chair for Decentralized Information Systems and Data Management. His research focuses on cost-efficient data systems, particularly in cloud environments, with expertise in core database topics like query processing, transaction management, and storage optimization. He earned his PhD from TUM in 2016 and previously held professorships at Friedrich Schiller University Jena and Friedrich-Alexander-Universität Erlangen-Nürnberg before returning to TUM in 2022. His work has been recognized with prestigious awards, including the ACM SIGMOD Dissertation Award, VLDB Early Career Research Contribution Award, and an ERC Starting Grant. Research Interests: Cloud computing, database systems, query optimization, storage engines, transaction processing, and NVMe-optimized systems. Key Projects: Developed the LeanStore storage engine and contributed to the Hyper database system. His recent publications emphasize cloud-native architectures, high-performance storage solutions, and hybrid transactional/analytical processing. He actively teaches courses on distributed systems, cloud databases, and blockchain technologies.

Marko Cetinaمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Optics
• Atomic Physics
• +۳ مورد دیگر

Marko Cetina is an Assistant Professor of Physics and the Department of Electrical and Computer Engineering at Duke University, affiliated with the Duke Quantum Center. He holds a B.S. from the California Institute of Technology (2004) and a Ph.D. from the Massachusetts Institute of Technology (2011). His research focuses on quantum computing, quantum optics, and atomic physics, with particular emphasis on trapped-ion systems, quantum error correction, and quantum simulation. He teaches courses including Atomic Physics and Quantum Optics, Advanced Topics in Physics, and introductory mechanics. His work explores foundational aspects of quantum mechanics and applied technologies for scalable quantum computers. Notable contributions include demonstrating multi-body interactions in trapped ions and advancing measurement-induced quantum phases. His team actively develops fault-tolerant qubit control and novel quantum architectures using cavity-mediated systems. Publications highlight advancements in quantum error correction protocols, lattice gauge theories, and quantum verification protocols. While no explicit awards are listed, his involvement in high-impact studies like the Duke Quantum Center underscores his contributions to the field. Current projects include optimizing stabilizer codes for logical qubit memory and simulating NMR experiments via digital quantum methods. His lab collaborates across disciplines to bridge theoretical quantum mechanics with experimental implementations in trapped-ion platforms.

Mendel Rosenblumمشاهده پروفایل

استاد

• System Software
• Distributed Systems
• Computer Architecture
• +۴ مورد دیگر

Mendel Rosenblum is the Cheriton Family Professor and holds dual appointments as Professor in the Departments of Computer Science and Electrical Engineering at Stanford University. He is a co-founder of VMware Inc. and served as its Chief Scientist for its first decade, playing a pivotal role in designing foundational virtualization technologies. Rosenblum's research focuses on system software, distributed systems, and computer architecture, with notable contributions to virtualization, data center networks, and operating systems. He leads the Platform Lab at Stanford, exploring next-generation data center technologies and high-performance computing systems. Administrative Role: Faculty Director of Stanford Computer Forum (2012–present) Education: PhD (UC Berkeley, 1992), MS (UC Berkeley, 1989), BA (University of Virginia, 1984) His research interests span disk storage management, computer simulation, scalable operating systems, and security. Recent work emphasizes deployable consensus algorithms, programmable smartNICs, and self-programming networks. Rosenblum has authored over 80 publications and holds multiple patents in virtualization and system software. Awards & Recognition: ACM System Software Award (2009) IEEE Computer Entrepreneur Award (2011) ACM Thacker Breakthrough in Computing Award (2018) Member, National Academy of Engineering (2013) He advises PhD and Master's students, including current advisees Sina Jandaghi Semnani and Zixi Liu. Rosenblum teaches advanced courses on web applications, distributed systems, and independent research projects.

Kristian J. Hammondمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Generation
• Narrative Generation
• +۷ مورد دیگر

Kristian J. Hammond is the Bill and Cathy Osborn Professor of Computer Science at Northwestern University's McCormick School of Engineering. He directs both the Master of Science in Artificial Intelligence Program and the Center for Advancing Safety of Machine Intelligence (CASMI). His research focuses on artificial intelligence, natural language generation, narrative generation, conversational interfaces, and ethical AI applications across domains like law, education, and journalism. Hammond co-founded Narrative Science, leveraging AI for automated journalism from data. Education: PhD, MS, and BA in Philosophy (all from Yale University). His work spans technical innovation and societal impact, with notable contributions to AI transparency, bias mitigation, and machine learning ethics. Hammond has authored influential articles on AI governance, conversational systems, and the future of work in an automated economy. His leadership roles emphasize interdisciplinary collaboration between computer science, business, and humanities. Research highlights include developing AI systems that enhance human capabilities, exploring ethical frameworks for machine intelligence, and advancing AI safety through initiatives like CASMI. He frequently engages in public discourse via TEDx talks and commentaries on AI's societal implications, emphasizing the need for human-centered technology design.

Guo Pingمشاهده پروفایل

دانشیار

• Precision Engineering
• Intelligent Metrology
• Advanced Manufacturing Technologies
• +۵ مورد دیگر

Guo Ping is an Associate Professor of Mechanical Engineering at Northwestern University, leading the Advanced Intelligent Manufacturing Laboratory (AIM). His research focuses on precision manufacturing, intelligent metrology via deep learning, and advanced manufacturing applications. He holds a Ph.D. from Northwestern University and a B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D. in Mechanical Engineering, Northwestern University, Evanston, IL B.S. in Automotive Engineering, Tsinghua University, Beijing, China Research Interests: Dr. Guo’s work emphasizes innovations in precision engineering, including ductile-regime machining, smart metrology systems, and robotics-driven manufacturing. Key areas include structural coloration, additive manufacturing, and human-robot collaboration in industrial settings. His lab explores cutting-edge techniques like ultrasonic vibration machining and machine learning for defect detection and process optimization. Publications Trends: Recent work spans AI-driven quality control (e.g., photometric stereo networks), robotic swarm patterning, and wearable fatigue monitoring systems. His research bridges machine learning, robotics, and traditional manufacturing to address scalability and precision challenges. Awards: F.W. Taylor Medal (CIRP, 2023) ASME Kornel F. Ehman Manufacturing Medal (2021) SME Outstanding Young Manufacturing Engineer Award (2020) Professional Service: Associate Editor of the Journal of Manufacturing Processes (2017–present). Active in organizing conferences and reviewing for top journals. Labs & Teams: Directs the AIM Lab, which integrates robotics, AI, and advanced materials to solve problems in precision fabrication and smart manufacturing. Current projects include structural coloration for anti-counterfeiting and fatigue prediction in industrial workers.

• Solid-state electronics
• Nanotechnology
• Semiconductor devices
• +۴ مورد دیگر

Scott Thompson is a Professor in the Department of Electrical & Computer Engineering at the University of Florida. He holds academic appointments within the College of Engineering and specializes in solid-state electronics, nanotechnology, and semiconductor device fabrication. His research focuses on advancing transistor technologies to extend Moore’s Law, including strained silicon innovations and low-power device designs. Thompson earned his BSEE (1987), MS (1988), and PhD (1992) in Electrical Engineering from the University of Florida. His industrial contributions include roles at Intel Corporation and leadership at SuVolta, recognized with the TiE 50 Top Startup Award (2014) and Semi Award North America (2008). He is an IEEE Fellow (2006) and Intel Fellow (2003). His research portfolio includes over 50 patents and publications on topics like deeply depleted channel transistors, CMOS process optimization, and strain-engineered semiconductor materials. Key themes in his work involve improving transistor performance, reducing power consumption, and advancing fabrication techniques for nanoscale electronics. Key Awards: IEEE Fellow, Intel Fellow, TiE 50 Startup Award Key Contributions: Strained silicon transistors, SuVolta startup innovations, CMOS process advancements