Find Academic Supervisors

Prof. Dr. Thomas Hofmann is a Full Professor and Head of the Department of Computer Science at ETH Zurich since 2014. He also leads the Institute for Machine Learning. His research focuses on machine learning, deep learning, natural language understanding, and text understanding. Hofmann holds a Ph.D. from the University of Bonn (1997) and has held academic positions at Brown University (1999–2004) and TU Darmstadt. He transitioned to industry as Director of Engineering at Google (2006–2014), leading the Zurich R&D center, before returning to academia. He co-founded Recommind (2000) and 1plusX (Swiss marketing tech company), currently serving as Chief Scientist and board member at 1plusX. Education: Ph.D. in Computer Science, University of Bonn (1997) Postdoctoral Work: MIT (CBCL/AI Lab), UC Berkeley (EECS/ICSI) His research explores advanced machine learning techniques, including diffusion models, generative adversarial networks, and optimization dynamics. Hofmann’s entrepreneurial ventures reflect his focus on applying AI to real-world challenges, such as e-discovery and marketing technology. His work spans theoretical contributions (e.g., neural network training dynamics, continual learning) and applied innovations (e.g., image editing, portrait generation). Hofmann actively bridges academia and industry, influencing both research and commercial AI applications.

• Machine Learning
• Deep Learning
• Natural Language Understanding
• +3 more

Prof. Christian Holz is an Associate Professor at the Department of Computer Science and Deputy Head of the Institute of Intelligent Interactive Systems at ETH Zürich. His work focuses on advancing human-computer interaction through innovations in wearable technologies, mixed reality systems, and sensor-driven applications. Key research areas include motion capture, physiological signal processing, and adaptive user interfaces. Holz leads the SIPLab (siplab.ethz.ch), producing influential work at the intersection of computer science and biomedical engineering. His research explores cutting-edge topics such as egocentric vision systems, wearable health monitoring devices, and VR/AR applications. Recent studies investigate cybersickness detection via EEG, heart rate estimation from eye-tracking cameras, and scalable motion capture using inertial/UWB sensors. Holz's work emphasizes practical applications in healthcare, education, and human-centered computing. Publications reflect a strong focus on interdisciplinary solutions, combining machine learning with sensor data analysis. Notable contributions include the EgoSim multi-view simulator, WildPPG biomedical dataset, and MiBOT cardiovascular modulation device. His research bridges theoretical advancements with real-world usability in domains like emergency response training, chronic disease monitoring, and immersive education.

• Human-Computer Interaction
• Wearable Technologies
• Mixed Reality
• +4 more

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +5 more

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +3 more

Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.

• Computer Vision
• Deep Learning
• Robotics Perception
• +5 more

Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).

• High Dimensional Probability
• Random Matrices
• Mathematical Statistics
• +3 more

Marc Pollefeys is a Full Professor at the Department of Computer Science, ETH Zurich, and Director of the Microsoft Mixed Reality and AI Lab. His work focuses on advanced perception systems for HoloLens, 3D computer vision, robotics, and machine learning. Key contributions include automated 3D modeling from video, real-time reconstruction pipelines, and vision-based autonomous systems. Education: PhD from KU Leuven (1999) Previous Affiliation: Professor at UNC Chapel Hill Research interests span 3D reconstruction , computer vision , robotics , SLAM , augmented reality , and privacy-preserving mapping . His work often integrates geometric modeling , feature matching , and deep learning . Recent projects emphasize implicit 3D representations , open-vocabulary scene understanding , and robust estimation using neural-guided algorithms. Recent publications highlight advancements in neural implicit fields , line-based correspondence , and vision-language integration . Trends include hybrid point-line methods, differentiable RANSAC, and privacy-aware localization frameworks. Scientific recognition includes: IEEE Fellow (2012) David Marr Prize (ICCV 1998) DAGM Best Paper Award (1999) Advisees include current and alumni PhD students such as Yagız Aksoy, Federico Camposeco, and Sudipta Sinha. Collaborations span institutions like UNC Chapel Hill, ETH Zurich, and Microsoft Zurich. Research sponsors include Microsoft, Google, and European research initiatives.

• Computer Vision
• 3D Reconstruction
• Robotics
• +12 more

Margarita Boenig-Liptsin is a tenure-track Assistant Professor of Ethics, Technology and Society at ETH Zurich's Department of Humanities, Social and Political Sciences, a position she has held since September 2022. Trained in Science, Technology and Society with dual PhDs from Harvard University (History of Science) and Université Paris-Sorbonne (Philosophy), she brings interdisciplinary expertise to examining the relationship between technology, power, democracy, and ethics. Her research spans several interconnected areas focusing on transformations to human identity and citizenship in relation to information technologies across time and cultures. She investigates the social and political aspects of digital technologies, identity and selfhood in technological societies, ethics of technology, and comparative cultures of innovation. Her work uniquely bridges historical analysis with contemporary ethical concerns, examining how concepts like human dignity evolve alongside technological developments from punch cards to AI governance frameworks. Her recent publications (2023-2025) reveal a strong focus on AI ethics, data justice, and the philosophical implications of algorithmic systems. These works demonstrate her consistent interest in how technology shapes democratic processes, citizenship, and ethical frameworks. Her research shows progression from historical studies of computer literacy programs to contemporary analyses of generative AI's impact on knowledge production and everyday ethics. Her scientific recognition includes: NSF funding for her collaborative project on Silicon Valley's innovation imaginary Mozilla Foundation Responsible Computer Science Challenge grant (2018-2021) Academic Data Science Alliance support (2020-2022) Sorbonne Université's Paris IAS Chair on 'Major Changes' (2021-2022) At ETH Zurich, she leads research and teaching initiatives that build connections between engineering and social sciences. Previously at UC Berkeley, she directed the Human Contexts and Ethics Program, developing the Human Contexts and Ethics component for data science education. Her work consistently emphasizes translating social science theory for diverse audiences and connecting scholarly work to community needs in Zurich, Switzerland, and Europe. She teaches courses including 'Artificial Intelligence and Human Values' and 'Research in Ethics, Technology and Society' for the Fall 2025 semester.

• Social and political aspects of digital technologies
• Identity and citizenship in technological societies
• Ethics of Technology
• +5 more

Claudia R. Binder is Full Professor at EPFL's School of Architecture, Civil and Environmental Engineering, leading the Laboratory for Human-Environment Relations in Urban Systems (HERUS) since 2016. Previously, she held professorships at the University of Munich (2011-2016), University of Graz (2009-2011), and University of Zurich (2006-2009). She served as Dean of EPFL's ENAC School from 2020-2023 and holds advisory roles with Swiss federal institutions including the Mercator Foundation since 2024. Her academic foundation includes a Biochemistry degree and PhD in Environmental Sciences from ETH Zurich, followed by postdoctoral research at the University of Maryland. This interdisciplinary background underpins her research approach spanning natural and social sciences. Professor Binder's work centers on urban sustainability transitions, examining urban metabolism dynamics through systems science frameworks. She investigates energy-food-transport interdependencies in cities using transdisciplinary methods that integrate material flow analysis, spatial modeling, and socio-technical assessments. Her research particularly emphasizes regulatory mechanisms and transformation drivers in human-environment systems, with case studies across Swiss and global urban contexts. Recent publications reveal evolving focus from foundational urban metabolism studies toward actionable transition strategies. Her 2024-2025 work increasingly addresses social tipping dynamics, circular decarbonization, and spatially explicit waste management, demonstrating methodological innovation through geo-referenced material flow analysis and participatory backcasting frameworks. Key thematic clusters include energy innovation diffusion, plastic waste governance, and demand-side flexibility in residential systems. She actively mentors 7 current PhD candidates while supervising 11 graduates since 2018, with research spanning urban metabolism modeling, sustainability assessment, and transition governance. Her leadership extends to Swiss National Science Foundation committees and National Research Program 71 on energy consumption management. At EPFL, she directs the HERUS laboratory which develops the Sustainability Solution Space methodology for urban assessment. The lab operates at the intersection of data science, environmental engineering, and social theory, maintaining strong field connections in Switzerland, Indonesia, and Germany for empirical validation of transition models.

• Urban Sustainability
• Transition Research
• Urban Metabolism
• +5 more

Dima Damen is a Professor of Computer Vision at the School of Computer Science, University of Bristol, where she leads the Machine Learning and Computer Vision Group. She also serves as a Senior Research Scientist at Google DeepMind. As an EPSRC Early Career Fellow (2020-2025) and a Fellow of ELLIS for Europe, her research focuses on advancing computer vision, particularly in egocentric (first-person) vision, video understanding, and action recognition. Her educational background and professional journey have positioned her as a leader in the field of computer vision, with a particular emphasis on understanding human activities from wearable cameras. She has received numerous awards including Best Paper at ACCV 2024 and Outstanding Paper at ICASSP 2021. Professor Damen's research interests span multiple areas of computer vision and machine learning. She specializes in egocentric vision, where she has made significant contributions to understanding human activities from first-person perspectives. Her work explores video understanding, action recognition, hand-object interactions, and the development of vision-language models that can interpret and generate instructions from visual data. She has pioneered approaches to unique video captioning, long video understanding through active memory representations, and spatial reasoning from egocentric videos. Her research often bridges the gap between theoretical computer vision and practical applications in human-centered AI. Her recent publications demonstrate a strong focus on egocentric vision, with papers like "AMEGO: Active Memory from long EGOcentric videos" (ECCV 2024) and "HOI-Ref: Hand-Object Interaction Referral in Egocentric Vision" (2024) advancing the state of the art in understanding long-form first-person videos. She has also contributed to vision-language models with works like "ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions" (CVPR 2025) and "It's Just Another Day: Unique Video Captioning by Discriminitave Prompting" (ACCV 2024, Best Paper). Her research shows a consistent trajectory toward building systems that can understand human activities in natural environments with human-like capabilities. Professor Damen has received significant recognition for her work, including: EPSRC Early Career Fellow (2020-2025) ELLIS Fellow for Europe (Nov 2024) Best Paper at ACCV 2024 Outstanding Paper at ICASSP 2021 (awarded to only 3 out of 1700 papers) Outstanding Reviewer for CVPR 2020 and 2021 Program Chair for ICCV 2021 She has successfully advised numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. Her group has secured significant research funding including the EPSRC Programme Grant Visual AI and the EPSRC UMPIRE grant. She actively collaborates with industry partners including Google DeepMind, Adobe, and Meta, ensuring her research has practical impact. Professor Damen leads the Machine Learning and Computer Vision Group at the University of Bristol, which focuses on egocentric vision, video understanding, and the development of vision-language models. The group has created influential datasets like EPIC-KITCHENS, which has become a standard benchmark in egocentric vision research. Her team regularly participates in and organizes workshops at major computer vision conferences including CVPR, ICCV, and ECCV.

• Computer Vision
• Egocentric Vision
• Machine Learning
• +5 more

Dr. Cesar Dario Cadena Lerma is a Lecturer at the Department of Mechanical and Process Engineering and a tenured Senior Scientist at the Institute of Robotics and Intelligent Systems (IRIS) at ETH Zurich. He leads the Perception, Mapping and Navigation team within the Robotics Systems Lab (RSL), co-founded and directs the ETH RobotX initiative focusing on educational robotics, and previously held roles at ETH Zurich's Autonomous Systems Lab, University of Adelaide, and George Mason University. His research focuses on robotics perception, particularly in SLAM (Simultaneous Localization and Mapping), semantic scene understanding, and robust perception systems for dynamic environments. Education: PhD in Computer Science and System Engineering from the University of Zaragoza, followed by postdoctoral research at George Mason University and The University of Adelaide. Professional roles include managing director of ETH RobotX and leadership in multi-modal mapping frameworks like maplab 2.0. Research interests emphasize integrating perception and learning in robotics, with a focus on semantic mapping, data association, place recognition, and navigation in unstructured environments. His work bridges traditional SLAM techniques with modern deep learning approaches to create robust, modular systems. Key contributions include the PHASER registration algorithm, SCIM obstacle avoidance framework, and C-Blox dense mapping system. Awards include the Best Paper Award at the 2017 IEEE International Symposium on Safety, Security, and Rescue Robotics. His articles span topics like semantic pointcloud filtering, volumetric mapping, and embodied domain adaptation. He collaborates widely, with over 50 peer-reviewed publications in top venues such as IEEE Robotics and Automation Letters and International Journal of Robotics Research.

• Robotics
• Computer Vision
• SLAM (Simultaneous Localization and Mapping)
• +3 more

Frédéric Kaplan serves as Director of the College of Humanities at École Polytechnique Fédérale de Lausanne (EPFL), where he holds the Chair of Digital Humanities. He also serves as President of the Time Machine Organisation, a nonprofit entity comprising over 600 institutions. His academic appointments span multiple departments including the Digital Humanities Laboratory (DHLAB), School of Architecture (SAR), and School of Humanities (SODH), demonstrating his interdisciplinary leadership across EPFL's academic structure. Dr. Kaplan's research focuses on the intersection of computational methods and humanities, particularly in historical urban analysis, cultural heritage digitization, and the development of the Mirror World concept. His work bridges computational techniques with historical scholarship, creating new methodologies for analyzing historical documents, maps, and urban structures through advanced digital tools. His research has significant implications for how we understand and reconstruct historical urban environments and cultural heritage. His publication record reveals a strong emphasis on computational approaches to historical data, with recent work focusing on LLM applications for historical cadastre navigation, historical map analysis through deformation patterns, 4D city modeling, and language technology applications for historical document processing. This research trajectory demonstrates an evolving focus from basic digitization toward sophisticated analytical frameworks that extract deeper historical insights from digital representations. Kaplan has supervised numerous doctoral students whose work spans digital heritage applications, historical document analysis, computational cartography, and language technology. His research has been supported through multiple institutional frameworks at EPFL and has resulted in practical applications demonstrated through exhibitions at major institutions including the Venice Architecture Biennale, Grand Palais, Centre Pompidou in Paris, and the Museum of Modern Art in New York. He leads the Digital Humanities Laboratory (DHLAB) which serves as a nexus for computational approaches to humanities research. The lab focuses on developing methodologies for historical data analysis, creating digital tools for cultural heritage institutions, and exploring the theoretical implications of computational approaches to historical scholarship. The lab's work with the Time Machine Organisation represents one of the most ambitious efforts to create comprehensive digital reconstructions of historical urban environments.

• Digital Humanities
• Computational History
• Historical Map Analysis
• +4 more

Varun Jog is Professor of Information Theory and Statistics in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, Faculty of Mathematics. Previously, he served as Assistant Professor at the University of Wisconsin-Madison (2016-2020) and at the University of Cambridge (2021-2024). His academic background includes a B.Tech. in Electrical Engineering from IIT Bombay (2010) and a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015). Professor Jog's research centers on fundamental questions at the intersection of information theory, statistics, and machine learning. He develops theoretical frameworks for statistical inference under constraints such as limited communication and privacy requirements, with significant contributions to hypothesis testing, differential privacy, adversarial risk analysis, and information-theoretic inequalities. His work bridges abstract mathematical principles with practical applications in data science and robust machine learning. Recent publications demonstrate a concentrated focus on distributed inference systems, particularly examining sample complexity limits in hypothesis testing under information constraints and privacy-preserving mechanisms. His research consistently reveals deep connections between information theory and statistical learning, with increasing emphasis on adversarial robustness and foundational inequalities. His scientific contributions have earned recognition through prestigious awards: NSF-CAREER Award (2020) R. Narasimhan Memorial Lecture Award (2020) Eli Jury Award from UC Berkeley EECS Department (2015) Jack Keil Wolf student paper award at ISIT (2015) Professor Jog maintains an active research group, currently supervising one PhD student while having graduated four PhD students and four Master's students. His mentorship extends to postdoctoral researchers including Amir Asadi, Deepanshu Vasal, and Andre Wibisono. Research funding includes the competitive NSF-CAREER grant. He co-organizes the Cambridge Information Theory Seminar, fostering academic exchange and collaboration within the theoretical research community.

• Information Theory
• Statistics
• Machine Learning
• +1 more

Jan Skaloud serves as an Adjunct Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He holds positions across multiple departments including SSIE (Institute of Earth Surface Dynamics), EDCE (Doctoral Program in Environmental Sciences and Engineering), and leads the Earth Sensing and Observation (ESO) Lab. His office is located at GC C2 397 in the EPFL campus in Lausanne, Switzerland. Dr. Skaloud's research expertise spans satellite positioning, inertial and integrated navigation systems, sensor orientation and calibration, attitude determination, mobile mapping, airborne laser scanning, and Kalman filtering techniques. His work bridges theoretical development with practical applications in UAV navigation, photogrammetry, and remote sensing. He teaches across three EPFL sections and two faculties, demonstrating his interdisciplinary approach to education. His publication record shows consistent contributions to the field, with recent work (2023-2025) focusing on vehicle dynamic model-based navigation for various UAV platforms, including delta-wing and fixed-wing drones. His research demonstrates a clear trajectory toward increasingly sophisticated navigation systems that integrate aerodynamic modeling with traditional sensor fusion approaches. This trend reflects the growing importance of model-based navigation in achieving higher precision and autonomy in UAV operations. 2021: Samuel Gamble Award for career contribution in photogrammetry & sensing (ISPRS) 2020: U.V. Helava Award for best paper in ISPRS Journal (2016-2019) 2017: Best Demo Award at IEEE International Workshop on Metrology & Aerospace 2014: Hansa Luftbild Award for best paper in PFG journal 2012: Karl Kraus Medal for best textbook in Photogrammetry 2009: GNSS Leader to Watch Innovation Award (GPS World) Dr. Skaloud has supervised numerous PhD students whose work focuses on advanced navigation systems, sensor calibration, and UAV applications. His research has received funding for projects involving direct georeferencing, mobile mapping systems, and UAV-based search and rescue operations. The ESO lab he directs serves as a hub for cutting-edge research in Earth observation technologies. The Earth Sensing and Observation Lab under Dr. Skaloud's direction brings together researchers working on navigation systems, sensor integration, and data processing techniques for geospatial applications. The lab maintains strong connections with industry partners and international research organizations, facilitating technology transfer and collaborative research projects.

• Satellite positioning
• Inertial navigation
• Integrated navigation systems
• +9 more

Brian W. Pogue, Ph.D., is the Robert A. Pritzker Chair in Biomedical Engineering at Dartmouth College's Thayer School of Engineering, with a joint appointment as an Honorary Fellow in Medical Physics at the University of Wisconsin-Madison. His academic background includes a Ph.D. in Medical/Nuclear Physics from McMaster University and a Research Fellowship at Harvard Medical School's Wellman Center for Photomedicine. He has led significant administrative roles, including Dean of Graduate Studies at Dartmouth (2008–2012) and Chair of Medical Physics at Wisconsin (2022–2025). Research Focus : Dr. Pogue pioneers Optics in Medicine , specializing in cancer imaging, photodynamic therapy, and surgical guidance. His work integrates fluorescence imaging, radiation therapy monitoring, and molecular diagnostics to improve cancer treatment precision. Key innovations include Cherenkov imaging for radiotherapy dosimetry and hypoxia-sensitive probes for tumor resection. Publication Trends : Recent articles (2023–2025) emphasize real-time surgical guidance, hypoxia quantification, and multimodal imaging systems. Dominant themes include fluorescence tomography, radiation dosimetry, and low-cost diagnostic devices, reflecting a translational focus from preclinical validation to clinical applications. Awards & Honors : Fellow, Optica (formerly OSA) Fellow, American Institute for Medical and Biological Engineering (AIMBE) Fellow, American Association of Physicists in Medicine (AAPM) Fellow, SPIE (International Society for Optics and Photonics) Funding & Innovation : Continuously funded by the NIH since 2001 ($52M+ total), Dr. Pogue founded three startups: DoseOptics LLC (radiotherapy dose imaging) and Hypoxia Surgical LLC (tissue hypoxia cameras), bridging academic research to clinical tools.

• Optics in Medicine
• Cancer Imaging
• Photodynamic Therapy
• +5 more