جستجوی استاد راهنمای دانشگاهی

Fatemeh Vafaeeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +۵ مورد دیگر

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .

Daniele Cattaneoمشاهده پروفایل

پژوهشگر

• Autonomous Robots
• Deep Learning for Localization
• Sensor Fusion
• +۳ مورد دیگر

Dr. Daniele Cattaneo is a Junior Research Group Leader at the Robot Learning Lab (University of Freiburg, Germany). He specializes in autonomous robotics, deep learning for perception and localization, and sensor fusion. His research focuses on embodiment-agnostic and environment-agnostic systems for robots, with applications in autonomous driving and healthcare robotics. Education: Ph.D. in Computer Science, Università degli Studi di Milano-Bicocca (2016–2020) M.Sc. and B.Sc. in Computer Science, Università degli Studi di Milano-Bicocca (2013–2016, 2010–2013) Research Interests: His work addresses challenges in LiDAR-camera calibration, SLAM (Simultaneous Localization and Mapping), unsupervised domain adaptation, and multimodal fusion for robust perception. Key projects include CMRNext (LiDAR-camera matching), Syn-Mediverse (healthcare scene understanding), and Continual SLAM (long-term autonomy). Awards & Grants: He leads funded projects like AI-Drive (next-gen autonomous driving algorithms) and iSUOR (operating room video analysis). Collaborations include work with the AIS Group and Robotic Learning Lab . Students & Labs: Supervises 12+ students in topics like LiDAR localization, HD maps, and radar-based navigation. Active in the Robot Learning Lab at Freiburg, contributing to open-source datasets and tools for robotics research.

Dries Daemsمشاهده پروفایل

استادیار

• Archaeology
• Digital Humanities
• Computational Modeling
• +۷ مورد دیگر

Dries Daems serves as Assistant Professor in Spatial Digital Humanities at Vrije Universiteit Amsterdam's Faculty of Social Sciences and Humanities within the Art and Culture, History, Antiquity department, while also holding a Visiting Professor position at Helsinki University. His archaeological expertise spans the Iron Age to Hellenistic Mediterranean with methodological specialization in computational approaches. His research integrates digital humanities with classical archaeology, focusing on social complexity through agent-based modeling, network science, and spatial analysis of material culture. Key interests include urbanism dynamics, artisanal production networks, and human-environment interactions, particularly through pottery studies and socio-ecological simulations. This interdisciplinary approach bridges traditional archaeological methods with advanced computational techniques. Recent publications demonstrate a clear trend toward methodological innovation in archaeological science, with emphasis on quantifying connectivity through ceramic networks, validating archaeological datasets via simulation frameworks, and developing open computational platforms for modeling past societies. These works consistently apply network theory and spatial analysis to Mediterranean material culture. SSH Open Competition XS Grant (2025) Daems leads significant research initiatives including the 3D-CERA-ML project for machine learning analysis of archaeological ceramics and the ModelPlaza platform for socio-ecological simulations. His collaborative projects involve international teams across Europe and Turkey, with funding supporting computational infrastructure development and methodological standardization in digital archaeology. Current courses include Mobility and Interaction in the Ancient Mediterranean and Digital Practice in Archaeology. As core faculty at VU Amsterdam's Network Institute, he contributes to interdisciplinary research infrastructure while organizing academic events like the CAA2025 workshop. His work emphasizes open science through platforms like ModelPlaza that provide shared simulation environments for archaeological research communities.

• Oral Radiology
• Dental Implantology
• 3D Printing in Dentistry
• +۳ مورد دیگر

Vinicius Dutra, DDS, PhD, MBA is a Clinical Associate Professor at the Department of Oral Pathology, Medicine and Radiology within the Indiana University School of Dentistry. His work focuses on innovative applications of 3D printing, virtual reality, and artificial intelligence in dental education and clinical practice. He specializes in oral radiology, implantology, and orthodontics, with a strong emphasis on improving diagnostic accuracy and surgical precision through advanced imaging and digital tools. Dr. Dutra holds a Doctor of Dental Surgery (DDS), a PhD, and an MBA, reflecting his interdisciplinary expertise in clinical practice, research, and healthcare management. His research explores topics such as guided implant surgery, 3D-printed anatomical models for training, and the integration of machine learning into dental diagnostics. His recent studies include evaluations of virtual reality in dental education, the accuracy of guided implant placement techniques, and comparisons between cone-beam CT scans and histological analyses. He has also contributed to advancements in complete denture fabrication workflows and bone thickness measurement methodologies. Despite his extensive publication record, no scientific awards were explicitly mentioned in the provided materials. Dr. Dutra’s clinical and educational roles involve mentoring students and contributing to the development of cutting-edge dental technologies. His work bridges traditional dental practices with modern digital innovations, aiming to enhance patient outcomes and streamline clinical workflows.

Al Bovikمشاهده پروفایل

استاد مهمان

• Digital Video
• Visual Neuroscience
• Deep Learning
• +۳ مورد دیگر

Al Bovik is a Visiting Professor at the University of Texas at Austin's Department of Electrical and Computer Engineering, part of the College of Engineering. His research focuses on digital video, visual neuroscience, and deep learning, with an emphasis on developing perceptual theories for high-quality streaming video and virtual/augmented reality systems. He has pioneered advancements in video quality assessment, compression, and AI-generated media evaluation. He has received prestigious awards including the IEEE Edison Medal, Primetime Emmy, and membership in multiple national academies. Bovik co-founded the IEEE Transactions on Image Processing and established the IEEE International Conference on Image Processing in 1994, which he chaired. His influential books include The Essential Guides to Image and Video Processing . Key Research Contributions: HDR/SDR video quality metrics, perceptual artifact detection, and AI-driven quality assessment frameworks. Labs/Teams: Leadership in IEEE initiatives and collaborations in media quality and signal processing. His work bridges theoretical research with practical applications in video streaming, medical diagnostics, and user-generated content analysis.

• Computer Graphics
• Virtual and Augmented Reality
• Facial Expression Analysis
• +۴ مورد دیگر

Joe Geigel is a Professor in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. He holds a DSc in Computer Science from George Washington University, an MS in Computer Science from Stevens Institute of Technology, and a BS in Mathematics from Manhattan College. His career includes industry positions at Eastman Kodak, Bell Laboratories, and RCA Solid State before transitioning to academia. Geigel's research explores the intersection of computer graphics and performing arts, with expertise in: Virtual and augmented reality systems Facial expression analysis and motion capture Interactive theatre and dance technologies Human-computer interaction for creative applications Interdisciplinary education methods His publications demonstrate consistent innovation in virtual performance technologies, with recent work focusing on mixed reality theatre, museum interpretation systems, and educational applications of immersive technologies. He has directed over 15 technology-enhanced productions including Singring and the Glass Guitar and Farewell to Dawn . As co-director of the CS Graphics and Applied Perception Lab, Geigel leads the XRLive team developing extended reality solutions for live performances. He teaches graduate courses in computer graphics, animation algorithms, and virtual reality applications.

• Soil micromorphology
• Urban geoarchaeology
• Phytolith analysis
• +۳ مورد دیگر

Yannick Devos is a postdoctoral researcher at the Archaeology, Environmental changes & Geo-Chemistry department of the Vrije Universiteit Brussel (VUB), within the School of History, Archeology, Arts, Philosophy and Ethics. His work focuses on urban geoarchaeology, soil micromorphology, and phytolith analysis, with particular emphasis on medieval and historical urban environments in Belgium and beyond. Education : Ph.D. in Archaeology (university details not explicitly stated in text). Affiliations : Active in multiple interdisciplinary projects, including the Revealing the Senne initiative and studies on ancient agricultural terraces in the Mediterranean. Research Interests : Urban environmental archaeology, particularly 'Dark Earth' formations and anthropogenic soils. Phytolith analysis as a tool for reconstructing past environments and fire events. Medieval land use, including agricultural practices and waste management in urban contexts. Soil micromorphology applied to historical trade hubs and urban stratigraphy. Recent Article Trends : Recent work emphasizes interdisciplinary methods, such as integrating 3D modeling, autofluorescent phytolith analysis, and biomarker studies to explore urbanization processes, historical environmental changes, and ancient human-environment interactions. Awards : Best poster award (2025) for research on Mediterranean agricultural terraces through soil analysis. Grants & Projects : FWOTM1260 (2024-2028): Investigating ancient agricultural terraces in the Eastern Mediterranean. BRGDOC11 (2023-2027): Uncovering the historical Senne River's role in Brussels' urban landscape. Labs/Teams : Collaborates with the Inside-Out project (x-ray analysis) and the Archaeology of Coastal Communities initiative, focusing on resilience and environmental adaptation.

• Robotics
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Kuan Fang is an Assistant Professor of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning-based perception and control systems. Previously, he was a postdoc at UC Berkeley under Sergey Levine and earned his Ph.D. and M.S. from Stanford University under Fei-Fei Li and Silvio Savarese, with a B.S. from Tsinghua University. He has also worked at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia. Education: Ph.D. & M.S., Computer Science, Stanford University Bachelor's Degree, Tsinghua University Research Interests: Robot manipulation and control Reinforcement learning and policy optimization Robot perception and vision-language integration Generalization in robotics across tasks, environments, and robots Open-world robotic systems leveraging large-scale data Teaching: CS 6758: Deep Learning for Robotics (Fall 2024) CS 4756: Robot Learning (Spring 2025) Lab & Collaborations: His lab at Cornell develops scalable algorithms and systems for robotic perception and control, emphasizing data-driven methods. Notable work includes ReLIC for interlimb coordination, GLIDE for bimanual manipulation, and TRA for compositional task execution. He collaborates with institutions like Boston Dynamics AI Institute and UC Berkeley.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

• Geothermal Energy
• Structural Geology
• Geophysical Modeling
• +۳ مورد دیگر

Prof. Florian Wellmann is a University Professor at RWTH Aachen University's Chair of Numerical Geosciences, Geothermal Energy and Reservoir Geophysics within the Faculty of Georesources and Materials Engineering. His research focuses on integrating machine learning with geoscience applications, particularly in geothermal energy, structural modeling, and uncertainty quantification. He leads the CG³ research group, developing open-source tools like GemPy and GemGIS for 3D geological modeling. Notably, he received a KI-Campus fellowship for advancing AI in geoscience education. His work includes projects such as the Horizon Europe GeoHEAT initiative, exploring geothermal systems and repurposing idle wells for energy. Education details are not explicitly provided, but his academic roles indicate expertise in numerical geosciences and geothermal engineering. Research interests span physics-based machine learning, subsurface characterization, and probabilistic modeling. His fellowship highlights contributions to digital education, blending AI with geological curricula. Recent publications emphasize sensitivity analysis, fault modeling, and geothermal reservoir optimization. Prof. Wellmann's awards include the KI-Campus fellowship (2021). His grants and advising efforts are reflected in collaborative projects like GeoHEAT and software development. Labs/teams include the CG³ group at RWTH Aachen, equipped with advanced geophysical instruments for field and lab analysis.

• Surgical Computing
• Simulation Science
• Virtual and Mixed Reality in Medicine
• +۶ مورد دیگر

Professor Fernando Bello holds the position of Professor of Surgical Computing and Simulation Science at Imperial College London's Department of Surgery and Cancer (Faculty of Medicine). He leads the Centre for Engagement and Simulation Science and directs the SiMMS research group, focusing on medical simulation and modeling. His career includes a PhD from Imperial College (1996), postdoctoral work at Guy's Hospital and the University of Kent, and roles as Lecturer in Electronic Engineering before rejoining Imperial in 2000. Education: PhD in Biomedical Systems, Imperial College London (1996) Postdoctoral Research Fellow at Guy's Hospital (1996-1998) Lecturer in Electronic Engineering at University of Kent (1998) Research Interests: Bello’s work bridges medicine, education, and technology. Key areas include virtual/mixed reality in surgical training, haptic feedback systems for medical simulations, and patient-specific surgical modeling. He pioneers innovations like robotic clinical examination tools and AI-driven surgical training platforms. His research emphasizes improving clinical education through immersive technologies and enhancing patient engagement via 3D imaging and VR. Publications Trends: Recent work focuses on surgical simulation validation (e.g., fracture models, anorectal exam tools), haptic interfaces for prosthetics and teleoperation, and VR applications in patient care. His articles highlight cross-disciplinary collaboration between engineering, computer science, and medicine. Affiliations & Leadership: Academic Co-director of Imperial’s MSc in Surgical Innovation, and collaborator with the Robotics Forum and Artificial Intelligence Network. He actively develops simulation-based training programs globally. Labs & Teams: Directs the SiMMS group, which specializes in modeling clinical processes and developing advanced simulators for surgical training. His team also explores distributed simulation for healthcare system optimization.

Anubhav Tripathiمشاهده پروفایل

استاد

• Biomedical Engineering
• Microfluidics
• Biomaterials
• +۷ مورد دیگر

Anubhav Tripathi is a Professor of Engineering and Director of Biomedical Engineering at Brown University's School of Engineering. His work focuses on developing scalable bioengineering solutions for diagnostics and life sciences, emphasizing microfluidics, biomaterials, and high-throughput screening. His lab, the Tripathi Biomedical Engineering Group, pioneers innovations in sample preparation, single-cell analysis, and point-of-care diagnostics. Research interests include understanding complex biomaterials through microfluidic platforms, proteomics, and lab-on-a-chip applications. Key technologies developed include electric-field-assisted sample preparation, automated liquid handling systems, and placental lipid bilayer models for molecular interaction studies. Recent work spans opioid quantification, amyloid-beta detection, and drought-resistant maize screening in Nepal. Publications highlight advancements in nucleic acid analysis, cell dissociation techniques, and diagnostic automation. His interdisciplinary approach bridges engineering principles with medical applications, addressing challenges in genetic testing, infectious disease detection, and reproductive health. Collaborative efforts extend to agricultural research, demonstrating versatility in applying microfluidic expertise to diverse domains. Contact details include his email Anubhav_Tripathi@brown.edu and lab website at tripathi_lab@brown.edu. His lab is located at 91 Waterman St, Providence, RI 02912.

John Kershawمشاهده پروفایل

استاد

• Forest Mensuration
• Forest Inventory Design
• Remote Sensing in Forestry
• +۵ مورد دیگر

John Kershaw is a Professor at the University of New Brunswick (UNB), holding the position of Associate Vice President Academic (Partnerships). He teaches Advanced Mensuration, Research Methods, and coordinates the Nanjing Forest University-UNB 3+1+1 program. His research focuses on forest mensuration, inventory design, LiDAR analysis, and growth modeling, with projects spanning North America, Asia, and beyond. He co-authored the widely used Forest Mensuration (5th Edition) . Key research interests include copula applications in forestry, large-scale forest inventory , and climate change impacts on forest dynamics. He collaborates internationally, with active projects in New Brunswick, Newfoundland, Maine, Florida, Oregon, Malaysia, Taiwan, Indonesia, and India. He serves on the editorial board of Forest Ecosystems and represents UNB in the Indo-Canadian Shastri Institute. Recent publications emphasize LiDAR-driven forest analysis , big data applications , and mixed-species stand modeling . His work bridges theoretical and applied forestry, focusing on sustainable management and technology integration. Outside academia, he is an avid outdoors enthusiast, engaging in hiking, canoeing, and mountain climbing.

• Southwest Archaeology
• Pueblo Cultures
• Ceramic Analysis
• +۱ مورد دیگر

James Allison is Professor of Anthropology at Brigham Young University, specializing in Southwest US archaeology and ceramic analysis. His research centers on Ancestral Pueblo communities and Fremont cultural dynamics. Key research interests: Early Pueblo village formation Ceramic production/exchange systems Kinship and social organization models Rocky Mountain indigenous archaeology Publications demonstrate methodological diversity including: Agent-based modeling of social networks 3D reconstruction from archival materials Reassessment of chronological frameworks Comprehensive regional syntheses (e.g., Crucible of Pueblos)

Karla Eitelمشاهده پروفایل

استاد

• Place-based Education
• STEM Education
• Environmental Education
• +۳ مورد دیگر

Dr. Karla Eitel is a Professor of Place-based Environmental Education and Director of the McCall Field Campus at the University of Idaho's College of Natural Resources. She holds a Ph.D. in Natural Resources (2007), M.Ed. in Curriculum & Instruction (2008), and M.S. in Conservation Social Sciences (2003). Her work focuses on culturally relevant STEM education, place-based learning methodologies, and technology integration in outdoor education contexts. She leads the McCall Outdoor Science School (MOSS) serving over 20,000 K-12 students annually through residential programs. Her outreach includes the MOSS Teacher Institute (277 teachers trained since 2009) and a decade-long service-learning partnership with Donnelly Elementary School's watershed restoration projects. Dr. Eitel has secured over $4.3M in grants including a 2023 NSF ITEST Award for drone-based STEM education with Native American students. Her research emphasizes energy literacy, biofuel education, and Indigenous science pedagogy, with publications in Research in Science Education and Journal of Sustainability Education .