جست‌وجوی استاد و پژوهشگر

George Vosselmanمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Laser Scanning
• +۷ مورد دیگر

George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.

Hongkai Wenمشاهده پروفایل

استاد

• Machine Learning Systems
• Artificial Intelligence
• Data Science
• +۵ مورد دیگر

Hongkai Wen is a Professor (Chair in Machine Learning Systems) in the Department of Computer Science at the University of Warwick, UK. He holds dual appointments as a Fellow of the Alan Turing Institute (serving as Independent Scientific Advisor for BridgeAI and member of Turing Research Ethics team) and previously worked as Senior Research Scientist at Samsung AI Centre Cambridge and postdoctoral researcher at Oxford University. Education: Computer Science, Keble College, University of Oxford Research Focus: Develops intelligent multi-modal perception systems for real-world deployment with extreme computational efficiency. Core expertise spans ML systems optimization, neural architecture search, and cross-disciplinary applications in robotics, urban mobility, and wearable/IoT security. Pioneered event-based vision techniques and training-free NAS frameworks. Publication Trends: Recent work (2023-2025) demonstrates accelerating innovation in diffusion model efficiency, on-device AI deployment, and sensor fusion techniques. Dominant themes include computational resource optimization for edge devices, multi-modal temporal modeling, and privacy-preserving spatial analytics, with significant contributions to NeurIPS, ICML, and CVPR venues. Scientific Recognition: Best Paper Award, AutoML Conf 2023 (T-CET) Best Paper Runner-up, SenSys 2024 (AdaFlow) Best Paper Awards: IPSN 2014 & EWSN 2013 1st/2nd Place, Zero Cost NAS Competition (AutoML'22) Mentorship & Funding: Actively supervises PhD candidates through thesis committees at Warwick, Ulster, and Queensland universities. Secured National AI Strategy Fund for Macro Neural Architecture Search research. Recruits annually for PhD positions with scholarships from UKRI, Turing Institute, and industry partnerships. Research Leadership: Heads the AI/ML Systems (AMS) Division at Warwick, directing a 15+ member team developing deployable ML frameworks for mobile/robotic platforms. Maintains active collaborations with Samsung AI Centre and Turing Institute's BridgeAI programme on ethical AI deployment.

Lionel C. Briandمشاهده پروفایل

استاد

• Software Engineering
• Software Verification and Validation
• Trustworthy AI
• +۶ مورد دیگر

Lionel C. Briand is a Professor of Software Engineering with shared appointments at the University of Luxembourg's SnT Centre for Security, Reliability, and Trust and the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Canada Research Chair (Tier 1) in Intelligent Software Dependability and Compliance and serves as Director of Lero, Ireland's national software research center. His academic leadership spans over 25 years of collaborative research with industry partners across automotive, aerospace, energy, financial, and legal domains. Professor Briand's research focuses on software verification and validation, trustworthy AI systems, model-driven engineering, and empirical software engineering methodologies. His work bridges theoretical foundations with industrial applications, particularly in cyber-physical systems where machine learning components interact with safety-critical control systems. He has pioneered techniques for testing AI-enabled systems, GDPR compliance automation, and mutation analysis for space systems. His publication portfolio demonstrates consistent innovation in software testing, with recent emphasis on large language models for test generation, automated compliance checking, and safety analysis of deep neural networks. Key trends include black-box testing methodologies, metamorphic testing for security, and search-based approaches for DNN validation. IEEE Fellow and ACM Fellow IEEE Computer Society Harlan Mills Award (2012) ACM SIGSOFT Outstanding Research Award (2022) IEEE Reliability Society Engineer-of-the-Year Award (2013) ERC Advanced Grant recipient (2016) Fellow of the Academy of Science, Royal Society of Canada (2023) ICSE 2011 Most Influential Paper Award As Director of Lero and holder of a Canada Research Chair, Professor Briand leads major research initiatives including an ERC Advanced Grant on cyber-physical system modeling and testing. His industrial collaborations generate substantial grant funding, particularly in automotive safety validation and regulatory compliance automation. He mentors numerous researchers through his dual appointments and serves on program committees for top software engineering conferences. Professor Briand directs research activities at the SnT Centre's SVV department, focusing on software verification and validation. His team develops practical tools like MASS for space system mutation analysis and COREQQA for compliance requirements understanding, with strong industry adoption in automotive and aerospace sectors.

• Artificial intelligence
• Data compression
• Deep learning
• +۱۵ مورد دیگر

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

Daniel Hershcovichمشاهده پروفایل

استادیار

• Natural Language Processing
• Cross-Cultural NLP
• Computational Linguistics
• +۷ مورد دیگر

Daniel Hershcovich is a Tenure-Track Assistant Professor at the Natural Language Processing section of the Department of Computer Science, University of Copenhagen. His research focuses on cross-cultural adaptation of language models, integrating human values into AI systems, and evaluating AI's real-world impact in domains like law, literature, and food culture. Research Themes Cultural value alignment in LLMs Multimodal models for accessibility Cross-cultural recipe and food knowledge Historical Scandinavian text analysis Ethical AI and bias mitigation Scientific Recognition SAC Highlight Award (ACL 2025) Outstanding Paper Award (ACL 2017) Advising & Grants : Mentions collaborations on multiple EMNLP/ACL/CoNLL papers. Leads Independent Research Fund Denmark project ALIKE (2025-2027) and contributes to Innovation Fund Denmark's XHAILe (2025-2028). Co-organized SemEval 2019 and CoNLL 2019/2020 shared tasks. Labs & Teams : Leads the CoAStaL research group. Collaborates with teams at IBM Research Haifa, University of Manchester, and Wuhan University of Science and Technology.

• Machine learning methods development
• Bioinformatics
• Biological sequence analysis
• +۸ مورد دیگر

Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.

Gabriella Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Jonathan Ragan-Kelleyمشاهده پروفایل

استادیار

• High-Efficiency Visual Computing
• Compilers
• Computer Architecture
• +۳ مورد دیگر

Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

• Human Activity Recognition
• Wearable Sensors
• Machine Learning
• +۴ مورد دیگر

Thomas Ploetz is an Adjunct Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His work focuses on sensor-based human activity recognition, wearable computing, and computational behavior analysis with applications in healthcare, smart homes, and education. He leads interdisciplinary research integrating machine learning, IoT systems, and human-centered design. His research interests include improving activity recognition robustness through synthetic data generation, developing explainable AI for time-series analysis, and advancing healthcare technologies like diabetic foot ulcer monitoring systems. He explores ethical implications of wearable sensing in diverse populations, including underrepresented groups. Key contributions include IMUTube (virtual sensor data generation), ProxiCycle (cyclist safety monitoring), and DISCOVER (smart home activity recognition framework). His work addresses challenges in data scarcity, domain adaptation, and long-term system maintenance in pervasive computing environments. He has been awarded grants such as the GVU/IPaT Research and Engagement Grant (2020), and his research appears in top venues like UbiComp and ISWC. He actively contributes to the wearable computing community through conference organization and editorial work.

Akram Houraniمشاهده پروفایل

استاد

• Satellite Communications
• Signal Processing
• Internet-of-Things (IoT)
• +۶ مورد دیگر

Professor Akram Hourani is a Discipline Leader and Professor in the Department of Electrical & Electronic Engineering at RMIT University's School of Engineering. He holds roles as Program Manager for the Master of Engineering (Telecom & Network Eng.) and Deputy Director of the Centre for Opto-electronic Materials and Sensors (COMAS). Prior to academia, he was an ICT Program Manager in the telecommunications industry, leading projects in satellite and telecommunications infrastructure. His research focuses on advanced signal processing, satellite communications, radar systems (including SAR), neuromorphic hardware, and IoT. He has secured grants from ARC, CRC, government departments, and DSTG, with over 130 publications. His work aligns with UN Sustainable Development Goals 9 (Industry, Innovation & Infrastructure), 11 (Sustainable Cities), and 10 (Reduced Inequalities). Education: PhD in Electronics & Telecommunications (2016, RMIT University) Non-academic roles: R&D Engineering Program Manager at Inteltec Emirates (2006–2013) Key research themes include interference mitigation, 5G/6G networks, neuromorphic sensing, and AI-driven satellite IoT. He is listed in Stanford's top 2% scientists for career-long and single-year impact. His teaching includes courses on satellite communications and wireless sensor networks. Grants & Funding: ARC, CRC, DSTG, and government-funded projects since 2017 Collaborations: CSIRO, industry partners in telecommunications and aerospace His lab focuses on next-generation communication systems, with active projects in mega satellite networks, neuromorphic hardware, and AI for IoT sensing. He supervises PhD/Masters research in areas like satellite connectivity and machine learning applications.

Michael Websterمشاهده پروفایل

استاد

• Cognitive neuroscience of human vision
• Visual adaptation
• Color and form perception
• +۳ مورد دیگر

Michael Webster is a Professor of Psychology at the University of Nevada, Reno, serving as Co-Director of the Graduate MS/PhD Neuroscience Program and Undergraduate BS Neuroscience Program, and Director of the NIH-funded COBRE for Integrative Neuroscience. His research focuses on visual perception, particularly how perception adapts to environmental and physiological changes. He leads major initiatives like the $10 million COBRE grant establishing an fMRI facility and neuroscience programs. Education: Ph.D., Psychology, University of California, Berkeley (1988); B.A., Psychology, University of California, San Diego (1981). Research Interests: Cognitive neuroscience of vision, visual adaptation mechanisms, color and face perception, and cultural/environmental influences on perception. Notable contributions include discoveries about face adaptation, color constancy, and blur correction. His work is funded by NIH grants and recognized through awards like the Outstanding Researcher Award. Grants/Awards: NIH COBRE Directorship, foundation professorship, grants for radiology adaptation studies. His lab (Visual Perception Lab) explores neural and cognitive bases of visual processing.

Pedro R. M. Inácioمشاهده پروفایل

دانشیار

• Cybersecurity
• Information Security
• Computer Simulation
• +۷ مورد دیگر

Pedro R. M. Inácio is an Associate Professor at the University of Beira Interior (UBI) , where he teaches information assurance, cybersecurity, and computer simulation courses in undergraduate and graduate programs. He serves as Pro-Rector for the Digital University and Data Protection Officer at UBI, and leads the Cross Cutting Skills Lab and the Network Security research group at Instituto de Telecomunicações. His work bridges academia and industry, including a PhD at Nokia Siemens Networks Portugal.

FENG Menglingمشاهده پروفایل

دانشیار

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +۳ مورد دیگر

**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.

• Medical Image Computing
• Tumor Growth Modeling
• Computational Physiology
• +۳ مورد دیگر

Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.