Find Academic Supervisors

Daniel DoktorView profile

Researcher

• Remote Sensing
• Climate Change
• Vegetation Ecology
• +5 more

Dr. Daniel Doktor is the Head of the 'Land-Cover & Dynamics' research group at the Helmholtz Centre for Environmental Research (UFZ), Department of Remote Sensing. He holds a PhD from Imperial College London and has been a Postdoc at UFZ since 2008, leading groundbreaking research in remote sensing applications for environmental monitoring. His work focuses on analyzing climate and land-use impacts on vegetation through satellite data, radiative transfer modeling, and machine learning. Education: Abitur, Martin-Luther-Schule, Marburg (1994) Diplom in Geography, Westfälische Wilhelms Universität Münster (2002) PhD in Biology from Imperial College London (2007), focusing on satellite imagery analysis of phenology and climate interactions Research Interests: Dr. Doktor's research integrates remote sensing with environmental science to understand land-use changes, plant traits, and ecosystem services. Key areas include: Optical time-series analysis for vegetation condition monitoring Hyperspectral data acquisition and trait measurements Machine learning for land-use classification and biodiversity mapping Linking land-use intensity to biodiversity and ecosystem health Radiative transfer modeling and sensor fusion techniques His work emphasizes innovative methods like the APiC algorithm for land-cover mapping and the Forest Condition Monitor for Germany. Grants & Collaborations: Key projects include: Helmholtz-funded 'Impacts of hydroclimatic extremes on forest condition' (2025-2029) BMEL 'Waldresilienz' project (2024-2025) iDiv-funded biodiversity detection studies (2022-2024) He collaborates with Leipzig University through the RSC4Earth initiative and coordinates the MoDEV PhD program on environmental remote sensing. Labs & Teams: Leads the Land-Cover & Dynamics group, collaborating with experts in radiative transfer modeling, ecological data science, and Earth observation. The team operates the UFZ satellite validation test site and develops processing chains for remote sensing data.

• Materials engineering
• Nanophotonics
• Plasmonics
• +5 more

Dr. Muhammad Alam is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He holds a PhD in Photonics (University of Toronto, 2012), MASc in Electromagnetics (University of Victoria, 2003), and BSc in Electrical Engineering (Bangladesh University of Engineering and Technology, 2000). His research focuses on advancing nanophotonics, plasmonics, and metasurface technologies for applications in energy, sensing, and optical systems. Education: PhD in Photonics, University of Toronto (2012) MASc in Electromagnetics, University of Victoria (2003) BSc in Electrical Engineering, Bangladesh University of Engineering and Technology (2000) Research Interests: His lab explores transformative nanophotonics applications, including plasmonic sensors, metasurface-based optical systems, and energy-efficient photonic devices. Key areas include solar azimuth prediction using bio-inspired polarization techniques, low-cost transparent solar absorbers, and quantum-optics metasurface arrays. Awards: Douglas R. Colton Medal for Research Excellence (2020) Over 371 citations (Google Scholar, 2023) Supervision and Grants: Current advisee: Dr. Zohreh Vafapour (Postdoctoral Fellow). His work has been supported by collaborations with institutions like NASA's Jet Propulsion Laboratory and Lawrence Berkeley National Laboratory. Labs/Teams: Research activities are conducted in Queen's Walter Light Hall labs, with affiliations to advanced photonics facilities. Ongoing projects bridge material science, optics, and engineering for real-world applications.

SERHAT KILIÇARSLANView profile

Associate Professor

• Data Mining
• Machine Learning
• Bioinformatics
• +2 more

Serhat Kiliçarslan is an Associate Professor at the Software Engineering Department of Bandırma Onyedi Eylül Üniversitesi, Faculty of Engineering and Natural Sciences. He also serves as Deputy Director of the Distance Education Application and Research Center and Deputy Head of the Software Engineering Department. His academic roles include Doctor Lecturer at Bandırma Onyedi Eylül Üniversitesi and Lecturer positions at Gaziosmanpaşa University since 2012. Education: No formal education details provided in the text. His research focuses on data mining, machine learning, bioinformatics, deep learning, and image processing. Key areas include feature extraction methods for agricultural pest classification, medical image analysis using deep learning architectures, and development of novel activation functions for neural networks. His work bridges computer science with applications in healthcare, agriculture, and industrial quality control. Recent publications (2023–2025) highlight advancements in AI-driven solutions for medical diagnosis (e.g., glaucoma, melanoma), agricultural product classification (hazelnuts, apples), and material science modeling. His research emphasizes hybrid approaches combining optimization algorithms with deep learning frameworks. Statistical Contributions: 41 articles (out of 329), 31 papers/congresses (out of 185), and 4 books/chapters (out of 46) within his department attributed to his work. No scientific awards or grant details are mentioned in the provided text. He holds student office hours on Tuesdays 11:00–13:00.

BUKET TOPTAŞView profile

Lecturer

• Machine Learning
• Medical Imaging
• Deep Learning
• +6 more

Buket Toptaş is a Lecturer at the Department of Software Engineering, Faculty of Engineering and Natural Sciences at Bandırma Onyedi Eylül University. She previously served as a Research Assistant (2019–2022) before transitioning to her current role. Her research focuses on machine learning, medical imaging, deep learning, and image processing, with applications in healthcare diagnostics and biomedical engineering. She has contributed to projects such as the Early Diagnosis of Diabetic Retinal Aneurysms with Retinal Image Analysis , completed in 2022. Her academic duties include teaching courses like Machine Learning, Data Mining, and Image Processing at both undergraduate and graduate levels. She is currently supervising a Master’s thesis on Colonoscopic Image Polyp Segmentation with Yaren Akgöl. Her work spans over 30 publications in journals like Soft Computing , Biomedical Signal Processing and Control , and IEEE Access , with a focus on retinal imaging, polyp detection, and algorithm optimization. Her conference contributions include papers on optimization algorithms (e.g., Biogeography-Based Optimization) and flame detection systems. She actively participates in university research projects and maintains an extensive portfolio of peer-reviewed articles addressing challenges in medical image analysis and deep learning.

Joni DambreView profile

Professor

• Machine Learning
• Deep Learning
• Embedded AI
• +5 more

Joni Dambre is a full-time tenured Professor at Ghent University leading the AIRO research group within the Internet Technology and Data Science Lab (IDLab). She transitioned from digital hardware research to machine learning in 2008 after earning her MSc and PhD in Electrical and Computer Science Engineering from Ghent University. Her educational background includes: MSc in Electrical Engineering from Ghent University PhD in Computer Science Engineering from Ghent University Professor Dambre's research spans machine learning, deep learning, embedded AI implementations, robotics, and brain-inspired unconventional computing. Her work uniquely bridges theoretical foundations with practical applications, evidenced by her team's competitive success in Kaggle competitions. Key thrusts include photonic reservoir computing, sign language technology development, and human-AI interaction studies, with emphasis on both hardware-efficient implementations and societal impacts. Recent publications (2023-2025) reveal strong interdisciplinary convergence, merging machine learning with photonics for unconventional computing architectures while advancing sign language translation through projects like SignON. A notable trend involves expanding from technical implementations to human-centric AI research, including studies on human alignment with large language models and societal implications of AI deployment. No major scientific awards were mentioned in the provided text. Although specific student names were not listed, Professor Dambre leads the AIRO group and serves as principal investigator for significant projects including SignON and cREAtIve, indicating active research supervision and substantial grant funding from both academic and industry sources. The AIRO research group maintains a dual focus on foundational theory and real-world applications, with active projects spanning photonic neural networks for telecommunications, sign language recognition systems, and robotics platforms. Their work demonstrates consistent innovation in translating brain-inspired computing concepts into practical solutions while maintaining strong industry partnerships and competitive performance in applied machine learning challenges.

• Solid State Memory
• Ionics
• Non-Volatile Memories
• +3 more

Michael Kozicki is a Professor and Graduate Program Chair in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), and a Senior Global Futures Scientist in the Julie Ann Wrigley Global Futures Laboratory. He joined ASU in 1985 and holds adjunct roles at the University of Edinburgh and Gwangju Institute of Science and Technology. His research focuses on novel materials and devices for memory technologies, supply chain security, and radiation-hardened systems. He has over 70 patents and founded multiple startups, translating lab innovations into commercial products. Notable awards include the 2015 National Academy of Inventors Fellowship and the 2019 Daniel Jankowski Legacy Award. He has led ASU's Center for Solid State Electronics Research and contributed to entrepreneurial programs. Education: Ph.D. and B.Sc. in Electronics and Electrical Engineering from the University of Edinburgh (1980, 1985). Research interests include solid-state memory, ionics, and programmable metallization cells (PMCs). His work bridges microelectronics with applications in cybersecurity and space technology. Recent projects involve dendritic identifiers for authentication, radiation-resistant memristors, and low-energy non-volatile memories. Publications span memristive systems, PUFs, and nano-identifiers, reflecting a trend toward secure, scalable electronics. His 15 most recent articles (2022–2025) emphasize nanostructuring, machine learning-driven authentication, and quantum conductance in devices. Scientific Awards: 2019 Daniel Jankowski Legacy Award, 2019–2020 Joseph C. Palais Scholar Award, Fellow of the National Academy of Inventors. Advising and grants include leadership in ASU's graduate programs and over 25 funded projects from NSF, NASA, DOD, and industry partners, totaling millions in research support. Notable grants focus on radiation effects in memristors and neuromorphic applications. Labs and collaborations involve the Center for Solid State Electronics Research (NanoFAB), interdisciplinary teams in optical biomolecular devices, and global partnerships in materials science.

Scott E FraserView profile

Professor

• Biomedical Engineering
• Cell Biology
• Developmental Biology
• +3 more

Scott E. Fraser is a Provost Professor of Biological Sciences & Biomedical Engineering at the University of Southern California, holding joint appointments in the Dornsife College of Letters, Arts and Sciences, Keck School of Medicine, and Viterbi School of Engineering. He is also Director of Science Initiatives for USC and leads multiple imaging centers, including the Translational Imaging Center and MRI Center. Fraser's research focuses on imaging technologies and quantitative biology, with emphasis on embryonic development, organogenesis, and medical diagnostics. He has pioneered advancements in light-sheet microscopy, optical coherence tomography, and fluorescent imaging techniques. His work integrates engineering, physics, and biology to address complex biological systems. Fraser earned his BS in Physics from Harvey Mudd College (1976) and PhD in Biophysics from Johns Hopkins University (1979). Before USC, he served as Chair of Physiology and Biophysics at UC Irvine and Anna L. Rosen Professor at Caltech. He founded the Caltech Brain Imaging Center and contributed to the Kavli Institute of Nanoscience. His interdisciplinary approach has led to innovations in imaging tools and systems biology. Research interests include imaging embryonic zebrafish, craniofacial development, and real-time metabolic tracking. Key projects involve developing hyperspectral imaging, label-free biomarkers, and multiplexed biosensors. Fraser has authored over 600 publications, including breakthrough studies on synapse dynamics, insulin secretion, and neural basis of quantity perception in zebrafish. Awarded the Edwin G. Conklin Medal (2021) for developmental biology, Fraser has led initiatives such as the Chan Zuckerberg Initiative’s science grant programs. His labs and centers provide cutting-edge facilities for USC researchers, fostering collaborations across medicine, engineering, and basic sciences.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +7 more

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

• Hybrid Variational/DL methods under topological constraints
• Hybrid Variational/DL methods under geometric constraints
• PDE Analysis and numerical simulation
• +5 more

Carole Le Guyader is an Associate Professor in the Mathematical and Software Engineering Department at the National Institute of Applied Sciences of Rouen (INSA Rouen), France. She has been actively contributing to the fields of image processing, mathematical analysis, and computational methods since joining INSA Rouen in 2009, after previously serving as an Assistant Professor at INSA Rennes from 2005 to 2009. Her academic leadership extends to various administrative roles including Head of Mathematics teaching at the STPI INSA Rouen department since 2019 and Director of the Mathematical Engineering Department since 2013. Dr. Le Guyader earned her Habilitation to Supervise Research on November 16, 2012, at Rouen University with the thesis titled "Contribution to the mathematical analysis of images: segmentation, registration and decomposition." Her habilitation jury included distinguished mathematicians such as Grégoire ALLAIRE from Ecole Polytechnique and Gabriel PEYRE from University Paris-Dauphine. Her primary research interests focus on the development and application of variational methods and deep learning techniques for image processing, with particular emphasis on incorporating topological and geometric constraints. She investigates mathematical models for image segmentation, registration, and decomposition, applying these techniques to biomedical imaging, geophysics, and other scientific domains. Her work bridges theoretical mathematics with practical applications in medical diagnostics and industrial settings. Le Guyader's publication record demonstrates a consistent focus on advancing image processing methodologies, particularly in the integration of traditional variational approaches with modern deep learning techniques. Her recent work shows increasing emphasis on medical applications, especially in radiotherapy planning and MRI reconstruction, where geometric and topological constraints play critical roles in ensuring clinically meaningful results. Dr. Le Guyader has successfully supervised multiple PhD students throughout her career, demonstrating her commitment to mentoring the next generation of researchers. Her collaborative approach is evident in her extensive network of academic and industrial partnerships across Europe and the United States. She has secured and managed numerous research projects including the LMI project "Mathematical Imaging and Digital Analysis" at CRIHAN since 2013, and the wind field modeling project with LABEX AMIES since 2014. Her leadership extends to coordinating INSA teams at the GENCI/CRIANN Hackathon since 2017 and participating in regional and European research initiatives.

Yasser RezaeiyanView profile

Researcher

• Neuromorphic Computing
• Spintronics
• Biomedical Circuits
• +3 more

Yasser Rezaeiyan is a Senior Researcher at the Department of Electrical and Computer Engineering (Electronics and Photonics group) at Aarhus University. His research focuses on neuromorphic computing, spintronics-based systems, and biomedical circuit design. He specializes in developing energy-efficient and high-performance analog/mixed-signal integrated circuits for applications in healthcare, environmental monitoring, and smart infrastructure. Key technical contributions include neuromorphic hardware leveraging spintronics and vortex oscillators, low-power sensor systems for leakage detection in district heating networks, and ultrasonically powered biomedical implants. His work bridges device physics, circuit design, and system-level integration to address challenges in next-generation computing and IoT. Publications highlight interdisciplinary innovations such as reservoir computing for seizure detection, STT-RAM-based in-memory computing architectures, and high-bandwidth operational amplifiers. His research has been published in top-tier journals like IEEE Transactions on Magnetics and IEEE Transactions on Biomedical Circuits and Systems. Rezaeiyan holds a strong record in analog circuit design, with expertise in chopper stabilizers, bio-impedance measurement systems, and energy harvesting solutions. His lab focuses on translating fundamental physics into practical systems with real-world impact.

• Computer Vision
• Machine Learning
• Natural Language Processing
• +3 more

Dr. Suman Kumar Ghosh is a Lecturer in Data Science and Computer Science at York St John University, affiliated with the York Business School in London. His research focuses on computer vision, machine learning, and natural language processing, with a decade of experience across diverse industries. He has contributed to advancements in handwritten character recognition, scene text understanding, and segmentation-free word spotting techniques. His work spans foundational research in document analysis, including contributions to datasets like the Versailles-FP for ancient floor plan analysis. He has published extensively in top-tier conferences such as ICDAR and CVPR, addressing challenges in word spotting, bi-gram indexing, and visual attention models for text recognition. His research bridges theoretical advancements with practical applications in multilingual systems and adaptive feature extraction. No scientific awards are explicitly mentioned in the profile. His teaching responsibilities include modules on machine learning and artificial intelligence concepts.

Alexander PachaView profile

Lecturer

• Optical Music Recognition
• Deep Learning
• Music Information Retrieval
• +3 more

Dr. Alexander Pacha is an External Lecturer at TU Wien, specializing in Optical Music Recognition (OMR) and deep learning applications. His work focuses on advancing OMR systems through graph neural networks, dataset development (e.g., DeepScoresV2), and music score analysis. He leads projects like DeepOMR and contributes to the Mixed Reality Lab and Virtual Meeting Room initiatives. Key research areas include music symbol detection, staff/measure segmentation, and cross-document alignment in music scores. Research interests span computer vision, artificial intelligence, and software tools for music information retrieval. His work bridges theoretical advancements with practical applications in digital libraries and music technology. Notable contributions include self-learning OMR frameworks and benchmarking tools for music object detection. Labs & Teams: Mixed Reality Lab (exploring AR/VR in music contexts), Virtual Meeting Room (collaborative digital environments).

John LoSeccoView profile

Professor

• Particle Physics
• Neutrino Physics
• CP Violation
• +3 more

John LoSecco is a Professor in the Department of Physics & Astronomy at the University of Notre Dame. His research focuses on CP violation in quark decays (via the BaBar experiment) and neutrino interactions (via the Double ChooZ experiment). He has contributed to major collaborations like DUNE, IceCube-Gen2, and IMB-3. His work bridges particle physics, astrophysics, and detector technology. B.S. in Engineering Physics, Cooper Union, 1972 A.M. & Ph.D. in Physics, Harvard University, 1973 & 1976 Research interests include neutrino oscillations, CP violation mechanisms, and precision measurements in high-energy physics. Recent work emphasizes the DUNE experiment’s neutrino oscillation program and IceCube-Gen2’s neutrino astronomy capabilities. His studies also explore proton decay and gravitational lensing effects on supernova neutrinos. Key contributions span neutrino mixing angle measurements, detector design for liquid argon time projection chambers (LArTPCs), and machine learning applications for event reconstruction. His articles highlight advancements in neutrino interaction modeling, accelerator upgrades, and software frameworks for large collaborations. Honors: Jefferson Science Fellow (2020-21), Bruno Rossi Prize (1989), DOE Outstanding Junior Investigator (1982-85) LoSecco advises on neutrino projects and collaborates internationally. He has pioneered experimental setups for neutrino flavor transitions and contributed to cosmic ray detection technologies. His labs focus on detector R&D for next-generation particle physics experiments.

Yue ZhuoView profile

Researcher

• Medical Imaging
• Biophotonics
• Machine Learning in Healthcare
• +3 more

Yue Zhuo is a Research Scientist in Radiology & Biomedical Imaging at Yale School of Medicine. His work focuses on developing advanced imaging technologies using photonic materials, machine learning, and molecular imaging techniques. Key areas include tumor volume delineation for radiotherapy planning, label-free stem cell differentiation imaging, and optimizing MRI/PET modalities for clinical applications. He holds appointments in the PET Core and collaborates extensively with researchers like Thibault Marin and Chao Ma. His lab develops novel biosensors and microscopy techniques, such as Photonic Resonator Outcoupler Microscopy (PROM) and Photonic Crystal Enhanced Microscopy (PCEM). These innovations enable high-resolution, non-invasive monitoring of cellular processes and biomolecular interactions. Zhuo's publications emphasize translating photonic technologies into clinical settings, with recent work addressing inter-observer variability in radiation oncology and improving cardiac imaging accuracy. His research bridges biomedical engineering, data science, and clinical radiology to advance diagnostic precision and therapeutic planning.

Torben JohansenView profile

Assistant Professor

• Machine Learning
• Econometrics
• Historical Data Analysis
• +3 more

Torben Johansen is an Assistant Professor in the Department of Economics VIP at the University of Southern Denmark. His research focuses on machine learning applications in historical data analysis, econometrics, and causal inference. He actively collaborates on projects combining machine learning with socioeconomic data, such as improving census transcriptions and analyzing breastfeeding impacts on educational outcomes. Johansen teaches courses in statistics and historical economics applications, supervises master’s students in economics and medicine, and engages in academic activities through conferences and peer reviews. His work bridges computer science and social sciences, with notable contributions to occupational standardization and policy optimization. He has contributed datasets to platforms like Harvard Dataverse and engages in public discourse on child health policies. Education: Implied PhD in Economics or related field (not explicitly stated) Research Interests: Machine Learning in socioeconomic contexts, causal inference, historical data digitization, and health economics. His methodologies include neural networks, transfer learning, and optimization algorithms applied to real-world policy problems. Recent research trends emphasize interdisciplinary approaches, blending econometric models with machine learning to address challenges in healthcare, labor markets, and historical record analysis. Key contributions include improving transcription accuracy in historical censuses and evaluating long-term effects of early childhood health interventions. Awards: None explicitly mentioned Advising involves supervision of master’s students in economics and medicine. His teaching portfolio includes applied statistics and historical perspectives on economics. Grants and funding details are not detailed in the provided text. Labs/Teams: Active member of the HEDG (Historical Economics & Development Group) and collaborates with researchers in computer science and public health.