Find Academic Supervisors

• Multispectral colour imaging
• Print and Image quality
• Colorimetric device characterization
• +3 more

Jon Yngve Hardeberg is a Professor at the Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He is a member of the Norwegian Colour and Visual Computing Laboratory, where he supervises students, manages international programs, and leads research projects. His career spans roles at Gjøvik University College and NTNU, with visiting positions at institutions like the University of Washington and French universities. MSc in Signal Processing from NTNU PhD in Signal and Image Processing from École Nationale Supérieure des Télécommunications, Paris Research Interests: Multispectral colour imaging, cultural heritage documentation, medical imaging, and material appearance modeling. His work integrates optics, signal processing, and computational methods for applications like gilded surface analysis, 3D printing, and translucency perception. Recent publications focus on advanced imaging techniques (e.g., spectral reconstruction, reflectance transformation imaging), material analysis (pigment mapping, skin gloss), and 3D digitization. Keywords span Computer Science , Optics , and Medical Imaging , with subfields like Hyperspectral Imaging and Image Quality Metrics . Scientific Awards: Fulbright Scholar SPIE Senior Member Advising and Grants: Supervised 46 master students and 12 PhD graduates; currently guides 4 postdocs and 5 PhD candidates. Led major projects such as MUVApp (NOK 24.9M), IQ-MED (NOK 16.2M), and HyPerCept (NOK 33.6M), funded by the Research Council of Norway and EU. Labs/Teams: Director of The Norwegian Color Research Laboratory (2005-2012), member of the International Commission on Illumination (CIE) Division 8, and founder of Forum Farge (Norway’s color association). Collaborates with institutions like the Swiss National Museum and Edvard Munch Museum.

Rodrigo ObandoView profile

Associate Professor

• Complex Systems Modeling
• Cellular Automata
• Data Mining
• +18 more

Rodrigo Obando serves as an Associate Professor in the Computer Science department at Columbus State University's TSYS School of Computer Science. His academic career spans theoretical and applied computer science with expertise across multiple computational domains. Education Background: Ph.D. in Electrical Engineering, Old Dominion University, Norfolk, VA M.E. in Electrical Engineering, Old Dominion University, Norfolk, VA B.S. in Electronics Engineering, Instituto Tecnologico de Costa Rica, Cartago, Costa Rica Dr. Obando's research spans multiple specialized areas with significant contributions to Complex Systems modeling using Cellular Automata, including Rule Space Classification and Transformation. His work in Data Mining and Warehousing incorporates advanced visualization techniques for diverse data types. He has developed expertise in Fault-Tolerant Computing systems, distributed computing architectures, and performance analysis methodologies. Additional research interests include 3D modeling, Computer Graphics, and VRML technologies. His visualization research encompasses both general Information Visualization techniques and specialized applications for accounting, financial, and marketing data analysis. Theoretical aspects of his work include Petri Nets modeling, Graph Theory applications, Network Modeling approaches, and Boolean Algebras with particular emphasis on Monotone Boolean Functions. Dr. Obando actively mentors students through the ACM Student Chapter and Campus Nerds organizations. His teaching portfolio includes foundational courses like Data Structures and Object-Oriented Design, as well as specialized courses in Computer Graphics, Game Programming, and Programming Challenges. His educational approach emphasizes practical application of theoretical concepts through hands-on programming assignments and projects, with a strong focus on academic integrity and professional standards in computer science.

Daniel HuttenlocherView profile

Professor

• Computer Vision
• Social Networks
• Autonomous Vehicles
• +4 more

Daniel Huttenlocher is the John P. and Rilla Neafsey Professor of Computing, Information Science, and Business at Cornell University, where he serves as Dean of Computing and Information Science. Concurrently, he holds the position of Dean at MIT's Stephen A. Schwarzman College of Computing and is Vice Provost of Cornell Tech. His academic roles span Cornell's Computer Science Department and Johnson Graduate School of Management. Huttenlocher's research encompasses diverse domains including computer vision (image matching, object recognition, video analysis), social networks (governance models, link prediction, community dynamics), autonomous systems (DARPA Urban Challenge), geometric algorithms , and interactive document systems . His work bridges theoretical algorithms and practical applications, with recent advancements in structured models for visual recognition and large-scale network analysis. His publications (2010-2006) demonstrate a consistent focus on algorithmic innovation in computer vision and social computing. Recent works emphasize real-world applications like location recognition, social media governance, and image restoration, employing machine learning, probabilistic modeling, and large-scale data mining techniques across diverse visual and network datasets. While no awards are listed, Huttenlocher co-chaired major conferences (CVPR 2006/2009) and serves on the MacArthur Foundation Board. He has developed educational tools like the CoNote annotation system to foster collaborative learning and chaired Cornell's computing initiatives leading to the Faculty of Computing and Information Science.

El Mahi AbderrahimView profile

Professor

• Composite Materials
• Mechanical Behavior of Materials
• Acoustic Emission Monitoring
• +5 more

Abderrahim El Mahi is a Professor at Le Mans University , affiliated with the Laboratoire d'Acoustique de l'Université du Maine (LAUM) . His research focuses on optimizing mechanical properties of composite materials, characterizing damage evolution, and predicting lifespan for industrial reliability. Current affiliations: LAUM (Materials Team) Key research areas: Biocomposites, Acoustic Emission Monitoring, 3D Printing His recent publications emphasize bio-based composites , auxetic core structures , and acoustic emission analysis for damage detection. Work spans experimental and computational studies under varying environmental conditions. Research trends include: Damping behavior in 3D-printed materials Multi-scale mechanical analysis Hybrid composites (flax-glass/epoxy) Water aging effects on bio-composites Poisson’s ratio influence in anti-trichiral designs

• 3D Object Retrieval
• Biometrics
• Shape Analysis
• +5 more

Theoharis Theoharis is a Professor at the Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU Trondheim), where he leads research in shape analysis and visual computing. He is a core member of the Visual Computing Lab , focusing on 3D object retrieval, biometrics, and cultural heritage applications. Research Interests : 3D Object Retrieval and Registration Biometric Identification (facial data, somatotype analysis) Shape Segmentation and Cultural Heritage Preservation Medical Imaging and Robotics Notable Projects : PRESIOUS : EU-funded initiative for cultural heritage using visual computing D4FLY : On-the-move biometric verification CHANGE ITN : Monitoring cultural heritage change with 15 PhD theses Publications highlight trends in: 3D shape descriptors (QUICCI, radial intersection count) Deep learning for 3D reconstruction and registration Applications in cultural heritage, medical imaging, and geophysics Education : BSc in Computer Science MSc in Computation DPhil in Computer Graphics and Parallel Processing Advising includes doctoral students like Evdokia Saiti (2023) and Peder Bergebakken Sundt (2024), with contributions to SHREC'16 and 3DOR2022 symposiums. Labs & Collaborations : Visual Computing Lab (NTNU) NTNU's Computing unit European Union FP7-ICT (D3.1 Deliverable)

Janusz KonradView profile

Professor

• Computer Vision
• Visual sensor networks
• Image and video processing
• +6 more

Professor Janusz Konrad is a distinguished faculty member in the Department of Electrical and Computer Engineering at Boston University's College of Engineering, where he has served since 1992 with increasing responsibilities, becoming a full Professor in 2008. He leads the Visual Information Processing (VIP) laboratory and is a key member of the Machine Learning, Information and Data Sciences (MINDS) group, the Center for Information and Systems Engineering (CISE), and the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. Dr. Konrad earned his PhD from McGill University in 1989 and his M.Eng. from the Technical University of Szczecin, Poland in 1980. His academic journey began as a Lecturer in Poland (1980-1984), followed by research positions at McGill University and INRS-Telecommunications in Montreal before joining Boston University. His research focuses on computer vision, visual sensor networks, image and video processing, stereoscopic and 3-D imaging, and multidimensional digital signal processing. Recent work includes developing the COSSY (Computational Occupancy Sensing System) for energy-efficient HVAC control, plasmonic phase-imaging meta-sensors for cell classification, and privacy-preserving techniques for human activity recognition. His work bridges theoretical signal processing with practical applications in biomedical imaging, cybersecurity, and sustainable building technologies. Analysis of his recent publications reveals a strong trajectory toward practical applications of computer vision in public health (particularly post-pandemic social distancing monitoring), energy conservation through intelligent building systems, and biomedical diagnostics using advanced optical techniques. His work increasingly integrates machine learning with traditional signal processing approaches, particularly in the development of optical-digital hybrid neural networks. IEEE Fellow (2008) IEEE Signal Processing Society Distinguished Lecturer (2019-2020) ECE Outstanding Faculty Teaching Award, BU (2011) Faculty Service Award, BU (2024) EURASIP Fellow (2025) Multiple best paper awards including PETS Challenge Winner (2021), AVSS Best Paper (2010) Professor Konrad has secured significant research funding from diverse sources including NSF, DOE, NIH, DOD, and NSERC of Canada. His $1 million DOE ARPA-E contract for the COSSY project exemplifies his ability to translate theoretical research into practical solutions addressing energy conservation and public health concerns. As Deputy Editor-in-Chief of IEEE Transactions on Image Processing and through extensive service on conference committees, he has significantly shaped his field. He leads the VIP laboratory, which focuses on computer vision applications including event recognition, clutter analysis, human-computer interfaces, fisheye camera networks, and visual privacy. His lab collaborates extensively with the MINDS group and CISE, creating synergies between theoretical signal processing and practical applications in healthcare, cybersecurity, and sustainable infrastructure.

• Computer Vision
• Signal Processing
• Machine Learning
• +4 more

Vedran Jovanović serves as a Senior Lecturer at the Department of General Electrical Engineering, Faculty of Electrical Engineering, University of Banja Luka. His academic career centers on interdisciplinary research bridging computer vision, signal processing, and biohybrid engineering systems with practical applications in security, environmental monitoring, and industrial automation. His primary research domains include Computer Vision, Signal Processing, Machine Learning, Biohybrid Systems, Robotics, Sensor Networks, and Land Use Classification. Jovanović has pioneered video analysis techniques for honeybee activity monitoring in explosive detection systems and developed novel algorithms for small object detection in UAV surveillance footage. His work in offshore aquaculture monitoring addresses splash and cage detection challenges through deep learning approaches. Analysis of his recent publications reveals a pronounced focus on applying machine learning to humanitarian and industrial challenges, particularly in biohybrid landmine detection systems and precision robotics calibration. His most impactful work appears in journals such as Sensors, Science of the Total Environment, and Remote Sensing, demonstrating consistent methodological innovation across computer vision and signal processing domains. Dr. Jovanović actively participates in multiple research initiatives including the NATO-funded "Biological Methods (Bees) for Explosive Detection" project (2017-2021, 300,415 BAM) and current national projects such as "Signal Processing using Embedded Computer Systems and Machine Learning" (2025, 9000 BAM) and "Development of STEM Skills and Interests in School Children" (2025, 3000 BAM), where he contributes expertise in machine learning and computer vision systems development. He collaborates extensively within the University of Banja Luka's research ecosystem, particularly with the Signal Processing and Bioengineering groups, focusing on smart sensor technologies for environmental monitoring and industrial applications.

• Signal Processing
• Image Processing
• Feature Extraction
• +3 more

Rodrigo Dalvit Carvalho da Silva serves as a Research Fellow in the Department of Neurosurgery at Yale School of Medicine, Yale University, holding a primary appointment as a Postdoctoral Associate focused on computational biomedical research. His work bridges advanced signal processing with clinical neurosurgery applications through algorithmic innovation. His academic credentials include: PhD in Biomedical Engineering from Western University, Canada MSc in Teleinformatics Engineering from Federal University of Ceara, Brazil BSc in Mechatronics Engineering from University of Fortaleza, Brazil His research centers on signal/image processing, feature extraction (PCA, ICA, t-SNE, UMAP, Orthogonal moments), machine learning, deep learning, and Intracranial EEG analysis. He develops robust computational frameworks for medical image segmentation, classification, and analysis with direct applications in neurosurgery, cardiology, and craniofacial surgery. His methodological innovations target clinical challenges in brain tumor research, concussion biomechanics, and surgical planning through quantitative imaging biomarkers. Analysis of his publication record reveals a consistent trajectory from industrial computer vision (2014-2016) toward biomedical applications, culminating in recent neurosurgical and cardiological imaging breakthroughs. His signature contributions involve orthogonal moment theory for invariant feature extraction, deep learning integration with traditional image processing, and 3D reconstruction techniques for anatomical structures. The work demonstrates exceptional methodological transferability across domains while maintaining clinical relevance in neurological disorders. No scientific awards were documented in the source materials. While student mentorship and grant funding details remain unspecified in available records, his position within Yale's Neurosurgery research ecosystem suggests active participation in departmental training programs and collaborative projects. His technical expertise likely supports multiple research initiatives through computational infrastructure development. He operates within Yale Neurosurgery's research infrastructure, contributing to the Yale Program in Brain Tumor Research through computational methodologies. His work interfaces with clinical teams for intracranial EEG analysis and surgical planning, leveraging the department's imaging resources for algorithm validation against real-world neurological cases.

Stefan HarmelingView profile

Professor

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +3 more

Prof. Dr. Stefan Harmeling is a Distinguished Professor at TU Dortmund University 's Faculty of Computer Science and leads the Harmeling Lab . Previously, he served as Professor for Machine Learning at Universität Düsseldorf (2014-2022) and held postdoctoral positions at the University of Edinburgh (2005-2007) and Max Planck Institutes in Tübingen (2007-2014). His research spans machine learning , deep learning , reinforcement learning , and causality , with applications in medical imaging , seismology , and political discourse analysis . Education : Dipl. Math. in Mathematical Logic, University of Münster (1998) M.Sc. in Computer Science, Stanford University (2000) Dr. rer. nat. in Machine Learning, University of Potsdam (2005) His scientific contributions include 15 recent publications (2021-2025) covering topics from cardiac MRI analysis to LLM applications in political deliberation . These works demonstrate expertise in generative models , self-supervised learning , and adversarial robustness . Notable awards : 2022: Helsinki Tomography Challenge winner 2012: Günter Petzow Prize 2011: DAGM Prize 2009: COSMOSTAT Best Contribution 2009: GREAT08 Challenge Top Score As an advisor, he has mentored 8 PhD students including Tobias Uelwer (2023 PhD), Sebastian Konietzny , and Maike Behrendt . His teaching portfolio includes courses on probabilistic reasoning, machine learning, and deep learning at TU Dortmund. He also serves as area chair for TinyPapers@ICLR 2024 and organizer for multiple NeurIPS/ICML workshops.

• Computer Science
• Computational Geometry
• Computer Vision
• +7 more

Vasyl Tereshchenko is a Professor and Head of the Mathematical Informatics Department at Taras Shevchenko National University of Kyiv, Ukraine. His work spans computational geometry, computer vision, and algorithmic design, with a focus on applications in rehabilitation systems and real-time visualization. PhD (1993) and Doctor of Sciences (2000) in Physics and Mathematics from Taras Shevchenko National University of Kyiv Research interests include: Computational geometry for 3D surface modeling and mesh deformation Real-time eye-gaze tracking and handwriting recognition systems Augmented reality applications for robotics and navigation Algorithmic frameworks for common algorithmic space in visualization Recent publications emphasize geometric algorithms, machine learning coreset discovery, and 3D reconstruction techniques. His research group at the Samsung Advanced Information Technologies Research Joint Lab develops tools for: Medical rehabilitation systems Traffic accident warning technologies Mobile device computer vision Labs and teams under his leadership focus on cross-disciplinary challenges in: Medical technology integration Human-computer interaction Geometric modeling for engineering applications

• Artificial intelligence
• Image processing
• Hybrid systems
• +9 more

Dr. José Carlos Dafonte Vázquez is a researcher in the Department of Computer Science and Information Technology at the University of A Coruña's Faculty of Computer Science. His work focuses on artificial intelligence, image processing, and bioengineering applications. Active faculty member since multiple six-year research evaluation periods Teaching roles include Infrastructure Management, Language Processing, and Final Year Projects Research interests span: Quantum key distribution for secure communications Machine learning in astrophysics data analysis IoT-based monitoring systems for IT infrastructure Recent publications focus on: AI applications in astronomical data processing Quantum cryptography implementations IoT environmental monitoring devices Behavioral biometric authentication Key funding sources: EU Horizon Europe program Ministry of Science, Innovation and Universities Galician Xunta regional government National Cybersecurity Institute (INCIBE) He leads projects at the Research Center in Information and Communication Technologies (CITIC) and collaborates with international institutions on Gaia mission data analysis.

• Bioengineering
• Remote Sensing
• Chemometrics
• +6 more

Dr. Serhiy Volodymyrovych Morshchavka is an Associate Professor at the Department of Information Security and Nanoelectronics, Faculty of Information Security and Electronic Communications, Zaporizhzhia National Technical University. With a career spanning since 2001, he specializes in bioengineering, remote sensing, and hyperspectral image processing.

• Radio Engineering
• Radar Systems
• Interference Immunity
• +4 more

Dmytro Makarovych Pisa is a Professor at Zaporizhzhia Polytechnic University specializing in Radio Engineering and Radar Systems within the Faculty of Information Security and Electronic Communications. With over 50 years of academic experience since graduating in 1969, he has established himself as a leading expert in interference immunity of complex radio systems. Educational Background: Zaporizhia Machine-Building Institute (1969), specialty: 'Radio Engineering' Candidate's thesis in specialty 05.12.04 'Radar and Radio Navigation' (1982) Doctoral dissertation in specialty 05.13.06 'Information Technologies' (1997) Professor Pisa's research primarily focuses on radio engineering, radar systems, and signal processing . His work centers on developing advanced methods for interference compensation, spatial and polarization filtering, and space-time signal processing in radar systems. He has pioneered techniques for forming classified training samples, correlation methods for spatial filter adaptation, and improving the efficiency of interference compensation in complex radio environments. His recent publications (2012-2022) demonstrate a consistent progression from theoretical foundations to practical implementations, with increasing sophistication in handling combined active and passive interference. His research bridges traditional radio engineering with modern computational approaches, particularly in adaptive filtering and signal processing algorithms. Scientific Awards: Medal for labor valor (1982) Excellent education worker of Ukraine (2003) Badge 'For Scientific Achievements' (2008) Diploma of the Cabinet of Ministers of Ukraine (2012) Professor Pisa has supervised numerous research projects and has been actively involved in training the next generation of radio engineers. His laboratory work on radar systems design and interference compensation has provided practical experience for students. He has authored over 225 scientific works, including 25 patents, 30 author's certificates, 5 textbooks, and 1 monograph. His research group at Zaporizhzhia Polytechnic University focuses on advancing radar technology, particularly in the areas of interference immunity and signal processing. The team has developed several innovative approaches to spatial and polarization filtering that have practical applications in both military and civilian radar systems.

• Classical Novae
• Recurrent Novae
• Nova Super-Remnants
• +10 more

Prof Matt Darnley is the Professor of Time Domain Astrophysics and Head of Teaching at the Astrophysics Research Institute (ARI) of Liverpool John Moores University (LJMU) , where he has worked since 2001. He also serves as Associate Dean for Education and Student Experience in LJMU's Faculty of Health, Innovation, Technology & Science (HITS) since 2022. Education : MPhys in Physics, University of Oxford (dissertation on cosmological object detection) PhD in Astrophysics, LJMU (Andromeda Galaxy nova populations) Research Interests focus on Classical and Recurrent Novae , their progenitor systems , and extragalactic populations , with significant work linking novae to Type Ia Supernovae . He pioneered hydrodynamic modeling of nova ejecta , discovering the first nova super-remnants . Recent work expands to observational astrophysics (X-ray/UV/optical), cataclysmic variables , and human spaceflight including astrodynamics and sustainable space missions to the Moon and Mars. Scientific Trends in his 15 most recent works (2024-2020) span hydrodynamic modeling of nova super-remnants ( KT Eridani , RS Ophiuchi ), machine learning applications in transient detection ( ZTF , Gaia Alerts ), and multiwavelength studies of novae and supernovae. His work combines observational campaigns with theoretical simulations to address stellar evolution and cosmological implications. Awards & Recognition : Shortlisted for THE Research Project of the Year (STEM) 2017 Won University of Liverpool Student Experience Staff Award 2017 Recognized in LJMU Faculty Teaching Excellence Awards 2024 for ED&I contributions Elected Fellow of the Royal Astronomical Society and Higher Education Academy Teaching & Leadership : Leads LJMU's Physics Icebreaker Project (Mission to Mars module), Undergraduate Astrophysics Programs , and MSc Time Domain Astrophysics module. Served as University of Liverpool Programme Director (2016-2019, 2023) and External Examiner at University College London and University of Hertfordshire . Professional Activities : Chaired EWASS 2018 Local Organising Committee Advisory role for EWASS 2019 Member of Armagh Observatory Management Committee (2021-) Editor for Open Astronomy (2017-) Active in Institute of Physics , International Astronomical Union , and European Astronomical Society (2015-) Grants & Collaborations : Awarded STFC Grant (2021) for Recurrent Nova Super-Remnants research. Collaborates with institutions including University College London , Armagh Observatory , and European Week of Astronomy and Space Science committees.

Alwin PouloseView profile

Assistant Professor

• Localization
• Human Activity Recognition
• Facial Emotion Recognition
• +5 more

Dr. Alwin Poulose is an Assistant Professor Grade I in Data Science at the School of Data Science, Indian Institute of Science Education and Research (IISER) Thiruvananthapuram, Kerala, India, since January 2023. Previously, he worked as a Researcher at the Center for ICT and Automobile Convergence (CITAC), Kyungpook National University, South Korea (2021-2022), and as a Research Fellow at the University of Michigan, Dearborn, United States (November-December 2022). His educational background includes a B.Sc. in Computer Maintenance and Electronics (2012), M.Sc. in Electronics (2014), M.Tech in Communication Systems (2017), and Ph.D. in Electronics and Electrical Engineering (2021) from Kyungpook National University, South Korea. Dr. Poulose's research spans multiple domains of intelligent systems, with a focus on localization technologies, human activity recognition, facial emotion recognition, and human behavior prediction. His work bridges computer vision, machine learning, and sensor technologies to develop practical applications in healthcare, transportation safety, and animal welfare. He has made significant contributions to indoor positioning systems using Wi-Fi, UWB, and IMU sensors, as well as to emotion recognition systems that have applications in driver monitoring and autonomous vehicles. His extensive publication record demonstrates a progression from fundamental research in localization and activity recognition to increasingly diverse applications across healthcare, agriculture, and social sciences. Recent work shows a trend toward broader interdisciplinary applications of machine learning techniques, including medical diagnostics, agricultural technology, and social data analysis. Technical Program Committee Member for the 12th International Conference on ICT Convergence (2021) and 1st Korea Artificial Intelligence Conference (2020) Session Chair for the 27th Asia-Pacific Conference on Communications (APCC 2022) and 3rd Korea Artificial Intelligence Conference (2022) Guest editor for special issues in Sensors Journal, Frontiers in Signal Processing, and Algorithms Journal Topic editor for Sensors, Sustainability, and Algorithms journals Dr. Poulose leads the Deep Intelligence Learning Lab (DiL Lab) at IISER TVM, where his team works on cutting-edge research in human-centered AI systems. His service activities include extensive reviewing for major IEEE journals including IEEE Access, IEEE Sensors Journal, IEEE Transactions on Circuits and Systems, and multiple MDPI journals since 2018-2022. His collaborative work extends across international institutions in South Korea, United States, and India, demonstrating a strong network in the academic community.