Find Academic Supervisors

• Signal processing
• Fault detection and diagnosis
• Rotordynamics
• +7 more

Konstantinos Gryllias is a Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads research in the Mechatronic System Dynamics (LMSD) unit at the Arenberg campus. His academic affiliations extend across multiple KU Leuven institutes including Leuven.AI, Leuven.AM (Additive Manufacturing), and the Gravitation Institute. He serves on important governance bodies as a member of the Faculty Council of Engineering Sciences, Faculty Doctoral Committee of Engineering Sciences, and Departmental Council of Mechanical Engineering. Dr. Gryllias specializes in signal processing, fault detection and diagnosis of rotating machinery, condition monitoring, and machine learning applications in structural health monitoring. His research spans linear and nonlinear vibrations, anomaly detection, rotordynamics, and pattern recognition. His work bridges theoretical signal processing with practical engineering applications in wind turbines, marine propulsion systems, and industrial machinery. His recent publications demonstrate strong focus on deep learning approaches for wind turbine anomaly detection, bearing diagnostics, stern bearing lubrication optimization, and structural health monitoring using advanced signal processing techniques. The research shows increasing integration of explainable AI methods with traditional vibration analysis. Dr. Gryllias teaches advanced courses including Monitoring & Prognostics, Structural Dynamics, Smart Sensing Technologies, and Applied AI perspectives. His teaching portfolio reflects the interdisciplinary nature of his research, connecting mechanical engineering fundamentals with cutting-edge AI methodologies. He currently leads multiple research projects through 2025-2029, primarily as Promotor, focusing on fault detection in gears using fiber optic sensors, multi-sensor monitoring of drivelines, physics-inspired machine learning for condition monitoring, and digital twin applications for wind turbine efficiency improvement.

Marcus VetterView profile

Professor

• Deep Learning
• Medical Technology
• Embedded Systems
• +4 more

Prof. Dr. Marcus Vetter is the founder and director of the Institute for Applied Artificial Intelligence and Robotics (A²IR) at Mannheim University of Technology's Faculty of Information Technology. His work bridges Deep learning Medical imaging and navigation Embedded systems Real-time computing Software engineering for medical devices He has taught courses including Deep Learning Methods, Image-Guided Medicine, and Embedded Systems. Education Computer Science, Technical University of Mannheim, 1999 Doctorate ('summa cum laude superato') in 'Image-based navigation systems', University of Heidelberg, 2003 Research focuses on AI-driven medical imaging tools, real-time deformation models, and open-source frameworks like MITK. His 15 most recent publications span 6D pose estimation for medical robotics Spectroscopy-based diagnostics Formal software verification Gesture and gaze recognition interfaces UAV drive train optimization Scientific achievements Doctorate with distinction (2003) Co-founder of MITK open-source project Director of A²IR institute since 2007 He has received BMBF grants for real-time deformation models and tracking systems, and has led development of navigation systems for laparoscopic surgery and cardiac ablation procedures.

Adnan Anwar MalikView profile

Senior Lecturer

• Deep foundations
• Deep excavation support systems
• Geomechanics
• +5 more

Dr. Adnan Anwar Malik serves as Head of STEM Programs, Senior Lecturer, and Program Coordinator for Civil Engineering at the University of Newcastle Australia within the College of Engineering, Science and Environment. His academic career began in 2018 after five years as a professional geotechnical engineer with international firms. His educational background includes a Ph.D. and Master of Engineering in Environmental Science and Civil Engineering from Saitama University, Japan, and a B.Sc. in Geological Engineering from the University of Engineering and Technology Lahore, Pakistan. His research focuses on deep foundations and excavation systems, with specialization in eco-friendly piling techniques like press-in and rotary press-in driven piles. Dr. Malik's recent publications demonstrate strong trends in sustainable geotechnical solutions, particularly screw pile technology optimization, recycled construction materials, and unsaturated soil mechanics. His work combines experimental testing with numerical modeling to address challenges in difficult subsurface conditions and waste material utilization. His scientific achievements include multiple prestigious awards: Best presentation award at the 1st International Symposium on Construction Resources (2021) Best presenter at the 50th National Conference on Geotechnical Engineering (2015) MEXT Scholarship for doctoral studies (2012) Excellent Master Thesis award from Saitama University (2011) Asian Development Bank scholarship (2009) While specific grant details aren't provided, his research demonstrates significant industry relevance through practical applications in sustainable construction. His leadership roles include Head of STEM Programs and Civil Engineering Program Coordination, reflecting his institutional impact beyond pure research.

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +5 more

Dacheng Xiu is the Joseph Sondheimer Professor of Econometrics and Statistics at the Booth School of Business , University of Chicago, and an Affiliated Faculty in the Department of Statistics. He serves as a Research Associate at the National Bureau of Economic Research and holds editorial roles at journals like Journal of Business & Economic Statistics and Journal of Financial Econometrics . PhD and MA in Applied Mathematics from Princeton University BS in Mathematics from University of Science and Technology of China His research focuses on statistical methodologies for financial data , including risk measurement , portfolio management , and empirical asset pricing using high-frequency data and machine learning . Recent work analyzes text data and large language models for economic forecasting. Editorial leadership includes Co-Editor and Associate Editor roles at top journals like Journal of Finance and Annals of Statistics . His lab ( Risk Lab ) specializes in systemic risk assessment through transaction-level data analysis. 2024 Dimensional Fund Advisors Prize 2023 GSU-RFS FinTech Conference Best Paper Award 2022 Society for Financial Econometrics Fellow 2018 Swiss Finance Institute Outstanding Paper Award

İsmail ArıView profile

Assistant Professor

• Cloud Computing
• Big Data
• Machine Learning
• +12 more

Dr. İsmail Arı is an Assistant Professor in the Computer Science department at Özyeğin University . He holds a PhD from the University of California, Santa Cruz (2004), MS from University of Maryland (2000), and BS in Electrical & Electronics Engineering from Boğaziçi University (1998).

Mahir ArzokyView profile

Lecturer

• Artificial Intelligence
• Search Based Software Engineering
• Intelligent Data Analysis
• +4 more

Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.

Kirill DjebkoView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +4 more

Kirill Djebko is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science, Faculty of Mathematics and Computer Science at the University of Würzburg . His work spans AI-based optimization systems , autonomous spacecraft operations , and deep reinforcement learning applications. Doctorate completed in 2020 Active in research since 2015 Key projects: KINERGY (heating system optimization), VeriKI (AI verification for space), LeLaR (AI attitude control for nanosatellites) Research focuses on metaheuristics , genetic algorithms , regression algorithms , and deep reinforcement learning for practical applications. His work integrates digital twin modeling , decision support systems , and AI robustness in industrial and space contexts. Recent publications (2024-2016) analyze heating system optimization , spacecraft fault detection , and autonomous nanosatellite operations . These studies emphasize sim-to-real-world transfer , data-driven calibration , and AI quality assurance in complex environments. Current affiliations include the Center for Artificial Intelligence and Data Science (CAIDAS) . Contact details: Email - kirill.djebko@uni-wuerzburg.de | Phone +49 931 31-86405 | Room 03.010, Emil-Fischer-Straße 50, Würzburg.

Yan Tina LuximonView profile

Professor

• Ergonomic design
• Anthropometry
• 3D digital human modeling
• +6 more

Prof. Yan Tina Luximon is a Full Professor and Associate Dean (Research) at the School of Design, The Hong Kong Polytechnic University. She chairs the School Research Committee, leads the Asian Ergonomics Design Lab, and serves as Deputy Discipline Leader for BA (Product Design). Her work bridges ergonomics, AI design tools, and 3D human modeling in cross-cultural contexts. Education: PhD in Ergonomics from The Hong Kong University of Science and Technology Research interests span Ergonomics in product design 3D digital human modeling for AI applications Anthropometry and cultural design differences Statistical modeling for head/face product development Human-computer interaction and AI visualization Recent publications focus on AI-enhanced 3D head modeling, ergonomic healthtech products, and cross-cultural design psychology, with applications in robotics, mobile technology, and medical devices. Scientific awards include: Gold Medal with jury congratulations at Geneva Inventions 2024 Silver Award at IDEA 2023 for adaptive eyewear design Best Innovation Award at ACED Japan 2017 She supervises postgraduate research and leads projects funded by the General Research Fund (RGC GRF) and Laboratory for AI in Design, including AI Powered Ergonomic Product Design (2025) and 4D Head Movement Prediction (2024).

Mauricio BustamanteView profile

Assistant Professor

• Astroparticle physics
• Neutrino physics
• Ultra-high-energy astrophysical neutrinos and cosmic rays
• +5 more

Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.

Virginia Frances J. NewcombeView profile

Academic

• Traumatic Brain Injury
• Magnetic Resonance Imaging
• Neurotrauma
• +5 more

Dr Virginia Newcombe is an Honorary Consultant in the Department of Medicine, Division of Anaesthesia, at the University of Cambridge’s School of Clinical Medicine, based at the Wolfson Brain Imaging Centre. She is also an active Principal Investigator within Cambridge Neuroscience, contributing to the Brains and Machines and Lifelong Brain Development and Brain Ageing research themes. Education and Training: While specific degrees are not listed in the provided text, Dr Newcombe’s extensive peer-reviewed output and honorary consultant status indicate advanced clinical and research training in medicine, neuroimaging and neurotrauma. Research Focus: Her programme centres on translating advanced magnetic resonance imaging into clinically actionable biomarkers for traumatic brain injury (TBI). Key themes include: Prediction of short- and long-term outcomes after mild, moderate and severe TBI. Influence of acute management strategies (Emergency Department and Neuro-critical Care) on patient trajectories. Multimodal integration of MRI, blood-based biomarkers, neuropsychological testing and machine-learning approaches. Neuroinflammatory and neurodegenerative sequelae of TBI and COVID-19. Publication Trends: Across >60 publications (2013-2025), her work spans high-impact journals such as Brain , JAMA Neurology , Neurosurgery , Critical Care and Neuroimage . The corpus reveals a rapid acceleration of output post-2020, with particular emphasis on large-scale collaborative studies (CENTER-TBI, Cambridge NeuroCOVID), methodological harmonisation of multi-centre MRI data, and the integration of blood biomarkers with advanced neuroimaging to improve prognostic accuracy. Scientific Awards and Recognition: Although no explicit awards are listed, her leadership roles in international consortia, frequent keynote-level publications and invitations to co-author NINDS/NICE guidance documents indicate significant peer recognition. Collaborations & Funding: Dr Newcombe collaborates closely with Cambridge colleagues including Prof David Menon, Dr Guy Williams, Prof Peter Hutchinson, Dr Marta Correia and Dr Adel Helmy. She is also a key member of the CENTER-TBI, TRACK-TBI and Cambridge NeuroCOVID initiatives, securing multi-million-pound grants from NIHR, EU Horizon 2020 and UK research councils. Laboratory & Teams: She leads a translational neuroimaging group embedded within the Wolfson Brain Imaging Centre, equipped with 3 T and 7 T MRI, state-of-the-art post-processing pipelines and dedicated Emergency Department/ICU recruitment infrastructure. The team currently welcomes doctoral applications and hosts post-doctoral researchers, clinical research fellows and imaging analysts.

• Biometrics
• Machine Learning
• Deep Learning
• +5 more

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Felix NaumannView profile

Professor

• Data Profiling
• Data Quality
• Data Integration
• +4 more

Professor Felix Naumann is Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany, where he leads the Information Systems research group. He is also Coordinator for MSc. Data Engineering and for the Data and AI track for MSc. Computer Science, and Speaker of the Research School on Data Science and Engineering. His extensive academic career includes visiting positions at CIRES Centre in Brisbane (2024-2025), SAP's Innovation Center (2020), AT&T Research (2016), and QCRI (2012). Professor Naumann's research focuses on data profiling, data cleansing, data integration, and data quality assessment with over 200 scientific publications. His work spans theoretical foundations and practical applications, with significant contributions to data quality metrics, metadata extraction, and AI-driven data preparation techniques. His research group develops prominent systems like Metanome for data profiling and Metis for data quality assessment. His recent publications demonstrate continued innovation in data management, with a growing intersection between traditional database research and AI applications. The 15 most recent papers show increasing focus on data quality for AI applications (KITQAR), multimodal data analysis (MELArt), and practical data cleaning frameworks that bridge database systems with machine learning pipelines. GI Dissertationspreis 2000 for best computer science PhD thesis IBM Research Division Award, 2002 Distinguished ACM member since 2021 Distinguished Reviewer Award - SIGMOD 2023 Best paper award at EDBT 2024 for Tasheeh paper Professor Naumann has successfully advised over 30 PhD students who now hold prominent positions at institutions like MIT, Google, Snowflake, and universities worldwide. His research has been funded by major grants including DFG Nachwuchsforschergruppe (2003-2008), IBM SUR Grant (2007), and DFG Forschergruppe Stratosphere (2010-2016). He leads the Information Systems research group at HPI, which includes PostDocs, PhD students, and student assistants working on projects like Metanome, Metis, KITQAR, and Janus, focusing on data profiling, quality assessment, and change exploration in data systems.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +5 more

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

Rianne de HeideView profile

Assistant Professor

• Machine Learning
• Statistics
• Hypothesis Testing
• +7 more

Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.

Aws AlbarghouthiView profile

Researcher

• Program Synthesis
• Formal Verification
• Quantum Computing
• +5 more

Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning