Physically-adaptive computing Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۹٬۴۹۹ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Hao Yangمشاهده پروفایل

استادیار

Johns Hopkins UniversityUnited States

Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.

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Wei Gaoمشاهده پروفایل

دانشیار

University of PittsburghUnited States

Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.

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Emil Lupuمشاهده پروفایل

استاد

Imperial College LondonUnited Kingdom

Computer System Security
Artificial Intelligence and Image Processing
Computer Communications Networks
+۵ مورد دیگر

Professor Emil Lupu is a Professor of Computer Systems at the Department of Computing , Imperial College London. He leads the Resilient Information Systems Security Group and serves as Co-Director of the National Research Institute in Trustworthy Inter-Connected Cyber-Physical Systems (RITICS) . As a Security Science Fellow at Imperial’s Institute for Security Science and Technology, his work bridges academic research with real-world security challenges. Education: PhD in Computing, Imperial College London (1994–1998) His research focuses on security and resilience of cyber-physical systems (CPS) , with emphasis on defending against data spoofing attacks , adversarial machine learning , and IoT vulnerabilities . He pioneered the Ponder policy systems for access control and the Self-Managed Cell framework for autonomic computing, and developed Bayesian Attack Graphs for scalable risk assessment in CPS. Recent publications highlight trends in adversarial robustness (2025–2022), including LIDAR spoofing defense for autonomous vehicles, LLM security , and attack graph analysis for IoT. His work explores the intersection of safety and security , applying model-checking to identify adversarial threats in train control, microgrids, and aviation systems. Scientific Awards: Security Science Fellowship, Imperial College London (2011–present) As co-founder of the PETRAS National Centre of Excellence in IoT Cybersecurity (2016–2021), he advanced security methodologies for interconnected systems. His collaborations with institutions like the Cyber Security Body of Knowledge (CyBoK) demonstrate his leadership in shaping cybersecurity research standards. Current projects include the RITICS Institute , focusing on trustworthy cyber-physical systems, and exploring generative AI for security poisoning with practical defenses against adversarial ML.

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Angela Sasic Kalagasidisمشاهده پروفایل

استاد

Chalmers University of TechnologySweden

Angela Sasic Kalagasidis is a Professor and Department Head at Chalmers University of Technology , leading the Building Physics research group. She serves as a board member of the Moisture Center at Lund University of Technology , contributing to interdisciplinary research in building science. Building Physics Heat and Mass Transfer Energy Efficiency Moisture Safety Indoor VOC Emissions Climate Change Adaptation Her recent publications focus on aerogel-based materials for insulation, urban heat island mitigation , and thermal energy storage systems . Key methodologies include CFD simulations , field testing , and life cycle assessment frameworks . Research trends show emphasis on: Advanced computational tools for hygrothermal analysis Integration of phase change materials in building systems Climate resilience in building envelopes Optimization of ventilation and moisture control

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Susanne Narcissمشاهده پروفایل

استاد

Dresden University of TechnologyGermany

Susanne Narciss is a Professor at the Psychology of Learning and Instruction department of Technische Universität Dresden, leading the Center of Tactile Internet with Human in the Loop (CeTI). Her research focuses on error processing in educational contexts, with 15 recent publications analyzing error climates, feedback strategies, and motivational frameworks. Key Research Areas : Learning from errors/failure, instructional feedback design, affective-motivational responses, error-related metacognition. Methodological Scope : Combines longitudinal studies, experimental designs, and qualitative analyses across K-12, university, and informal learning settings (museums, home contexts). Her work emphasizes context-specific interventions for educators, parents, and students, including error-competency training programs and metacognitive scaffolding tools. Current projects examine vibrotactile feedback systems for motor learning and cultural responsiveness in psychology education. Collaborative Networks : Works with international teams on the International Competences for Undergraduate Psychology model and cyber-physical system pedagogy. Recent Trends : 2025 articles focus on collaborative error processing, scenario-based human-machine interaction, and generative learning tasks in digital environments.

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Jennifer Jakobiمشاهده پروفایل

استاد

University of British ColumbiaCanada

Jennifer Jakobi is a Professor at the School of Health and Exercise Sciences , Faculty of Health and Social Development, UBC Okanagan. She serves as Director of the Institute for Healthy Living and Chronic Disease Prevention and holds the NSERC Chair for Women in Science and Engineering . Her work bridges neuromuscular physiology and STEM diversity initiatives . Research Themes : Frailty, aging in place, sex-differential muscle function, and motor unit modulation. Labs : Leads the Healthy Exercise and Aging Lab (HEAL) and coordinates the iSTAND diversity program. Publications focus on aging biomechanics, cannabis impacts in elderly populations, social VR interventions, and pandemic-era health behaviors. Her NSERC-funded basic research examines cortical-spinal pathways, while UBCO grants support applied studies on vibration therapy and assistive technologies. Scientific Contributions : Identified sex-specific force steadiness mechanisms Developed prehabilitation frameworks for functional independence Advocated for gender-inclusive STEM education Outreach includes youth STEM camps, equity workshops, and community partnerships on aging-in-place policies. Current research explores VR-based social engagement and cannabis safety protocols for seniors.

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Qian Zhangمشاهده پروفایل

استادیار

Queen's University at KingstonCanada

Dr. Qian Zhang serves as Assistant Professor in the Robert M. Buchan Department of Mining at Queen's University's Smith Engineering, leading the Green Mining Value Chain (GreeMVC) Lab. His research develops strategic frameworks for sustainability and resilience throughout mining value chains, with emphasis on climate change mitigation and resource efficiency in global mineral systems. His academic foundation includes a Ph.D. in Urban Engineering from the University of Tokyo (awarded Japanese Government MEXT Scholarship), complemented by MSc and BSc degrees in Environmental Science plus a Minor in Economics from Peking University. Prior to his current role, he conducted postdoctoral research at the University of Victoria and University of Tokyo while consulting for the World Resources Institute on climate-energy initiatives. Dr. Zhang's expertise spans carbon footprint analysis , life-cycle assessment , and industrial ecology applied to mining systems. He employs advanced methodologies including input-output analysis and material flow accounting to model environmental pressures across urban infrastructure and mineral supply chains. His work specifically addresses greenhouse gas accounting, water-energy nexus challenges, and circular economy implementation in resource-intensive sectors. Recent publications reveal strong methodological convergence between artificial intelligence and environmental assessment, particularly in optimizing mining operations through reinforcement learning and geospatial analysis. Key thematic clusters include carbon accounting standardization, critical mineral sustainability, and policy-oriented modeling of environmental pressures throughout mineral value chains. His research program is supported by major competitive grants: NSERC Discovery Grant (2022-2027) SSHRC Institutional Grant (2023, 2025) NSERC Alliance Missions Grant (2023, 2024) Mitacs Accelerate Grant (2023, 2025) NFRF Exploration Grant (2025-2027) NRCan Energy Innovation Program (2025) Dr. Zhang actively mentors a dynamic research group comprising 10+ graduate students and postdocs, securing collaborative funding through institutional and federal channels. His GreeMVC Lab maintains active partnerships with industry leaders and government agencies to translate research into practical sustainability solutions for the mining sector, with current projects focusing on AI-driven fleet management and life-cycle assessment of mineral supply chains. The GreeMVC Lab operates as a multidisciplinary hub with structured mentorship programs, regular industry engagement events, and international collaborations including the COM symposium on sustainable circularity. The lab's physical space in Goodwin Hall supports advanced computational analysis of mining value chains while fostering innovation in green mining technologies through student-led research initiatives.

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Christopher Bronk Ramseyمشاهده پروفایل

استاد

University of OxfordUnited Kingdom

Christopher Bronk Ramsey is Professor in Archaeological Science and Head of the School of Archaeology at the University of Oxford, affiliated with Merton College. As chair of the INTCAL committee, he oversees global radiocarbon calibration. His expertise spans archaeological science, Quaternary environmental research, and nuclear instrumentation development. Research focuses on radiocarbon dating, Bayesian chronological modeling, and AMS techniques. Key areas include Quaternary chronology, human evolution, climate change impacts, and archaeological applications in the eastern Mediterranean, Amazonia, and Anglo-Saxon England. He develops the OxCal software for statistical chronology analysis. Recent publications emphasize radiocarbon calibration advancements, pre-Columbian land-use in Amazonia, European Neolithic chronologies, and high-precision dating methods. His work integrates archaeology, environmental science, and data science to address chronological challenges across diverse temporal and spatial scales. No specific scientific awards were detailed in the source material. He supervises numerous doctoral students on topics ranging from dendrochronology to digital archaeology and Middle Stone Age chronology. Major projects include FeedSax (Anglo-Saxon agriculture) and HERCA (Amazonian human-environment interactions), securing research grants for interdisciplinary teams. Professor Ramsey directs the OxCal project and co-leads IntCal, IntChron, FeedSax, and HERCA initiatives. These teams develop calibration standards, integrate chronological data, and investigate past human adaptations to environmental change, particularly in Amazonia and Europe.

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Minh Hoai Nguyenمشاهده پروفایل

استادیار

Stony Brook UniversityUnited States

Minh Hoai Nguyen is an Assistant Professor in the Department of Computer Science at Stony Brook University. He received his PhD in Robotics from Carnegie Mellon University and a Bachelor of Engineering from the University of New South Wales. Prior to Stony Brook, he was a post-doctoral research fellow at Oxford University and a Kurti Junior Research Fellow at Brasenose College. Education: PhD in Robotics, Carnegie Mellon University Bachelor of Engineering, University of New South Wales His research focuses on computer vision , machine learning , and time series analysis , particularly in developing algorithms for human action recognition , gesture detection , and expression analysis in video data. Applications include video surveillance , human-computer interaction , and medical diagnosis of behavioral disorders . His work integrates computer vision for video processing, time series analysis for modeling human behavior, and machine learning for training complex algorithms. Notable awards include: CVPR 2012 best student paper award Winner of PASCAL VOC 2012 Challenge for Human Action Recognition He teaches courses such as Video Analysis (CSE 594) and Introduction to Robotics (CSE 525) .

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Jukka K Nurminenمشاهده پروفایل

استاد

University of HelsinkiFinland

Jukka K Nurminen is a Professor of Computer Science at the University of Helsinki (since 2019) and a Research Professor at VTT. He leads the Empirical Software Engineering research group and supervises doctoral students in the Doctoral Programme in Computer Science. His career spans academia and industry, including roles as Adjunct Professor at Aalto University (part-time, 2016-2021) and Principal Scientist at VTT (2016-2019). His research focuses on efficient software systems , particularly energy-efficient software , mobile cloud computing , and data-intensive systems . Recent work addresses AI system testing , ethical decision-making in software , and quantum computing software . His publications highlight trends in quantum algorithms , machine learning for edge computing , and ethical AI . Best Paper Award (2023) Nurminen has supervised 6 PhD theses, 48 MSc theses, and 21 BSc theses. He has secured over 1 MEUR in research funding, including projects like FrameQ and EM4QS for quantum middleware. His teaching innovations include hackathons and summer schools, with excellence recognized in tenure-track evaluation (2018) and adjunct professorship (2015).

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Brian D. Gerardotمشاهده پروفایل

استاد

Heriot-Watt UniversityUnited Kingdom

Brian D. Gerardot is a Professor at the School of Engineering & Physical Sciences , Heriot-Watt University , where he leads the Quantum Photonics Laboratory within the Institute of Photonics and Quantum Sciences. His research focuses on creating ultra-coherent quantum photonic devices that bridge quantum optics, condensed-matter physics, materials science, and nano-optics. BSc in Materials Science from Purdue University (1998) PhD from UC Santa Barbara (2004) His work explores semiconductor quantum dots and defect centers in diamond, utilizing advanced nano-fabrication techniques to design and characterize photonic structures. Research outputs highlight quantum technologies, entangled imaging, exciton dynamics in 2D materials, and coherence in photon emission systems. Scientific Awards include: Chair in Emerging Technologies (Royal Academy of Engineering, 2018) Wolfson Merit Award (Royal Society, 2018) ERC Consolidator Grant (2018) ERC Starting Grant (2013) Personal Research Fellowship (Royal Society of Edinburgh, 2006-2009) University Research Fellowship (Royal Society, 2009-2017) Challenging Engineering award (2011) He manages the NanoFab Disco Saw facility and has secured significant grants for quantum technologies and nanophotonic research, with collaborations spanning international institutions and datasets supporting breakthroughs in exciton-polarons, quantum imaging, and photonic coherence.

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Lionel C. Briandمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Lionel C. Briand is a Professor of Software Engineering with shared appointments at the University of Luxembourg's SnT Centre for Security, Reliability, and Trust and the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Canada Research Chair (Tier 1) in Intelligent Software Dependability and Compliance and serves as Director of Lero, Ireland's national software research center. His academic leadership spans over 25 years of collaborative research with industry partners across automotive, aerospace, energy, financial, and legal domains. Professor Briand's research focuses on software verification and validation, trustworthy AI systems, model-driven engineering, and empirical software engineering methodologies. His work bridges theoretical foundations with industrial applications, particularly in cyber-physical systems where machine learning components interact with safety-critical control systems. He has pioneered techniques for testing AI-enabled systems, GDPR compliance automation, and mutation analysis for space systems. His publication portfolio demonstrates consistent innovation in software testing, with recent emphasis on large language models for test generation, automated compliance checking, and safety analysis of deep neural networks. Key trends include black-box testing methodologies, metamorphic testing for security, and search-based approaches for DNN validation. IEEE Fellow and ACM Fellow IEEE Computer Society Harlan Mills Award (2012) ACM SIGSOFT Outstanding Research Award (2022) IEEE Reliability Society Engineer-of-the-Year Award (2013) ERC Advanced Grant recipient (2016) Fellow of the Academy of Science, Royal Society of Canada (2023) ICSE 2011 Most Influential Paper Award As Director of Lero and holder of a Canada Research Chair, Professor Briand leads major research initiatives including an ERC Advanced Grant on cyber-physical system modeling and testing. His industrial collaborations generate substantial grant funding, particularly in automotive safety validation and regulatory compliance automation. He mentors numerous researchers through his dual appointments and serves on program committees for top software engineering conferences. Professor Briand directs research activities at the SnT Centre's SVV department, focusing on software verification and validation. His team develops practical tools like MASS for space system mutation analysis and COREQQA for compliance requirements understanding, with strong industry adoption in automotive and aerospace sectors.

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Annette Byrneمشاهده پروفایل

استاد

Royal College of Surgeons in IrelandIreland

Professor Annette Byrne is a leading academic at RCSI University of Medicine and Health Sciences , where she serves as Professor of Physiology and Head of the Precision Cancer Medicine (PCM) Group. She has held this position since 2019 after progressing through roles as Lecturer (2008), Senior Lecturer (2013), and Associate Professor (2017). Her research focuses on precision medicine approaches for colorectal and brain cancers , integrating multi-modality molecular imaging , Next Generation Sequencing , and patient-derived xenograft models . PhD in Cell Biology (University of York, 1999) John Kerner Fellowship in Gynaecologic Oncology (UCSF, 1999-2001) Scientist at Pharmacyclics Inc. (2001-2003) Senior Scientist at Angion Biomedica Corp. (2003-2005) Principal Investigator at UCD Conway Institute (2005-2008) Her research interest lies in precision cancer medicine , particularly elucidating predictive biomarkers (genomic, transcriptomic, proteomic) and identifying novel therapeutic targets . Key methodologies include radiomics , fluorescence-guided surgery , and systems modeling of apoptosis pathways. She has pioneered Ireland's first Tumour Xenograft Facility and Translational In Vivo Imaging Centre . Recent publications highlight her work on cross-species radiomics , cell-free DNA analysis , and glioblastoma microenvironment subtyping . Her Marie Curie networks (Gliotrain, Glioresolve) and COLOSSUS project have trained 25+ PhD researchers in brain cancer therapeutics. Over €45M in national/international grants Member of Royal Irish Academy (2025) Highly cited in Cancer Discovery , Annals of Oncology , and Nature journals She supervises multiple PhD candidates and leads the RCSI Precision Cancer Medicine Group , which utilizes computational approaches and molecular imaging to improve cancer treatment outcomes. Her GLIORESOLVE and EDIReX projects focus on tumor microenvironment manipulation and distributed PDX infrastructure.

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Marie Rochمشاهده پروفایل

استاد

San Diego State UniversityUnited States

Professor Marie Roch is a distinguished faculty member in the Department of Computer Science at San Diego State University within the College of Sciences . Her groundbreaking research bridges Bioacoustics and Machine Learning , focusing on advanced algorithms for automated detection, classification, and analysis of marine mammal vocalizations using passive acoustic monitoring. Core research in marine bioacoustic signal processing and deep learning applications for echolocation click detection Published extensively in Journal of the Acoustical Society of America , Biological Reviews , and IEEE Transactions Developed deep learning frameworks for whale whistle extraction without human annotation Created open-source tools like Silbido Profundo for automated marine mammal call analysis Marie's work has been supported by over $3 million in grants from the DOD Office of Naval Research , Bureau of Ocean Energy Management , and Human Frontier Science Program . She actively mentors graduate students and serves on numerous thesis committees, with recent advisees working on deep learning for baleen whale calls and terrestrial animal recognition . Her Marine Acoustic Research Lab (MAR Lab) leads in developing the Tethys metadata workbench for ocean acoustic data management.

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James Chelikowskyمشاهده پروفایل

استاد

University of Texas at AustinUnited States

James Chelikowsky is Professor and W. A. "Tex" Moncrief, Jr. Chair in Computational Materials at The University of Texas at Austin's Oden Institute for Computational Engineering and Sciences (ICES). His research pioneers quantum mechanical simulations for materials design and discovery across multiple domains. His educational background includes: B.S. in Physics from Kansas State University (1970) Ph.D. in Physics from University of California at Berkeley (1975) Chelikowsky's research spans computational materials science with focus on quantum models for functionalized nanostructures, simulations of liquids and crystal growth, "green magnetism" in dilute magnetic semiconductors, oxide defects, materials informatics, and high-performance electronic structure algorithms. His work bridges theoretical physics with practical materials engineering to solve complex problems in energy and electronics. Analysis of his 2012-2022 publications reveals evolving focus from fundamental quantum simulations toward machine learning integration for magnetic materials discovery, while maintaining strong contributions to two-dimensional materials and interfacial phenomena. Key trends include increased computational complexity and interdisciplinary collaboration with experimental groups. His distinguished honors include: Feynman Prize for Theory (2022) FMD John Bardeen Award (2021) Aneesur Rahman Prize (2013) Multiple society fellowships (MRS, AAAS, APS) Guggenheim Fellowship (1996) As leader of an active research group, Chelikowsky mentors graduate students in computational methods development. While specific grant details aren't provided, his sustained publication record and named chair position indicate substantial ongoing research funding. His group maintains strong industry and national laboratory collaborations evident in co-authorship patterns. The Computational Materials Group operates through ICES with research facilities supporting high-performance computing for materials simulations. Current projects focus on machine learning-guided materials discovery and quantum mechanical modeling of novel electronic materials.

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