Find Academic Supervisors

Ganesh KrishnasamyView profile

Lecturer

• Computer Vision
• Machine Learning
• Pattern Recognition
• +6 more

Ganesh Krishnasamy is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Electrical Engineering from the University of Malaya, with earlier degrees from Universiti Kebangsaan Malaysia (B.Eng and M.Eng in Electrical and Electronic Engineering). His research focuses on computer vision, pattern recognition, manifold learning, and semi-supervised learning, with applications in multimedia processing and understanding. He has led three research projects including 'AEinstein: Adversarial AI amongst Materials Discovery Domains' and pioneered semi-supervised action recognition in videos. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure. Research Interests: Computer Vision Pattern Recognition Manifold Learning Semi-Supervised Learning Optimization Algorithms HDR Image Reconstruction Recent Projects: AEinstein Project (2024-2026): Focuses on adversarial AI in materials discovery. Research Management System (2023-2024): Aims to optimize research workflows. Semi-Supervised Image-to-Video Adaptation (2020-2023): Developed ActNetFormer and other hybrid models. Publications: Over 16 outputs since 2014, with recent emphasis on HDR imaging, sports video analysis (badminton rally detection), and optimization techniques. His work bridges theoretical machine learning with commercial applications.

Alexander AsterothView profile

Professor

• Machine Learning
• Stochastic and Probabilistic Learning Methods
• Surrogate Modelling
• +5 more

Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.

Decky AspandiView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Decky Aspandi is a Researcher at Universitat Stuttgart in the Analytic Computing department. He holds a Ph.D. in Information and Communication Technologies from Universitat Pompeu Fabra, Barcelona, an M.Sc. in Computer Engineering from King Mongkuts University of Technology Thonburi, and a Bachelor in Computer Science from University of Mulawarman. Research Focus: Machine Learning, Deep Learning, Computer Vision, Affective Computing, Temporal Modeling, and Human-Computer Interaction. Teaching Experience: Teaching Fellow at Universitat Stuttgart (2022-2023, 2021-2022), Universitat Pompeu Fabra (2017-2020), and University of Mulawarman (2009-2013). Key Publications: 14 recent works on topics including eye-gaze prediction, facial alignment, lie detection, and affective computing applications.

Asja FischerView profile

Professor

• Machine Learning
• Adversarial Robustness
• Deepfake Detection
• +4 more

Asja Fischer is a Professor at the Faculty of Computer Science, Ruhr-University Bochum. Her research focuses on machine learning, adversarial robustness, deepfake detection, and generative models. Assistant Professor at RUB (previous) Akademische Rätin at Bonn University (previous) Post-doctoral researcher at MILA Her work explores uncertainty quantification, stochastic neural networks, and federated learning. Recent studies analyze adversarial attacks on diffusion models, semantic watermarks, and robustness in AI-generated image detectors. Key trends in her publications include: Adversarial robustness in computer vision and speech recognition Deepfake detection using autoencoder reconstruction and frequency analysis Uncertainty estimation for improving model reliability Security challenges in diffusion and autoregressive models She has no publicly listed scientific awards or student advising records in the provided texts.

Alexander BuckschView profile

Associate Professor

• Plant Phenomics
• Computational Imaging
• Root Architecture Analysis
• +7 more

Alexander Bucksch is an Associate Professor in the School of Plant Sciences at the University of Arizona , part of the College of Agriculture, Life & Environmental Sciences . His research focuses on root phenomics, combining computational imaging and geometric/topological analysis to study plant development and environmental interactions. He develops innovative tools like the DIRT platform for root trait analysis and collaborates on cyberinfrastructure projects such as CyVerse. Education: PostDoc, Georgia Institute of Technology, USA Ph.D., Delft University of Technology, Netherlands M.Sc. and B.Sc., Brandenburg Technical University, Germany Research Interests: Root architecture and phenotypic variation Non-destructive sensing technologies (e.g., Fiber Bragg gratings) Plant-microbiome interactions 3D imaging and machine learning applications Grants & Projects: CAREER Grant (2019): Quantifying phenotypic variation in crop roots CyVerse: Cyberinfrastructure for open science Labs/Teams: Plant IT Lab (focus: computational plant biology)

Zhirong YangView profile

Professor

• Machine Learning
• Information Visualization
• Data Representation Learning
• +6 more

Zhirong Yang is a Professor at the Department of Computer Science at the Norwegian University of Science and Technology (NTNU). He is affiliated with the Norwegian Open AI Lab and focuses on machine learning, information visualization, and data representation learning. His work integrates techniques like neural attention models, nonnegative matrix factorization, and dimensionality reduction algorithms such as Neighbor Embedding (NE) for visualization. Yang's research has applications in bioinformatics, medical imaging, and engineering systems. His educational background includes advanced studies in pattern recognition and computational methods, though specific details are not provided. Research collaborations span institutions globally, including work on bacterial population genomics and chromosome classification. Key contributions include scalable optimization frameworks for visualization (NE) and novel clustering algorithms using graph random walks and low-rank matrix decompositions. Yang has developed software tools like ChordMixer for sequence processing and HSSNE for robust data visualization. His recent publications emphasize advancements in attention mechanisms, sparse factorization, and medical diagnostics. He actively contributes to open-source projects and collaborates with industry on applications like subsea pipeline inspection and energy scheduling systems.

• Internet Security
• Operating Systems
• Programming
• +12 more

Dr. Xiaochun Cheng is a Senior Lecturer in the Department of Computer Science at Middlesex University, School of Science and Technology. Their research spans Internet Security, Operating Systems, and Programming, with a strong focus on cybersecurity, artificial intelligence, and IoT applications. They are actively contributing to both academic and applied research in secure systems and intelligent computing. Research Interests: Dr. Cheng's work is centered on cybersecurity, deep learning, and intelligent systems. Key areas include intrusion detection in IoT, secure communication in autonomous vehicles, medical image analysis using neural networks, and privacy-preserving data sharing in healthcare systems. Their interdisciplinary approach bridges computer science with biomedical and social applications. The recent publications reflect a strong trend toward applying deep learning and hybrid AI models to critical domains such as healthcare (e.g., diabetic retinopathy, melanoma detection), cybersecurity (e.g., malware classification, threat detection), and smart infrastructure (e.g., smart buildings, UAVs). These works often combine advanced neural architectures with real-world constraints like resource limitations and data privacy. Scientific Contributions: Dr. Cheng has contributed significantly to journals such as IEEE Transactions on Intelligent Transportation Systems, Computers & Security, PLoS ONE, and Applied Sciences. Their collaborative research spans multiple continents and disciplines, including computer science, public health, and bioinformatics. Grants and Advising: While specific grants and students are not mentioned in the provided text, Dr. Cheng's extensive publication record suggests active research supervision and project leadership. They likely mentor students in AI, cybersecurity, and software engineering, and may be involved in funded projects related to secure IoT and intelligent transportation systems. Laboratories and Teams: Dr. Cheng appears to be part of a multidisciplinary research group at Middlesex University focused on intelligent systems and cybersecurity. Their collaborations with researchers in blockchain, cloud computing, and medical AI suggest involvement in a dynamic, cross-functional research environment.

Laetitia ChapelView profile

Professor

• Machine Learning
• Remote Sensing
• Time Series Analysis
• +3 more

Laetitia Chapel is a full professor in computer science at Institut Agro Rennes-Angers , conducting research within the OBELIX team at IRISA , a mixed research unit in computer science, signal/image processing, and robotics. Her work focuses on machine learning for structured data (time series, graphs, hierarchical representations) with applications in remote sensing, and she obtained her HDR in 2022. Research Interests center on advancing optimal transport theory for structured datasets, including time series alignment, hyperbolic embeddings, and Riemannian neural networks. She actively participates in international workshops and conferences, delivering invited talks at venues like NeurIPS and IEEE MLSP. Ambroise Odonnat (2024, Huawei-funded PhD) Léo Buecher (2024, supervised with Nicolas Courty) Sergio Osson (2025, supervised with Charlotte Pelletier) Guillaume Mahey (2024, with Gilles Gasso) Manal Hamzaoui (2023, with Sébastien Lefèvre and Minh-Tan Pham) Titouan Vayer (2020, now at INRIA) Pierre Gloaguen (2017, now assistant professor at UBS) Scientific Awards include the Best paper award at MajecSTIC 2005 and Best paper award at ICPRAI 2022 . Her research projects span from theoretical foundations (e.g., hyperbolic metric learning) to applied remote sensing tasks (e.g., cropland mapping in the Brazilian Amazon).

Nicola StrisciuglioView profile

Associate Professor

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

Nicola Strisciuglio is an Associate Professor at the University of Twente, affiliated with the Data Management and Biometrics group. His research focuses on robustness and generalization in computer vision and machine learning, with applications in biomedical imaging, privacy in face recognition, and efficient model architectures. Key research areas include Bias mitigation in training data Physics-informed models Energy-efficient AI Robustness against image corruptions His recent work emphasizes sparse training, vision transformers, and data-efficient models, as seen in publications at ICLR, CVPR, and IEEE conferences. He supervises ongoing PhD candidates in the Netherlands and abroad. Scientific contributions span theoretical advancements and practical applications in robotics and biometrics. He was awarded the NeurIPS 2023 Outstanding Reviewer Award.

Guru Swaroop BennabhaktulaView profile

Researcher

• Computer Science
• Artificial Intelligence
• Image Processing
• +3 more

Guru Swaroop Bennabhaktula is a Postdoctoral Researcher at the University of Groningen's Faculty of Science and Engineering, affiliated with the Bernoulli Institute. He holds an MSc and has conducted research leading to a doctoral thesis titled 'Leveraging image noise: source camera identification and increased robustness of convolutional neural networks' (2023). His research focuses on computer vision, artificial intelligence, and deep learning with particular emphasis on image forensics, CNN robustness, and sensor pattern noise analysis. He has supervised multiple student theses including projects on camera identification, siamese networks for image retrieval, and noise-resistant neural architectures. Key contributions include the PushPull-Net architecture for robust image classification and video camera identification techniques leveraging sensor noise. His work bridges fundamental computer science research with practical applications in digital forensics and neural network optimization. Award recognition includes the Ben Feringa Impact Award (2023). Bennabhaktula has taught courses like Information Retrieval (2021-2024) and served as teaching assistant for Introduction to Computing Science. His research portfolio features 8 peer-reviewed publications spanning conference papers and journal articles in prestigious venues like Pattern Recognition and Expert Systems with Applications.

Gangbing SongView profile

Professor

• Structural health monitoring
• Non-destructive testing
• Wind energy
• +7 more

Dr. Gangbing Song is a Moores Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston , where he has been a tenured Full Professor since 2008. He previously served as an Assistant Professor (1998–2002) and Associate Professor with Tenure (2002–2008) at the University of Houston. Ph.D. , Mechanical Engineering, Columbia University, 1995 M.S. , Mechanical Engineering, Columbia University, 1991 B.S. , Energy Engineering, Zhejiang University, 1989 His research spans structural health monitoring , machine learning , and smart materials , focusing on non-destructive testing , wind energy , and structural vibration control . Recent work includes entropy-based fault diagnosis , underwater bolt monitoring , and upcycling of wind turbine blades in a circular economy framework. Dr. Song has secured over $3.2 million in external funding (including 13 NSF awards) and established the Smart Materials and Structures Laboratory . He has published 96 peer-reviewed journal papers , 191 conference papers , and holds 3 U.S. patents . Outstanding Technical Contribution Award (ASCE, 2008) NSF CAREER Award (2001) Best Paper Award (Earth and Space’08) General Chair, ASCE Earth and Space Conference (2010) He has advised 13 Ph.D. and 31 M.S. students , delivered 10 keynote speeches , and developed educational programs including a remote laboratory and REU initiative . His work integrates robotics , smart sensors , and data-driven algorithms for structural integrity and sustainability applications.

Erfu YangView profile

Senior Lecturer

• Robotics
• Autonomous Systems
• Computer Vision
• +16 more

Dr Erfu Yang is a Senior Lecturer in the Robotics and Autonomous Systems (RAS) Group within the Department of Design, Manufacturing and Engineering Management (DMEM) at the University of Strathclyde, UK. He holds a Ph.D. in Robotics from the University of Essex (2008) and has held research fellow positions at the University of Stirling, Tokyo Institute of Technology, and the University of Edinburgh. He is an active researcher, principal investigator, and supervisor in advanced robotics and intelligent systems. Ph.D. in Robotics, University of Essex, UK (2008) Research Fellow, University of Stirling, UK Research Fellow, Tokyo Institute of Technology, Japan Research Fellow, University of Edinburgh, UK Dr Yang's research focuses on robotics, autonomous systems, computer vision, AI, machine learning, mechatronics, and optimization. His work integrates multi-agent reinforcement learning, fuzzy logic, neural networks, and bio-inspired algorithms into applications such as manufacturing automation, healthcare robotics, and autonomous inspection. He emphasizes intelligent human-robot collaboration, safety, and adaptability in dynamic environments. The recent publications reflect a strong trend in applying deep learning and AI to industrial quality control, cognitive assessment with social robots, autonomous navigation, and multi-objective path planning. His work bridges theoretical algorithm development with practical deployment in smart manufacturing and healthcare. Topics span from ResNet-based inspection systems to bio-inspired robotic design and fractional-order system modeling. TOP CITED ARTICLE 2023-2024 Recipient Top 2% Scientists Recipient IEEE Outstanding Service Award Best Poster for RoVER VIP Project at ESD@Strath Conference 2023 TOP CITED ARTICLE 2021-2022 Recipient Excellent Paper Recipient Best Paper Award (2019) Best Paper Award Nominee (2017) Dr Yang has secured over 15 research grants as PI or CI, including projects on intelligent cobots for farming, human-robot healthcare collaboration, and autonomous inspection in manufacturing. He supervises multiple PhD and research students and leads initiatives in smart manufacturing and assistive robotics. He is an Associate Editor for Cognitive Computation (Springer) and serves on IEEE and IET committees, including as Publicity Co-Chair for IEEE UK and Ireland Industry Applications Chapter. He is actively involved in the Robotics and Autonomous Systems research group at DMEM, leads the Bio-Inspired Robotics Design and Development project, and utilizes equipment such as the NMIS-DMEM Cobot UR10e. His work contributes to UN Sustainable Development Goals in industry, innovation, and health.

Mable FokView profile

Professor

• Photonics
• Soft Robotics
• Bio-inspired Photonics
• +5 more

Mable Fok is a Professor and Assistant Dean for Academic and Faculty Affairs at the University of Georgia's College of Engineering. Her research focuses on photonics and soft robotics, with the WAVE Lab advancing interdisciplinary applications in fiber optics, bio-inspired systems, and microwave photonics. She holds 5 patents and has secured funding from NSF, DARPA, and other agencies. Dr. Fok's work emphasizes biomimetic approaches, neuromorphic algorithms, and real-world applications in healthcare, agriculture, and robotics. Education: BSc, MSc, and PhD from Chinese University of Hong Kong, followed by postdoctoral training at Princeton University. Current roles include leading the Photonics and Soft Robotics Lab and overseeing academic affairs in the College of Engineering. Research Interests: Soft Robotics with embedded fiber optic sensors Bio-inspired photonics for adaptive systems Microwave photonics for dynamic RF systems Deep learning integration in sensing applications Key Awards: NSF CAREER Award (202X), UGA Creative Research Medal (202X), Ralph E. Powe Award (202X). Notable achievements include student-led award wins and groundbreaking work in photonics-based sensing. Teaching: Courses include Fiber Optics and You , Fundamentals of Logic Design , and Principles of Lasers and Photonics . Emphasizes foundational knowledge and self-learning skills. Labs/Teams: WAVE Lab (Photonics and Soft Robotics Laboratory) with specialized equipment for photonics, RF, and biomaterials research. Collaborates with industry partners and K-12 outreach programs.

Mariya ZhelevaView profile

Associate Professor

• Wireless Networks
• Spectrum Measurement
• Dynamic Spectrum Access
• +5 more

Mariya Zheleva is an Associate Professor in the Department of Computer Science at the University at Albany (SUNY), where she directs the UbiNET Lab. She holds a PhD and MS in Computer Science from UC Santa Barbara and MEng/BEng degrees from Technical University of Sofia. Her research develops wireless solutions for infrastructure-challenged regions, focusing on spectrum measurement, dynamic access, IoT networks, and rural emergency systems. She leads NSF-funded projects including a CAREER award (2019) for spectrum management and a $1.5M Smart and Connected Communities grant for rural emergency networks. Awards include the University at Albany President’s Award for Exemplary Public Engagement (2019) and the Dynamic Spectrum Alliance Research Award (2019). Current doctoral advisees include Vaasu Taneja, Andrew Okoro, and Ishrat Mohima. Recent publications address spectrum sensing algorithms, machine learning for modulation classification, and community network resilience. She co-founded the NSF SpectrumX Center for spectrum innovation.

• Machine Learning
• Computer Vision
• Hyperspectral Imaging
• +5 more

Associate Professor Ivan Lee is affiliated with the University of South Australia's STEM division at Mawson Lakes Campus. He actively supervises research degrees and contributes to interdisciplinary research intersecting computer science, food safety, and smart materials. Research Interests: Specializes in machine learning applications for hyperspectral imaging in food contamination detection Develops novel graph learning algorithms for real-time driver fatigue monitoring Investigates eco-friendly synthesis techniques for graphene-based nanomaterials Advances intelligent sensing systems for structural health monitoring Publications Trends: Recent work focuses on spatio-temporal graph modeling, multi-objective optimization algorithms, and applying deep learning to food safety challenges like aflatoxin detection. His research spans computer vision, IoT systems, and sustainable materials. Supervision: Currently serves as a research degree supervisor at University of South Australia. No scientific awards mentioned in available records.