Find Academic Supervisors

Lesedi MasisiView profile

Senior Lecturer

• Electrical Machines
• Power Electronics
• Renewable Energy Systems
• +5 more

Dr. Lesedi Masisi is a Senior Lecturer at the School of Electrical & Information Engineering at the University of the Witwatersrand (Wits), specializing in electrical machines, power electronics, and renewable energy systems. With a PhD from Concordia University and engineering degrees from Wits, Dr. Masisi contributes significantly to research in sustainable power systems and electrified transportation. Dr. Masisi's educational background includes a BSc(Eng) and MSc(Eng) from the University of Witwatersrand, followed by a PhD from Concordia University. This strong academic foundation supports their expertise in power systems and electrical machine design. Research interests focus on electrical energy conversion, rotating electrical machines and drives, power electronics, renewable energy, and sustainable electrical system development . Specific areas include variable flux machines, wound-field flux switching machines, battery energy storage systems, electric vehicle infrastructure, and port crane energy optimization. Current research openings focus on renewable energy systems, rotating electric machines and drives, control of rotating electrical machines, and condition monitoring of rotating electrical machines. The 15 most recent publications demonstrate a clear focus on electric machine design (particularly flux switching machines), power system integration for electric vehicles, and energy optimization in industrial applications. The research spans from fundamental electromagnetic analysis to practical implementation of grid-connected hybrid power systems, with strong emphasis on both theoretical modeling and experimental validation. Dr. Masisi actively contributes to the academic community through publications in high-impact journals including IEEE Transactions on Industry Applications, IEEE Access, and Energies, as well as presentations at major conferences such as IEEE ECCE and PowerAfrica. As a Senior Lecturer, Dr. Masisi supervises research students and is currently recruiting for projects in renewable energy systems, rotating electric machines, and condition monitoring. While specific grant information isn't detailed in the provided materials, the extensive publication record suggests active research funding supporting these investigations. Research activities are centered around electrical machines and drives, with experimental work likely conducted in the School of Electrical & Information Engineering's laboratories at Wits. The focus on prototyping and experimental validation of novel machine designs indicates hands-on laboratory work with electrical machine testing facilities.

Máté LengyelView profile

Professor

• Computational Neuroscience
• Cognitive Science
• Machine Learning
• +9 more

Máté Lengyel is a Professor at the Department of Cognitive Science , Central European University, and holds a joint appointment as Professor of Computational Neuroscience at the Computational and Biological Learning Lab , Department of Engineering, University of Cambridge. His research spans computational neuroscience, cognitive science, and machine learning. Key collaborations with experimental neuroscience groups Focus on neural oscillations and cortical dynamics Applications to human psychophysics and fMRI His computational work emphasizes Bayesian inference , reinforcement learning , and dynamical systems analysis of biophysical models. Recent publications analyze neural variability, memory mechanisms, and optimized cortical coding. He co-organizes interdisciplinary workshops like SCENE: Theoretical Foundations of Ecological Neuroscience and maintains an open-access lab website at lengyellab.org with code repositories in MATLAB and R.

• Machine Learning
• Deep Learning
• Bioinformatics
• +6 more

Wolf Beat is an Associate Professor at the Fribourg School of Engineering and Architecture, part of the University of Applied Sciences of Western Switzerland (HES-SO). He is affiliated with the Institute of Complex Systems (iCoSys) where he conducts interdisciplinary research at the intersection of machine learning, bioinformatics, and document analysis. His educational background includes: BA HES-SO in Architecture from Fribourg School of Engineering and Architecture MSc HES-SO in Business Administration MSc HES-SO in Engineering PhD in "Reducing the complexity of OMICS data analysis" from University of Würzburg (2017) Dr. Beat's research spans multiple disciplines with a focus on applying machine learning techniques to solve complex problems. His work in bioinformatics has contributed to advancements in DNA sequencing analysis and genomic research, while his more recent work explores time series forecasting, document analysis, and anomaly detection. He has developed innovative approaches for handling historical manuscripts through computer vision techniques and has applied machine learning to geotechnical engineering problems. His research often bridges theoretical machine learning concepts with practical industrial applications. An analysis of his recent publications reveals a strong trend toward foundation models and diffusion models for time series analysis, alongside continued work in bioinformatics and document analysis. His research demonstrates a consistent pattern of applying cutting-edge AI techniques to domain-specific problems across diverse fields including healthcare, engineering, and digital humanities. Dr. Beat is actively involved in multiple research projects including ModIA (2024-2025, 15,000 CHF) and as principal investigator for the Foundation model for time series forecasting project (2024-2025, 100,000 CHF). His past projects include Monitoring des Transformateurs de Puissance (2023-2024, 220,000 CHF) and GREENum (2022, 24,700 CHF), demonstrating his ability to secure funding for interdisciplinary research. He collaborates extensively within the HES-SO network and with external partners, often serving as a co-researcher or principal investigator on projects that combine expertise from engineering, computer science, and domain-specific applications. His research team at iCoSys Institute includes multiple researchers working on AI and complex systems, and he has been involved in numerous projects with colleagues including Montet Frédéric, Pasquier Benjamin, Von Barnekow Alec, and Maillard Philippe. His work demonstrates a consistent commitment to developing practical AI solutions that address real-world challenges across multiple domains.

• Artificial Intelligence
• Ophthalmology
• Deep Learning
• +6 more

Kaveri Thakoor, Ph.D., is an Assistant Professor of Ophthalmic Science (in Ophthalmology) at Columbia University Irving Medical Center. She directs the Artificial Intelligence for Vision Science (AI4VS) Laboratory, specializing in developing robust and interpretable deep learning systems for ophthalmology applications. Her research addresses key challenges in AI translation to clinical practice, including model generalization across imaging modalities and institutions. Education BS, Chemistry (Honors) – Stanford University (2010) MS, Computer Science – University of Southern California (2015) PhD, Biomedical Engineering – Columbia University (2022) Research Focus Dr. Thakoor's research integrates AI with ophthalmology to enhance disease diagnosis. Key areas include: glaucoma detection using OCT scans, multimodal analysis of age-related macular degeneration, generative adversarial networks for portable OCT image enhancement, and eye-tracking-informed AI systems. Her lab aims to develop clinically deployable tools that improve accessibility and diagnostic accuracy across diverse populations. Publication Trends Dr. Thakoor's recent publications demonstrate a strong focus on deep learning applications in medical imaging, particularly glaucoma and AMD detection. Her work evolves from foundational computer vision research to clinically oriented AI systems, emphasizing multimodal data fusion, model interpretability, and real-world robustness. Earlier publications reflect interdisciplinary contributions to seismology and assistive technologies. Awards and Honors Biomedical Engineering Young Scientist Award, NEBEC (2022) Morton B. Friedman Memorial Prize for Excellence (2022) Three Minute Thesis Finalist (2022) NIH Director’s Early Independence Award Candidate (2021) NSF Graduate Research Fellowship (2018) 2nd Place Best Student Paper Award (2015) Best Student Paper Award (2013) National Merit Commended Scholar (2006) Girl Scout Gold Award (2005) Laboratory The AI4VS Laboratory develops AI systems as clinical collaborators for ophthalmologists. Current projects include constraint-based AI using expert eye-tracking patterns, portable OCT integration, and cross-institutional validation of diagnostic algorithms.

Mohammed AbuhamadView profile

Assistant Professor

• AI Security
• Deep Learning Security
• Software Security
• +9 more

Mohammed Abuhamad is an Assistant Professor in the Department of Computer Science at Loyola University Chicago. He holds dual Ph.D. degrees in Computer Science from the University of Central Florida and Electrical and Computer Engineering from INHA University (both 2020), and a Master's in Information Technology (Artificial Intelligence) from the National University of Malaysia (2013). His research focuses on AI/Deep-Learning Security, Software and Mobile/IoT Security, and Privacy, with applications in autonomous vehicles, healthcare, and social media. Key interests include adversarial machine learning robustness, behavioral biometrics, malware analysis, and misinformation detection. Dr. Abuhamad leads two research labs: the Cybersecurity Lab and AI for Secure Computing Research Lab (AISeC) . He teaches COMP 358/458: Big Data Analytics and COMP 388/487: Deep Learning during Fall 2024. He has secured significant research funding including: $3,800,000 NSF Grant for 'CyberCorps Scholarship for Service: CyberRamblers Bolster National Security' $50,000 from Dr. Scholl Foundation for online misinformation research $300,000 NSF Grant for experiential cybersecurity training

Kaori IdemaruView profile

Professor

• Speech Perception
• Speech Production
• Phonetics
• +4 more

Kaori Idemaru serves as Professor and Department Head of East Asian Languages and Literature in the School of Global Studies and Languages at the University of Oregon. Her office is located in Friendly Hall, and she actively contributes to academic leadership while maintaining a robust research profile in linguistic sciences. Her research centers on speech perception and production mechanisms, particularly how listeners achieve perceptual constancy despite variability from speaker characteristics, phonetic context, and speech rate. She investigates second language speech learning challenges—especially for adult learners—and develops evidence-based training methodologies for phonetic acquisition. Her work bridges theoretical linguistics with practical language education applications. Recent publications demonstrate concentrated expertise in Korean and Japanese phonetics, with recurring themes of dialect acquisition (notably North Korean refugees adapting to Seoul Korean), prosodic features of politeness, and cross-linguistic perception. Her methodological approach integrates sociolinguistic analysis with controlled experimental phonetics to examine language variation and learning trajectories. Professor Idemaru is affiliated with the Spoken Language Research Laboratories (SLRL) at the University of Oregon, which provides infrastructure for acoustic analysis, perception experiments, and speech production studies supporting her research on phonetic variation and language learning.

Andreas HenriciView profile

Researcher

• NMR spectroscopy
• Dynamical systems
• Mathematical analysis
• +4 more

Andreas Henrici is a research-focused academic at the Zurich University of Applied Sciences (ZHAW), School of Engineering, within the Applied Complex Systems Science research centre. His core activities revolve around NMR spectroscopy, dynamical systems theory, and the development of machine-learning methods for automated spectral analysis. Education & professional development: Certificate of Completion, ZHAW Life Sciences and Facility Management, 02/2019 Doctorate (Dr.) – exact discipline and institution not specified in the source Research interests: Henrici combines analytical mathematics with practical spectroscopy. Early work concentrated on stability and symmetry analyses of nonlinear lattices (Toda lattice). More recently he leverages deep-learning architectures (CNNs, DETR-style networks) to deconvolute and classify 1D/2D-NMR data, pushing automation in metabolomics and fragment-based drug discovery. Bayesian inference, image-processing techniques, and trustworthy AI are recurring themes. Recent publication trends (2022-2025): Over 15 peer-reviewed articles and conference posters illustrate a clear shift toward AI-driven NMR: deconvolution networks, automated multiplet segmentation, spin-system identification, and uncertainty-aware classification. Collaborative projects yield open data, open code, and cross-disciplinary authorship with chemists, computer scientists, and industry partners. Editorial & organisational service: Guest editor for Frontiers in Artificial Intelligence volumes summarising European COST conferences on AI in Industry & Finance (2022, 2023) Project leader for the European conference series AI in Industry and Finance (completed) Current projects & funding: Smart Acquisition for Ultra-High field NMR Spectroscopy – project leader (ongoing) NMR-based drug discovery – co-project leader (ongoing) Several completed COST and ZHAW-internal grants on machine-learning for spectroscopy and mathematics-for-industry Labs & teams: Henrici is embedded in the ZHAW Applied Complex Systems Science focus area, working closely with the NMR, metabolomics, and data-science groups. Shared facilities include high-field NMR spectrometers and GPU clusters for deep-learning experiments.

Corentin DumeryView profile

Researcher

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +4 more

Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.

Andrew CresswellView profile

Professor

• Neuromechanics
• Neurophysiology
• Biomechanics
• +5 more

Professor Andrew Cresswell is a faculty member at the University of Queensland's School of Human Movement and Nutrition Sciences, where he served as Head of School from 2014-2019. He holds a medical doctorate in Neuroscience from Karolinska Institute, Sweden (1993), and previously held positions at Karolinska Institute and University College of Physical Education and Sports before joining UQ in 2005. His research integrates neurophysiology and biomechanics (neuromechanics) to investigate human movement control, with specific interests in: Motoneurone, reflex and cortical excitability during muscle actions Neuromuscular fatigue mechanisms Abdominal musculature activation during postural tasks Muscle mechanics and gait analysis Recent publications (2021-2024) demonstrate consistent focus on experimental biomechanics, neurophysiological measurements, and computational modeling of human locomotion. Research themes include foot biomechanics, muscle-tendon mechanics, motor unit behavior, and metabolic cost of movement, employing techniques like electromyography, motion capture, and musculoskeletal modeling. As affiliate of the Centre for Sensorimotor Performance, he leads research on neuromuscular control mechanisms during functional movements. He maintains active collaborations nationally and internationally, evidenced by co-authorships across multiple institutions.

Enrico GlereanView profile

Researcher

• Cognitive Neuroscience
• Neuroimaging
• Functional MRI
• +7 more

Dr. Enrico Glerean is a Staff Scientist at the Aalto School of Science , core member of Aalto Scientific Computing (managing the Triton high-performance computing cluster), and active in research software training. He contributes to GDPR compliance, data anonymization, and secure research workflows as part of the Aalto Data Agents network , European Data Protection Board, and Finnish Reproducibility Network . His academic affiliation is with the Department of Neuroscience and Biomedical Engineering . Expertise: Cognitive neuroscience, brain imaging, open science advocacy Leadership: Academic Affairs Committee, Making Waves initiative for women in STEM His research combines neuroimaging with real-world digital phenotyping , focusing on functional brain connectivity, embodied emotions, and MRI methodological development. Recent work explores environmental influences on brain networks (2024) and emotion-body mapping (2024). Publications span journals like NeuroImage and PLoS Biology , often integrating machine learning with neuroscience. Scientific awards include: 16th Novartis Neuroscience Award (2016) Best doctoral dissertation 2016 (Aalto School of Science) Aalto Scientific Communication Award (2018) Kordelin Foundation scholarship (2018) He contributes to research ethics education and promotes responsible generative AI use. His work bridges technical computing expertise with cognitive neuroscience applications, particularly in autism spectrum disorders and emotion processing.

Karlo JosićView profile

Lecturer

• Information and Communication Technologies (ICT)
• Computer Networks
• Project Management
• +4 more

Karlo Josić is a lecturer at Algebra University College , Zagreb, Croatia. His professional work focuses on information and communication technologies (ICT), computer networks, project management, and their multidisciplinary applications in education, security, and expert systems. Educational Background Specialist graduate study in Project Management (2011) Holdings: Multiple international certificates in computer networks and project management Research Interests : Josić's research spans wireless networking, network security (including SDN, serverless architecture, and next-generation firewalls), smart systems (e.g., WiFi signal-based lighting control), and interdisciplinary applications of ICT in education and biomedical imaging. His work often addresses practical challenges in network performance, security, and automation. Article Trends : His publications emphasize network security, wireless communication, and emerging computing paradigms. Key areas include adaptive routing in disrupted environments, zero-trust architectures, and electromagnetic interference impacts on home networks.

Alper ÇelikelView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +2 more

Dr. Alper Çelikel is an Associate Professor at the Department of Computer Engineering, Faculty of Engineering and Natural Sciences, İstanbul Okan University. He specializes in machine learning, artificial intelligence, and data science with specific focus areas including natural language processing, computer vision, and optimization of neural network architectures. Education: PhD in Computer Science from Boğaziçi University (2014) Research Focus: Deep learning, cross-lingual systems, and multi-modal learning Leadership: Director of Research and Development Policy at İstanbul Okan University His recent research explores hybrid CNN-Transformer architectures, federated learning in healthcare, and explainable AI frameworks. He has received prestigious awards including the TÜBİTAK Career Award (2018) and Turkish Academy of Sciences Young Scientist Award (2015). Dr. Çelikel has supervised multiple graduate researchers and actively contributes to curriculum development and academic quality assurance initiatives. TÜBİTAK Career Award (2018) Turkish Academy of Sciences Young Scientist Award (2015) As an academic leader, he chairs the Research and Application Centers Directorate and contributes to university-wide policy development including information security, education, and research protocols.

Mahya Mohammadi KashaniView profile

Researcher

• Underwater Robotics
• Risk Assessment
• Probabilistic Planning
• +3 more

Mahya Mohammadi Kashani is a Researcher in Software Engineering at the IT University of Copenhagen, specializing in risk assessment for underwater robotics. She serves as an Early Stage Researcher in the REMARO (Reliable AI for Marine Robotics) network, a European Commission-funded project advancing trustworthy AI for marine applications. Her academic background includes: PhD in Computer Science (2021-2024), IT University of Copenhagen: Focused on statistical assessment of plans via probabilistic optimization of reliability. MSc in Artificial Intelligence and Robotics (2016-2019), Shahid Rajaee Teacher Training University: Researched search-based image annotation using deep models. BSc in Software Engineering (2010-2014): Developed filter drivers for disk access control at low-level driver hierarchies. Her research develops Bayesian-inference-based methods for risk-averse decision-making in underwater robotics, creating probabilistic models to assess operational risks and select reliable robot plans. Previously, she investigated pattern recognition algorithms, sparse reconstruction/coding techniques, and search space reduction for automatic image annotation systems. Her publications (2022-2025) establish a cohesive framework for risk-aware marine robotics, addressing fault recovery, risk quantification, and plan assessment through probabilistic optimization and belief-based planning models. No scientific awards were documented in the source material. Funded by the REMARO project (2020-2025), she contributes to European Commission research on trustworthy AI, active learning, and image segmentation for underwater robotics as a co-investigator. Her collaborative work spans risk-averse planning methodologies and reliability engineering for autonomous marine systems. Within the REMARO network, she collaborates on advancing reliable AI for marine robotics through active learning techniques and image segmentation solutions for challenging underwater environments.

Eran SegalView profile

Professor

• Computational and Systems Biology
• Nutrition
• Genetics
• +4 more

Prof. Eran Segal serves as a faculty member in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. His laboratory (accessible at http://genie.weizmann.ac.il) conducts cutting-edge interdisciplinary research at the intersection of computational methods and biological systems. His research program centers on Computational and Systems Biology with specific focus areas in Nutrition, Genetics, Microbiome , and Gene Regulation . Prof. Segal's work aims to translate computational insights into practical applications for personalized nutrition and personalized medicine. His approach combines quantitative modeling with experimental validation to address complex biological questions. Analysis of his publication record reveals a research trajectory evolving from fundamental genomic principles to applied health solutions. His early work established foundational understanding of nucleosome positioning and gene regulation, while more recent research focuses on microbiome dynamics and personalized dietary responses. The interdisciplinary nature of his publications across Science, Cell, and Nature demonstrates significant impact across computational biology, genomics, and clinical applications. Prof. Segal maintains an active professional presence through his laboratory website and Twitter account (@segal_eran), with research that has received notable media coverage including features in The New York Times and TEDx presentations. His work represents a successful integration of computer science methodologies with biological discovery and medical applications.

W. BenderView profile

Professor

• Developmental Biology
• Epigenetics
• Genetics
• +3 more

W. Bender, Ph.D., serves as the Harold T. White Professor of Biological Chemistry and Molecular Pharmacology at Harvard Medical School, Harvard University. He leads an active research laboratory investigating epigenetic mechanisms using the Drosophila bithorax complex as a foundational model system for Polycomb-mediated repression. His research centers on how Polycomb group proteins establish stable cellular memory of developmental decisions through chromatin modifications, particularly H3K27me3 methylation. The lab studies the 300+ kb bithorax complex where homeotic genes are collinearly arranged with body segments, dissecting regulatory domains, boundary elements, and non-coding RNA functions. Current work includes analyzing segment-specific chromatin states through nuclei isolated from individual embryonic segments and investigating long-range enhancer interactions and structural organization of the complex. In collaboration with Bob Kingston's lab, the team has identified linear progression of active/repressed chromatin domains across regulatory regions. They also examine functional roles of non-coding transcripts, including a microRNA whose deletion causes reproductive defects through derepression of homeotic genes in the nervous system, aiming to uncover novel molecular mechanisms in epigenetic regulation.