جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

Christa Searleمشاهده پروفایل

استادیار

• Sustainability
• Logistics
• Operational Research
• +۳ مورد دیگر

Christa Searle is an Assistant Professor in Business Analytics at Heriot-Watt University's School of Social Sciences and Edinburgh Business School. She joined in 2021 and serves as Programme Director for the MSc Business Analytics and Consultancy. Her research focuses on sustainability, logistics, and human behavior, employing operational research, agent-based simulation, and data analytics. She holds an extraordinary senior lecturer position at Stellenbosch University, where she earned her PhD in Industrial Engineering (2019). Her work addresses challenges like hydrogen refuelling infrastructure design, supply chain optimization, and modeling complex systems such as tuberculosis spread in informal settlements. Key projects include the ATHENA framework for hydrogen infrastructure analysis and Agent-Based Modeling of forced migration and consumer behavior. Christa has been recognized with awards including the Tom Rozwadowski Medal (2022) and Fellow of the Higher Education Academy (FHEA, 2023). She actively participates in conferences and workshops, such as the 53rd Annual Conference of the Operations Research Society of South Africa (2024) and the Sustainable Road Freight Workshop (2023). She contributes to research groups like the Centre for Logistics & Sustainability and the FeMe Network. Her teaching emphasizes authentic, industry-engaged assessments and spans business and data analytics. Current research collaborations explore decarbonizing freight transport and SDG-aligned solutions, reflecting her commitment to practical sustainability outcomes.

Stephen Arrowsmithمشاهده پروفایل

استاد

• Seismoacoustics
• Infrasound Propagation
• Earthquake Detection
• +۳ مورد دیگر

Stephen Arrowsmith is a Professor and Hamilton Chair in Earth Sciences at Southern Methodist University (SMU). His research focuses on seismoacoustics, infrasound propagation, and geophysical monitoring of natural and anthropogenic phenomena. He holds a Ph.D. from the University of Leeds and has contributed significantly to nuclear test monitoring, atmospheric dynamics, and machine learning applications in geophysics. Key research areas include: Integration of seismic and acoustic data for event detection and discrimination Analysis of infrasound signals from earthquakes, explosions, and bolide impacts Development of automated detection algorithms using machine learning Urban seismology and distributed acoustic sensing Recent work emphasizes multi-modal data fusion, with studies on the Korean Peninsula, Tonga eruptions, and North Korean nuclear explosions. His contributions include advancing global infrasound networks and improving stratospheric atmospheric modeling through infrasound observations. He maintains an active research website at allthespheres.com .

Shahin Shahrampourمشاهده پروفایل

استادیار

• Machine Learning
• Optimization and Control
• Distributed Learning
• +۵ مورد دیگر

Shahin Shahrampour is an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University, with a courtesy appointment in Electrical & Computer Engineering. He holds a Ph.D. in Electrical & Systems Engineering from the University of Pennsylvania, an M.A. in Statistics from The Wharton School, and a B.S. in Electrical Engineering from Sharif University of Technology. Prior to Northeastern, he was at Texas A&M University and held a postdoctoral fellowship at Harvard University. Research Interests: Machine learning, optimization and control, distributed and sequential learning, with a focus on computationally efficient methods for data analytics. Specific areas include manifold optimization, online and reinforcement learning, non-convex optimization, and statistical signal processing. His work bridges theory and applications in networked systems and dynamic environments. Awards & Grants: Received a $500,000 NSF grant (2023) for distributed optimization in non-convex environments. Best Paper Award at IEEE ICASSP 2022 for contributions to signal processing. TEES Engineering Genesis Award (2020) for multidisciplinary research at Texas A&M. Principal Investigator on NSF-funded projects in collaborative online optimization and decentralized learning. Labs & Teams: Leads research in machine learning and control systems at Northeastern University, focusing on interdisciplinary challenges in distributed optimization and real-time learning. His lab emphasizes theoretical foundations and practical implementations in networked and dynamic systems.

Yun Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۲ مورد دیگر

Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.

Allison Jingمشاهده پروفایل

مدرس

• Virtual and Mixed Reality
• Human-Computer Interaction
• Digital Health
• +۳ مورد دیگر

Allison Jing is a Lecturer (Assistant Professor) at RMIT University’s School of Computing Technologies and an adjunct Researcher at the Empathic Computing Lab at the University of South Australia. Her research focuses on Mixed Reality (MR) technologies to enhance human abilities through multimodal interaction, including gaze-based cues, biosignals, and AI-driven empathy. She holds a PhD in Information Technology from the University of South Australia (2023) and an MSc in Information Technology from the University of St Andrews (2017). Dr. Jing’s academic roles include coordinating Mixed Reality and Programming courses at RMIT. She has extensive industry experience, working at Meta Reality Labs, Microsoft, Amazon, and startups, which informs her user-driven research approaches. Her work spans collaborative VR/AR systems, empathic computing, and digital health applications. Key research interests include cross-reality collaboration, empathetic AI, and physiological signal integration. She collaborates with global institutions like CSIRO Data61 and Meta RLR. Her awards include runner-up in the XR Workshop (2023) and finalist in Deloitte Cyber Capture the Flag (2018). Teaching and supervision: Coordinates COSC2476/77 (Mixed Reality) and COSC2801 (Programming Bootcamp Java). Currently supervises PhD students and mentors research interns from Oxford, NYU, and other universities. Serves on program committees for CHI, MobileHCI, and ISMAR. Labs/Teams: Empathic Computing Lab, Australian Research Centre for Interactive and Virtual Environments (IVE), and ARIVE network. Active in industry partnerships for collaborative projects in MR and AI.

• Reinforcement Learning
• Robotics
• Deep Learning
• +۳ مورد دیگر

Glen Berseth is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal and a Senior Academic Fellow at Mila – Quebec Institute for Artificial Intelligence. He is also a Canada CIFAR Chair in AI and Co-Director of the Montreal Robotics and Integrative AI Laboratory (REAL). His work focuses on reinforcement learning, robotics, and deep learning applied to autonomous systems. He holds a postdoctoral background from Berkeley Artificial Intelligence Research (BAIR), working under Sergey Levine. His research emphasizes real-world applications, including human-robot collaboration, continual learning, and multi-agent systems. He teaches courses on robot learning at the University of Montreal and Mila, covering cutting-edge techniques for general-purpose robots. Key research interests include reinforcement learning for robotics, adaptive interfaces, and sim-to-real transfer. His recent work addresses challenges in autonomous learning systems, such as robust locomotion control and efficient exploration strategies. Notable awards include the Canada CIFAR AI Chair. He has supervised numerous students, including PhD candidates Ozgur Aslan and Siddarth Venkatraman, and Master’s students like Roger Creus-Castanyer and Léa Demeule, focusing on topics like reinforcement learning and robotic control. Berseth leads research projects funded by organizations like the CRSNG, FCI, and MITACS, addressing topics such as modular lifelong learning and generalization in robotics. His lab, REAL, explores embodied AI and robotics integration.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Hady W. LAUW is an Associate Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), serving as Director of the BSc (Computer Science) Programme and a Lee Kong Chian Fellow. He holds a PhD from Nanyang Technological University (2008). His research focuses on artificial intelligence, data science, machine learning, and recommender systems, with notable contributions to multimodal recommendation frameworks like Cornac and collaborative filtering techniques. He teaches advanced courses including IS712 Machine Learning for postgraduate students and CS608 Recommender Systems for MITB programme participants. His work emphasizes practical applications, such as designing explainable recommendation systems and integrating A/B testing into frameworks. Key research interests include web mining, preference learning, representation learning, and decision support systems. He has advised multiple students on topics like neural networks, collaborative filtering, and comparative analysis of reviews. Scientific achievements include the Lee Kong Chian Fellowship and over 100 publications in top venues like ACM WWW and IEEE TKDE. He actively contributes to conferences as a PC member and chairs events like PAKDD. His Cornac framework supports reproducible research in multimodal recommendations. He leads the Preferred.AI research group, fostering undergraduate and postgraduate research in computing. His work bridges theoretical advancements with industry applications, particularly in data-driven decision making and automated evaluation metrics.

Zeynep Işıl Kalaylıoğluمشاهده پروفایل

استاد

• Bayesian Statistics
• Environmental Statistics
• Health Statistics
• +۳ مورد دیگر

Prof. Zeynep Işıl Kalaylıoğlu is a Professor of Statistics at the Department of Statistics, Middle East Technical University (METU), Ankara, within the Faculty of Arts and Sciences. She holds a Ph.D. in Statistics from North Carolina State University (2002) and has held roles including Associate Professor (2014–2022), Assistant Professor (2009–2014), and researcher at the National Cancer Institute (2002–2007). She has served in administrative roles such as Assistant to the Department Head and member of METU’s Wind Energy Research Center and Faculty Board. Ph.D.: Statistics, North Carolina State University, 2002 M.S.: Statistics with Computational Engineering minor, North Carolina State University, 1999 B.S.: Statistics with Computer Engineering minor, Middle East Technical University, 1995 Her research focuses on Bayesian statistical methodologies for environmental and health phenomena. Key areas include spatial/temporal modeling of atmospheric and ecological variables, prediction of migratory patterns (e.g., wind direction, bird movements), and risk modeling for breast cancer using mammogram data. She develops flexible models for circular and extremal data, emphasizing applications in ecology, epidemiology, and environmental science. Her recent publications address topics like goodness-of-fit tests for statistical distributions, predictive model selection for circular data, and Bayesian approaches for missing covariates in generalized linear models. Current projects include mobile apps for olive harvest optimization and frost预警 systems, reflecting her commitment to applied statistical solutions. Labs/Teams: She leads the BayeZian Research Group, collaborating with METU’s Ecosystem Research Center on interdisciplinary projects. Her teaching expertise spans mathematical statistics, Bayesian theory, and statistical inference at undergraduate and graduate levels.

Kemal Tepeمشاهده پروفایل

استاد

• Vehicular Networks
• Cognitive Radio
• Wireless Communication
• +۶ مورد دیگر

Professor Kemal Tepe is a faculty member in the Faculty of Engineering at the University of Windsor, specializing in wireless communication and information processing. His research focuses on vehicular networks, cognitive radio systems, and smart grid technologies. He leads the Wireless Communication and Information Processing Lab , where he develops solutions for autonomous driving systems, cybersecurity in vehicle-to-infrastructure communication, and spectrum sensing techniques. In 2016, he was awarded the Medal of Excellence by the Faculty of Engineering for his dedication and service. Tepe’s work bridges theoretical advancements and practical applications, addressing challenges in autonomous systems, machine learning for anomaly detection, and IoT security. His contributions include pioneering methods for detecting adversarial behavior in vehicular networks and improving spectrum utilization through probabilistic modeling. Collaborations with industry partners like Ford Motor Company and involvement in initiatives such as the Perspective Magazine automotive research highlight his industry-relevant research. His research interests span a wide range of domains including: Autonomous vehicle safety and communication protocols Cognitive radio networks and spectrum management Machine learning for network security and anomaly detection Wireless sensor networks and energy-efficient protocols Smart grid integration and communication architectures Tepe’s publications emphasize practical implementations, such as real-time routing protocols for wireless sensor networks and hardware designs for cognitive radio systems. His lab’s innovations have been showcased in industry-relevant platforms, demonstrating the real-world impact of his work.

Johannes Twiefelمشاهده پروفایل

پژوهشگر

• Speech Recognition
• Language Understanding
• Reservoir Computing
• +۷ مورد دیگر

Dr. Johannes Twiefel is a Researcher at the Knowledge Technology Research Group within the Department of Informatics at the University of Hamburg. His work focuses on speech recognition, language understanding, and brain-inspired models using echo state networks. He leads the LemonSpeech project, funded by the German Federal Ministry for Economic Affairs and Energy, which aims to develop German automatic speech recognition (ASR) systems for local hardware deployment. His research spans noise robustness in ASR, robotic interaction, and multimodal learning. Education: Doctor rerum naturalium (Dr. rer. nat.) in Computer Science, University of Hamburg (2020) Master's degree in Computer Science, University of Hamburg (2014) Research Interests: Reservoir Computing and Echo State Networks Machine Learning and Neural Networks Speech Recognition and Language Understanding Human-Robot Interaction Robotics and Multimodal Systems Grants and Projects: EXIST Scholarship (2021–present) for LemonSpeech DOCKS Project (post-processing ASR hypotheses) Labs/Teams: Active in the Knowledge Technology Research Group, collaborating on neuro-inspired architectures and ASR systems.

Rishabh Dabralمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +۶ مورد دیگر

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

Emmanuel Stamatakisمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Neuroimaging
• Cognitive Neuroscience
• +۵ مورد دیگر

Dr. Emmanuel Stamatakis is a Senior Research Associate at the University of Cambridge, affiliated with the School of Clinical Medicine and the Division of Anaesthesia. He maintains research ties with the Centre for Speech, Language, and the Brain (CSLB) while focusing on neuroimaging, consciousness studies, and traumatic brain injury (TBI) mechanisms. Institution: University of Cambridge Primary Affiliation: School of Clinical Medicine, Division of Anaesthesia Research Affiliation: Centre for Speech, Language, and the Brain (CSLB) Academic Rank: Research Fellow His research spans neuroscience, neuroimaging, and anaesthesia, with particular emphasis on: Consciousness dynamics under pharmacological and pathological conditions Functional and structural brain connectivity in TBI and delirium Neural mechanisms of anaesthetic agents Evolutionary and comparative neuroscience of brain states Long-term outcomes after neurological trauma Neurotechnology applications in clinical settings Recent publications reveal trends in: Quantifying consciousness through functional gradients and harmonic decomposition Linking anaesthesia-induced neural changes to clinical outcomes Exploring genetic and inflammatory factors in TBI recovery Multi-modal imaging approaches to neurological disorders Translational studies bridging animal models and human conditions Methodological advancements in brain network analysis

• Urban Sustainability
• Urban and Regional Planning
• Transportation and Freight Services
• +۴ مورد دیگر

Dr. Ahmadreza Faghih Imani is a Teaching Fellow in Urban Sustainability at the Centre for Environmental Policy, part of the Faculty of Natural Sciences at Imperial College London. He also serves as co-convenor of the Urban Sustainable Environments (USE) option for the MSc in Environmental Technology. Previously, he was a Research Associate at the Centre for Transport Studies (Imperial College London) and held an NSERC Postdoctoral Fellowship at the University of Toronto. He earned his PhD from McGill University in 2017, recognized with the Benjamin H. Stevens Graduate Fellowship. His research focuses on urban sustainability, exploring how built environments, transport infrastructure, and advanced modeling techniques can enhance environmental and social sustainability. Key interests include transportation planning, energy consumption modeling, and policy analysis with over 30 publications in top-tier journals. He has contributed to industry/government projects (9 technical reports), volunteered with TRB's Bicycle Transportation Committee, and reviewed for academic journals. His work bridges disciplines such as urban planning, civil engineering, and applied mathematics. Recent efforts emphasize health integration into mobility policies, smart energy systems, and innovative data collection methods (e.g., smartphone apps). He collaborates widely with over 10 faculty members and 20 researchers globally. Notable achievements include modeling air pollution exposure from transit policies and analyzing cycling infrastructure impacts in cities like Toronto and Montreal. His research addresses urgent urban challenges through interdisciplinary frameworks and policy-relevant insights.

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Vehicular Networks
• +۶ مورد دیگر

Daxin Tian is a prominent professor at Beihang University's School of Transportation Science and Engineering, specializing in intelligent transportation systems and vehicular networks. With over 170 publications spanning from 2006 to 2025, his research has significantly contributed to the advancement of connected and autonomous vehicle technologies. His work appears consistently in top-tier IEEE journals including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, and IEEE Internet of Things Journal, establishing him as a leading authority in the field. Professor Tian's research interests encompass several critical areas in modern transportation technology: Connected and Autonomous Vehicle Systems Vehicular Networking and Communication Protocols Vehicle Platooning and Cooperative Driving Algorithms Edge Computing Applications for Transportation Computer Vision for Autonomous Driving Perception Traffic Flow Optimization and Prediction Models Resource Allocation in Vehicular Networks His recent publications demonstrate an increasing sophistication in addressing complex multi-vehicle scenarios while maintaining practical considerations like communication reliability, energy efficiency, and safety constraints. The research trajectory shows a clear evolution from foundational networking and control problems toward more integrated AI-driven solutions that combine computer vision, natural language processing, and advanced control theory for next-generation transportation systems. Professor Tian maintains extensive international collaborations, particularly with researchers at Canadian institutions including Victor C. M. Leung's group, while leading a substantial research team at Beihang University. His work frequently bridges theoretical advances with practical transportation challenges, resulting in numerous high-impact publications that address real-world implementation barriers in intelligent transportation systems.