جست‌وجوی استاد و پژوهشگر

Stephanie Forrestمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Computational Immunology
• +۸ مورد دیگر

Stephanie Forrest is a Professor of Computer Science at Arizona State University and serves as Director of the Biodesign Center for Biocomputation, Security and Society . She holds affiliations with the School of Computing and Augmented Intelligence , Global Futures Laboratory , and Santa Fe Institute External Faculty . Education: B.A. from St. John's College M.S. and Ph.D. in Computer Science from the University of Michigan Research Interests: Forrest specializes in the intersection of biology and computation , with key contributions to cybersecurity (anomaly detection, instruction-set randomization), automated software repair (evolutionary methods), and biological modeling (immune systems, SARS-CoV-2 spread). Her work bridges complex adaptive systems , AI/ML , and defense applications . Scientific Contributions: 2020 IEEE S&P Test of Time Award 2019 ICSE Most Influential Paper Award 2011 ACM/AAAI Allen Newell Award NSF Presidential Young Investigator (1991) IEEE Fellow Evolutionary Computation Pioneer award Publications & Grants: Her research appears in top venues (ICSE, IEEE S&P, PNAS) and is funded by the National Science Foundation , DARPA , Air Force Research Lab , and Santa Fe Institute . Key projects include Crispy (CRISPR-inspired DoS defense), GenProg (automated bug correction), and SIMCoV-GPU (agent-based pandemic modeling).

Ajitha Rajanمشاهده پروفایل

استاد

• Software Testing
• Software Verification
• Automated Testing
• +۱۲ مورد دیگر

Ajitha Rajan is a Professor (Personal Chair of Software Testing and Verification) at the School of Informatics, University of Edinburgh. Previously, she was a post-doctoral researcher at Oxford University's Computer Science Department and Laboratoire d'Informatique de Grenoble (LIG) in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under the supervision of Prof. Mats Heimdahl. Her research spans two main directions: Automated Software Testing Techniques covering test input generation, test oracles, and coverage metrics; and Biomedical Artificial Intelligence focusing on cancer survival models, interpretability for biological sequences, and medical images. Her work bridges software engineering and biomedical applications, particularly in the development of trustworthy AI systems for healthcare. Professor Rajan leads several significant research projects including a Royal Society Industry Fellowship (2022-2025) on AutoTest for autonomous vehicle perception safety, the H2020 European Project KATY (2021-2025) on AI for genomics and personalized medicine where she serves as Edinburgh Lead PI, and an EPSRC Trustworthy Autonomous Systems Node project (2020-2024). Her recent publications demonstrate strong activity across software testing, explainable AI, and biomedical applications, with numerous papers accepted to top conferences in 2025 including ML for Healthcare, IJCAI, and ESEM. Among her scientific recognitions, she received the Best Reviewer Award at ISSTA'25. Her work has been consistently published in leading venues including ICSE, ICASSP, and Communications Biology. Professor Rajan actively mentors PhD students working on diverse topics from automated testing of speech recognition systems to explainable AI for medical image analysis and cancer immunotherapy. She teaches undergraduate courses in Software Testing, Computer Programming, and Embedded Systems, and has been instrumental in establishing several fully funded PhD positions through Centres for Doctoral Training at the University of Edinburgh.

George Ghineaمشاهده پروفایل

استاد

• Multimedia Quality Assessment
• Mulsemedia Computing
• Human-Computer Interaction
• +۷ مورد دیگر

Professor George Ghinea is a distinguished academic in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 350 publications and 33 successfully supervised PhD students, he leads cutting-edge research at the intersection of computer science, media studies, and psychology. His educational background includes a PhD from the University of Reading (1999) where he pioneered the Quality of Perception (QoP) metric - a precursor to today's widely adopted Quality of Experience (QoE) concept. He holds multiple degrees with distinction from the University of the Witwatersrand in South Africa, including BSc, BSc (Hons), and MSc in Computer Science. Professor Ghinea's research focuses on perceptual multimedia quality and human-centered e-systems, with particular emphasis on mulsemedia (multiple sensorial media) - his own conceptual framework extending multimedia to engage non-traditional senses. His work spans eye-tracking applications, telemedicine, multi-modal interaction, and ubiquitous computing. Current research explores mulsemedia integration in autonomous vehicles, security-enhanced systems, and accessibility solutions. His publications reveal strong trends in multisensory computing (42% of recent works), telemedicine applications (28%), accessibility research (18%), and network optimization (12%). The work consistently bridges theoretical frameworks with practical implementations, often incorporating physiological data and user perception metrics. Distinguished Visiting Fellow of the Royal Academy of Engineering (2018) SPARC DUO-India 2020 Fellowship Programme recipient Principal Investigator for multiple EU Horizon 2020 projects Research featured in major media including BBC, Forbes, and Daily Telegraph Professor Ghinea has secured substantial research funding through projects like the EU H2020 NEWTON initiative, Royal Academy of Engineering partnerships, and multiple Newton Fund collaborations. His supervision portfolio includes 33 PhD completions with diverse research spanning security behavior in Ghana, physiological QoE in VR, smart city adoption in Oman, and sustainable digital transformation in Qatar. He leads the IMUSY research group focusing on mulsemedia systems and human perception. His laboratory work centers on the IMUSY research group where they develop mulsemedia applications integrating thermal, wind, and olfactory devices for enhanced user experiences. Current team projects include mulsemedia in autonomous vehicles (MulsEAV), physiological data for QoE assessment, and smart city adoption studies.

Bo Chenمشاهده پروفایل

استاد

• Mechatronics
• Embedded Systems
• Agent Technology
• +۷ مورد دیگر

Bo Chen is a Professor in the Department of Mechanical Engineering – Engineering Mechanics and the Department of Electrical & Computer Engineering at Michigan Technological University. She directs the Intelligent Mechatronics and Embedded Systems (IMES) Laboratory, focusing on advanced controls, optimization, and artificial intelligence for connected and autonomous vehicles, electric vehicle–smart grid integration, and smart mobility. PhD in Mechanical and Aeronautical Engineering from the University of California, Davis (2005) Visiting Professor at Argonne National Laboratory (2014–2015, 2016) Sabbatical at Oak Ridge National Laboratory (2022–2023) Dr. Chen's research spans Mechatronics , Embedded Systems , Hybrid Electric Vehicles , and Cyber-Physical Systems . Her work includes vehicle-to-grid integration , battery control systems , and cybersecurity for automotive systems . Recent publications highlight advancements in predictive control algorithms for hybrid vehicles, consensus-based frequency regulation , and plausibly deniable encryption systems for mobile devices. Funded by the National Science Foundation, Department of Energy, and industry partners, her research has secured over $10 million in grants. ASME Fellow Best Paper Award (2008 IEEE/ASME MESA Conference) Top Cited Article Award (Journal of Computers & Graphics) Best Survey Paper Award (IEEE Transactions on ITS) Co-recipient of four Best Student Paper Awards Dr. Chen has held leadership roles as Chair of the Technical Committee on Mechatronics and Embedded Systems (IEEE ITS Society), Chair of the ASME Design Engineering Division's Technical Committee, and Associate Editor for IEEE Transactions on Intelligent Transportation Systems (2012–2019). She organized multiple international conferences and co-edited special issues on intelligent transportation systems.

Tanvir Arafinمشاهده پروفایل

استادیار

• Hardware Security
• Trusted Computing
• IoT Security
• +۴ مورد دیگر

Tanvir Arafin serves as an Assistant Professor in the Department of Cyber Security Engineering at George Mason University, where his research focuses on hardware security and trust mechanisms for emerging computing platforms. With publications in premier venues including IEEE Transactions on Very Large Scale Integration Systems, IEEE Transactions on Computers, and ACM International Conference on Computer-Aided Design, he addresses critical security challenges in next-generation systems through rigorous hardware-software co-design approaches. His research portfolio spans Hardware Security, Trusted Computing, and IoT Security, with specialized expertise in Side-Channel Attacks and Secure Hardware Design. Dr. Arafin investigates electromagnetic side-channel vulnerabilities in O-RAN networks, develops countermeasures for autonomous vehicle cybersecurity, and pioneers RRAM-based security solutions for memory-constrained devices. His work bridges theoretical security models with practical implementations, emphasizing real-world applicability in edge computing environments and autonomous navigation systems. Current projects explore machine learning integration for anomaly detection in connected vehicles and secure acceleration of cryptographic operations. Analysis of Dr. Arafin's 2022-2025 publications reveals strategic focus areas: electromagnetic fingerprinting for radio units in O-RAN (2025), spatial acceleration of Kolmogorov-Arnold Networks (2025), and NTT-based cryptography accelerators (2024). His research demonstrates consistent innovation in securing autonomous navigation systems and edge devices, with emerging work on in-memory computing architectures using resistive memory technologies. Key trends include hardware-centric defense against model inversion attacks, voltage overscaling for lightweight authentication, and robust multi-robot coordination in dynamic environments. Scientific Awards: No scientific awards, fellowships, or medals were documented in the source materials. Dr. Arafin leads significant collaborative research, including the NSF CISE-MSI grant (DP: CNS) for edge-based robust multi-robot systems. His educational initiatives feature Capture-the-Flag competitions targeting underrepresented students in cybersecurity. Current grant activities emphasize practical security solutions for autonomous navigation, multi-robot coordination, and IoT edge devices, with demonstrated focus on translating research into deployable countermeasures for real-world threats in dynamic operational environments.

• Signal Processing
• Wireless Communications
• Information Theory
• +۵ مورد دیگر

Luis Castedo Ribas is a Professor at the Faculty of Informatics , University of A Coruña (UDC) , Spain, since 2001. Previously held research positions at the University of Southern California (USC) and École supérieure d'électricité (SUPELEC). PhD in Telecommunications Engineering (1993), Technical University of Madrid Department of Computer Engineering Research Group: Electronic and Communications Technology Group Research Interests: Specializing in Signal Processing and Information Theory for Wireless Communications Engineering , with focus on MIMO Communication Systems , 5G Radio Interfaces , Joint Source-Channel Coding , and High-Speed Wireless Communications . His work bridges theoretical advancements with practical prototyping of digital communication systems. Recent Article Trends: Publications span Massive MIMO , Beamforming , AI/ML for Wireless , and Quantum-Inspired Coding , reflecting his leadership in evolving telecom standards (5G→6G). Collaborative work with institutions like IEEE and European consortia. Scientific Awards: Best Student Paper (2007, 2013, 2017) General Co-Chair, IEEE Sensor Array Workshop (2014) General Co-Chair, European Signal Processing Conference (2019) Grants & Collaborations: Principal Investigator in over 50 projects funded by Spanish Ministry of Science, EU programs, and companies like Atos Origin. Key roles in national and international research consortia.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +۳ مورد دیگر

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

Jie Gaoمشاهده پروفایل

استادیار

• Machine Learning for Communications and Networking
• Internet of Things (IoT) and Industrial IoT
• Cloud and Multi-Access Edge Computing
• +۷ مورد دیگر

Dr. Jie Gao is an Assistant Professor at the School of Information Technology , Carleton University, with cross-appointments at Dalhousie University (Adjunct Faculty, 2024) and Carleton University (2025). He holds a Ph.D. in Electrical and Computer Engineering from the University of Alberta (2014) and has held postdoctoral and research associate positions at Ryerson University (2017-2019), University of Waterloo (2019-2020), and Marquette University (2020-2022). His research focuses on machine learning for communications/networking , 6G wireless networks , cloud/multi-access edge computing , IoT/industrial IoT solutions , and network virtualization/digital twins . Research Leadership: Co-investigator on projects related to AI-assisted network slicing, digital twin-driven resource allocation, and integrated satellite-terrestrial networks Led work on energy-efficient UAV-assisted edge computing (IEEE Best Land Transportation Paper Award 2024) Professional Roles: Senior Member, IEEE Lead Associate Editor, IEEE Access Vehicular Technology Society Section (2020-present) Associate Editor, Springer Peer-to-Peer Networking and Applications (2020-present) IEEE Vehicular Technology Society Young Professional Ambassador (2024) Notable Contributions: Authored/co-authored books on Intelligent Computing and Communication for the Internet of Vehicles (Springer, 2023) and Connectivity and Edge Computing in IoT (Springer, 2021) Holds patents on medium access control methods (US Patents 2022, 2024) Received multiple IEEE service awards (2018-2024)

Matthew B. Panzerمشاهده پروفایل

استاد

• Traumatic Brain Injury
• Injury Biomechanics
• Computational Biomechanics
• +۲ مورد دیگر

Matthew B. Panzer serves as Associate Dean for Graduate Education and Post-Doctoral Affairs at the University of Virginia's School of Engineering and Applied Sciences, holding professorships in Mechanical & Aerospace Engineering and courtesy in Biomedical Engineering. As Deputy Director of the Center for Applied Biomechanics, he leads research in impact biomechanics and injury prevention. Education: B.S. in Mechanical Engineering, University of Waterloo, 2003 M.S. in Mechanical Engineering, University of Waterloo, 2006 Ph.D. in Biomedical Engineering, Duke University, 2012 Research Focus: Panzer's work employs computational and experimental methods to investigate high-rate non-linear mechanics in tissue mechanics, impact biomechanics, vehicle crashworthiness, military blast/ballistics, and sports injury. Current projects include traumatic brain injury mechanisms in football helmet impacts, biological tissue characterization, human body model development for automotive safety, and protective system design. His approach integrates finite element modeling with experimental validation to translate biomechanical findings into clinical and safety applications. Publication Trends: Recent work (2022-2025) emphasizes brain injury metrics, sex-specific biomechanical responses, and finite element model validation across automotive, sports, and military contexts. Key themes include rotational loading effects, biofidelic model development, and injury risk function derivation, demonstrating strong interdisciplinary integration of neuroscience, engineering, and computational science. Awards: Shannon Fellow (2024-2027) Copenhaver Fellow (2023) MAE Early Career Researcher of the Year (2021, 2022) UVA Research Achievement Award (2019) MAE Young Research of the Year (2018, 2019) Grants and Teaching: Panzer has secured over 40 research grants as Principal Investigator from federal agencies and industry partners. He teaches graduate courses including MAE 6710: Finite Element Analysis (annually since 2015), MAE 7030: Injury Biomechanics, and MAE 6952: Impact Mechanics, mentoring graduate students in biomechanics research despite no specific advisees listed in source materials. Laboratory Leadership: At the Center for Applied Biomechanics, Panzer directs teams conducting experimental testing and computational modeling to advance injury mechanism understanding and develop protective technologies for automotive, sports, and military applications.

• Medical Informatics
• Statistical Science
• Bioinformatics
• +۵ مورد دیگر

Dr. Masato Inoue is a Professor at the Faculty of Science and Engineering , School of Advanced Science and Engineering at Waseda University. He holds a Doctor of Medical Science from Kyoto University. Education: 2003 - Kyoto University Graduate School of Medicine 2003 - Kyoto University His research spans multiple disciplines at the intersection of Medical Informatics , Bioinformatics , and Statistical Mechanics . Key areas include: Medical Imaging : Developing Bayesian super-resolution algorithms and Prior Ensemble Learning for improved MRI reconstruction Voice Analysis : Creating innovative voice quality quantification systems for clinical diagnostics Genetic Analysis : Advancing haplotype inference methods and gene network modeling Signal Processing : Applying statistical mechanics to diverse problems from coding theory to neuroscience His recent publications (2021-2012) demonstrate consistent contributions to medical imaging algorithms , voice disorder classification , and genetic data analysis . Notable collaborations include work with Kyoto University researchers , Swedish medical institutions , and cross-disciplinary teams in bioengineering.

• Computational Design
• Secure Design and Manufacturing
• Socio-Technical Systems
• +۷ مورد دیگر

Professor Jitesh H. Panchal is a Professor of Mechanical Engineering and Associate Head of Undergraduate Programs at Purdue University's School of Mechanical Engineering. His research focuses on computational design of socio-technical systems, secure manufacturing, and collaborative engineering processes. He holds a PhD from Georgia Tech and has received numerous awards including the NSF CAREER Award (2010) and ASME's Guest Associate Editor Award (2019). Education: Ph.D. (Mechanical Engineering), Georgia Tech, 2005 M.S. (Mechanical Engineering), Georgia Tech, 2003 B.Tech. (Mechanical Engineering), IIT Guwahati, 2000 Research Interests: His work integrates computational methods with human factors, addressing challenges in secure design, additive manufacturing, and decision-making frameworks. Recent trends show focus on AI-driven design optimization, cybersecurity in manufacturing, and policy frameworks for defense acquisitions. Awards: ASME Journal of Mechanical Design Guest Associate Editor Award (2019) NSF CAREER Award (2010) Robert E. Fulton SEIKM Best Paper Award (2013) Ruth and Joel Spira Award (2018) Advising & Grants: His research is supported by grants from NSF and industry partnerships. He advises graduate students in design engineering and manufacturing systems, though no specific student names are listed. Labs: Directs the Design Engineering Lab at Purdue (DELP) , focusing on computational design methodologies and secure manufacturing technologies.

Mustafa Abdallahمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Game Theory
• +۷ مورد دیگر

Mustafa Abdallah is an Assistant Professor at the Computer and Information Technology (CIT) department of Purdue University in Indianapolis, with a courtesy appointment at the Purdue Polytechnic Institute. He holds a PhD in Electrical and Computer Engineering from Purdue University (2022) and prior degrees from Cairo University (MS: 2016, BS: 2012). His research focuses on game theory, behavioral decision-making, explainable AI, and deep learning, applied to cybersecurity, autonomous systems, and IoT anomaly detection. His work has been recognized by the prestigious Bilsland Fellowship and grants from IEEE and IUPUI. Industrial collaborations include Adobe Research (meta-learning for time-series forecasting), Principal Financial Group (financial risk prediction using Kalman filters), and RDI Company (deep learning for pronunciation systems, resulting in a US patent). He has published extensively in top venues like IEEE S&P, IEEE TCNS, and ACM AsiaCCS.

Pierre Lermusiauxمشاهده پروفایل

استاد

• Ocean Data Assimilation
• Numerical Ocean Modeling
• Uncertainty Quantification
• +۷ مورد دیگر

Pierre Lermusiaux is the Nam Pyo Suh Professor of Mechanical Engineering and Associate Department Head for Operations Research at MIT. His research focuses on numerical ocean modeling, data assimilation, uncertainty quantification, and applications to ocean dynamics and engineering. He holds a B/M.Eng. from University of Liège, M.Sc. and Ph.D. from Harvard University. Recognized with awards like the MIT Doherty Chair and Spira Teaching Award, his work bridges computational science and marine systems. Education: B/M.Eng., University of Liège, 1992 M.Sc., Harvard University, 1993 Ph.D., Harvard University, 1997 Research Interests: Combines oceanographic modeling with advanced computational methods. Specializes in stochastic ocean prediction, autonomous systems optimization, and interdisciplinary applications of uncertainty quantification. His work enables real-time environmental forecasting and marine robotics navigation strategies. Publications: Focus on developing computational frameworks for probabilistic ocean forecasts, acoustic propagation modeling, and machine learning-based environmental prediction systems. Recent work emphasizes adaptive sampling strategies and energy-efficient path planning for underwater vehicles. Awards: Fulbright Foundation Fellowship (1993-96) Ruth and Joel Spira Award for Teaching (2010) MIT Doherty Chair in Ocean Utilization (2009-2011) Advising & Grants: Leads MIT’s MSEAS group and has secured funding from NSF, ONR, and international collaborations. Advises projects on ocean robotics, climate modeling, and underwater acoustics. Key contributions include the MIT Red Sea Modeling System and real-time forecasting platforms. Labs/Teams: Directs MIT’s multidisciplinary ocean modeling efforts, integrating mechanical engineering, computer science, and environmental science. Co-leads initiatives like the Ocean Predictions with Ensembles (OPEN) project and the METEOR autonomous observatory system.

Hussein Gharakhaniمشاهده پروفایل

استادیار

• Agricultural Robotics
• Automation in Agriculture
• Artificial Intelligence
• +۳ مورد دیگر

Hussein Gharakhani is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. He specializes in agricultural robotics and automation, focusing on robotic cotton harvesting systems, sensor integration, and precision agriculture applications. His research addresses challenges in end-effector design, object detection, and field testing of robotic prototypes. Dr. Gharakhani holds a Ph.D. in Biosystems Engineering from Mississippi State University, an M.S. in Mechanical Engineering of Agricultural Machinery from the University of Tehran, and a B.S. in Agricultural Machinery Engineering from the University of Tabriz. His academic background includes roles as a graduate research and teaching assistant, as well as industry experience as a research and application engineer. His research interests span robotic manipulators, artificial intelligence, 2D/3D perception, and off-road robotics. Key projects include developing vision-guided robotic harvesters, evaluating end-effectors, and exploring UAV applications in cotton farming. His work emphasizes practical solutions to enhance agricultural efficiency and sustainability through automation. No scientific awards or grants are explicitly listed in the provided text. Dr. Gharakhani’s advising and mentorship activities are not detailed here, though his academic role suggests involvement in graduate student guidance. His research is centered on advancing robotic systems for precision agriculture, with a particular focus on cotton production challenges and robotic harvesting innovations.

Renata Dividinoمشاهده پروفایل

استادیار

• Graph Knowledge Representation
• Machine Learning
• Web Science
• +۵ مورد دیگر

Renata Dividino is an Assistant Professor in the Department of Computer Science at Brock University, Canada. She holds a BSc from the University of Campinas (Brazil), an MSc from Universität des Saarlandes (Germany), and a PhD from Universität Koblenz – Landau (Germany). Her research focuses on graph knowledge representation, machine learning, and their applications in web science, semantic web foundations, and provenance systems. She has worked at institutions like DFKI, Fraunhofer IGD, and the Big Data Analytics Lab at Dalhousie University, bridging academic and industrial sectors. Her industry experience includes roles as an AI Scientist and Director of Data Science in the maritime sector, where she developed patented technologies for AI-driven maritime operations and risk assessment systems. Key research contributions include improving AI system reliability via provenance analysis and advancing knowledge graph applications. Education: BSc in Computer Science, University of Campinas MSc in Computer Science, Universität des Saarlandes PhD in Computer Science, Universität Koblenz – Landau Research Interests: Provenance systems, semantic web foundations, knowledge graphs, graph-based AI, maritime AI applications, and federated learning. Her work emphasizes practical applications in complex networks, web-scale data, and social networks. Awards: No specific awards mentioned, but her contributions include patented technologies and peer-reviewed publications on provenance-driven AI transparency. Advising & Grants: Secured industry grants for R&D projects in maritime AI and data science. Her work on vessel risk assessment and infectious disease prediction demonstrates applied research impact.