Find Academic Supervisors

• Generative Models
• Computer Science
• Machine Learning
• +3 more

Tuomas Kynkäänniemi is a Doctoral Researcher at the School of Science's Department of Computer Science, affiliated with Professor Jaakko Lehtinen's professorship. His research focuses on generative models, diffusion models, and evaluation metrics for machine learning systems. He has contributed to advancements in image generation, algorithm optimization, and feature space analysis. Education highlights include an MSc Thesis awarded the prestigious 'Best MSc Thesis Award' in 2020. His work bridges theoretical computer science with practical applications in image processing and time-series analysis. Key research areas include disentangling cyclic/random effects in time-lapse sequences, improving diffusion model performance through novel guidance techniques, and evaluating generative models using metrics like Fréchet Inception Distance. Collaborations span academic institutions, with recent work appearing in top conferences like NeurIPS and ICLR. Award-winning contributions include pioneering studies on ImageNet class impacts on evaluation metrics and scalable training methods for StyleGAN2. His research emphasizes both foundational machine learning theory and real-world applicability in domains like planetary science and environmental monitoring.

Zafer DoganView profile

Assistant Professor

• Machine Learning
• Data Analytics
• Signal Processing
• +5 more

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

Marlous HallView profile

Associate Professor

• Epidemiology
• Cardiovascular Disease
• Multimorbidity
• +6 more

Dr. Marlous Hall is an Associate Professor in Epidemiology at the University of Leeds, affiliated with the Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM). She serves as Deputy Head of the Clinical and Population Sciences Department and co-leads the Leeds Institute for Data Analytics (LIDA) Health Community. Her expertise lies in advanced analytical methods applied to large clinical datasets, focusing on cardiovascular survivorship, multimorbidity, and causal inference. She holds a Sir Henry Wellcome Fellowship and has established the Survivorship and Multimorbidity Epidemiology Research Group. Educations: BSc Mathematics (University of Sheffield), MSc Statistics (University of Sheffield), PhD in Paediatric Cancer Epidemiology (University of Leeds) Professional Roles: Co-Director of the British Heart Foundation PhD Programme, Member of LICAMM Executive Committees, and Fellow of the Higher Education Academy Her research emphasizes multimorbidity patterns in cardiovascular patients, frailty management, and improving guideline adherence. Recent work includes analyzing 1.6 million cardiac surgery patients and 56 million myocardial infarction survivors. She supervises multiple PhD students exploring topics like anticoagulation in atrial fibrillation and blood pressure management in frail populations. Awards: Sir Henry Wellcome Fellowship (2014), University Academic Fellowship (2017) Labs/Teams: Survivorship and Multimorbidity Epidemiology Research Group, Leeds Institute for Data Analytics

Donna AnkerstView profile

Associate Professor

• Biostatistics
• Clinical Risk Prediction
• Prostate Cancer Research
• +3 more

Professor Donna Ankerst holds an Associate Professorship in Biostatistics at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. She also serves as a Research Professor at the University of Texas Health Sciences Center in San Antonio. Her work spans clinical risk prediction (e.g., prostate cancer risk calculators), deep learning for medical imaging (e.g., 3D mammograms), and climate change impacts on Bavarian forests and health. She has pioneered internationally validated tools like the prostate cancer risk calculator and collaborates with TUM's Rechts der Isar Hospital and School of Life Sciences. Education: PhD in Statistics (Carnegie Mellon University), postdoctoral training at Harvard University and the Fred Hutchinson Cancer Research Center. Awards include the 2018 TUM PhD Supervisor Award and the 2015 Prostate Cancer Dream Challenge win. She teaches courses on applied regression, statistical computing, and data analysis. Research focuses on integrating biostatistical methods with clinical and environmental data, including multi-center trials, electronic health records, and climate modeling. Key projects include predictive algorithms for cancer diagnosis and climate-driven health outcomes. Grants managed include collaborations on kidney/pancreas transplantation outcome tools and prostate cancer decision support systems. Labs/Teams: Part of TUM's Data Science initiatives and interdisciplinary groups like the TUM School of Life Sciences. She chairs the Data Safety Board for the WISDOM breast cancer trial and edits Biometrics reviews.

• Sensor Systems
• Embedded Systems
• Ultrasound Imaging
• +7 more

Stefan Johann Rupitsch is a Professor at the University of Freiburg, leading the Professorship for Electrical Measurement Technology and Embedded Systems since December 2020. He holds a prominent role in academic and industrial research collaborations, focusing on interdisciplinary fields such as sensor systems, biomedical engineering, and embedded technologies. His work bridges theoretical research with practical applications, including drug delivery systems, environmental monitoring, and disaster response technologies. **Education**: Information not explicitly provided in the text. **Research Interests**: His primary research areas include wireless sensor networks, ultrasound imaging for biomedical applications, magnetic nanoparticle localization, and energy-efficient embedded systems. He has pioneered advancements in magnetomotive ultrasound for drug targeting and developed innovative sensor platforms for industrial and environmental monitoring. His projects often integrate machine learning and robotics to enhance system precision and autonomy. **Projects**: As a project manager or coordinator, he oversees initiatives such as wireless microsensor probes for tree canopy monitoring (B1), cochlear implant sensing (CIAS), and disaster-site radar systems (avaRES). Recent efforts focus on sustainable sensor technologies for wear part optimization (Smart Belt) and gas infrastructure resilience (LEAKALERT). **Grants & Advising**: Rupitsch has supervised numerous theses in fields like sensor design, AI for building acoustics, and earthquake monitoring. He actively participates in reviewing academic works and industry projects, reflecting his leadership in technical communities. **Labs & Teams**: His research is conducted through collaborations with the Microsystems for Biomedical Imaging Laboratory and the Sustainability Performance Center, emphasizing interdisciplinary teamwork and real-world impact.

Jun LiView profile

Lecturer

• Data Analytics
• Machine Learning
• Deep Learning
• +7 more

Dr. Jun Li is a Lecturer in Mathematics and Computer Science at Cranfield University's Centre for Computational Engineering Sciences. His expertise spans Data Analytics, Machine Learning, Deep Learning, and Mathematical Modelling applied to diverse domains including aerospace, urban planning, and cybersecurity. BSc in Computer Science from QingDao University MSc in Software Engineering from LondonMet University PhD in Neural Networks from LondonMet University Dr. Li's research focuses on integrating Machine Learning with High Performance Computing and Mathematical Modelling. Key areas include: Large-scale Neural Network simulations in Neuroscience Deep Learning for aircraft engine defect detection via computer vision Neural Network language modeling with noise resilience Transport flow analysis using Complex Networks Urban agglomeration formation and imbalanced economy recovery modeling His recent publications highlight interdisciplinary applications of Machine Learning in transport systems, aerospace diagnostics, and cybersecurity. These works emphasize data-driven methods, GANs, and predictive analytics for real-world challenges. Current grants include funding from China's National Key Research and Development Program (2016YFC0503506) and CAS's Bingwei Excellent Young Scientists Programme (2015RC202). Dr. Li supervises MSc and PhD students in Artificial Intelligence, Machine Learning, and Big Data analytics. He collaborates with Cranfield's Centre for Computational Engineering Sciences, leading projects like: Airport service quality evaluation using Skytrax reviews Urban land-use transport modeling for policy impact analysis Imbalanced economic recovery frameworks post-disasters

Pinjia HeView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Natural Language Processing
• +5 more

Pinjia He is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen, where he leads research in software engineering, AI systems, and log analysis. He received his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and his B.E. from South China University of Technology. His research focuses on three interconnected areas: AI for Software Engineering : Developing LLM-based tools for automated logging, code repair, and AIOps Software Engineering for AI : Ensuring safety and reliability of AI systems through testing and validation Software Systems : Log analysis, anomaly detection, and performance optimization in distributed systems Recent publications demonstrate strong emphasis on testing methodologies for AI systems (LLMs, vision-language models), automated log analysis techniques, and AI reliability. His work consistently appears in top-tier venues including ICSE, FSE, ASE, and ICLR. He has received significant recognition including: Most Influential Paper Award (ISSRE) IEEE Open Software Services Award IEEE CS TCSE Rising Star Award He leads a substantial research team with 9 PhD and 8 Master's students, focusing on AI/SE integration. The group maintains active industry collaborations and open-source projects with over 5,000 GitHub stars.

Przemysław ŚliwińskiView profile

Professor

• Image Processing
• Behavioral Modeling
• Nonlinear Systems
• +4 more

Przemysław Śliwiński serves as a Professor at Wroclaw University of Science and Technology within the Faculty of Information and Communication Technology, specifically in the Department of Control Systems and Mechatronics. His academic credentials include DSc, PhD, and Engineering degrees, reflecting extensive expertise in advanced computational methodologies. His research spans three primary domains: Image Processing focusing on object detection, 3D mapping, and autofocusing algorithms Autistic Behavior Modeling utilizing stochastic frameworks, emotion detection, and augmented reality applications Nonlinear System Identification specializing in nonparametric algorithms and computational methods for complex systems His work demonstrates consistent innovation in bridging theoretical mathematics with practical engineering solutions. His publication trends reveal increasing focus on intelligent systems and machine learning applications since 2019, with particular emphasis on event processing, neural network implementations, and real-time system identification. Earlier work centered on foundational nonlinear system modeling techniques. While no formal awards are listed in available documentation, his research has consistently appeared in high-impact journals including IEEE Transactions and Springer proceedings. Professor Śliwiński maintains active supervision of research projects with collaborators including Wachel P., Lagosz S., and Helt K., though formal student advising relationships aren't explicitly documented. His laboratory work appears centered on control systems validation and image processing applications within the Department of Control Systems and Mechatronics infrastructure.

• Structural Health Monitoring (SHM)
• Machine Learning
• Seismic Engineering
• +7 more

Eloi Figueiredo is a Full Professor at the Faculty of Engineering of Lusófona University in Lisbon. His expertise lies in Structural Health Monitoring (SHM) , Machine Learning , and Bridge Management . He leads the Civil Research Group focused on sustainable infrastructure and serves as Associated Editor for the Structural Health Monitoring: An International Journal . Research Interests : SHM algorithms, climate change adaptation of bridges, damage identification via machine learning, finite element modeling. Projects : ClimaBridge Project (EEA Grant), Buildingadapt Project on climate resilience. Collaborations : European Union, United States, Brazil, UK, Norway institutions. Awards : EEA Grant for climate-bridge research. His 15 most recent publications address transfer learning , Bayesian calibration , and climate impact on structural integrity , with 100+ total SHM publications and 70+ opinion pieces promoting science. He has delivered international keynote speeches and developed tools like SHMTools and smartphone-based monitoring applications.

Başak Esin KÖKTÜRK-GÜZELView profile

Assistant Professor

• Statistical Learning Theory
• Signal Processing
• Pattern Recognition
• +5 more

Başak Esin KÖKTÜRK-GÜZEL is an Assistant Professor at İzmir Democracy University's Electrical and Electronics Engineering Department and Co-Founder of Zoi Data. Her research focuses on Image Processing, Machine Learning applications in Marketing AI, and interdisciplinary projects in biotechnology and healthcare. She actively collaborates on initiatives like the ERA-NET NEURON project (Brain-Body interactions) and the INSECTAI Annual Meeting (insect conservation). Recently published work combines statistical methods and machine learning to optimize xylanase production in Fermentation (MDPI). Education: Unspecified in text. Affiliations: İzmir Democracy University, Zoi Data. Research Interests include Statistical Learning Theory, Signal Processing, and real-world applications like Sales Prediction and Customer Segmentation. She emphasizes translating machine learning into practical tools for clinicians and researchers through Zoi Data. Key Projects: Participated in the 2025 ERA-NET NEURON kick-off meeting and presented at the Workshop on Advancing Medical Imaging. Active in COST Action CA22129 for insect monitoring. Grants/Awards: None explicitly mentioned. Collaborative projects highlighted instead. Zoi Data focuses on automated bioanalytical imaging solutions, aligning with her mission to drive healthcare innovation through AI-driven tools.

Yan ZhuView profile

Research Fellow

• Photovoltaic systems
• Solar cell technology
• Semiconductor characterization
• +3 more

Dr. Yan Zhu is a Research Fellow at the University of New South Wales (UNSW), affiliated with the School of Photovoltaic and Renewable Energy Engineering. She is currently part of the ACDC research team, focusing on advanced characterization techniques for photovoltaic materials and devices. Dr. Zhu holds a PhD in Photovoltaic Engineering from UNSW, complemented by international education including a Master's from École Polytechnique (France) and a Bachelor's from Shanghai Jiao Tong University. Her research specializes in photovoltaic systems and semiconductor characterization, with expertise in: Development of novel characterization methods for solar materials Defect analysis in silicon and semiconductors using photoluminescence Performance optimization of monocrystalline and multijunction solar cells Advanced metrology for photovoltaic device quality improvement Her publication portfolio demonstrates strong focus on photovoltaic innovation (60% of recent works), complemented by interdisciplinary contributions in computer vision applications for biometrics (25%) and medical imaging (15%). The research shows consistent emphasis on measurement accuracy, material reliability, and efficiency enhancement in renewable energy systems. Awards and Honors: Dean's Award for Outstanding PhD Theses (UNSW) Best Student Award - 7th World Conference on Photovoltaic Energy Conversion Steve Robinson Memorial Prize (UNSW) Dr. Zhu leads research within the ACDC photovoltaics characterization laboratory at UNSW, collaborating on projects developing next-generation solar cell diagnostic techniques. Her work bridges fundamental semiconductor research and industrial applications for renewable energy solutions.

• Digital Signal Processing
• Spectral Analysis
• Pattern Recognition
• +7 more

Maria Ragousi is a faculty member in the Department of Electrical and Electronic Engineering at the University of West Attica, affiliated with the School of Engineering. She holds a PhD in Digital Signal Processing and Pattern Recognition from NTUA (1994), an MSc from the University of Virginia (1990), and a Diploma in Electrical Engineering from NTUA (1987). Her academic role is as a Lecturer, focusing on courses such as Digital Signal Processing, Biomedical Signal Processing, and Educational Technologies. Education: PhD (1994): Digital Signal Processing & Pattern Recognition, NTUA MSc (1990): Digital Signal Processing, University of Virginia Diploma (1987): Electrical Engineering, NTUA Her research interests span Digital Signal Processing (DSP), Spectral Analysis, Pattern Recognition (including neural networks and genetic algorithms), Biomedical Signal Processing, Wireless Sensor Networks for environmental monitoring, and E-Learning technologies. She has contributed to over 30 peer-reviewed publications, including work on EEG-based person identification, environmental sound classification, and educational data mining. Recent research emphasizes integrating machine learning into education and developing tools for environmental acoustic monitoring. She has collaborated on projects like the ESOUNDMAPS initiative, deploying wireless sensor networks for real-time environmental sound mapping. Her work also explores active learning methodologies in physics education and visualization of e-learning platform data. Maria advises courses at both undergraduate and postgraduate levels, including specialized programs in medical signal analysis and smart electronic systems. She actively participates in academic governance and has led several educational innovation projects funded by national and international grants.

Sercan YALÇINView profile

Associate Professor

• Wireless Sensor Networks
• Machine Learning
• Deep Learning
• +6 more

Sercan YALÇİN is an Associate Professor at the Department of Computer Engineering, Faculty of Engineering, Adiyaman University. He holds a PhD in Computer Engineering from Firat University (2021), with research focused on wireless sensor networks, machine learning, and AI applications in healthcare. Previously held roles include Research Assistant at Firat University (2014-2021) and Bayburt University's IT Department as a Computer Engineer (2013-2014). His expertise spans clustering algorithms, network optimization, and embedded systems design. Education: Bachelor's in Computer Engineering, Firat University (2013) Bachelor's in Business Administration, Anadolu University (2015) Master's in Computer Engineering, Firat University (2016) PhD in Computer Engineering, Firat University (2021) Research Interests: Specializes in wireless sensor networks, AI-driven healthcare solutions, cognitive radio networks, and optimization techniques. Recent work focuses on federated learning for edge computing, fault detection in industrial systems using deep learning, and energy-efficient routing protocols. Publications & Awards: Authored over 30 peer-reviewed papers in journals like Expert Systems with Applications and IEEE transactions. Notable awards include TÜBİTAK Project Support (2016-2021) and the ÖYP Scholarship during doctoral studies. Current projects include AI-based reservoir water level prediction and aortic disease diagnosis using deep learning. Grants & Roles: Deputy Head of Department at Adiyaman University. Active in interdisciplinary collaborations, including projects on climate dynamics estimation and EV battery management optimization. Supervises research on federated learning frameworks and wireless network security.

Yijin LiuView profile

Associate Professor

• Energy Storage
• Materials Science
• Battery Technology
• +4 more

Associate Professor Yijin Liu is affiliated with the Walker Department of Mechanical Engineering at the University of Texas at Austin. Prior to this role, he served as a Lead Scientist (2020), Staff Scientist (2015), and Associate Staff Scientist (2012) at the SLAC National Accelerator Laboratory, Stanford University. His academic background includes a B.S. (2004) and Ph.D. (2009) in Optics from the University of Science & Technology of China (USTC), followed by postdoctoral research at Stanford. **Research Focus**: Dr. Liu specializes in energy storage materials, particularly leveraging X-ray characterization methods combined with machine learning and data mining. His work addresses challenges in battery manufacturing, safety, degradation, and failure analysis. Key areas include high-throughput experimental methods for battery cathodes, chemomechanical interactions, and in-situ visualization of material dynamics. **Key Technical Contributions**: He pioneered techniques such as operando microscopy for studying particle network dynamics in composite cathodes and developed machine learning frameworks for analyzing material defects and cathode detachment. His methodologies have advanced understanding of oxygen redox mechanisms in layered oxides and thermal-healing processes in battery materials. **Awards**: No specific awards are listed, but his impactful contributions to battery science are reflected in his extensive publication record and leadership in national lab research programs. His work bridges fundamental material science with industrial applications, informing both academic and industrial battery development. **Labs/Teams**: The Liu Group at UT Austin focuses on interdisciplinary approaches to energy storage, integrating advanced imaging, computational modeling, and experimental innovations. Visit his group website for ongoing projects and collaborations.

• Sports Science
• Sports Psychology
• Talent Development in Sports
• +4 more

Dr. Daniel Leyhr is a researcher at the Institute of Sports Science and Methods Center of the Eberhard Karls University of Tübingen. He serves as Head of the junior research group 'Data Science in Sports' since 2021 and is a habilitation candidate at the Faculty of Economics and Social Sciences since 2020. His work bridges sports psychology, biomechanics, and data science, focusing on talent development, biological maturation, and motor performance in youth athletes. First State Examination for secondary school teaching (2014) Diploma in Mathematics (2014) Promotion/PhD (2019) Habilitation candidate (2020–present) Dr. Leyhr's research examines the predictive validity of talent characteristics in youth soccer, biological development in youth competitive sports, and data-driven approaches to performance assessment. He has co-authored studies on relative age effects, maturity-related selection biases, and the intersection of psychological need satisfaction in NCAA student-athletes. His publications span high-impact journals like European Journal of Sport Science and Journal of Sports Sciences , with methodological expertise in longitudinal studies, MRI diagnostics, and deep learning-based factor analysis. Current projects include integrating data science into sports talent identification and refining maturity assessment protocols. Dr. Leyhr serves as a reviewer for journals such as PLoS One , Frontiers in Psychology , and Annals of Human Biology . He has presented at major conferences including the European College of Sport Science (ECSS) and World Conference on Science and Soccer (WCSS). As Equal Opportunities Officer (2023) and member of the Methods Center Advisory Board (since 2019), he contributes to academic governance and diversity initiatives.