جست‌وجوی استاد و پژوهشگر

Liu Liliمشاهده پروفایل

مدرس

• Machine Learning
• Computer Vision
• Multi-modal Learning
• +۷ مورد دیگر

Liu Lili is a Lecturer (Educator Track) in the Department of Computer Science at the School of Computing, National University of Singapore. She holds a Ph.D. from Nanyang Technological University and a Master's in Computer Science from Shanghai University. Prior to NUS, she served as a Senior Research Scientist at Singapore Polytechnic and a Scientist at A*STAR's Institute of High-Performance Computing. Her research focuses on Machine Learning, Computer Vision, and Multi-modal Learning, with applications in FinTech, Social Media Analysis, and Algorithms & Theory. Notable projects include AI-driven coating inspection systems for marine assets and behavioral competency assessment tools for navigational safety. She has contributed to robotics for construction quality assessment and interactive virtual environments for rehabilitation. Liu's publications span AI applications in finance, robotics, and material science, reflecting her expertise in bridging theoretical computer science with practical industrial solutions. Her work emphasizes automation, anomaly detection, and multi-modal data integration.

Suranga Chandima Nanayakkaraمشاهده پروفایل

دانشیار

• Intelligent Systems
• Human Augmentation
• Human-Computer Interaction
• +۳ مورد دیگر

Suranga Chandima Nanayakkara is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the Centre for Holistic Inquiry into Lifelong Learning (CHILL) and the Smart Systems Institute (SSI). He holds roles such as AI+HCI Theme Lead at NUS+CNRS IPAL Lab and Residential Fellow at NUS College. His research focuses on assistive human-computer interfaces, emphasizing technologies that enhance perceptual and cognitive capabilities for individuals with sensory deficits. He earned his PhD and BEng from NUS, followed by postdoctoral work at MIT Media Lab. Notable projects include the Augmented Human Lab, iTILES for rehabilitation, and the FingerReader device for visually impaired shoppers. His work has garnered awards like the MIT TR35, TOYP, and INK Fellowship. Research interests include Intelligent Systems, Human Augmentation, and Design Science. He explores applications in assistive technologies, healthcare informatics, and educational tools. Over 15 publications highlight innovations in wearables, stress management, and multimodal interaction. Awards: 40+ accolades, including MIT TR35, TOYP, and multiple design awards for projects like Kiwrious and FingerReader. Grants: Led projects funded by institutions like NUS and Singapore’s innovation initiatives. Labs: Augmented Human Lab (founded 2011), focusing on humanizing technology through natural interfaces.

Liyi Zhouمشاهده پروفایل

مدرس

• Systems Security
• Blockchain Technology
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Liyi Zhou is a Lecturer in the School of Computer Science at the University of Sydney, specializing in systems security, blockchain, and AI. His research focuses on developing automated and adaptive security tools using machine learning and reinforcement learning. He co-founded D23E.ch, a platform addressing blockchain security and privacy challenges. Research interests include AI-driven vulnerability detection, large security models, real-time intrusion prevention, advanced program analysis (fuzzing/symbolic execution), and privacy-preserving systems. He actively recruits PhD students for projects advancing AI in cybersecurity. Notable achievements include pioneering 'sandwich attacks' discovery in DeFi protocols, contributing to Ethereum Foundation grants, and receiving bug bounties from Flashbots and Ethereum Foundation for vulnerability disclosures. His work has been published in venues like IEEE S&P, USENIX Security, and SIGMETRICS. Teaching includes the course INFO2222. He seeks collaborations and funding to bridge academic research with real-world industry problems, emphasizing practical impact.

Zhongying Dengمشاهده پروفایل

پژوهشگر ارشد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Zhongying Deng is a Research Fellow in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work focuses on advancing medical imaging technologies and computer vision through deep learning and domain adaptation techniques. Key contributions include developing benchmark datasets like TrafficCAM and TrafficMOT for traffic analysis, A-Eval for abdominal organ segmentation, and foundational models for medical AI such as GMAI-VL. His research bridges theoretical advancements in neural networks and practical applications in healthcare and transportation. His research interests span image segmentation, domain adaptation, neural network architectures, and multimodal data integration. Notable projects include FCN+ for enhanced convolutional networks and Brain Foundation Models for neurodegenerative disease analysis. Deng collaborates extensively on interdisciplinary projects, combining mathematical modeling with computational tools to address real-world challenges in medical diagnosis and autonomous systems. Publications emphasize scalable medical image analysis frameworks (e.g., STU-Net, Sa-med2d-20m) and robust domain adaptation methods for cross-dataset performance. His datasets and models are widely recognized for enabling reproducible research and advancing state-of-the-art performance in critical areas like MRI reconstruction and multi-organ segmentation.

Jakoah Brgochمشاهده پروفایل

استادیار

• Machine Learning in Materials Science
• Solid-State Lighting
• Superhard Materials
• +۳ مورد دیگر

Jakoah Brgoch is an Assistant Professor in the Department of Chemistry at the University of Houston. His research focuses on leveraging machine learning to design inorganic compounds for applications in LED-based lighting and superhard materials. Key areas include phosphor development, sparse data handling, and predicting material formation. He leads the Brgoch Group, which emphasizes interdisciplinary approaches combining computational modeling and experimental synthesis. Research interests span luminescent materials, crystal chemistry, and defect engineering, with a particular emphasis on optimizing phosphors for solid-state lighting and high-performance materials under extreme conditions. His work bridges data science and traditional materials discovery to accelerate innovation in optoelectronics and mechanical materials. Recent publications highlight advancements in cyan-emitting nitridation processes, machine learning-guided phosphor discovery, and understanding oxidation resistance in silicides. His team has developed novel phosphors like Na2CaZr2Ge3O12:Cr³⁺ for NIR bioimaging and explored luminescent properties of Sr-based solid solutions. Active in translational research, Dr. Brgoch collaborates on applications like smartphone-readable diagnostic platforms using nanophosphors and point-of-care testing. His lab emphasizes open science practices and has pioneered methods like Single-crystal automated refinement (SCAR) for structural determination.

David Blaauwمشاهده پروفایل

استاد

• Ultra-Low Power Circuits
• IoT and Sensor Networks
• RF Communication
• +۷ مورد دیگر

David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)

Hasan Davulcuمشاهده پروفایل

استاد

• Sociocultural Modeling
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.

• Environmental Science
• Climate Change Mitigation and Adaptation
• Geographic Information Systems (GIS)
• +۳ مورد دیگر

Dr. Jennifer Koch is an Associate Professor at the Laboratory of Geo-information Science and Remote Sensing, part of Wageningen University & Research. Previously, she served as an Associate Professor and Associate Research Director at the University of Oklahoma's Data Institute for Societal Challenges. Her research integrates data-driven methods like simulation modeling to address socio-economic and climate change challenges, focusing on sustainable urbanization and environmental management. Education: She holds a Diplom (Univ.) in Geoecology from the University of Bayreuth and a Dr.-Ing. in Electrical Engineering/Computer Science from the University of Kassel. She teaches courses on geo-information management and data analytics. Research emphasizes multi-scale modeling, stakeholder engagement, and participatory approaches to socio-ecological systems. Recent work explores urban growth in Africa, methane emission monitoring, and renewable energy siting. Articles highlight interdisciplinary methods in GIS, climate policy, and community geography. Professional service includes roles with iEMSs, IALE, and the AAG. No ancillary activities reported. Her work bridges technical geospatial tools with societal challenges, emphasizing practical policy applications.

• Marine Propulsion Systems
• Hydrodynamics of High-Speed Vehicles
• Ship Performance Analysis
• +۶ مورد دیگر

Sverre Steen is a Professor and Head of the Department of Marine Technology at the Norwegian University of Science and Technology (NTNU). He leads the Kongsberg Maritime University Technology Centre focused on 'Ship Performance and Cyber-physical Systems' and is a member of the standing committee for the Symposium of Marine Propulsors. His research emphasizes ship propulsion, hydrodynamics, and big data analysis of in-service vessel performance. Key interests include seakeeping, high-speed marine vehicles, and model testing techniques. Steen teaches TMR 4217 Hydrodynamics of High-Speed Marine Vehicles , covering cavitation, experimental hydrodynamics, and propulsion systems. He collaborates internationally on projects like the Norwegian Ocean Technology Centre. His recent work explores wave-energy extraction via hydrofoil vessels, resistance modeling for fast ferries, and propulsion efficiency in real sea states. He has contributed to global shipping emission models (MariTEAM) and reliability analysis of structural components under vibration. Steen's publications span propulsion in waves, engine-propeller dynamics, and data-driven methods for ship performance monitoring. His applied research bridges experimental testing and computational modeling to address challenges in sustainable maritime transport and operational safety.

• Fluid Dynamics
• Turbulence
• Machine Learning
• +۶ مورد دیگر

Adrián Lozano-Durán is an Associate Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Guggenheim Laboratory for Aeronautics (GALCIT). He holds a B.S., M.S., and Ph.D. from the Polytechnic University of Madrid (2010–2015) and joined Caltech as a Visiting Associate in 2024 before becoming a faculty member in the same year. His research focuses on fluid dynamics, turbulence, and machine learning applications in computational fluid dynamics (CFD), particularly for aerospace systems. He leads the Aerofluids, Learning & Discovery (ALD) Lab, collaborating with MIT’s AeroAstro department. Key research areas include causal inference in fluid systems, reduced-order modeling, and machine-learning-based closure models for large-eddy simulation (LES). His work addresses challenges in low-speed aerodynamics, supersonic, and hypersonic flows. Notable recent contributions include advancements in LES wall models and information-theoretic approaches to turbulence control. He frequently presents at international conferences and has co-authored high-impact papers in Nature Communications , Journal of Fluid Mechanics , and Physical Review Research . Education: B.S., Polytechnic University of Madrid (2010) M.S., Polytechnic University of Madrid (2012) Ph.D., Polytechnic University of Madrid (2015) Affiliations: GALCIT, Caltech AeroAstro, MIT (collaboration) Advising focuses on students like Álvaro Martínez-Sánchez and Tristan, whose work spans causality in turbulence and flow control. He actively engages in interdisciplinary research, bridging fluid mechanics with machine learning and information theory to advance aerospace engineering solutions.

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +۶ مورد دیگر

Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.

Yanchao Liuمشاهده پروفایل

دانشیار

• Industrial Engineering
• Operations Research
• Transportation Systems Optimization
• +۴ مورد دیگر

Dr. Yanchao Liu is an Associate Professor at Wayne State University's College of Engineering, Department of Industrial and Systems Engineering. He has received research funding from the National Science Foundation and the State of Michigan, including the NSF Career Award. His academic career spans prior industry roles as a Data Scientist and Manager of Advanced Analytics at Sears Holdings Corporation (2016-2017) and Director of Brand Marketing Analytics at Catalina Marketing Corporation (2017). He teaches courses in data science, IoT, and stochastic processes. B.S. Industrial Engineering, Huazhong University of Science and Technology (2006) M.S. Industrial Engineering, University of Arkansas (2008) Ph.D. Industrial and Systems Engineering, University of Wisconsin-Madison (2014) Dr. Liu's research focuses on mathematical modeling for transportation systems, industrial AI, and data analytics. His work addresses drone traffic management, battery-constrained delivery routing, and optimization algorithms for urban mobility. He has developed novel methods for UAV safety diagnostics, random forest implementations, and fairness-aware path planning in urban air mobility. His publications span journals like Journal of Guidance, Control and Dynamics , Transportation Research Part C , and IEEE Transactions on Intelligent Transportation Systems , with conference contributions at IISE and FAIM. His research combines theoretical advancements with practical applications in smart cities and logistics. NSF Career Award (2020) Faculty Research Excellence Award (2021) IEEE PES Best Conference Paper (2015) IEEE Transactions on Smart Grid Best Reviewer (2015) Hubei Province Distinguished Bachelor’s Thesis Award (2006) Dr. Liu advises PhD students like Zhenyu Zhou and J. Chen. He has contributed to energy market modeling (with M.C. Ferris) and published extensively on drone operations, machine learning algorithms, and stochastic processes. His work includes U.S. patent pending applications for UAV safety systems.

• Geomechanics
• Offshore Geotechnics
• Soil-Structure Interaction
• +۱۲ مورد دیگر

Richard Jardine is a Professor of Geomechanics in the Department of Civil and Environmental Engineering at Imperial College London's Faculty of Engineering. He also serves as a College Proconsul and co-chairs the College Artworks Group. His work spans advanced geotechnical research, offshore renewable energy foundations, and international collaboration. Research Interests: His expertise includes soil properties, advanced laboratory and field measurement techniques, soil characterization, offshore geotechnics, foundation analysis, slope stability, driven pile behavior, soft ground engineering, full-scale monitoring, geotechnical instrumentation, and cold region geotechnics. His research is central to climate change adaptation and renewable power systems. Publication Trends: Recent articles focus on the mechanical behavior of chalk, sand, and glacial tills under monotonic and cyclic loading, particularly in offshore contexts. Emphasis is placed on numerical modeling (FE, MPM), pile-soil interaction, aging effects, and design method validation for offshore wind foundations. Fellow of the Royal Society (2024) Fellow of the Royal Academy of Engineering (2002) RAEng Medal (1997) British Geotechnical Association Medal (1990, 2015, 2021, 2023) Canadian Geotechnical Society Quigley Award (2019) ISSMGE McClelland Honour Lecture (2023) BGA Rankine Lecture (2016) Advising and Grants: Richard has led major international Joint Industry Projects including ALPACA, PISA, PAGE, and Unified Pile Design Method JIP, involving partners such as Orsted, NGI, Fugro, Oxford, and Zhejiang University. He has advised UK government, contractors, and energy firms on offshore projects in the Baltic Sea, Taiwan Strait, and North Sea. He holds a Royal Society Newton Advanced Fellowship with ZJU. Labs and Teams: He leads research within Imperial’s Geotechnics group and the Imperial Centre for Geohazards. His team conducts advanced laboratory and field testing, collaborates with Deltares (Netherlands), and maintains strong ties with Zhejiang University, where he is a Visiting Professor and Distinguished International Scholar.

Geoffrey S.D. Beachمشاهده پروفایل

استاد

• Spintronics
• Magnetic Materials
• Nanotechnology
• +۳ مورد دیگر

Geoffrey S.D. Beach is the Toyota Professor in Materials Processing and Professor of Materials Science and Engineering at MIT, and Co-director of the Materials Research Laboratory . His research focuses on spin dynamics, spintronics, and nanoscale magnetic materials, aiming to revolutionize data storage and computation through advanced instrumentation. Education: Bachelor of Science in Physics, California Institute of Technology (1997) PhD in Physics, University of California San Diego (2003) Research Interests: Professor Beach investigates the manipulation of magnetic properties using electric fields, voltage-controlled magnetic order, and the dynamics of skyrmions and domain walls. His work emphasizes materials like ferrimagnetic insulators and garnets, leveraging innovations in solid-state hydrogen gating and interfacial phenomena. Awards: Fellow, IEEE (2023) Junior Bose Award (2009) Labs & Teams: His lab, the Beach Group , develops cutting-edge optical and electrical tools to study magnetization dynamics at nanoscale. Key projects include voltage-gated optical devices and magneto-ionic control of magnetism. Grants & Collaborations: His work is supported by initiatives such as MIT’s Materials Research Laboratory and industry partnerships, though specific grants are not listed here.

Rayadurgam Srikantمشاهده پروفایل

استاد

• Machine Learning
• Communication Networks
• Cloud Computing
• +۷ مورد دیگر

Rayadurgam Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, affiliated with the Coordinated Science Lab. He co-directs the C3.ai Digital Transformation Institute, focusing on AI-driven solutions for global challenges. His research spans machine learning, communication networks, stochastic systems, and game theory. Srikant has authored influential textbooks including Communication Networks: An Optimization, Control and Stochastic Networks Perspective . He holds IEEE Fellow status and has received prestigious awards like the ACM SIGMETRICS Achievement Award (2021) and IEEE Koji Kobayashi Award (2019). Over 20 of his advisees hold faculty positions globally. Education: PhD (1991), MS (1988) in Electrical Engineering from UIUC; B.Tech (1985) from IIT Madras. He has taught advanced courses on optimization, stochastic systems, and game theory. His work bridges theory and practice, with contributions to congestion control, cloud computing, and reinforcement learning. Current projects include AI applications for pandemic response and digital transformation initiatives. Research highlights include foundational work on Lyapunov drift methods for network stability and distributed algorithms. He serves as Area Editor for Mathematics of Operations Research and has led editorial roles for IEEE/ACM Transactions on Networking. His lab collaborates with industry leaders like Microsoft and C3.ai, leveraging supercomputing resources for societal impact.