Find Academic Supervisors

Brian M. DeeganView profile

Researcher

• Computer Vision
• Automotive Imaging Systems
• Image Processing
• +3 more

Brian M. Deegan is an academic researcher affiliated with the University of Galway, Ireland, and previously with Valeo (2011–2022). His primary focus is on automotive imaging systems, sensor technology, and computer vision applications for autonomous vehicles. Research Interests: His work spans LED flicker analysis, camera calibration, object detection robustness in varying conditions, and wireless communication challenges for vehicle-to-everything (V2X) systems. He contributes to improving automotive sensor performance through advanced image processing techniques and simulation methodologies. Publications: Over 30 peer-reviewed articles since 2014, with recent emphasis on optimizing camera systems for autonomous applications. Key topics include fisheye optics, exposure time optimization, and hyperspectral imaging for perception tasks. Collaborations: Regular co-authorship with researchers from Valeo, NUI Galway, and industry partners. His interdisciplinary work bridges academia and automotive industry R&D.

• Biomedical Engineering
• Automotive Systems
• Computer Vision
• +3 more

Martin Glavin is a Professor at the National University of Ireland, Galway, affiliated with the Department of Electronic & Electrical Engineering within the College of Engineering and Informatics. His research focuses on cutting-edge areas such as biomedical engineering, automotive systems, and computer vision. Key contributions include advancements in image processing for low-light environments, driver gaze analysis for cyclist safety, and optimizing automotive camera systems. He has collaborated extensively with co-authors like Edward Jones and Darragh Mullins, yielding over 70 peer-reviewed publications since 2002. His work spans journals like IEEE Transactions and conferences such as ICC and ICARCV. Research interests include safety-critical object detection for autonomous vehicles, microwave imaging for medical diagnostics, and 5G-enabled V2X communication systems. Glavin's interdisciplinary efforts bridge signal processing, machine learning, and engineering applications, addressing real-world challenges in healthcare and transportation.

• Automotive Systems
• Biomedical Engineering
• Computer Vision
• +3 more

Edward Jones is a prominent academic researcher with a focus on interdisciplinary studies at the intersection of computer vision, biomedical engineering, and automotive systems. His work spans multiple domains including image processing, sensor optimization for autonomous vehicles, and medical imaging technologies. Collaborating extensively with institutions and researchers in Ireland and beyond, he has contributed to advancing perception systems in challenging environments and improving safety-critical applications in transportation. Research Interests: Automotive perception systems, medical imaging, low-light imaging, wireless communication for vehicular networks, and algorithm optimization for embedded systems. Key Contributions: Published over 90 papers in top-tier journals/conferences such as IEEE Transactions on Intelligent Transportation Systems, Sensors, and IEEE Access. Notable work includes low-light image enhancement algorithms, cyclist safety analysis via driver gaze tracking, and optimizing camera exposure for automotive applications. Recent trends in his publications emphasize cross-disciplinary solutions for autonomous systems, leveraging advances in computer vision and signal processing to address real-world challenges like adverse weather conditions and sensor degradation. Recipient of grants for projects involving automotive networks, breast cancer detection via microwave imaging, and distributed speech recognition systems.

• Wireless Communication
• Internet of Things (IoT)
• Network Protocols
• +4 more

Jong-Deok Kim is an active researcher in wireless communication and IoT systems, with frequent collaborations on publications spanning dynamic channel bonding, network optimization, and low-power protocols. His work addresses challenges in Wi-Fi, LoRa, and millimeter-wave networks, focusing on throughput, latency, and reliability. Research Focus: Wireless networks, edge computing, and blockchain for IoT. Key Topics: Channel allocation, federated learning, and error compensation methods. His recent publications highlight trends in adaptive algorithms for dense networks, hybrid positioning systems, and federated learning applications. Awards and grants are not explicitly mentioned in the provided data.

• Cross-Modal Retrieval
• Image-Text Matching
• Agricultural Robotics
• +4 more

Mingyong Li is a Professor in the Department of Agricultural Engineering at Huazhong Agricultural University's College of Engineering, specializing in agricultural robotics, computer vision, and cross-modal retrieval systems. His research bridges agricultural engineering with advanced machine learning techniques, focusing particularly on rice transplanting automation and precision agriculture systems. His primary research interests include Cross-Modal Retrieval, Image-Text Matching, Agricultural Robotics, Computer Vision, Machine Learning, Semantic Analysis, and Emotion Recognition. Li's work demonstrates strong integration between theoretical machine learning advancements and practical agricultural applications, with particular emphasis on developing vision-based systems for crop monitoring and robotic manipulation in farming environments. Analysis of his recent publications (2023-2025) reveals a strong trend toward multi-modal learning systems that integrate visual perception with textual understanding, particularly applied to agricultural robotics. His work shows increasing sophistication in handling ambiguity in image-text matching while maintaining practical applications in agricultural machinery automation. The research spans both theoretical machine learning advancements and their concrete implementations in agricultural equipment. Professor Li has mentored several researchers who have become frequent collaborators, including Junyu Chen, Yewen Li, and Mingyuan Ge. His work demonstrates consistent funding support through numerous collaborative projects focused on agricultural automation and intelligent systems. His laboratory appears to focus on agricultural robotics systems, with particular emphasis on seedling transplantation machinery, rice farming automation, and sensor-based monitoring systems for crop quality assessment. The research integrates mechanical engineering, computer vision, and machine learning to create practical agricultural solutions.

• Machine Learning
• Time Series Forecasting
• Credit Card Fraud Detection
• +3 more

Gianluca Bontempi is a Professor in the Department of Computer Science at the Université Libre de Bruxelles (ULB), Faculty of Sciences. His research spans machine learning, time series forecasting, and big data analytics, with significant applications in credit card fraud detection, biomedical engineering, and causal inference. He leads projects on adaptive learning systems, financial cybersecurity, and high-dimensional data modeling. Research Focus: Bontempi's work integrates theoretical machine learning with real-world challenges. Key areas include: Developing dynamic factor models for multivariate time series forecasting Designing incremental learning frameworks for fraud detection in evolving data streams Advancing causal inference methods for personalized decision-making Creating interpretable AI systems for biomedical and financial applications Publication Trends: His recent articles (2019–2026) show a strong emphasis on combating concept drift in financial fraud systems, improving fairness in feature selection, and enhancing cross-domain adaptation techniques. Methodological innovations in multi-task learning and adversarial modeling are recurrent themes. Awards & Recognition: No awards explicitly mentioned in the source text. Research Infrastructure: Bontempi collaborates with interdisciplinary teams across Europe on large-scale projects involving biomedical data (TCGAbiolinks), urban mobility (traffic forecasting), and AI ethics (CLAIRE COVID-19 initiative).

Philipp NeumannView profile

Professor

• High Performance Computing
• Molecular Dynamics Simulations
• Multiscale Simulations in Fluid Dynamics
• +5 more

Philipp Neumann is a Professor of Informatics with a focus on High Performance Computing and Data Science, holding a joint appointment at DESY and the University of Hamburg. As a Leading Scientist and Head of IT at DESY, he coordinates the Helmholtz Federated IT Services (HIFIS) and specializes in HPC for molecular and multiscale simulations. Diploma in Engineering Mathematics (2008), Friedrich-Alexander-University Erlangen-Nuremberg Doctorate in Scientific Computing (2013), Technical University of Munich Habilitation in Scientific Computing (2019), University of Hamburg Neumann's research spans high-performance computing, molecular dynamics, machine learning, and exascale applications. He develops tools like MaMiCo and AutoPas for multiscale and particle simulations, with applications in climate modeling, proteomics, and industrial use cases. His work emphasizes energy efficiency, fault tolerance, and automated algorithm selection. Recent publications highlight advancements in HPC software (e.g., xbat ), data harmonization (e.g., HarmonizR ), and simulation methodologies (e.g., aerodynamic lens systems, multiscale fluid dynamics). These reflect his expertise in bridging molecular and continuum-scale simulations with data-driven techniques. At DESY, Neumann leads IT infrastructure initiatives, including federated IT services for the Helmholtz Association. He contributes to projects like hpc.bw and serves on committees for international HPC conferences, demonstrating leadership in software development and computational science education.

• Recommender Systems
• Machine Learning
• Graph Neural Networks
• +7 more

Yiwen Zhang is a researcher affiliated with Anhui University in Hefei, China, with a focus on Recommender Systems , Machine Learning , and Graph Neural Networks . Their work spans Data Mining , Imbalanced Data challenges, and Big Data optimization. Recent publications highlight advancements in robust recommendation via heterogeneous graphs, causal feature selection , and anomaly detection in time series. Key research areas: Recommender Systems, Machine Learning, Graph Neural Networks, Data Mining, Edge Computing. Notable applications: Imbalanced Data Classification, Social Recommendation, Edge Server Placement, Causal Discovery. Their 2025 and 2024 contributions include 15+ papers on topics like parameter-free sampling , graph attention networks , and causal inference . These works emphasize robustness , uniformity , and low-cost optimization in complex scenarios. Collaborations: Lei Sang, Dengcheng Yan, Qiang He, Yun Yang, Zhaolong Ling. Published in journals: Applied Intelligence , IEEE Transactions on Knowledge and Data Engineering , Expert Systems with Applications .

Klaus NeymeyrView profile

Professor

• Numerical Analysis
• Linear Algebra
• Chemometrics
• +3 more

Prof. Dr. Klaus Neymeyr is a Professor of Numerical Mathematics at the University of Rostock, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. Since 2023, he serves as Prodekan of the faculty. Previously, he was Dean (2014–2023) and an associated member at the Leibniz Institute for Catalysis (LIKAT). He obtained his habilitation in mathematics at the University of Tübingen (2001) and has held academic roles since 2003. His research spans numerical linear algebra, chemometrics, and catalysis, with a focus on eigenvalue problems, multivariate curve resolution (MCR), and spectral data analysis. Notable contributions include advancements in preconditioned eigensolvers and the development of FAC-PACK for feasible solution computation. Neymeyr’s work integrates computational methods with chemical applications, such as catalyst characterization via FTIR and NMR spectroscopy. He has been recognized with awards including the Kowalski Prize (2016) and a highly cited paper designation by ISI (2003). His collaborations extend to interdisciplinary projects in catalysis and biopharmaceutical data analysis. Publications emphasize algorithm development, convergence analysis, and data-driven methodologies. Scientific achievements include patents on energy-optimized pump regulation and contributions to the theoretical foundations of MCR and eigensolver convergence. His research group actively publishes in top journals like SIAM Journal on Matrix Analysis , Analytica Chimica Acta , and Journal of Chemometrics .

Alina GeigerView profile

Researcher

• Genetic Programming
• Genetic Improvement
• Machine Learning
• +3 more

Alina Geiger is a Researcher at the Chair of Business Informatics within the Faculty of Law and Economics at Johannes Gutenberg University Mainz. She currently serves as a research assistant and supervises final theses and seminar papers. Her expertise lies in Genetic Programming (GP), Genetic Improvement (GI), Machine Learning, and Deep Learning. Education: Master of Science in Management (2020-2023, JGU Mainz) Bachelor of Science in Economics (2017-2020, JGU Mainz) Research Interests: Alina’s work focuses on advancing evolutionary algorithms like Genetic Programming and Genetic Improvement, particularly leveraging machine learning techniques. She explores applications in symbolic regression, software evolution, and algorithmic optimization. Her recent studies investigate the integration of large language models (LLMs) to enhance mutation operators in genetic improvement and evaluate explainability in AI-generated software patches. Teaching: Since 2022/23, she has taught 'Introduction to IT' courses and oversees student research projects. Her academic contributions reflect a strong emphasis on bridging theoretical advancements with practical software engineering challenges.

Dominik SobaniaView profile

Researcher

• Large Language Models in Software Development
• Genetic Programming for Program Synthesis
• Genetic Improvement
• +3 more

Dr. Dominik Sobania is a researcher at the Department of Business Informatics at Johannes Gutenberg University Mainz. His work focuses on the intersection of artificial intelligence and software development, particularly in the areas of Large Language Models (LLM), Genetic Programming (GP), and Genetic Improvement (GI). He explores how evolutionary computation can enhance program synthesis and improve software systems. His research interests include applying genetic algorithms to solve complex programming challenges, integrating LLMs with traditional GP techniques, and optimizing selection methods for better performance in symbolic regression and program analysis. Notable projects include ImageBreeder (combining diffusion models with evolutionary methods) and ComfyGI (automated image workflow improvement). Dr. Sobania's publications emphasize efficient algorithm design, such as down-sampled lexicase selection for GP, and critical assessments of LLM-generated software patches. He actively contributes to advancing AI-driven software development through empirical studies and comparative analyses of machine learning techniques. No scientific awards or formal advising records are mentioned in the provided texts.

Kathrin DörflerView profile

Professor

• Digital Design
• Robotics in Architecture
• Additive Manufacturing
• +3 more

Prof. Dr. Kathrin Dörfler is a Tenure Track Professor at the Technical University of Munich (TUM), leading the Chair of Digital Fabrication. She specializes in the intersection of architecture and robotics, focusing on digital design and robot-supported construction processes. Her research explores mobile robotics, augmented reality, and additive manufacturing in construction. She joined TUM in 2019 to establish a research group within the Department of Architecture and Civil, Geo and Environmental Engineering. Education: M.Sc. Architecture (TU Vienna, 2012), Ph.D. (ETH Zurich, 2018). Prior to TUM, she was a postdoc at Gramazio Kohler Research (ETH Zurich), working on the National Centre of Competence in Research (NCCR) Digital Fabrication. Her work emphasizes collaborative human-robot systems, on-site fabrication, and sustainable materials. Research Interests: Digital design methodologies, robotic construction automation, augmented reality for assembly, and additive manufacturing in architecture. Notable projects include the DFAB HOUSE demonstrator and the development of the In Situ Fabricator robot. Key Projects: TRR 277 Additive Manufacturing in Construction (collaborative research center), Mobile Additive Manufacturing systems, and the Robotic AeroCrete technology. Her work bridges academic research with practical applications in construction innovation.

Constantin Jacob SchneiderView profile

Researcher

• Capital Markets
• Asset Pricing
• Corporate Finance
• +5 more

Constantin Jacob Schneider is a Researcher at the Department of Finance under the School of Business and Economics at the University of Münster. He holds an M.Sc. in Economics from the Paris Lodron University of Salzburg (2021–2023) and bachelor’s degrees in Economics from the Frankfurt University of Applied Sciences and Seinäjoki University of Applied Sciences. His research focuses on Capital Markets, Asset Pricing, Behavioral Finance, and Corporate Finance, particularly dividend policy. He has published in Finance Research Letters on AI-driven portfolio selection using ChatGPT. Professionally, he serves as Managing Director at CCP Centauri Capital Partners (M&A Advisory) since 2021 and previously led Schneider Sports Management (2017–2021). He also lectures courses like Rating and Capital Market and Risk Management in Insurance in German and English. His work integrates empirical finance with practical applications in corporate strategy and behavioral economics.

Yahya YilmazView profile

Researcher

• Text Mining
• Machine Learning
• Finance
• +3 more

Yahya Yilmaz is a Researcher at the University of Münster's School of Business and Economics, affiliated with the Chair of Finance led by Prof. Dr. Schneider. His research focuses on integrating text mining and machine learning techniques into financial analysis, particularly in portfolio management and risk assessment. He holds an M.Sc. in Business Administration (Ruhr University Bochum, 2017–2020) and a B.Sc. in Economics (Ruhr University Bochum, 2014–2017). His teaching responsibilities include advanced courses such as Advanced Corporate Finance and Mergers & Acquisitions , primarily in English. Recent work includes a 2025 publication in Finance Research Letters exploring ChatGPT's role in portfolio selection based on risk appetite. No scientific awards are explicitly listed, but his contributions to AI-driven finance research are notable. He advises students through formal course structures but no specific advisees are named. His office hours are by appointment, and he is located in Room J496 at Universitätsstr. 14-16, Münster.

Stefan HelberView profile

Professor

• Production Planning and Control
• Logistics Optimization
• Call Center Management
• +4 more

Prof. Stefan Helber is a full Professor (W3) and Director of the Institute of Production Management at Leibniz University Hannover. His roles include Executive Director of the Institute, representative in academic committees (e.g., Curriculum and Teaching Committee), and member of the Cluster of Excellence SE²A. Education: PhD (1993) and Habilitation (1998) from Ludwig-Maximilians-Universität München Research focuses on stochastic production systems, logistics optimization, call center management, and automated production systems Recent work emphasizes regeneration processes, dynamic charging infrastructures, and stochastic flow line analysis. His research integrates operations research methods with practical industrial applications, particularly in manufacturing and service systems. Key publications include works on buffer allocation, lot sizing, and call center modeling, with a focus on optimization under uncertainty. He has supervised numerous theses across diploma/master/bachelor levels.