جست‌وجوی استاد و پژوهشگر

• Internal Combustion Engines
• Laser Diagnostics
• Turbulent Flow
• +۳ مورد دیگر

Jaal Ghandhi is a Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison. His research focuses on combustion and fluid mechanics in internal combustion engines, utilizing laser-based diagnostics to study temperature and concentration fields. He holds significant academic roles and has received multiple prestigious awards, including the John Bollinger Chair and ASME/Society of Automotive Engineers Fellowships. Education: PhD 1995 (Princeton University), MS 1988 (UW-Madison), BS 1986 (UW-Madison) His research interests include laser diagnostics, turbulent flow, and advanced engine design. Recent work emphasizes thermal barrier coating performance, diesel engine efficiency, and hydrogen-based fuels. Over 20 years, his publications span combustion dynamics, material durability, and engine thermodynamics. Awards include the NSF CAREER Award, Grainger Professorship, and multiple teaching accolades. He teaches graduate courses in energy sustainability, combustion, and engine experiments. His research contributes to sustainable engineering through improved engine efficiency and reduced emissions, with collaborations in automotive and energy sectors.

• Aerospace Engineering
• Sustainable Aviation
• Hydrogen Propulsion Systems
• +۵ مورد دیگر

Phillip J Ansell is an Associate Professor in the Department of Aerospace Engineering at the University of Illinois. He serves as the Director of the Center for High-Efficiency Electrical Technologies for Aircraft (CHEETA), focusing on advancing sustainable aviation through innovative propulsion and energy systems. His research interests include aerodynamics optimization, hydrogen propulsion, electric aircraft integration, and cryogenic technologies. Ansell has received prestigious awards such as the AFOSR Young Investigator Award (2015), ARO Young Investigator Award (2017), and the Lawrence Sperry Award (2023), recognizing his contributions to sustainable aviation and flow control technologies. His work spans interdisciplinary areas like hydrogen fuel cell systems, airfoil design, and electrified aircraft architectures. Recent research emphasizes sustainable aviation frameworks, cryogenic hydrogen storage, and propulsion-airframe integration. Ansell has collaborated on projects involving distributed propulsion systems, wind energy optimization, and advanced plasma actuators for flow control. His leadership in CHEETA drives innovations in superconductivity and high-temperature superconducting components for next-generation aircraft. Notable contributions include studies on laminar flow control, transonic aerodynamics, and the technical challenges of integrating MW-scale hydrogen propulsion systems. His publications reflect a blend of theoretical modeling, experimental validation, and systems engineering approaches to address aviation's sustainability challenges.

Moinuddin Qureshiمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Hardware Security
• +۱ مورد دیگر

Moinuddin Qureshi is a Professor of Computer Science at Georgia Institute of Technology, affiliated with the School of Computer Science and involved in the Online Master of Science in Computer Science (OMSCS) program. He holds a Ph.D. and M.S. from the University of Texas at Austin. His research focuses on computer architecture, memory systems, hardware security, and quantum computing, with notable contributions to mitigating rowhammer vulnerabilities and advancing quantum error correction. Previously, he was a Research Staff Member at IBM T.J. Watson Research Center (2007–2011), where he contributed to caching algorithms for Power-7 processors. He has held leadership roles, including Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017. His work has been recognized with prestigious awards, including the 2019 Persistent Impact Prize and multiple best paper awards. Key research areas include secure memory design (e.g., rowhammer mitigation techniques like MINT and Moat), quantum computing (e.g., Flag-Proxy Networks and Élivágar), and hardware vulnerability analysis (e.g., Roguerfm attacks and COAXIAL memory systems). His publications span 2009–2025, addressing topics like error correction, secure tracking, and quantum annealing optimization. Awards include membership in ISCA, MICRO, and HPCA Hall of Fame, alongside contributions to conferences like HiPC and IEEE MICRO. His work bridges theoretical advancements with practical implementations in both classical and quantum domains.

Hossein Jahanshahlooمشاهده پروفایل

دانشیار

• Blockchain Technology
• Cryptocurrencies
• Market Microstructure
• +۳ مورد دیگر

Dr. Hossein Jahanshahloo is an Associate Professor of Financial Technology at Alliance Manchester Business School, The University of Manchester. Previously, he served as an Assistant Professor of Finance at Cardiff Business School (University of Cardiff). He holds a PhD in Finance from Leeds University Business School, alongside MSc and BSc degrees in Finance and Computer Software Engineering. His research focuses on Blockchain Technology, Cryptocurrencies, Market Microstructure, and Algorithmic Trading. He created the Cardiff University Bitcoin Database (CUBiD), which provides accessible cryptocurrency network data to academics and practitioners. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. He currently serves as an associate editor at the Journal of Research in International Business and Finance and holds a visiting researcher position at Technical University of Munich. His recent publications explore Bitcoin arbitrage dynamics, cryptocurrency market efficiency, and banking acquisition failures. Jahanshahloo advises PhD students and collaborates internationally on projects involving blockchain applications and financial technology innovations.

Benjamin Safdiمشاهده پروفایل

دانشیار

• Dark Matter Physics
• Astrophysics
• Particle Physics
• +۳ مورد دیگر

Benjamin Safdi is an Associate Professor at the University of California, Berkeley, affiliated with the Berkeley Center for Theoretical Physics. He holds a PhD from Princeton University (2014), a Master of Advanced Study from Cambridge University (as a Churchill Scholar), and an undergraduate degree from the University of Colorado at Boulder. Prior to UC Berkeley, he was a Pappalardo Fellow at MIT (2014–2017), an Assistant Professor at the University of Michigan (2017–2020), and part of LBNL before joining Berkeley in 2021. Research Focus: Dr. Safdi specializes in dark matter physics, particularly axion dark matter detection via novel laboratory experiments and astrophysical probes. His work integrates high-performance computing simulations, data analysis techniques for astrophysical datasets, and collaborations with experimental facilities like the Fermi gamma-ray telescope and the Green Bank radio telescope. He co-founded the ABRACADABRA axion dark matter experiment. Awards: Department of Energy Early Career Award (2018) IUPAP C11 Young Scientist Prize (2020) His research bridges theoretical particle physics and observational astrophysics, emphasizing interdisciplinary approaches to uncovering dark matter's microscopic nature.

• Structural Health Monitoring
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.

• Virtual Reality
• Human-Computer Interaction
• Computer Vision
• +۴ مورد دیگر

Robert S. Allison is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. His research focuses on human perceptual responses in virtual environments, stereoscopic vision, and eye movement analysis. He is affiliated with the York Centre for Vision Research, Sensorium (Digital Arts & Technology), and the Centre for Innovation in Computing at Lassonde. His research interests include depth perception in natural and virtual environments, human-computer interface design for VR, machine vision applications, and the measurement of human motion. He has supervised multiple graduate students and contributed to over 260 publications. His work spans topics like cybersickness mitigation, display lag effects, and perceptual adaptation in VR. Key grants include NSERC-funded projects on perception in virtual environments and collaborations with institutions like the Australian Research Council. His teaching includes courses on human perception in human-computer interaction and digital logic design. Recent articles highlight advancements in understanding motion perception, VR-induced sickness, and multisensory integration. He collaborates widely, with affiliations including the VISTA program and York's Connected Minds initiative.

Christian Krupitzerمشاهده پروفایل

استاد

• Food Informatics
• Self-adaptive Software Systems
• Machine Learning
• +۴ مورد دیگر

Jun.-Prof. Dr. Christian Krupitzer is a Tenure Track Professor in Food Informatics at the University of Hohenheim's Institute of Food Science and Biotechnology, part of the Faculty of Natural Sciences. He leads the Department of Food Informatics and is a member of the Computational Science Hub (CSH). His research focuses on self-adaptive software systems, machine learning (especially edge computing), IoT technologies, and software engineering applied to food processing and agricultural systems. Education: PhD in Business Information Systems (Dr. rer. pol.), University of Mannheim (2018) M.Sc. and B.Sc. in Business Information Systems, University of Mannheim (2010–2012) High School Diploma (Abitur) from Wilhelmi-Gymnasium Sinsheim (2007) Research Interests: Krupitzer’s work integrates computational methods with food science, emphasizing adaptive systems for food quality monitoring, IoT in agriculture, and machine learning for predictive analytics. He explores edge computing’s role in real-time decision-making and secure group communication schemes for IoT networks. Publications: His recent work spans predictive maintenance in Industry 4.0, digital twins in food systems, and blockchain applications in supply chain authentication. The articles highlight trends in interdisciplinary approaches combining AI, IoT, and domain-specific challenges in food production and logistics. Awards: No scientific awards explicitly listed in the provided materials. Grants & Advising: While specific grants are unmentioned, his roles as department head and tenure-track professor suggest involvement in research funding. No formal advisee list provided, though his team includes postgraduate researchers like Dana Jox, Daniel Einsiedel, and others. Labs & Teams: Leads the Food Informatics department and collaborates with the Computational Science Hub. His team focuses on developing innovative solutions for food systems through computational methods.

Lin Caiمشاهده پروفایل

دانشیار

• Wireless Communication
• Networking
• Green Radio Communication Networks
• +۵ مورد دیگر

Lin Cai is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, serving as Director of Graduate Affairs. She holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2010). Her research focuses on sustainable wireless communication and networking, particularly in energy harvesting, IoT, and deep reinforcement learning for resource management. Key research projects include energy sustainability in renewable-energy-powered networks, energy-efficient 5G protocols, and radio resource management over unlicensed bands. She has received the NSF CAREER Award (2016-2020), Best Paper Awards, and NSERC Postdoctoral Fellowship. Her work spans theoretical frameworks and practical implementations in HetNets, MIMO, and UAV-based systems. Education: Ph.D., E&CE, University of Waterloo, Canada (2010) Affiliations: Executive Editorial Committee member, IEEE Transactions on Wireless Communications (2021–present); Associate Editor for multiple IEEE journals Teaching: Courses include ECE 407/408 (Computer Networks) and ECE 517 (Modern Wireless Network Protocols) Her research emphasizes cross-layer protocol design, sustainable capacity planning, and topology control. Notable contributions include frameworks for energy sustainable radio resource management and optimization in dense HetNets. She actively contributes to conference organization and technical program committees for IEEE INFOCOM, GLOBECOM, and ICC.

Yiting Xiaمشاهده پروفایل

استادیار

• Computer Networks
• Data Center Networking
• Optical Communications
• +۳ مورد دیگر

Dr. Yiting Xia is a tenure-track faculty member at the Max Planck Institute for Informatics (MPI-INF), leading the Network and Cloud Systems research group. She previously worked as a research scientist at Facebook and holds a PhD in Computer Science from Rice University (2018) and a B.S. in Telecommunications Engineering from Beijing University of Posts and Telecommunications and Queen Mary University of London (2011). Her research focuses on high-performance and energy-efficient networking for cloud computing, including reconfigurable data center networks, optical communications, and network protocols. Notable contributions include innovations in transport protocols, time synchronization for optical networks, and failure-resilient network design. Education: PhD in Computer Science, Rice University, 2018 M.S. in Computer Science, Rice University, 2014 B.S. in Telecommunications Engineering, BUPT & QMUL, 2011 Research Interests: Data center networking, optical communications, cloud systems, network protocols, distributed systems, and network security. Her work bridges theoretical contributions with practical implementations, addressing challenges in latency-sensitive flows, traffic engineering, and system reliability. Awards include the Ken Kennedy-Cray Fellowship and the N2Women Rising Star Award (2021). She has co-lectured courses on distributed systems and data networks at Saarland University and previously contributed to teaching at Rice University. Key projects include Aurora (for MoE inference optimization), Lighthouse (an open research framework for optical networks), and Occam (a reliable network management system). Grants & Projects: Focus on deployable optical network architectures and resilient backbone management during pandemic-driven traffic shifts. Labs/Teams: Leads the Network and Cloud Systems group at MPI-INF, collaborating with academia and industry on cutting-edge networking solutions.

Feng Yanمشاهده پروفایل

دانشیار

• Big Data
• Machine Learning
• Systems
• +۶ مورد دیگر

Dr. Feng Yan is an Associate Professor at the University of Houston's Computer Science Department, leading the Intelligent Data and Systems Lab (IDS Lab). He previously held an Associate Professor position at the University of Nevada, Reno. His research focuses on bridging Big Data, Machine Learning, and Systems, with interdisciplinary applications in wildfire science, materials engineering, and civil infrastructure. He has received prestigious awards such as the NSF CAREER Award and the Regents' Rising Researcher Award. Education: Ph.D. (2016) and M.S. (2011) in Computer Science from College of William and Mary; B.S. (2008) in Computer Science from Northeastern University. Research experience includes roles at Microsoft Research and HP Labs. Research Interests: Large Language Models (LLM), Distributed Deep Learning, AutoML, Serverless Computing, Federated Learning, and AI-driven domain sciences. His work emphasizes real-world impact through collaborations with industry and national labs. Publications: Over 60+ papers in top-tier venues like NeurIPS, ICLR, KDD, AAAI, SOSP, SC, and VLDB. Key contributions include ZeRO++ (collective communication optimization), Gradient Compression techniques, and Federated Learning frameworks like TiFL and HDFL. Awards: NSF EPSCoR Award ($20M), NSF CAREER Award, FAA BAKFAA Grant, and multiple best paper awards (IEEE CLOUD 2018, CLOUD 2019). Active in program committees for HPDC, ICAC, ICPE, and AAAI. Advising: Supervised over 30+ graduate/undergraduate students, with placements at Microsoft Research, IBM, Oak Ridge National Lab, Facebook, and MathWorks. Runs a vibrant lab with a focus on interdisciplinary AI/Systems research.

John Wickersonمشاهده پروفایل

دانشیار

• Computer Hardware
• Electrical and Electronic Engineering
• Computer Software
• +۷ مورد دیگر

Dr. John Wickerson is an Associate Professor in the Circuits and Systems group at the Department of Electrical and Electronic Engineering, Imperial College London. His research focuses on improving the reliability of high-performance computing through formal methods, with contributions to high-level synthesis, memory models, and concurrency verification. He holds leadership roles including Course Director for the Electrical and Information Engineering degree and Deputy Tutor for PhD students. Research Interests: Formal Verification of Hardware/Software Systems High-Level Synthesis (HLS) and FPGA Compilation Weak Memory Models and Concurrency Semantics Fuzz Testing for Hardware Tools Compiler Optimization and Correctness Digit Elision and Arbitrary-Precision Arithmetic Notable Achievements: Best Paper Award at EuroSys 2024 (database isolation validation) Pioneered formal methods for HLS tools (e.g., QuteFuzz, C4) Co-developed the C4 C compiler concurrency checker Published over 60 peer-reviewed papers across top venues (ASPLOS, PLDI, FPGA) Lab/Team: Part of the Circuits and Systems group at Imperial College, collaborating with industry partners like Kaihong Yann and ARM.

Turhan Hilmi Demirayمشاهده پروفایل

پژوهشگر

• Energy Systems
• Electrical Power Systems
• Power System Analysis
• +۴ مورد دیگر

Turhan Hilmi Demiray is the Head of the Research Center for Energy Networks (FEN) at ETH Zürich, Switzerland , where he leads industrial and academic research projects in energy systems. His academic background includes a Ph.D. in Electrical and Computer Engineering from ETH Zürich (2008) and an M.Sc. in Telecommunications from TU Vienna (2003). He has held academic and industry roles at ABB Vienna AG, NEPLAN AG, and ETH Zürich’s Power Systems Laboratory.

Véronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

• Video Coding
• Machine Learning
• FPGA Acceleration
• +۳ مورد دیگر

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.