جستجوی استاد راهنمای دانشگاهی

Bob Fieldsمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Mental Health Technology
• Rail Safety
• +۵ مورد دیگر

Dr. Bob Fields is an Associate Professor in the Department of Computer Science at Middlesex University. His research focuses on interdisciplinary areas including human-computer interaction, mental health technology, railway safety, and collaborative work analysis. He has conducted extensive studies on suicide prevention strategies in railway environments, leveraging both qualitative and quantitative methodologies. His work integrates digital technologies with real-world applications, such as developing open-source robotic platforms for education and exploring federated learning frameworks with differential privacy for GIS systems. Fields' research spans over two decades, with notable contributions to understanding decision-making processes in complex systems like air traffic control and legal investigations. He has published extensively on topics such as distributed sensemaking, user experience design for immersive technologies (e.g., 360-degree video), and the application of mobile health (mHealth) for mental health monitoring. His publications often bridge theoretical models with practical implementations, emphasizing human-centered design principles. Key themes across his work include improving safety through technology, enhancing collaborative workspaces, and advancing educational tools via robotics and interactive systems. While no specific awards are listed, his prolific publication record reflects sustained academic engagement with high-impact societal issues such as public safety and digital health innovation.

Richard Yuمشاهده پروفایل

استاد

• Wireless Networks
• Edge Computing
• Federated Learning
• +۷ مورد دیگر

Richard Yu is a Professor at the Carleton School of Information Technology, Carleton University. His research focuses on cross-layer design in wireless systems, security in wireless networks, green information technologies, and multimedia over wireless networks. He holds a Ph.D. from the University of British Columbia and is a Professional Engineer (P.Eng). His work integrates machine learning, edge computing, and reinforcement learning to address challenges in vehicular networks, autonomous systems, and IoT. Education Ph.D., University of British Columbia Research Interests Professor Yu’s research spans wireless communication systems , edge computing , and AI-driven optimization . He explores novel paradigms such as federated learning for industrial IoT, secure UAV-assisted satellite networks, and generative AI for video understanding. His work emphasizes practical applications in autonomous vehicles, smart infrastructure, and industry 4.0 systems. Recent Trends in Publications His recent articles highlight advancements in reinforcement learning frameworks for resource allocation, safety-critical AI in video generation, and cross-modal fusion for embodied agents. He also investigates hybrid NOMA/OMA systems and quantum-inspired algorithms for next-generation networks. Advising & Grants No specific advisees are listed, but his research is supported by grants focusing on edge intelligence, vehicular networks, and secure computing. Labs & Teams Associated with Carleton’s School of Information Technology research groups, focusing on wireless systems and AI for industrial applications.

Thomas Naselarisمشاهده پروفایل

دانشیار

• Neuroscience
• Cognitive Neuroscience
• Neuroimaging
• +۳ مورد دیگر

Thomas Naselaris is an Associate Professor at the University of Minnesota, specializing in cognitive neuroscience and neuroimaging. His research focuses on decoding brain activity using advanced fMRI techniques and machine learning models, particularly in understanding visual perception, mental imagery, and memory. He leads the development of benchmark datasets like the Natural Scenes Dataset (NSD) and NSD-Imagery, which have become foundational for studying human visual cortex dynamics. His work integrates computational methods with neuroimaging to uncover how the brain represents and processes complex visual information. Notable contributions include reconstructing seen images from fMRI data and analyzing neural representations across different brain regions. He collaborates on large-scale studies involving ultra-high-field MRI and interdisciplinary approaches combining electrophysiology (iEEG) with fMRI. Key research themes include the role of generative models in visual processing, signal-to-noise dynamics in neural responses, and systems consolidation in memory. His grants include collaborative research proposals funded by CRCNS, focusing on evaluating machine learning architectures using benchmark datasets. Ongoing projects aim to bridge cognitive neuroscience with artificial intelligence, emphasizing interpretable models and scalable brain mapping techniques.

Claire Conceisonمشاهده پروفایل

استاد

• Contemporary Chinese theater
• Contemporary French theater
• Cross-cultural exchange and performance
• +۳ مورد دیگر

Claire Conceison (康开丽) is Quanta Professor of Chinese Culture and Professor of Theater Arts at the Massachusetts Institute of Technology's School of Humanities, Arts, and Social Sciences. She serves as Chair of the DAPER Advisory Board and is faculty advisor of the MIT football team, the Baker Foundation, and Wuming Theater Club. Her academic journey spans prestigious institutions including Harvard and Cornell, establishing her as a leading scholar in cross-cultural theater studies. Ph.D. in Theater Studies from Cornell University (2000) A.M. in Regional Studies—East Asia from Harvard University (1992) B.A. with Honors in East Asian Studies and Theatre from Wesleyan University (1987) Additional studies at Central Academy of Drama, Shanghai Theatre Academy, Fudan University, and Peking University in China Conceison's research focuses on contemporary Chinese theater, contemporary French theater, cross-cultural exchange and performance, Asian American theater, translation studies, and sport as performance. Her work bridges academic scholarship with practical theater production, exploring how performance reflects and shapes cultural identity across national boundaries. She has made significant contributions to understanding how Chinese theater engages with global influences while maintaining distinctive cultural expressions. Her extensive scholarly output reveals consistent focus on Chinese theatrical traditions and innovations, with particular attention to key figures like Meng Jinghui and Gao Xingjian. Her research demonstrates how theater serves as a crucial site for examining cultural exchange, political expression, and social change in contemporary China, with increasing attention to the intersection of sports and performance in recent years. MIT Everett Moore Baker Memorial Award for Excellence in Undergraduate Teaching (2023) Levitan Teaching Award in the School of Humanities, Arts, and Social Sciences (2017) Oriental Scholar Distinguished Professorship at Shanghai Theatre Academy (2015-2018) As an advisor and mentor, Conceison has guided numerous students and theatrical productions across multiple institutions. Her research has been supported by prestigious grants including the Marion and Jasper Whiting Foundation Fellowship (2025), Research Fellowships from Duke University's Asian/Pacific Studies Institute, and grants from the Asian Cultural Council. She has directed student productions and staged readings of contemporary Chinese and Asian American plays at MIT, Duke, Harvard, Tufts, and University of Michigan, fostering cross-cultural dialogue through performance.

Mehrdad Jazayeriمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Dynamics
• Sensory-Motor Integration
• +۵ مورد دیگر

Mehrdad Jazayeri is a Professor in the Department of Brain and Cognitive Sciences at MIT and an Investigator at the McGovern Institute for Brain Research. He directs educational initiatives in the department and teaches 9.014: Quantitative Methods and Computational Models in Neuroscience , focusing on mathematical tools for analyzing neurobiological data. His research centers on the neural mechanisms of timing, sensorimotor integration, and decision-making, with contributions to understanding how the brain encodes time and generates flexible behaviors. Education and Background: Jazayeri earned his PhD in Neuroscience from New York University (NYU) and completed postdoctoral training there, supported by the Helen Hay Whitney Foundation. His work has been honored with prestigious awards including the Howard Hughes Medical Institute Investigator designation (2024), McKnight Scholars Award (2017), and multiple teaching awards from MIT. Research Interests: His lab explores how neural circuits in the frontal cortex and other regions support temporal processing, mental simulation, and hierarchical reasoning. Key topics include neural dynamics underlying interval timing, Bayesian computation in sensory-motor systems, and mechanisms of cognitive control. Recent work highlights the role of entorhinal cortex in mental navigation and the dynamics of working memory. Awards: Over 20 awards/fellowships span career milestones, including the 2024 HHMI Investigatorship, Sloan Research Fellowship (2014), and recognition for both research and teaching excellence. His early career was supported by the Goldsmith Fellowship (NYU) and NSERC Scholarship (Canada). Teaching and Education: As Director of Education in BCS, he shapes curricula for MIT’s Brain and Cognitive Sciences programs. His 9.014 course emphasizes computational modeling, MATLAB programming, and analysis of neurobiological data, reflecting his commitment to bridging theory and experiment. Lab and Collaborations: The JazLab develops innovative techniques like MEDiCINe for neural recordings and leverages primate models to study decision-making circuits. Collaborations integrate experimental neuroscience with computational modeling, addressing questions in temporal processing and neural plasticity.

Jen Jen Chungمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Associate Professor Jen Jen Chung holds a position in Mechatronics at the School of Electrical Engineering and Computer Science, The University of Queensland. Her research focuses on robotic perception, planning, and learning, particularly in mobile manipulation, navigation through human crowds, and adaptive sampling. Prior to UQ, she was a Senior Researcher at ETH Zurich (2018–2022) and a Postdoctoral Scholar at Oregon State University (2014–2017). Her PhD from the University of Sydney (2014) centered on exploration strategies for autonomous systems. She also holds a B.E. in Engineering from the University of Sydney (2010). Education: PhD in Robotics, University of Sydney (2014) Bachelor of Engineering, University of Sydney (2010) Research interests span robotic manipulation , navigational algorithms , and learning-based systems . Current projects include developing robots that understand human-centric environments and optimizing path planning in dynamic settings. Her work integrates machine learning and perception to solve real-world robotics challenges. Grants and collaborations involve multi-agent systems and autonomous navigation. She leads the Robotic Perception, Planning, and Learning (RPPL) Lab , advancing research in manipulation and environmental interaction. Her contributions include over 60 peer-reviewed publications, with recent work on soft robotics and environmental impact-aware path planning.

Lian Xuمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Deep Learning
• Annotation-efficient learning
• +۵ مورد دیگر

Lian Xu is a Research Fellow in the Department of Computer Science and Software Engineering at the University of Western Australia (UWA). She holds a PhD from UWA (2021) and specializes in computer vision, deep learning, and annotation-efficient methodologies. Her work focuses on weakly supervised learning, multi-modal data fusion, and applications in healthcare, agriculture, and maritime surveillance. Education: PhD in Computer Science, University of Western Australia (2021) Research Interests: Weakly supervised semantic segmentation Multi-modal learning with vision and language Computer vision applications in healthcare and agriculture Efficient learning strategies for limited labeled data Her research output emphasizes techniques such as auxiliary task integration, token-based transformers, and spectral analysis for hyperspectral imaging. Recent work includes advancements in small object detection for maritime surveillance and crop stress classification using hyperspectral data. Awards: Recipient of the Award for Growth in Innovation and Entrepreneurship (2021) for contributions to applied research. Teaching & Engagement: Facilitated courses on computer vision (CITS4402) and visualization (CITS2401). Co-organizer of workshops at DICTA 2024, ACCV 2022, and VCIP 2021 on small object detection and deep learning limits. Grants: Co-investigator on the Empowering Robots with 3D Vision and Reasoning for Enhanced Home Assistance project (2024–2025).

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Iaroslav Melekhov is a Postdoctoral Researcher at Aalto University's Department of Computer Science, affiliated with the Professorship Kannala Juho. His work focuses on advancing computer vision techniques, particularly in camera relocalization, 3D reconstruction, semantic segmentation, and autonomous systems. He has contributed to high-impact datasets like ECLAIR and developed methods for memory-efficient visual localization and medical image analysis. Melekhov holds a Nokia Foundation Scholarship (2018) and has engaged in collaborative research with institutions such as Wayve Technologies and ETH Zurich. Research Interests: 3D Reconstruction and Gaussian Splatting Camera Relocalization and Visual Localization Semantic Segmentation and Dataset Development Deep Learning for Autonomous Systems Self-Supervised Learning and Feature Descriptors Key Contributions: Co-developed the ECLAIR aerial LiDAR dataset for semantic segmentation. Advanced memory-efficient camera relocalization via differentiable product quantization. Explored medical image segmentation using SAM-generated annotations. Grants & Awards: Nokia Foundation Scholarship, 2018 Labs & Collaborations: Associated with the imedslab research group, contributing to open-source tools like SOLT. Collaborative efforts include projects with Wayve Technologies and ETH Zurich, focusing on autonomous driving and robotics.

Siyu Huangمشاهده پروفایل

استادیار

• Computer Vision
• Deep Learning
• Generative Models
• +۳ مورد دیگر

Siyu Huang is an Assistant Professor in the School of Computing at Clemson University, leading the Vision and Learning Lab (ViL). His research focuses on 2D/3D vision, generative models, and data-efficient learning with applications in biomedical and AI-driven systems. Huang holds a Ph.D. from Zhejiang University (2014–2019), a visiting scholar position at Carnegie Mellon University (2018–2019), and prior industry experience at Baidu Research and Harvard University's Visual Computing Group. He teaches advanced courses like Machine Learning-based Image Synthesis and Applied Computer Vision. Education: Ph.D., Information Science & Electronic Engineering, Zhejiang University (2014–2019) Visiting Scholar, Carnegie Mellon University (2018–2019) Bachelor's, Zhejiang University (2010–2014) Research Interests: Generative AI, 3D reconstruction, biomedical image analysis, and scalable unpaired translation. His work bridges vision and generative models for small-data scenarios. Publications: Over 30+ peer-reviewed papers in top venues like CVPR, ICCV, ICLR, and TPAMI, focusing on generative models, active learning, and medical imaging. Awards: Includes Baidu Research’s Outstanding Research Award (2020), ACM MM Travel Award (2018), and multiple scholarships from China’s Ministry of Education. Lab & Team: The ViL lab emphasizes interdisciplinary research, with current students focusing on 3D vision and generative models. Collaborations include Harvard, NTU, and industry partnerships.

Alistair Riderمشاهده پروفایل

مدرس ارشد

• 20th-Century Art
• Sculpture
• Environmental Humanities
• +۷ مورد دیگر

Dr. Alistair Rider is a Senior Lecturer in Art History at the School of Art History, University of St Andrews. His research focuses on 20th-century North American and European art, with emphases on sculpture, environmental humanities, and durational art practices. He has authored monographs on Carl Andre and James Howell, and his work explores ecological and materialist perspectives in art history. Rider is actively involved in environmental humanities research networks, examining how materials in late-twentieth-century sculptures reflect global resource flows. He teaches and supervises doctoral students in related fields. Education: PhD in Art History, University of Leeds (2005) Research Interests: Rider’s work bridges sculpture studies with environmental critiques, examining how artworks engage with ecological systems and material histories. He investigates durational art’s capacity to represent ‘living’ forms and the collective dimension of artistic creation. Recent projects explore sculpture’s materiality as a lens for understanding global trade networks. Publications: Rider has contributed to journals like Art History and Parallax , with publications analyzing artists such as Roman Opalka, Jeffrey Rubinoff, and On Kawara. His work often bridges art theory with socio-historical contexts, emphasizing interdisciplinary approaches. Activities: Rider has organized and participated in academic events, including a 2012 lecture with Manfred Mohr on algorithmic art and a 2015 seminar on Richard Long’s topographies. His activities highlight engagement with contemporary art discourse and public scholarship.

Hao Zhuمشاهده پروفایل

پژوهشگر ارشد

• Fusion Plasma Physics
• Nonlinear Dynamics
• Turbulence Modeling
• +۷ مورد دیگر

Hao Zhu is a CFSA Postdoctoral Visiting Fellow at the University of Warwick, supervised by Prof. Sandra Chapman and Prof. Richard Dendy. His research focuses on modeling nonlinear structures in fusion plasmas using Lotka-Volterra equations, bridging plasma physics with computational methodologies. He holds a BSc in Physics from Dalian University of Technology (DLUT, 2006–2010) and is affiliated with the Physics Centre for Fusion, Space and Astrophysics (CFSA). His interdisciplinary work spans plasma turbulence, machine learning accelerators, computer vision, and sustainable materials engineering. Key contributions include FPGAs for Tsetlin Machine training, dense captioning algorithms, and multispectral pedestrian detection systems. Notably, he explores applications of large language-vision models and circular economy strategies in steel production. Publications highlight advancements in edge computing, 3D model retrieval, and bias-mitigation in AI systems. Despite no listed awards, his research demonstrates cross-disciplinary innovation across physics, computer science, and environmental engineering. Labs and collaborations are centered at CFSA, with potential affiliations in computer vision labs given his dual focus areas. No advising roles or grants are explicitly stated in the provided texts.

Patrick Kanoldمشاهده پروفایل

استاد

• Systems Neuroscience
• Brain Development
• In Vivo Imaging
• +۵ مورد دیگر

Patrick Kanold, PhD is a Professor of Biomedical Engineering at Johns Hopkins University, co-director of the Biomedical Engineering PhD program, and affiliated with the Center for Hearing and Balance and Kavli Neuroscience Discovery Institute. His research focuses on understanding how sensory experience shapes cortical circuits during development and adulthood, particularly in the auditory system. Kanold employs advanced in vivo imaging, optogenetics, and electrophysiological techniques to study neuronal activity patterns and plasticity mechanisms. Education: PhD in Biomedical Engineering from Johns Hopkins (2000), Postdoctoral training in Neurobiology at Harvard Medical School (2000-2005). He holds Dipl.Ing. (MSE) in Electrical Engineering from Technische Universität Berlin. Research interests include systems neuroscience, brain development, neuroengineering, and neurophysiology. His lab investigates how early sensory experience influences cortical circuit formation, functional architecture of auditory cortex, and age-related changes in neural processing. Recent findings include studies on subplate circuits, auditory learning, and age-related hearing deficits. Publications span over 114 peer-reviewed articles in journals like Science, Nature, and Neuron, focusing on cortical circuitry, developmental neurobiology, and neurotechnological methods. His work bridges engineering and neuroscience to advance understanding of brain function and plasticity.

Pier Luca Lanziمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Multiagent Systems
• +۷ مورد دیگر

Pier Luca Lanzi is a Full Professor at Politecnico di Milano, affiliated with the Department of Electronics, Information, and Bioengineering (DEIB). He leads the Artificial Intelligence and Robotics Lab (AIRLab), focusing on interdisciplinary research at the intersection of AI, robotics, and human-centric technologies. His work emphasizes applications in gaming, healthcare, and education, leveraging machine learning, evolutionary algorithms, and virtual reality. Education: Not explicitly stated in the provided texts. Affiliations: Director of the AIRLab; core member of DEIB. His research interests span artificial intelligence, multiagent systems, procedural content generation for games, serious games for rehabilitation, and AI-driven medical imaging. He explores how AI can enhance learning experiences (e.g., VR classrooms for physics and history) and improve therapeutic outcomes through interactive frameworks like the IGER system for stroke rehabilitation. Notable contributions include frameworks for automated level design in games (e.g., DOOM level generation via GANs), AI-assisted diagnostic imaging systems, and exergaming solutions for juvenile arthritis patients. His work often bridges computational intelligence with real-world applications, emphasizing human-centered design. Advising & Grants: While no specific grant details are provided, his prolific publication record suggests sustained research activity. Students and collaborators include prominent names in AI and robotics, though formal advisee名单 are not listed here. Labs/Teams: Directs the AIRLab, which develops cutting-edge technologies in AI, robotics, and human-technology interaction. Collaborates with industry and academic partners on projects ranging from VR educational tools to medical exergames.

• Population Health
• Public Health Research
• Primary Care Studies
• +۳ مورد دیگر

Diana Romei is a Temporary Part-Time Research Assistant affiliated with the Wolfson Institute of Population Health at Queen Mary University of London. She contributes to the SCENE project, focusing on population health research and primary care studies. Her work aligns with the institute's mission to advance understanding of health determinants and improve healthcare delivery. Research interests include epidemiological analysis, healthcare service evaluation, and clinical research methodologies. While specific publications are not listed here, her role suggests involvement in collaborative studies addressing public health challenges. No awards or grants are explicitly mentioned in the provided text. As part of the NIHR School for Primary Care Research ecosystem, she collaborates with multidisciplinary teams to advance translational research initiatives.

• Music Education
• Arts Education
• Music Policy
• +۵ مورد دیگر

Professor David Forrest is a Professor of Music Education in the School of Art at RMIT University, Australia. He serves as HRD Coordinator and is actively involved in research supervision and academic leadership. His expertise spans music education policy, Russian/Soviet music pedagogy, and arts management, with a focus on the integration of music into educational curricula and community engagement initiatives. Professional Affiliations include the Association of Music Educators (Victoria), Australian Society for Music Education, and International Society for Music Education. He has received notable awards such as the 2021 Vice-Chancellor’s Award for Research Supervision Excellence and the 2015 Honorary Life Membership from the Australian Society for Music Education. Research interests center on music policy development, the legacy of composer Dmitri Kabalevsky, and the role of music in fostering intercultural understanding. His work emphasizes the importance of music for children and the symbiotic relationship between arts education and societal well-being. Professor Forrest supervises PhD and Master’s research in diverse areas including music education, arts policy, and creative practices. He has mentored over 17 research projects, ranging from music’s role in memory to contemporary musician career dynamics. He actively contributes to international academic discourse through editorial roles and peer-reviewed publications, advocating for the centrality of arts in education and policy frameworks.