Find Academic Supervisors

• Mass Spectrometry
• Proteomics
• Metabolomics
• +5 more

Dr. Ljiljana (Lili) Paša-Tolić is a distinguished Lab Fellow and Lead Scientist for Visual Proteomics at Pacific Northwest National Laboratory (PNNL), where she works within the Environmental Molecular Sciences Division and the Environmental Molecular Sciences Laboratory (EMSL) user program. She brings world-leading expertise in native-state and top-down proteomics, mass spectrometry, and the Biomolecular Pathways Integrated Research Platform, combining these with Functional Bioimaging and Cell Signaling platforms to develop transformational capabilities for dynamic spatio-temporally resolved proteomic imaging in cells. Shipley Capturing Federal Business Course (2013) Emerging Leader Program (2012) Management Skills Development Program (2003) Postdoc, National High Magnetic Field Laboratory (1995–1997) Postdoc, PNNL (1993–1995) PhD in Chemistry, University of Zagreb (1992) MS in Theoretical and Physical-Organic Chemistry, University of Zagreb (1989) BS in Chemistry, University of Zagreb (1986) Dr. Paša-Tolić specializes in developing sophisticated analytical methods with emphasis on Fourier transform mass spectrometry and micro-separations. Her research focuses on applying these techniques to accurately quantify spatiotemporal changes in protein (metabolite) abundance, identity, and activity. Her work bridges the gap between advanced instrumentation development and biological applications, particularly in environmental systems, microbial communities, and plant-microbe interactions. She has pioneered approaches for single-cell metabolomics, top-down proteomics, and mass spectrometry imaging that have transformed how researchers study complex biological systems at unprecedented resolution. The trend in Dr. Paša-Tolić's recent publications demonstrates her leadership in advancing mass spectrometry technologies while applying them to increasingly complex biological questions. Her work spans environmental science, microbiology, plant biology, and immunology, showing how fundamental advances in analytical methodology can be leveraged across diverse research domains. Key themes include single-cell analysis, interlaboratory standardization, top-down proteomics, and the development of novel instrumentation approaches that push the boundaries of detection sensitivity and spatial resolution. The Analytical Scientist Power List (2019) Spectrometry Global Impact Award (2015) Director's Award, EMSL (2010) Systems Biology Fellows Mentor Award (2007) Outstanding Merit Award, International Immunology (2007) Key Contributor Award, PNNL (2002, 2003) Outstanding Performance Award, PNNL (1998, 1999, 2000) International Institute of Quantum Chemistry and Solid-State Theory Award (1988) As a leader in her field, Dr. Paša-Tolić has served in numerous professional capacities including Treasurer of the American Society for Mass Spectrometry (2020–2022), Editorial Board Member for the Journal of the American Society for Mass Spectrometry (2017–Present), and Member at Large on the Board of Directors for the Consortium for Top-Down Proteomics (2012–Present). She has organized and lectured at the 'Mass Spectrometry in Biology and Medicine' summer school in Dubrovnik, Croatia since 2007, demonstrating her commitment to training the next generation of scientists. Her research has been supported by multiple federal agencies including the Department of Energy, National Institutes of Health, and National Science Foundation. Dr. Paša-Tolić leads a dynamic research team at PNNL focused on visual proteomics, which combines advanced mass spectrometry with imaging techniques to study biological systems at unprecedented resolution. Her group works at the intersection of the Functional and Systems Biology group, the Biomolecular Pathways Integrated Research Platform, and the Functional Bioimaging platform, creating a synergistic environment for innovation. The team has developed novel capabilities for single-cell analysis, top-down proteomics, and mass spectrometry imaging that are being applied to diverse research areas from environmental systems to human health.

Klaus BöhmView profile

Professor

• Geoinformatics
• Spatial Data Analysis
• Explainable Artificial Intelligence
• +4 more

Klaus Böhm serves as a Professor at Mainz University of Applied Sciences within the School of Engineering, affiliated with the i3mainz institute (Institute for Spatial Information and Surveying Technology). His research bridges geospatial technologies with artificial intelligence, focusing on practical applications in urban planning, healthcare, and educational environments through projects like BAM (Big Data Analytics) and TOPML (Machine Learning). His primary research domains include geospatial explainable AI (GeoXAI), mixed reality decision support systems, and health informatics applications. He pioneers methods for visualizing uncertainty in AI models, developing geoparsing techniques using LLMs, and creating spatial navigation frameworks for VR environments. His work consistently addresses real-world challenges in elderly mobility, smart city infrastructure, and medical education through spatial analytics. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Integration of XAI with geospatial data for transparent decision-making in urban planning, (2) Development of mixed reality tools for STEM education and dermatology training, and (3) Spatio-temporal correlation analysis for smart city applications like parking optimization and public transport accessibility. His methodology emphasizes interactive visualization of complex spatial relationships and uncertainty quantification. Dr. Böhm leads multiple funded research initiatives including AIMR (AI-based decision support in mixed reality), RAFVINIERT (spatial intelligence for senior care), and FlexGeo (geo-service integration). These projects demonstrate sustained grant acquisition capability across EU and national funding programs, typically involving interdisciplinary teams from computer science, urban planning, and healthcare sectors. As a core member of the i3mainz research institute, he contributes to Germany's geospatial technology ecosystem through collaborative projects with municipal authorities and healthcare providers. The institute's work focuses on translating academic research into operational tools for spatial data infrastructure, particularly in environmental monitoring and public service optimization contexts.

• Computer Vision
• Object Detection
• Video Object Detection
• +7 more

Daniel Cores Costa is an Associate Professor of Computer Science and Artificial Intelligence at the University of Santiago de Compostela (USC) and a researcher at the Research Center on Intelligent Technologies (CiTIUS). He completed his PhD in Research in Technologies in November 2022 with a dissertation on 'Spatio-temporal convolutional neural networks for video object detection' under the supervision of Victor Manuel Brea Sanchez. Dr. Cores Costa's research focuses on computer vision with specialized expertise in several interconnected areas: Video object detection leveraging spatio-temporal information Small object detection challenges Advancing few-shot learning techniques for object detection with limited training data Developing open-vocabulary and category-free detection approaches Practical applications in medical imaging (particularly COVID-19 diagnosis) and UAV technology His recent publications (2023-2025) demonstrate a strong research trajectory with multiple first-author papers in top venues including ICCV, BMVC, and CAIP, as well as high-impact journals. Dr. Cores Costa's work consistently addresses fundamental challenges in object detection while developing practical solutions for real-world applications across various domains. Key contributions include: Development of the Downsampling GAN for small object data augmentation Innovative approaches to few-shot object detection through pseudo-label mining (xPAND) Pioneering work on category-free detection using open-vocabulary models Creation of TVBench, a new temporal benchmark for video-language models Advancements in spatiotemporal tubelet feature aggregation for small object detection Dr. Cores Costa actively collaborates with researchers both within USC and internationally, as evidenced by his diverse co-authorship patterns. His research bridges theoretical computer vision with practical implementations, making significant contributions to both academic knowledge and real-world applications in healthcare, autonomous systems, and visual recognition technologies.

• Applied Remote Sensing
• Spectral Signature Processing and Analysis
• Environmental Science
• +1 more

Brian Becker is a Professor in the Department of Geography and Environmental Studies and affiliated with the Institute for Great Lakes Research. His expertise bridges remote sensing technology and environmental science, with specialized focus on wetland ecology and aquatic system monitoring through advanced spectral analysis techniques. His academic foundation includes: Ph.D. from Michigan State University (2002) M.S. from University of Illinois (1993) B.S. from Eastern Illinois University (1989) Professor Becker's research program centers on developing and applying remote sensing methodologies for environmental assessment. He pioneers techniques for water quality parameter estimation (particularly CDOM and chlorophyll-a) in optically complex inland waters, utilizes hyperspectral data for wetland characterization, and creates algorithms for invasive species mapping. His work integrates field spectroscopy with satellite imagery to address challenges in freshwater ecosystems, with emphasis on spatio-temporal dynamics and algorithm validation for practical environmental management applications. Analysis of his 14 publications (2005-2018) reveals consistent innovation in remote sensing of aquatic environments. Key contributions include developing semi-analytical models for CDOM in shallow waters, optimizing spectral band selection for wetland monitoring, and creating spatial analysis techniques for large raster datasets. His research demonstrates strong methodological rigor while addressing real-world environmental challenges across the Great Lakes region and international sites like China's West Lake. As an educator, Professor Becker teaches Geographic Information Sciences, Remote Sensing, Image Processing, Environmental Science, and CAD Mapping for GIS. His affiliation with the Institute for Great Lakes Research provides students access to interdisciplinary freshwater research opportunities. While specific current grant details aren't provided, his extensive publication record in high-impact journals indicates sustained research activity with potential for student involvement in field campaigns, algorithm development, and environmental monitoring projects.

Arne SolliView profile

Associate Professor

• Historical Demography
• Urban History
• Historical GIS
• +5 more

Arne Solli is an Associate Professor in History at the Department of Archaeology, History, Cultural Studies and Religion, University of Bergen. His academic work bridges historical scholarship with digital methodologies, with a particular focus on historical demography, urban history, and GIS applications in historical research. Dr. Solli's research spans multiple interconnected domains including historical demography, urban development of Bergen, historical GIS applications, and the evolution of property registration systems. His work often combines microhistorical approaches with macrohistorical perspectives, creating innovative frameworks for understanding historical population patterns. He has made significant contributions to the study of 19th century cholera epidemics in Norway, examining how factors like topography, social status, and water supply affected mortality rates. His research demonstrates a consistent focus on spatial and temporal analysis of historical phenomena, with particular attention to Bergen's urban development over centuries. Dr. Solli has pioneered the integration of GIS technology with historical research through his development of BerGIS, a map portal for historical maps of Bergen that has become an essential resource for urban historians. Historical demography and population structures Urban history and development of Bergen Historical GIS applications Property registration systems through time 19th century Norwegian social and health history Cholera epidemiology and public health Dr. Solli has received recognition for his innovative approaches to historical research, particularly in developing digital tools that transform how historical data can be analyzed and visualized. His work on BerGIS has provided valuable resources for researchers studying urban development patterns over time. As an educator, Dr. Solli has contributed to the development of digital history methodologies and has supervised numerous students in historical research. His publications span from the 1990s to present, demonstrating a sustained commitment to advancing historical scholarship through technological innovation.

• Surgical Robotics
• Medical Image Computing
• Computer Vision
• +4 more

Danail Stoyanov is a Professor of Robot Vision at University College London's Department of Computer Science, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies. He serves as Co-Director of the UCL Hawkes Institute and previously directed the Wellcome/EPSRC Centre for Interventional and Surgical Sciences until September 2024. His academic journey at UCL progressed from Lecturer (2014) to Senior Lecturer (2015), Associate Professor (2016), and ultimately Professor of Robot Vision (2018). His educational background includes a Bachelor of Engineering in Computer Systems and Electronics from King's College London (2001) and a PhD in Computer Science specializing in medical image computing from Imperial College London (2006). His progression through Royal Academy of Engineering Research Fellowships at both Imperial College and UCL demonstrates his exceptional research trajectory. Professor Stoyanov's research focuses on developing surgical robotics and artificial intelligence to improve minimally invasive interventions. His work spans vision-based surgical guidance systems, computational biophotonic imaging modalities, and quantitative measurements during robotic-assisted procedures. His research bridges computer vision, medical robotics, and clinical applications with emphasis on gastrointestinal surgery, pituitary surgery, and ophthalmic interventions. Analysis of his recent publications reveals a strong trend toward real-time AI applications in surgical contexts, with particular emphasis on stereo vision, depth estimation, surgical workflow recognition, and error detection systems. His work increasingly incorporates foundation models and self-supervised learning approaches while maintaining clinical relevance across multiple surgical specialties including neurosurgery, ophthalmology, and gastrointestinal procedures. His distinguished recognition includes multiple prestigious fellowships: Fellow of the Royal Academy of Engineering (2022) Fellow of the Institute of Engineering and Technology (2020) Fellow of the MICCAI Society (2023) Fellow of the Institute of Electrical and Electronics Engineers (2024) Fellow of the Academy of Medical Sciences (2025) As an educator, Stoyanov founded and directs the MSc Robotics and Computation program while teaching the advanced Robotic Systems Engineering course. His leadership extends to conference organization including roles as Programme Chair for IPCAI (2014), General Chair for IPCAI (2017-2020), and Programme Co-Chair for MICCAI (2018, 2020). He maintains an active industry collaboration as Chief Scientist for Digital Technologies at Medtronic since 2016. His SRV Group at UCL serves as the primary research hub driving innovation in surgical robotics and vision, with strong connections to the Wellcome/EPSRC Centre for Interventional and Surgical Sciences and the UCL Hawkes Institute which he now co-directs. The group maintains active collaborations with clinical partners and industry including Medtronic, facilitating rapid translation of research into clinical practice.

Yu CaoView profile

Professor

• Medical Imaging
• Multimodal Deep Learning
• Computer Vision
• +5 more

Yu Cao, Ph.D., is a tenured full professor at the Miner School of Computer & Information Science, University of Massachusetts Lowell, where he also serves as Director of the UMass Center for Digital Health. His academic journey includes faculty positions at The University of Tennessee (2010-2013) and California State University (2007-2010), followed by a Visiting Fellowship at Mayo Clinic. Dr. Cao holds a Ph.D. in Computer Science from Iowa State University (2007), where he also earned his M.S. (2005), along with an M.Eng. from Huazhong University of Science and Technology (2000) and a B.Eng. from Harbin Engineering University (1997), all in Computer Science. His educational background includes: Visiting Fellow, Biomedical Engineering, Mayo Clinic (2007) Ph.D., Computer Science, Iowa State University (2007) M.S., Computer Science, Iowa State University (2005) M.Eng., Computer Science, Huazhong University of Science and Technology, China (2000) B.Eng., Computer Science, Harbin Engineering University, China (1997) Dr. Cao's research spans multiple domains of knowledge discovery from complex data, with particular focus on Medical Imaging, Multimodal Deep Learning, Computer Vision, Artificial Intelligence, and Digital Health. His work emphasizes intelligent, multi-modal, and data-intensive medical image analysis and retrieval; motion tracking, analyzing, and visualization; and intelligent data analysis for electronic medical records and pervasive healthcare monitoring. His research program has produced over 150 peer-reviewed publications with more than 8,000 citations and an h-index of 40+, appearing in top venues including IEEE CVPR, IJCAI, ICLR, ACM MM, and IEEE ICME, as well as prestigious journals like IEEE TNNLS, TBME, TPAMI, TSC, and JBHI. Analysis of Dr. Cao's recent publications reveals a strong focus on applying deep learning techniques to medical imaging problems, particularly in endoscopy and diagnostic imaging. His work spans multiple subfields including polyp detection in colonoscopy videos, tuberculosis detection in chest X-rays, diabetic retinopathy analysis, and food recognition systems for dietary assessment. The publications demonstrate a consistent pattern of applying cutting-edge AI techniques to solve practical healthcare challenges, with increasing emphasis on multimodal approaches and real-world deployment considerations. Dr. Cao has received numerous accolades for his work, including Best Paper Awards from ACM/IEEE CHASE (2023), IEEE IJCNN (2020), and IEEE NAS (2015). His paper was the most downloaded from Smart Health Journal by Elsevier (2017-2018), and he was recognized for having the highest number of peer-reviewed publications among faculty members in the College of Sciences (2017-2018). He was named a Senior Member of IEEE in 2013, an honor granted to only 8% of IEEE members worldwide. His research has been supported by dozens of NSF/NIH/Industry sponsored grants totaling approximately $10 million. Notable projects include NIH/NSF Award #1R01EB021900 ($1.29 million) as Principal Investigator, NSF Award #1547428 ($500,000) as Co-PI, and NSF Award #1541434 ($1 million) as Co-PI. Dr. Cao has successfully mentored numerous graduate and undergraduate students, with current advisees working on medical image retrieval, data analysis for body sensor networks, and motion tracking and visualization. He has served on organizing committees for over 30 international conferences and workshops, demonstrating strong leadership in the academic community. As Director of the UMass Center for Digital Health, Dr. Cao leads a multidisciplinary team focused on developing innovative solutions for healthcare challenges using digital technologies. His lab maintains active collaborations with medical institutions including Mayo Clinic, Harvard Medical School, and Erlanger Hospital, facilitating the translation of research findings into clinical practice. The center's work spans multiple research areas including medical video/image analysis, motion tracking and visualization, context-aware data analysis for body area sensor networks, and risk analysis for acute coronary syndromes.