Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Human-Computer Interaction
• +3 more

Mohammad Lataifeh serves as an Assistant Professor in the Department of Computer Science at the University of Sharjah. He earned his transdisciplinary Ph.D. in Design and Information Technology from De Montfort University, UK, in 2015, and brings industry experience as a solution consultant specializing in ERP systems, e-commerce, and digital transformation initiatives. Education Ph.D. in Design and Information Technology, De Montfort University, UK (2015) Research Interests Dr. Lataifeh's work centers on AI-driven innovation across multiple domains: Generative AI for creative augmentation (particularly GANs in character design) Digital transformation in public sector governance Robust biometric systems for speech processing under emotional stress His research uniquely bridges cognitive science with machine learning to enhance human-AI collaboration in practical applications. Publication Trends His 2022-2024 publications reveal three converging research threads: (1) Human-AI co-creation frameworks using GANs for character design, (2) UAE government digital transformation initiatives analyzing IT competence and identity platforms, and (3) Hybrid neural architectures for speaker verification in challenging environments. This demonstrates consistent interdisciplinary work connecting theoretical AI advancements with real-world implementation challenges. Scientific Awards No awards documented in source material Advising and Grants While specific student supervision details are unavailable, his collaborative publications with UAE government entities and international researchers (Carrasco, Elnagar, Ahmed) suggest active participation in publicly funded digital transformation projects. His industry background indicates strong applied research orientation. Laboratory and Team Affiliations Based at the University of Sharjah's Department of Computer Science, Dr. Lataifeh collaborates with cross-institutional teams including De Montfort University's Institute of Creative Technologies and UAE government digital initiatives, though specific lab affiliations aren't detailed in the source.

• Machine Learning
• Optimization Algorithms
• Data Mining
• +3 more

Václav Snásel is a Professor at the Department of Informatics, VSB - Technical University of Ostrava, Czech Republic. He holds a PhD from Masaryk University (Brno, Czech Republic). His research focuses on optimization algorithms, machine learning, metaheuristics, data mining, and their applications in engineering and computational intelligence. Key research interests include developing novel metaheuristic algorithms (e.g., Walrus Optimizer, Artificial Protozoa Optimizer), optimization frameworks for engineering problems, and applications in wireless sensor networks, power systems, and medical diagnostics. He also explores computational methods for data analysis, including graph-based techniques and surrogate-assisted evolutionary algorithms. His recent work emphasizes multi-objective optimization, algorithm design for high-dimensional problems, and interdisciplinary applications in agriculture, energy systems, and bioinformatics. Collaborations span institutions globally, with frequent co-authorship on topics like swarm intelligence and evolutionary computation.

• Machine Learning
• Pattern Recognition
• Computer Vision
• +5 more

David M. J. Tax is a researcher affiliated with Delft University of Technology, Netherlands. His work spans machine learning, pattern recognition, and computer vision, with a focus on neural networks, anomaly detection, and medical image analysis. He has collaborated extensively with researchers like Marco Loog, Robert P. W. Duin, and Marcel J. T. Reinders on topics including multiple instance learning, dissimilarity-based methods, and physics-informed neural networks. Key research contributions include advancements in incremental learning for neural networks, personalized anomaly detection in biomedical signals, and frameworks for evaluating time-series anomalies. His recent work (2023–2025) emphasizes physics-informed neural networks, stochastic scheduling algorithms, and proximity-aware evaluation techniques. Tax’s publications reflect a strong emphasis on interdisciplinary applications, integrating computer science with biomedical engineering and operations research. His methodologies often address challenges in scalability, robustness, and interpretability, with applications ranging from healthcare monitoring to automated project scheduling. Tax maintains an active presence in top-tier conferences like AAAI, ICPR, and NeurIPS, contributing to both theoretical foundations and practical implementations of machine learning systems.

Bettina FinzelView profile

Researcher

• Explainable Artificial Intelligence
• Neuro-Symbolic Artificial Intelligence
• Human-AI Interaction
• +4 more

Bettina Finzel is a doctoral candidate and Teaching Assistant at the Cognitive Systems Group, Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg. She specializes in Explainable Artificial Intelligence (XAI) with a focus on human-centered approaches for medical and educational applications.

Gustavo A. OlivaView profile

Adjunct Professor

• Programmable Blockchains
• Software Ecosystems
• Code Analytics
• +6 more

Gustavo A. Oliva is an Adjunct Professor at Queen's University in Canada, where he leads the blockchain research team at the Software Analysis and Intelligence Lab (SAIL). His research focuses on enabling cost-effective decentralized applications on programmable blockchain platforms like Ethereum, alongside empirical studies in software ecosystems, code analytics, and explainable AI. Dr. Oliva earned his PhD from the University of São Paulo (USP) in Brazil under Professor Dr. Marco Gerosa. Prior to his current role, he was a Post-Doctoral Fellow at Queen's University supervised by Professor Dr. Ahmed Hassan. His primary research spans programmable blockchains, software ecosystems (particularly npm), code analytics, and explainable AI. He employs static analysis, historical repository mining, and machine learning to investigate software evolution, dependency management, and smart contract development. Current projects address gas efficiency challenges in Ethereum, upgradeability patterns in smart contracts, and the impact of foundation models on software engineering practices. Recent publications reveal a dominant focus on blockchain systems (70% of recent work), with growing emphasis on foundation model challenges (FMware). His Ethereum research explores transaction processing, gas optimization, and technical debt, while newer work catalogs software engineering challenges in trustworthy AI-powered systems. Scientific recognition includes: Microsoft Azure for Research sponsorship Capes/CNPq scholarship for Visiting Research at Queen's University (2014) HPE scholarships for Smart Cities and Cloud Service Choreography projects European Commission FP7 funding for CHOReOS project Dr. Oliva actively mentors 8+ students across academic levels. His PhD advisees include Muhammad Ahasanuzzaman (ongoing), Amir Mohammad Ebrahimi, and Filipe Cogo (now at Huawei). Master's students Michael Pacheco and Ahmad Abdullah Zarir now work at Huawei and Amazon respectively. He also supervises visitor and undergraduate researchers in blockchain projects. His service includes program committees for ICSE, SANER, and MSR conferences, plus tutorial leadership at ASE, KDD, and FSE. As director of SAIL's blockchain research team, he manages projects on Ethereum smart contract analysis, npm dependency ecosystems, and AI-driven software engineering. Current initiatives include SPICE (automated issue labeling) and foundational work on trustworthy FMware development, with industry collaborations at Huawei and Amazon.

Alessio BenavoliView profile

Associate Professor

• Bayesian Probability Theory
• Bayesian Nonparametric Methods
• Skew Gaussian Processes
• +8 more

Alessio Benavoli serves as Associate Professor in Statistics within the School of Computer Science and Statistics at Trinity College Dublin, Ireland. His interdisciplinary research bridges theoretical statistics with practical applications in smart manufacturing, renewable energy forecasting, finance, and social sciences through advanced computational methodologies. Benavoli's core research program centers on Bayesian probability theory and its extensions, with significant contributions to Bayesian nonparametric methods, Skew Gaussian processes for regression and classification, and Bayesian optimization frameworks. He simultaneously explores foundational aspects of imprecise probability theory and develops novel connections between Bayesian principles and quantum information theory, particularly investigating quantum paradoxes through probabilistic lenses. His work demonstrates exceptional breadth across theoretical statistics, machine learning, and quantum computation. Analysis of his recent publications reveals a cohesive research trajectory focused on creating unified Bayesian frameworks for complex data problems. His 2021 Skew Gaussian Processes paper establishes a comprehensive methodology for diverse machine learning tasks, while his quantum mechanics research pioneers Bayesian interpretations of quantum phenomena. The 2017 classifier comparison tutorial exemplifies his commitment to making advanced statistical techniques accessible for practical machine learning evaluation. Benavoli currently leads the HLOOP project (Humans-in-the-Loop towards a more effective AI in manufacturing), which integrates human expertise with AI systems for industrial optimization, reflecting his emphasis on human-centered AI solutions that address real-world manufacturing challenges.

Marc FernandesView profile

Professor

• Electronic Negotiations
• Machine Learning
• Artificial Intelligence
• +2 more

Prof. Dr. Marc Fernandes serves as Professor in Business Informatics at Aalen University's Faculty of Business and Health. He coordinates the Business Informatics (B.Sc.) program and holds the dual administrative role of Deputy Head for both the Admissions and Recognition Office (covering Business Informatics, Cyber Security, Data Science, and Financial Management programs) and the Internship Office. His research integrates Electronic Negotiations , Machine Learning , and Artificial Intelligence across healthcare and business domains. Key contributions include emotion recognition frameworks for negotiation training, schizophrenia detection via EEG analysis, and digital transformation solutions for healthcare media discontinuities. His work bridges theoretical negotiation models with practical AI implementations. Publication analysis (2012-2020) reveals an evolution from ontology-based e-negotiation foundations to cutting-edge deep learning applications. Current work emphasizes interdisciplinary AI solutions, particularly in medical informatics and negotiation support systems, with strong industry relevance in digital healthcare transformation and business analytics.

• Machine Learning Applications in Agriculture
• Sensor Technology for Animal Health Monitoring
• Data Science in Bioeconomy
• +3 more

Lukas Minogue is a researcher at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam , affiliated with their Sensor Technology and Modeling department. He holds a Master's degree in Mathematics (University of Potsdam, 2019-2023) with focus on machine learning and statistical data analysis, and a prior Bachelor's degree in Mathematics (University of Potsdam, 2013-2019) concentrating on statistical modeling. His research specializes in applying machine learning models to agricultural sensor data , particularly for mastitis detection in dairy cows through automated milking systems (AMS). Key projects include: MEDICow (German-Irish collaboration): Developing AI-driven mastitis risk assessment using multisensor data AMRAfrica : Strengthening antibiotic resistance monitoring through data management systems in DR Congo INNOVAFRIKA (DFG-funded): Digital innovations in crop-livestock systems Recent publications demonstrate his work on: Time series analysis of AMS data with ML classifiers (logistic regression, random forest, neural networks) Comparative performance of SMOTE resampling techniques Feature engineering for dairy health metrics (e.g., fat-protein ratio) His technical expertise spans Python (scikit-learn, NumPy), data visualization (Matplotlib), and statistical modeling frameworks.

Prashant SrivastavaView profile

Researcher

• Machine Learning
• Deep Learning
• Image Analysis
• +5 more

Prashant Srivastava is a Researcher at the Department of Systems Biology and Bioinformatics under the Faculty of Computer Science and Electrical Engineering at the University of Rostock, Germany. His work bridges machine learning with bioinformatics and financial risk modeling. Education: MSc Data Science and Analytics (2018-2020) from Royal Holloway, University of London Integrated B.S.-M.S. in Physics (2013-2018) from Indian Institute of Science Education and Research, Kolkata Master's Thesis in Computer Science (2020) on statistical risk methods in financial markets Master's Thesis in Physics (2017-2020) on extraordinary transmission in silicon-based micro-meter wire grid polarizers Research Focus: Prashant specializes in machine learning and deep learning applications. His work spans bioinformatics (bitter peptide prediction), data science (ConvGeN algorithm for imbalanced datasets), and financial risk modeling using VaR and Expected Shortfall frameworks. Recent Publications: His research trends include graph neural networks for peptide analysis, convex space learning for generative oversampling, and self-attention mechanisms for molecular interaction extraction from biological texts. Technical Expertise: Proficient in Python (numpy, scipy, tensorflow), R, C++, MATLAB, cloud computing (Azure), and database systems (SQL, NoSQL). He develops libraries like mgarch for GARCH modeling and applies advanced ML techniques (CNN, RNN, GAN, NLP) across domains. Labs & Teams: Active member of the Systems Biology and Bioinformatics group at the University of Rostock, contributing to interdisciplinary research at the intersection of computer science and life sciences.

• Software Security
• Complex Systems
• Artificial Intelligence
• +6 more

Pierre Parrend is a researcher at INSA de Lyon's College of Engineering, focusing on software security, complex systems, and artificial intelligence applications. He has published extensively on topics including OSGi platform security, graph-based attack detection, and biologically inspired computing models. His research bridges cybersecurity with machine learning, emphasizing explainable AI for threat detection. Recent work includes Agentic AI frameworks for offensive security Graph processing methodologies for ML Multilayer architectures in cyber-physical systems Key collaborations include Pierre Collet, Stéphane Frénot, and Julio Navarro. While no formal education details appear in the data, his work spans 2006-2025 with significant contributions to zero-day attack detection and security architecture.

• Machine Learning
• Big Data
• Computational Linguistics
• +4 more

Jakob Andersen is a Researcher at the Department of Computer Science, Faculty of Engineering and Computer Science, Hamburg University of Applied Sciences. His work focuses on machine learning, big data, and computational linguistics, with specific emphasis on text classification, uncertainty sampling, and human-in-the-loop systems. His recent publications explore real-time active learning, sustainable text classification, and edge device reliability. He contributes to open-source frameworks like Forum 4.0 and MINTFIT, focusing on user comment analysis and computer science education. Andersen is a member of the Association for Computational Linguistics (ACL) and has published extensively in top-tier conferences like ACL-IJCNLP, EACL, COLING, and IEEE BigData Congress since 2016.

• Cancer Research
• Medical Imaging
• Radiotherapy
• +4 more

Dr. Klein, L. is a prominent researcher at Heidelberg University's Faculty of Medicine, Department of Clinical Radiology, with a primary focus on medical imaging, cancer research, and radiation therapy applications. Their work bridges clinical oncology with advanced imaging technologies and artificial intelligence methodologies, contributing significantly to the German Cancer Research Center (DKFZ) publications database. Research interests center on tumor heterogeneity, radiation dose optimization in CT imaging, pancreatic cancer microenvironments, and AI applications in medical diagnostics. Dr. Klein's work demonstrates particular expertise in translating technical imaging advancements into clinical applications, especially in neuro-oncology and metastatic disease management. Their research spans both theoretical frameworks and practical implementations in radiation physics and cancer treatment. The publication record reveals a strong trend toward interdisciplinary research combining oncology, radiology, and artificial intelligence. Recent articles show increasing focus on spatial tumor biology, radiation dose minimization techniques, and explainable AI applications in medical imaging. This work represents a convergence of traditional radiology with cutting-edge computational approaches to improve cancer diagnosis and treatment. Dr. Klein actively collaborates with researchers across multiple institutions, particularly evident in multicenter studies on hydrocephalus management in leptomeningeal disease and large-scale analyses of radiation therapy techniques. These collaborations span clinical departments, physics laboratories, and AI research groups, reflecting the interdisciplinary nature of modern medical research. The research program operates within DKFZ's infrastructure, leveraging advanced imaging facilities and computational resources for both clinical and preclinical studies. Current work appears focused on optimizing radiation delivery systems while simultaneously developing AI-driven approaches to personalize cancer treatment based on imaging biomarkers and tumor microenvironment characteristics.

• Cognitive Psychology
• Educational Technology
• Human-Computer Interaction
• +5 more

Maria Wirzberger is an Assistant Professor at the University of Stuttgart, specializing in Teaching and Learning with Intelligent Systems (LLiS). She serves as Spokesperson of the Stuttgart Research Focus IRIS and Co-Director of the AI Software Academy, focusing on adaptive teaching systems, cognitive modeling, and human-AI interaction. Her work bridges cognitive psychology, educational technology, and human-computer interaction, with a strong emphasis on self-regulation and attention control. Research Interests: Modeling human cognition, statistical analysis of behavioral data, distraction/interruption handling in digital environments, development of AI-driven educational tools, multimodal cognitive load assessment, and trust in AI systems. Scientific Contributions: 15 recent articles explore topics like AI feedback mechanisms for focus, emotion-performance dynamics in tutoring systems, sustainable behavior cognition, and neuroergonomic approaches to workload analysis. Grants & Collaborations: Involved in projects like the AI Software Academy and interdisciplinary studies on sustainability personas and cognitive pathways. Labs & Teams: Affiliated with the Stuttgart Research Focus IRIS and University of Stuttgart's LLiS department.

• Computer Graphics
• Shape Modelling
• Computer Animation
• +10 more

Marie-Paule Cani is a Professor of Computer Science at Ecole Polytechnique (IP Paris), where she serves as Dean of the Master of Science and Technology program. She is a member of the Académie des sciences and leads research in the LIX laboratory (CNRS/Ecole Polytechnique, IP Paris). Her career spans several prestigious institutions including Grenoble INP and Collège de France. Dr. Cani earned her M.Sc. in Computer Science from Ecole Normale Supérieure & University Paris XI in 1987, followed by a Ph.D. in Computer Graphics from University Paris XI in 1990 under advisor Claude Puech. She completed her Habilitation in Computer Science from Institut National Polytechnique de Grenoble in 1995. Professor Cani's research focuses on advancing user-centered, creative 3D modeling and animation systems. Her work aims to develop intelligent systems that help users express shapes in motion they have in mind, whether they are computer artists, engineers, or scientists from other disciplines. She pioneered methodologies that provide expressive, gesture-based control (such as sketching, deformation, copy-pasting) while augmenting graphical models with knowledge from priors or statistics learned from examples. Her research spans multiple areas including implicit surfaces, developable surfaces, procedural models, physically-based animation, and sketch-based modeling. Her recent publications demonstrate a continued focus on innovative approaches to 3D modeling, animation, and simulation. Her work bridges computer graphics with applications in geology, biology, and virtual environments. Recent trends show increasing integration of machine learning techniques with traditional graphics approaches, particularly in procedural modeling and animation control. ACM Siggraph Steven A. Coons Award (2023) Member of the ACM Siggraph Academy (2019) Member of Academia Europaea (2013) Silver medal from CNRS (2012) EUROGRAPHICS award for Outstanding Technical Contributions (2011) Irène Joliot Curie national award, mentorship category (2007) Junior member of the Institut Universitaire de France (1999) Professor Cani has supervised numerous students throughout her career and has led multiple significant research projects funded by prestigious organizations. Her ERC Advanced Grant EXPRESSIVE (2012-2017) focused on EXPloring RESponsive Shapes for the creation of Interactive Virtual Environnements. She created and led the STREAM Team at LIX from 2016-2019. More recently, she has been Principal Investigator in the chair between Google and Ecole Polytechnique on Artificial Intelligence & Visual Computing (2018-2021), and in the International Training Network CLIPE (2020-2024), funded by the European Research Council. She currently leads the interdisciplinary project Paleomob-3D (2023-2026) between LIX and HNHP, funded by CNRS 80-Prime. Professor Cani has led several research teams throughout her career, including the Imagine joint team (Inria/LJK lab, 2011-2016) and the Evasion joint team (Inria/GRAVIR lab, 2003-2010). She currently leads research in the LIX laboratory at Ecole Polytechnique, where she was the leader of the "Modeling Simulation and Learning" pole from 2019 to 2023.

Christin SanderView profile

Researcher

• Middle Eastern History
• Political History
• Postcolonial Studies
• +3 more

Christin Sander is a Scientific Associate at the Institute for Islamic Studies, Free University of Berlin, since April 2022. Her research focuses on the intersection of Middle Eastern history, political movements, and postcolonial historiography, particularly examining neglected narratives of the Syrian Left during 1976-1982. She holds an MA and BA in History from the University of Hamburg, with a minor in Philosophy. MA in History, University of Hamburg (2019-2021) BA in History (major), Philosophy (minor), University of Hamburg (2015-2019) Research Interests Sander’s work challenges dominant narratives in Syrian historiography by exploring oppositional unrests in the 1970s and 1980s. She investigates the Communist Labor League’s (CLL/CLP) role in shaping postcolonial Syria’s intellectual history, situating it within the broader context of the defeated Arab New Left. Her analysis of primary sources and oral histories aims to reconstruct a non-sectarian history of resistance. Article Trends Her publications emphasize computational methods in molecular dynamics, machine learning applications for coarse-graining, and data-driven modeling techniques. Despite this seeming divergence, her core focus remains on historical analysis within Islamic and Middle Eastern studies. Contact: christin.sander@fu-berlin.de | Book consultation hours