جست‌وجوی استاد و پژوهشگر

Houying Zhuمشاهده پروفایل

مدرس

• High-dimensional learning
• Statistical computing
• Modelling and visualisation
• +۴ مورد دیگر

Dr. Houying Zhu is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. Her research focuses on high-dimensional learning, statistical computing, and modelling/visualization, with an emphasis on developing efficient methodologies for modern data analysis. She holds a PhD in Applied Mathematics from the University of New South Wales and has held research roles at City University of Hong Kong and the University of Melbourne as a Maurice Belz Research Fellow in Statistics. Dr. Zhu is Co-Director of the Statistical Modelling Research Group, providing mentorship to early-career researchers and HDR students through regular supervision and collaborative projects. She actively engages in academic leadership, serving on the Board of Directors for the IASC-ARS (2023-2027) and the SSA NSW branch council (2021-2023). Her teaching spans undergraduate and postgraduate units, including Bayesian Data Analysis, Generalized Linear Models, and Statistical Graphics. Research Highlights: Innovations in quasi-Monte Carlo methods, feature selection algorithms, and medical imaging applications. Awards: Includes the President’s Award for Leadership in Statistics (2022) and multiple funding grants (MATRIX Family Funding, SSA Fellowship). Community Engagement: Organized the MATRIX Computational Mathematics for High-Dimensional Data program (Feb 2023) and contributes to statistical education initiatives. Her interdisciplinary work bridges computational statistics and real-world applications, with recent projects addressing wildfire risk modelling, protein engineering, and 6G communication systems.

Kris Hauserمشاهده پروفایل

استاد

• Robot Motion Planning
• Robot Control
• Autonomous Systems
• +۶ مورد دیگر

Kris Hauser is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), with affiliate appointments in the Departments of Electrical and Computer Engineering and Mechanical Science and Engineering. He holds a PhD in Computer Science from Stanford University and B.A. degrees in Computer Science and Mathematics from UC Berkeley. Prior to UIUC, he served as faculty at Indiana University (2009–2014) and Duke University (2014–2019), where he founded the Intelligent Motion Lab. He has also consulted for Waymo since 2019 and is the Director of the Coordinated Sciences Lab Robotics Group at UIUC. His research focuses on robot planning and control, semi-autonomous systems, and applications in intelligent vehicles, medical robotics, and legged locomotion. Key methodologies include optimization, probabilistic methods, AI, and physics simulation. His work bridges theory with real-world applications, such as robotic surgery, warehouse automation, and autonomous driving. Prof. Hauser has received notable awards including the NSF CAREER Award, Siebel Scholar Fellowship, and multiple Amazon Research Awards. He actively engages in teaching advanced robotics courses at UIUC and has contributed to open-source robotics frameworks like the Klamp't package. His lab emphasizes interdisciplinary collaboration, with projects ranging from UV disinfection robots to telepresence avatars.

Mihai ANDRIESمشاهده پروفایل

دانشیار

• Cognitive Robotics
• Ambient Systems
• Automated Functional Design
• +۳ مورد دیگر

Mihai ANDRIES is an Associate Professor in Computer Science (specializing in Artificial Intelligence and Robotics) at IMT Atlantique, Brest, France. He holds a PhD in Artificial Intelligence from the University of Lorraine (2015), an M.Sc. in Software and Data Engineering from the University of Strasbourg (2012), and a B.Sc. in Computer Science from the same institution (2010). His research focuses on cognitive robotics, ambient systems, and automated functional design, emphasizing human-centric technologies such as assistive robots and healthcare applications. He has held postdoctoral positions at Inria Nancy, Institute for Systems and Robotics (Lisbon), and Institute of Intelligent Systems and Robotics (Paris), with industry experience at PSA Peugeot Citroën and Dassault Systèmes. His work integrates artificial intelligence, robotics, and sensor systems to enhance quality-of-life solutions, including robotic rehabilitation assessment and dementia detection through speech analysis. Key projects include the DiscoBot (mental health chatbot), Veggie Breizh Bot (cooking assistance robot), and SUMMA-Sound (sound-based activity recognition). He has secured grants totaling over €1M for projects like HEAP sorting, RoboErgoSum (robotic consciousness), and Mementop (sound-based mental health diagnosis). His software contributions include tools for 3D model conversion (stl2sdf, binvox2sdf) and multi-robot simulators.

• Robotics
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Dr. Yi-Chi Liao is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in intelligent interactive systems. Their research focuses on human-robot interaction, wearable technology, and optimization techniques for user interface design. Key areas include developing datasets for naturalistic handover behaviors with robotic limbs, human-in-the-loop optimization methods, and computational workflows for designing input devices. Research Interests: Explores the intersection of robotics, machine learning, and human-centered design. Recent work emphasizes Bayesian optimization techniques, affordance theory, and tactile feedback systems. Projects like the 3HANDS dataset and ThirdHand wearable robotic arm demonstrate innovation in human augmentation and interaction design. Advising: Supervises doctoral student Peizhuo Li in the D-INFK program. Research outputs span 2015–2025, with notable contributions to haptic interfaces, multi-objective optimization, and wearable computing. Active in conferences and journals addressing HCI, robotics, and design automation.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۴ مورد دیگر

Feng Jiang is a Professor at Nanjing University's School of Computer Science and Technology, Department of Computer Science and Technology. With an extensive publication record spanning from 1999 to 2025, Professor Jiang has established himself as a prominent researcher in multiple computer science domains. His research interests include Machine Learning, Artificial Intelligence, Data Mining, Software Engineering, and Computer Vision. Professor Jiang's work demonstrates significant interdisciplinary applications, ranging from healthcare and bioinformatics to industrial manufacturing and agricultural technology. His research approach often combines theoretical advancements with practical implementations, addressing real-world challenges across diverse sectors. Analysis of his recent publications reveals a strong focus on deep learning applications, graph neural networks, and ensemble methods. His work shows consistent contributions to both theoretical frameworks and practical implementations, with particular emphasis on software engineering applications, computer vision systems, and biomedical informatics. The breadth of his research indicates active collaborations across multiple institutions and disciplines. Professor Jiang has supervised numerous research projects resulting in publications across top-tier journals including IEEE Access, Expert Systems with Applications, and Knowledge-Based Systems. His work has been consistently published in high-impact venues, demonstrating sustained research productivity and relevance in the field.

Aaron D Lantermanمشاهده پروفایل

استاد

• Radar Systems
• Image Reconstruction
• Target Recognition
• +۲ مورد دیگر

Aaron D Lanterman is Professor at Georgia Institute of Technology's School of Electrical and Computer Engineering. He holds triple B.S. degrees in music, computer science and electrical engineering (1993), plus M.S. (1995) and D.Sc. (1998) in electrical engineering from Washington University. Research spans radar systems, image reconstruction, target recognition, music synthesis, and signal processing. Recent work focuses on statistical inference methods, astronomical imaging, audio circuit analysis, and radar target identification. Honors include the 2006 Richard M. Bass Teaching Award and NIC Certificate of Excellence for intelligence community contributions. Active in IEEE and SPIE societies with chair roles at SPIE Aerosense conference. Industry experience includes concert promotion, recording engineering, and editorial roles in music publications. Maintains keyboard/guitar performance practice alongside technical research.

Bacha Rehmanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Bacha Rehman is a Senior Lecturer in the Department of Computing (AI & Data Science) at Solent University, affiliated with the Science and Engineering Research Group. His research focuses on artificial intelligence, machine learning, medical imaging, and metamaterials design, with a strong emphasis on practical applications in healthcare technology, computer vision, and optoelectronics. He has contributed to over 16 peer-reviewed publications from 2011 to 2025, exploring topics such as brain tumor detection through CNNs, infrared ship tracking, and machine learning-driven metamaterial optimization. His work bridges theoretical advancements with real-world challenges, such as developing robust solar energy systems using nanostructured materials and enhancing facial expression recognition frameworks. Collaborations span international research networks, though specific partnerships remain unspecified in the provided data. No academic awards are documented, though his consistent output reflects sustained research engagement. Rehman’s email is bacha.rehman@solent.ac.uk , and his ORCID is 0000-0003-2081-5728 . His research trajectory demonstrates a commitment to interdisciplinary innovation within AI, materials science, and computational engineering.

Yun Raymond Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Yun Raymond Fu is the COE Distinguished Professor of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Sciences. His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Big Data Analytics, leading the SMILE Lab. He holds a PhD from the University of Illinois at Urbana-Champaign and joined Northeastern in 2012. His work spans anomaly detection, video analysis, and multimodal learning, with over 200 publications in top journals and conferences. Education: PhD in Electrical and Computer Engineering, University of Illinois, 2008. Research Interests: Machine Learning, Computer Vision, Pattern Recognition, Deep Learning, and Cyber-Physical Systems. His lab develops algorithms for video anomaly detection, social media analytics, and domain adaptation. Awards: Includes 7 Young Investigator Awards, 12 Best Paper Awards, and fellowships from IEEE, IAPR, AAAS. Recently recognized as a AAAI Fellow and recipient of the Edward J. McCluskey Technical Achievement Award (2024). Labs & Entrepreneurship: Founded AI startup Giaran (acquired by Shiseido in 2017). SMILE Lab explores cutting-edge AI applications in healthcare, autonomous systems, and multimedia.

David Mercierمشاهده پروفایل

استاد

• Information Fusion
• Belief Functions
• Uncertainty Management
• +۳ مورد دیگر

David Mercier is a Professor at the University of Artois, France, affiliated with the LGI2A laboratory and the IUT de Béthune's Department of Networks and Telecommunications. He holds a HDR (Habilitation à Diriger des Recherches) from 2015 and a PhD from the University of Technology of Compiègne (UTC), France (2006). His research focuses on information fusion, belief functions, and uncertainty management, with applications in electrical engineering, transportation systems, and machine learning. Education DEA (M.S. Equivalent) in Artificial Intelligence, University Paul Sabatier, Toulouse (2003) PhD in Information Fusion for Postal Address Recognition, UTC (2006) HDR (Research Habilitation), University of Artois (2015) Research & Awards Notable contributions include work on contextual belief function corrections, vehicular network information fusion, and multi-target tracking. He received a co-best student paper award at BELIEF 2016. His research spans electrical machines diagnosis, stochastic simulations, and deep learning integration with Dempster-Shafer theory. Students & Involvement Supervised PhD theses include studies on evidential deep learning and motor fault diagnosis. Active in the Belief Functions and Applications Society (board member since 2010). Co-edited special issues in International Journal of Approximate Reasoning and authored book chapters on belief function applications. Labs & Teams Primary affiliation with LGI2A laboratory, collaborating with institutions like LSEE and industrial partners such as Solystic and SNCF. Research integrates theoretical advances with practical applications in smart mobility and fault detection.

• Electrical and Electronic Engineering
• Artificial Intelligence and Image Processing
• Distributed Computing
• +۱ مورد دیگر

Dr. Du Yong Kim is a Senior Lecturer in the School of Engineering at RMIT University, Australia. His research focuses on Electrical and Electronic Engineering, Artificial Intelligence and Image Processing, Distributed Computing, and Biomedical Engineering. He is actively involved in supervising research projects, including topics like mental health monitoring using wearables, decentralized multi-platform search algorithms, and sensor fusion for autonomous systems. His work spans advanced tracking algorithms, autonomous systems, and biomedical visualization tools such as CellTrackVis. Recent publications emphasize innovations in multi-object tracking, radar systems, and optimization frameworks for target localization. Dr. Kim collaborates on projects with organizations like CSIRO and has a strong record of industry-relevant research. Key research trends include the application of possibility theory to autonomous search, integration of machine learning in tracking systems, and development of sensor fusion techniques for maritime and indoor environments. His contributions bridge theoretical advancements with practical implementations in fields like healthcare monitoring and autonomous robotics.

• Computer Vision
• Robotics
• Intelligent Automation
• +۵ مورد دیگر

Professor Alireza Bab-Hadiashar is a Professor of Mechatronics at RMIT University's School of Engineering, leading the Intelligent Automation Research Group. He holds a PhD in Robotics from Monash University and has held academic roles at Swinburne University and the University of Copenhagen. His research focuses on computer vision, robotics, and intelligent automation, with industrial applications in manufacturing and healthcare. He leads the Master of Robotics and Mechatronics Engineering program and has extensive experience in interdisciplinary projects involving AI, biomedical imaging, and advanced manufacturing. Research interests span computer vision, AI-driven automation, and robotics with emphasis on safety-critical systems like autonomous vehicles and medical imaging. Notable projects include defect detection in additive manufacturing and domain generalization in medical imaging. He collaborates with industry partners like Ford Australia on lightweight vehicle design and quality control. His recent publications emphasize distributed tracking systems, robotic calibration, and domain adaptation techniques. He actively supervises research projects in automated inspection, computer vision for transportation safety, and cancer detection via CT imaging. Academic memberships include IEEE, FIEAust, and professional organizations in computer vision and robotics. Labs/Teams: Director of the Intelligent Automation Research Group at RMIT. Current initiatives include developing AI solutions for Industry 4.0 and smart manufacturing systems.

Ashwin Daniمشاهده پروفایل

دانشیار

• Robotics
• Autonomous Navigation
• Estimation and Control Theory
• +۵ مورد دیگر

Ashwin Dani is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Connecticut, affiliated with the School of Engineering. His research focuses on robotics, control systems, and autonomous navigation, with applications in manufacturing, biomedical engineering, and environmental monitoring. He holds a Ph.D. in Mechanical and Aerospace Engineering from the University of Florida (2011) and a B.S. in Mechanical Engineering from the University of Pune, India (2005). Prior to his faculty role, he served as a Post-doctoral Research Associate at the University of Illinois at Urbana-Champaign (2011–2013). His research interests span estimation theory, autonomous systems navigation, human-robot collaboration safety, sensor fusion, and neuro-prosthesis control. Notable contributions include advancements in vision-based control, GPS-denied navigation for UAVs, and adaptive control algorithms with safety guarantees. He leads the Robotics & Controls Lab, which develops solutions for industrial automation and assistive robotics. Dr. Dani has been awarded the Innovator’s Invention Award from the University of Florida for his work on nonlinear dynamic estimation. His research has produced patents on structure-motion estimation and over 50 peer-reviewed publications in top journals like IEEE Transactions on Automatic Control and Journal of Field Robotics. He actively explores intersections between machine learning and control theory to enhance system safety and adaptability in dynamic environments. His academic contributions include pioneering work on concurrent learning observers for depth estimation and developing probabilistic frameworks for human intent prediction in collaborative tasks. Ongoing projects focus on safe human-robot interaction protocols, deformable object manipulation, and real-time adaptive control systems for uncertain environments.

• Bayesian Search and Optimization
• Evolutionary Algorithms
• Multi-objective Optimization
• +۴ مورد دیگر

Dr Alma Rahat is an Associate Professor of Data Science at Swansea University, affiliated with the School of Mathematics and Computer Science. He specializes in Bayesian search and optimization, evolutionary algorithms, and multi-objective optimization. His work focuses on solving computationally expensive problems with applications in engineering, healthcare, and education. Education: BEng (Hons) in Electronic Engineering from the University of Southampton (UK), PhD in Computer Science from the University of Exeter (UK), and a Postgraduate Certificate in Teaching in Higher Education from Swansea University. He is a Fellow of the Higher Education Academy (FHEA). Research Interests: Dr Rahat’s expertise includes data-driven evolutionary optimization, surrogate-assisted methods, and active learning. He has contributed to pandemic response modeling for the Welsh Government and the UK Health Security Agency, leveraging machine learning and parameter optimization. His work on healthcare decision-making systems and educational assessment tools demonstrates a commitment to interdisciplinary applications of optimization. Key Contributions: He leads the Surrogate-Assisted Evolutionary Optimization (SAEOpt) workshop at GECCO and is a member of the IEEE Computational Intelligence Society Task Force on Data-Driven Optimization. His research bridges academic theory and industry needs, with patents and publications in top journals and conferences like IEEE Transactions and ACM. Grants: £750k from Welsh Government (Co-PI/Co-I), £230k EPSRC grant (EP/W01226X/1 as PI). Awards: Best Paper in Real-World Applications Track at GECCO, Patent for industrial fluid separation technology. Supervision: Currently guiding PhD students across AI, healthcare, education, and environmental modeling. His supervision emphasizes Bayesian methods and human-in-the-loop systems. Lab/Teams: Active in Swansea’s Computational Foundry, collaborating on projects like beach change forecasting and clinical decision support systems.

• Power Electronics
• Renewable Energy Systems
• Control Systems
• +۳ مورد دیگر

Santiago Cóbreces Álvarez is an Associate Professor at the Department of Electrónica, Universidad de Alcalá. His primary research group is GEISER (Ingeniería Electrónica Aplicada a Sistemas de Energías Renovables), focusing on electronic engineering applied to renewable energy systems. He holds a Ph.D. from Universidad de Alcalá, awarded in 2009 for his thesis on optimizing current control loops in voltage source converters (VSCs) connected to uncertain grids via LCL filters, supervised by Dr. Francisco Javier Rodríguez Sánchez and Dr. Emilio José Bueno Peña. His research interests span power electronics, renewable energy systems, control systems, and grid integration. Notable contributions include work on grid-forming converters, admittance shaping, H-infinity control, and stability enhancement in DC catenary systems. He has extensively published on topics such as DC/AC converter design, renewable energy harvesting, and robust control techniques. His articles reflect a strong focus on practical applications in power electronics, including railway systems, solar inverters, and microgrid stability. While no awards are listed, his prolific publication record underscores his academic and technical influence. Advising and grants details are not explicitly mentioned in the provided text. He is affiliated with the GEISER research group, emphasizing applied electronic engineering for renewable energy solutions. His work bridges theoretical control methodologies with practical implementations in high-voltage systems and grid-connected applications.