جستجوی استاد راهنمای دانشگاهی

Matthew Lee Smithمشاهده پروفایل

استاد

• Aging
• Health Behavior
• Chronic Disease Management
• +۶ مورد دیگر

Matthew Lee Smith is a Professor at the Texas A&M School of Public Health , part of Texas A&M University . He is a core faculty member of the Center for Community Health and Aging (CCHA) and the Center for Health Equity and Evaluation Research (CHEER) , and serves as the Director of the Texas Research, Analytics, Innovations, and Research Lab (TRAIL) . Education: Post-Doctoral Fellowship, Health Science Center, Texas A&M University (2010) PhD in Health Education, Texas A&M University (2008) MPH, Indiana University Bloomington (2004) BS in Public Health Education, Indiana University Bloomington (2002) Research Interests: Dr. Smith’s research focuses on aging , chronic disease management , and evidence-based public health interventions . He is particularly interested in health behavior change , health risk assessment , and survey research methodology . His translational work bridges research and practice across healthcare, aging services, and public health systems. He has a strong focus on social determinants of health , including social isolation , caregiving , diabetes self-management , and fall prevention among older adults. His work often targets underserved populations, particularly Black/African American men and rural communities . Scientific Awards: Immunization Neighborhood Champion Award (2024) Responsible Research in Management Award (2023) J. Mayhew Derryberry Award (2022) Consumer Education Program Award (Silver) (2022) Bluebonnet Award (2021) Innovators in Aging Award (2019) Redefining American Healthcare Award (2019) Community ConnecTivity Award (2019) Phillip G. Weiler Award for Leadership in Aging and Public Health (2018) Leadership & Mentorship: Dr. Smith holds leadership roles in several national and state-level initiatives. He is the Co-Director of the Advancing Gerontology through Exceptional Scholarship (AGES) Program and serves on the Steering Committees of the Texas Falls Prevention Coalition , Texas Alzheimer’s Research and Care Consortium , and Texas Social Isolation and Loneliness Coalition . He is also the Director of the Research Scholars & Mentorship Program (RSMP) at the American Academy of Health Behavior. Labs & Centers: He leads the Texas Research, Analytics, Innovations, and Research Lab (TRAIL) , which focuses on developing and evaluating community-based interventions. He is also affiliated with: Center for Community Health and Aging (CCHA) Center for Health Equity and Evaluation Research (CHEER)

ZHANG Liمشاهده پروفایل

استاد

• Small-scale Robotics
• Intelligent Systems
• Miniature Medical Robots and Platforms
• +۵ مورد دیگر

ZHANG Li is a Professor in the Department of Mechanical and Automation Engineering (MAE) and a Professor by Courtesy in the Department of Surgery at The Chinese University of Hong Kong (CUHK). He serves as Director of the SIAT-CAS-CUHK Joint Laboratory of Robotics and Intelligent Systems. Research Focus: Small-scale robotics, magnetic slime robots, microrobotic swarms, functional materials for biomedical applications. Editorial Roles: Associate Editor/Editorial Board Member of 10 journals including IEEE T-RO, IEEE/ASME T-MECH, and Advanced Intelligent Systems. His research spans translational biomedicine and minimally invasive interventions, with publications in high-impact journals like Science Robotics , Nature series, and Science Advances . His work on artificial bacterial flagella and microrobotic swarms has been recognized in the Guinness Book of World Records (2012) and Hong Kong’s Top 10 Innovation and Technology News (2022-2024). Major Awards: Fellow of IEEE, ASME, RSC, FAAIA, and FHKIE Gold medals at Geneva (2023) and IIFME2024 Multiple IEEE conference best paper awards RGC Early Career Award (2013), RGC Research Fellow Award (2021/22) He has secured over 30 competitive grants from Hong Kong RGC, ITC, Croucher Foundation, NSFC, and Shenzhen government. His projects include META System for stroke treatment and 3D Printing of Miniature Robots for ophthalmology.

Eric Saxمشاهده پروفایل

استاد

• Automotive Systems
• Autonomous Driving
• Cybersecurity
• +۷ مورد دیگر

Prof. Dr.-Ing. Eric Sax is a Professor of Electronic Systems Engineering and Management at the Karlsruhe Institute of Technology (KIT), serving as Dean of the Department of Electrical Engineering and Information Technology (ETIT). He leads the Institut für Technik der Informationsverarbeitung (ITIV) and directs the Forschungszentrum Informatik ESS division . As Program Director of the Electronic Systems Engineering & Management (ESEM) master's program at the HECTOR School, he focuses on integrating academic and professional education. His research spans automotive systems engineering , self-learning functions , cybersecurity , and data-driven validation . Key themes include over-the-air updates, scenario-based testing, and the synergy between machine learning and automotive systems. His work addresses challenges in autonomous driving validation, software-defined mobility, and cyber-physical system security. Prof. Sax's contributions include frameworks for automotive software partitioning, cloud-enabled vehicle architectures, and methodologies for quantifying data quality impacts on perception systems. He actively collaborates with industry partners to bridge academic research with industrial application. His recent projects include OptiCAM (cloud/edge function offloading), Drive4C (autonomous driving benchmarking), and UNCOVER (data-driven security monitoring). He holds leadership roles in both KIT and the HECTOR School's technology business programs.

René van de Molengraftمشاهده پروفایل

استاد

• Autonomous Robotics
• Robotics Control Systems
• Human-Robot Interaction
• +۳ مورد دیگر

René van de Molengraft is a Full Professor of Robotics at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e). He holds dual professorships in EAISI High Tech Systems and EAISI Foundational. His research focuses on autonomous robotic systems operating in human environments, emphasizing adaptability and task execution in varying conditions. Academic Background: MSc in Mechanical Engineering (TU/e, 1986) PhD in Identification of Mechanical Systems for Control (TU/e, 1990) Career progression: Assistant Professor (1990–2008) → Associate Professor (2008–2024) → Full Professor (2024) Research Interests: Autonomous robotics, human-robot interaction, agricultural automation, and control systems. His work emphasizes practical applications, such as the Tech United RoboCup team (world champion in multiple years) and large-scale robotics competitions. Projects: Key initiatives include the Tech United RoboCup team, the AI-matters manufacturing network, and the Frontiers in Autonomous Systems Technology (FAST) project. He has organized major robotics events like the 2013 and 2024 RoboCup World Championships in Eindhoven. Labs/Teams: Founder and leader of Tech United, a robotics team renowned for its achievements in robotic soccer and service robotics. Active in TU/e's Robotics research group and collaborations with industry partners.

Mykola Pechenizkiyمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Predictive Analytics
• +۵ مورد دیگر

Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.

Tara Boroushakiمشاهده پروفایل

استادیار

• Wireless Networking
• Cyber-Physical Systems
• Robotics
• +۳ مورد دیگر

Tara Boroushaki is an incoming Assistant Professor in Electrical & Computer Engineering at Yale University. She completed her Ph.D. at MIT (expected May 2025), advised by Prof. Fadel Adib, with a focus on sensing and mobile technologies. Her research spans wireless networking, robotics, and human-computer interaction, emphasizing multi-modal sensing for environmental perception. Key achievements include the Microsoft Research PhD Fellowship (2022–2024) and the IEEE RFID '23 Best Paper Award. Her work on RF-based 'X-ray vision' has been featured in TEDxMIT and media outlets like the BBC and World Economic Forum. She co-founded Cartesian Systems, deploying sensing technologies in retail and supply chain. Research interests include non-line-of-sight perception, RFID localization, and robotic grasping. She has developed systems like FuseBot and RFusion, highlighted as transformative in MIT's '103 Ways to Make the World Better' initiative.

Luca Demetrioمشاهده پروفایل

استادیار

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +۳ مورد دیگر

Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.

Fethiye Irmak Doganمشاهده پروفایل

پژوهشگر

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

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.

Randall D. Beerمشاهده پروفایل

استاد

• Artificial Life
• Neuroscience
• Computational Biology
• +۵ مورد دیگر

Randall D. Beer is a Provost Professor at Indiana University with affiliations across multiple departments and centers, including the Cognitive Science Program , Program in Neuroscience , School of Informatics, Computing, and Engineering , and the Center for Complex Networks and Systems Research . His research focuses on understanding how organisms function as integrated wholes, emphasizing the interplay between brains, bodies, and environments. He develops computational models of neuromechanical systems, biologically-inspired robotics, and dynamical systems approaches to cognition. Education: While formal educational details are not explicitly listed, Beer's academic trajectory is reflected in his extensive publications and roles in interdisciplinary research programs. Research Interests: Beer investigates: - Embodied cognition and enaction frameworks - Neurodynamics and central pattern generators - Evolution of behavior in artificial agents - Metabolic and developmental systems biology - Dynamical systems theory - Computational modeling of C. elegans locomotion Software Contributions: Beer has developed tools like Dynamica (for dynamical systems analysis), CTRNN (neural network simulation), and Evolutionary Agents (robotics control frameworks). These tools are widely used in computational neuroscience and robotics research. Advising and Teams: He supervises a large group of graduate students and postdocs, contributing to projects such as neuromechanical modeling and evolutionary robotics. His work is supported through grants focusing on embodied cognition and systems biology. Labs and Collaborations: Active in the Center for Complex Networks and Systems Research and collaborates with interdisciplinary teams exploring topics like autopoiesis, viability theory, and robotic embodiment.

Reza Ghabchelooمشاهده پروفایل

استاد

• Robotics
• Automation Engineering
• Control Systems
• +۷ مورد دیگر

Reza Ghabcheloo is a Professor at Tampere University, affiliated with the Faculty of Engineering and Natural Sciences and the Department of Automation Technology and Mechanical Engineering. He leads the Robotics major and the international Automation Engineering program. His research focuses on autonomous mobile machines, robotics, control systems, and safety engineering, with specific interests in construction robotics, sensor fusion, and hydraulic systems. He co-leads the Autonomous Mobile Machines Group and is associated with the Robotics and Intelligent Machines Lab and the Innovative Hydraulics and Automation Lab. His research emphasizes developing autonomous systems for off-road machinery, safe control strategies, and energy-efficient automation. He has published extensively on topics such as reinforcement learning for crane control, radar-based perception, and safety architectures for autonomous systems. His work bridges robotics, control theory, and industrial automation, addressing challenges in heavy-duty machinery and real-world robotic applications. Research Group: Autonomous Mobile Machines Group Labs: Robotics and Intelligent Machines Lab, Innovative Hydraulics and Automation Lab Key Projects: Safety of automated off-road machinery, machine learning for autonomous loading, and trajectory optimization

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Professor Mounim A. El Yacoubi holds positions at Institut Polytechnique de Paris, Institut Mines-Télécom, and Telecom SudParis. His research focuses on AI, machine learning, and deep learning applied to e-Health (neurodegenerative disease detection, diabetes management), biometrics (gait, vein, and handwriting recognition), and smart systems (agriculture, surveillance, robotics). He leads the SAMOVAR CNRS Lab and has supervised 17 PhDs and 30+ master's students. Education: PhD (1996, Université de Rennes 1), HDR (2014, Paris-Saclay University). Experience: Senior Researcher at Parascript (2001–2008), Visiting Scientist at CENPARMI (1997–1998), Associate Professor at PUCPR (1998–2001). Research Interests: AI applications in healthcare, biometrics, pattern recognition, and smart technologies. Recent work includes Alzheimer’s detection via handwriting analysis, diabetes prediction using PPG signals, and palm/vein recognition systems. Grants & Leadership: Program Chair of ICPRAI 2022, ICCPRA 2024. Editor of IEEE Access and journals on cyber-physical intelligence. Authored books on Pattern Recognition and AI.

Hassan Ashtianiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Algorithms and Complexity
• +۴ مورد دیگر

Dr. Hassan Ashtiani is an Associate Professor in the Department of Computing and Software at McMaster University and a faculty affiliate at the Vector Institute. He holds a PhD in Computer Science from the University of Waterloo (2018), a master’s in AI and Robotics, and a bachelor’s in computer engineering from the University of Tehran. His research focuses on machine learning, statistical learning theory, and theoretical computer science, with emphasis on adversarial robustness, privacy-preserving algorithms, and sample-efficient learning. Current projects include differentially private machine learning, robustness against adversarial perturbations, and distribution shifts. Recent work highlights include NeurIPS 2018 best paper award for pioneering distribution compression schemes in Gaussian mixtures. He routinely serves as an area chair for NeurIPS and other ML conferences. His CAS 775 course explores modern distribution learning theory, covering topics like PAC learning, computational complexity, and differential privacy. Awards: NeurIPS Best Paper Award (2018) Advising: Open PhD/MSc positions are listed on his homepage. His research group collaborates with the Vector Institute, focusing on advancing theoretical foundations of machine learning with practical applications.

John P. Spencerمشاهده پروفایل

استاد

• cognitive development
• brain development
• computational neuroscience
• +۷ مورد دیگر

John P. Spencer is a Professor of Psychology at the University of East Anglia , UK, and a member of its School of Psychology . He previously served as a Professor of Psychology and founding Director of the Delta Center at the University of Iowa. His research focuses on the development of executive function, working memory, attention, and word learning, with a pioneering emphasis on dynamical systems theory and dynamic neural field models of cognition and action. Sc.B. with Honors, Brown University (1991) Ph.D. in Experimental Psychology, Indiana University (1998) His work utilizes fMRI , fNIRS , and eye-tracking to explore neural-cognitive dynamics in infancy and childhood, particularly in low-resource settings. Recent articles highlight cross-situational word learning, air quality impacts on cognition, and brain network myelination studies. Scientific awards include: 2003 Early Research Contributions Award (Society for Research in Child Development) 2006 Robert L. Fantz Memorial Award (American Psychological Foundation) APA Fellow since 2007 Current projects include brain imaging centers, infant cognition in India, and theoretical advancements in dynamic systems. He leads the Developmental Dynamics Lab and collaborates with institutions worldwide.