جست‌وجوی استاد و پژوهشگر

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +۵ مورد دیگر

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

• Wireless Power Transfer
• Harmonics
• Inductive Power Transfer
• +۵ مورد دیگر

Dr. Prasanth Venugopal is an Associate Professor specializing in Power Electronics with a focus on advanced energy transfer systems and battery technology. His research spans wireless power transfer, electric vehicle charging, and electrochemical impedance spectroscopy for battery diagnostics. Primary research areas: Wireless Power Transfer (100%), Harmonics (88%), Inductive Power Transfer (87%), Battery Engineering (48%) Recent publications demonstrate expertise in transformerless converter designs, multi-level architectures, and AI-driven battery capacity estimation. He has pioneered meander coil topologies for harmonic mitigation and developed computation-light models for battery aging analysis. His work includes collaborations on Li-ion battery degradation, onboard chargers for electric vehicles, and hybrid power systems for electric aircraft. Despite significant output in IEEE Transactions, no explicit awards or student mentoring data appears in the provided texts.

Serge Fehrمشاهده پروفایل

استاد

• Cryptology
• Post-Quantum Cryptography
• Information-Theoretic Cryptography
• +۵ مورد دیگر

Serge Fehr is a Senior Researcher in the Cryptology Group at CWI (Centrum Wiskunde & Informatica) in Amsterdam and a part-time Professor at the Mathematical Institute of Leiden University. His research focuses on foundational aspects of cryptology, including post-quantum cryptography, information-theoretic security, zero-knowledge proofs, and secure multiparty computation. He participates in AMSec (Amsterdam Cyber Security Center) and leads work packages in the NWO-funded HAPKIDO consortium. Education: M.Sc. in Mathematics from ETH Zürich (1998) Ph.D. in Cryptography from ETH Zürich and University of Aarhus (2003) Postdoc at Macquarie University (2003-2004) Research Interests: Post-quantum cryptographic primitives (e.g., digital signatures, lattice-based schemes) Quantum-resistant protocols (e.g., non-resignable signatures, Fiat-Shamir transforms) Foundational security proofs in the quantum random oracle model (QROM) Secure multiparty computation and privacy-preserving healthcare systems Key Activities: Editorial Board Member: Journal of Cryptology , IEEE Transactions on Information Theory Program Committee Co-Chair: EUROCRYPT 2025 Steering Committee Member: Beyond IID Information Theory, QCrypt Co-organizer: Symposium Series on Post-Quantum Cryptography Grants/Awards: NWO Cybersecurity consortium grant (HAPKIDO - 2021) NWO Veni Grant (2005) NWO Open & Free Competition Grants (2008, 2013) Students/Advising: Supervised or served on committees for over 15 Ph.D. students, including work on post-quantum signatures, MPC applications, and lattice-based cryptography. Labs/Teams: Active in CWI’s Cryptology Group and Leiden’s Mathematical Institute, collaborating with industry partners (e.g., KPN, Microsoft) in cybersecurity initiatives.

Paul Boersmaمشاهده پروفایل

استاد

• Phonetics
• Phonology
• Computational Linguistics
• +۷ مورد دیگر

Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.

Matthias Schlottbomمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +۴ مورد دیگر

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

Geoffrey Nelissenمشاهده پروفایل

استادیار

• Real-Time Systems
• Schedulability Analysis
• Multicore Platforms
• +۳ مورد دیگر

Geoffrey Nelissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. He holds additional roles as a Research Scientist and Investigador Auxiliar at INESC TEC (Portugal), contributing to interdisciplinary research in real-time systems. His work focuses on schedulability analysis, parallel task execution, and real-time communication protocols, with applications in embedded systems and multicore architectures. Research Interests: Real-Time Scheduling Response Time Analysis Multi-core and Parallel Systems Time-Sensitive Networking (TSN) Formal Verification Recent work emphasizes schedule abstraction frameworks, memory contention analysis, and deterministic communication protocols. His research has received recognition through multiple best paper awards including ICESS 2021 and RTAS 2022. He actively contributes to conferences like RTSS and ECRTS while teaching courses on Real-Time Systems and Operating Systems. Collaborations span European institutions with focus on embedded systems, autonomous driving, and safety-critical applications. Current projects explore holistic approaches to WCRT analysis and resilient real-time communication architectures.

Dierck Hillmannمشاهده پروفایل

دانشیار

• Optical Coherence Tomography
• Biophotonics
• Medical Imaging
• +۳ مورد دیگر

Dr. Dierck Hillmann is an Associate Professor at the Faculty of Science, Department of Biophotonics and Medical Imaging, Vrije Universiteit Amsterdam. He holds a PhD in Holoscopy from Luebeck University (2013). His research focuses on advanced optical imaging techniques, particularly Optical Coherence Tomography (OCT), with applications in retinal imaging, functional signal analysis, and computational imaging. He is affiliated with the LaserLaB - Biophotonics and Microscopy research group. Key research areas include improving OCT resolution through holographic methods, functional imaging of retinal neurons and photoreceptors, and developing computational adaptive optics to enhance imaging quality. His work addresses challenges like speckle reduction, aberration correction, and real-time data processing in biomedical imaging. Dr. Hillmann’s contributions span over 37 publications, including innovations in full-field OCT, optoretinography, and phase-sensitive measurements. He teaches courses such as Computational Optical Imaging and Light-Tissue Interaction. His current project explores imaging individual retinal cells and their functions using advanced techniques. No scientific awards are explicitly listed, but his extensive publication record reflects significant academic impact. Students advised are not specified in the provided materials.

Jaco van de Polمشاهده پروفایل

استاد

• Model Checking
• Formal Methods
• Computer Science
• +۳ مورد دیگر

Jaco van de Pol is a Full Professor of Computer Science at Aarhus University, holding dual roles in the Digital Society Institute and Formal Methods and Tools. He earned his PhD from Utrecht University in 1996, specializing in Termination of Higher-order Rewrite Systems, and a Master's in Computer Science (Term Rewriting) in 1992. His research focuses on model checking, formal methods, algorithms, and automated verification, contributing to UN Sustainable Development Goals related to innovation and education. Education: PhD, Termination of Higher-order Rewrite Systems, Utrecht University (1996) Master's in Computer Science (Term Rewriting), Utrecht University (1992) Research Interests: His work spans model checking, formal verification, parallel algorithms, and their applications in software engineering and bioengineering. He emphasizes practical formal methods, such as SCC algorithms and timed automata analysis, to solve complex computational challenges. Awards: Best Paper Award SPIN 2017 (2017) Best Student Paper Award (2018) Advising & Grants: Supervised 12 students and contributed to collaborative projects in formal methods and computational biology. His research has been applied to areas like cartilage phenotype modeling and parallel algorithm design. Labs/Teams: Engages with interdisciplinary teams, including computational biology and distributed systems groups, to advance formal methods in practical contexts.

Rianne Conijnمشاهده پروفایل

استادیار

• Learning Analytics
• Human-Technology Interaction
• Artificial Intelligence
• +۶ مورد دیگر

Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants

Nicola Calabrettaمشاهده پروفایل

استاد

• Optical Communication Systems
• Photonics
• Telecommunications Engineering
• +۶ مورد دیگر

Nicola Calabretta is a Full Professor in Electro-Optical Communication Systems and Senior Research Fellow at Eindhoven University of Technology (TU/e). His work focuses on smart optical networks, high-speed electronics, FPGA implementations for scheduling algorithms, and photonic integrated circuits. He holds a PhD from TU/e (2004) and previously conducted research at DTU Fotonik and the Sant'Anna School of Advanced Studies. His expertise spans optical signal processing, multi-level modulation formats, and applications in data center and metro networks. Key research areas include optical switching architectures (e.g., SOA-based switches), WDM systems, and low-latency interconnect networks. He has led projects like ADAPTOR (resource optimization), SmartTWO (future telecom technologies), and 5G-MOBIX (cross-border mobility). His courses include 'Optical Fibre Communication Technology' and 'Optical Interconnection Networks.' Collaborations involve institutions globally, with recent work emphasizing photonic integration for neural networks, ultra-fast switching, and edge computing. His contributions align with UN SDGs through sustainable telecom infrastructure advancements.

• machine learning
• robotics
• machine vision
• +۹ مورد دیگر

Dr. Hamidreza Mohades Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He holds positions in both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG, focusing on Robotics and image-guided minimally-invasive surgery. His work bridges theoretical advances in machine learning with practical robotic applications. Dr. Kasaei's research focuses on developing algorithms for adaptive perception systems through interactive environment exploration and open-ended learning. His specific interests include 3D object perception, grasp affordance detection, object manipulation, and active perception. He has evaluated his research on various robotic platforms including PR2, UR5e, Kinova, Franka robotic arms, and humanoid robots. His work enables robots to learn from past experiences and intelligently interact with non-expert human users using data-efficient techniques. Analysis of his recent publications reveals strong trends toward increasingly sophisticated manipulation capabilities, particularly in dual-arm coordination and handling dense clutter. There's a clear progression toward integrating language models with robotic control systems, as seen in works like 'Lifelong Robot Library Learning' and 'Towards Open-World Grasping with Large Vision-Language Models.' His research consistently addresses real-world challenges in agricultural robotics, assistive technologies, and service robotics applications. Gratama Science Award (2022) Google Research Scholar Award in Machine Learning (2023) Outstanding Associate Editor for IEEE Robotics and Automation Letters (2023) Dr. Kasaei has successfully supervised multiple PhD students including Zhenxing Zhang (thesis on 'Generative Adversarial Networks for Diverse and Explainable Text-to-Image Generation') and Hamed Ayoobi (thesis on 'Explain What You See: Argumentation-Based Learning and Robotic Vision'). His research is supported by significant grants including the Google Research Scholar Award for 'Continual Robot Learning in Human-centered Environments' and various conference organization roles including workshops at RSS 2023 and NeurIPS 2022. He leads the Lifelong Interactive Robot Learning Lab (IRL-Lab), which focuses on six key research directions: Perception and Perceptual Learning, Object Grasping and Manipulation, Lifelong Interactive Robot Learning, Dual-Arm Manipulation, Dynamic Robot Motion Planning, and Exploiting Multimodality. The lab develops cutting-edge approaches for robots to learn in open-ended fashion through interaction with non-expert human users, with applications in assistive robotics for people with disabilities.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +۵ مورد دیگر

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.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +۴ مورد دیگر

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

Tiziano De Matteisمشاهده پروفایل

استادیار

• Parallel and Distributed Computing
• High-Performance Computing
• Energy Efficiency in Computing
• +۴ مورد دیگر

Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar

• Alzheimer's Disease
• Neurodegeneration
• Mild Cognitive Impairment
• +۹ مورد دیگر

Hiroko H. Dodge is a Full Professor of Neurology at Harvard Medical School and serves as Director of Research Analytics at the Interdisciplinary Brain Center, Massachusetts General Hospital (MGH) in Boston, Massachusetts, USA. Her work bridges clinical neurology, data science, and epidemiology with a focus on dementia research. Dr. Dodge's research spans multiple critical areas in aging and cognitive decline, with particular emphasis on clinical trials (both behavioral and pharmacological) in dementia research. Her expertise includes the epidemiology of dementia, application of demographic methods to clinical fields, and advanced data science approaches to understanding cognitive aging. She has made significant contributions to understanding the relationship between social isolation and cognitive decline, as demonstrated through her leadership of the Internet-Based Conversational Engagement Clinical Trial (I-CONECT). Her work explores how social engagement, environmental factors, and nutritional status impact cognitive trajectories in older adults. Dr. Dodge has pioneered research on digital biomarkers for early detection of cognitive impairment, utilizing computerized cognitive testing, speech analysis, and facial expression recognition through projects like the ARMADA study. She has also investigated the role of environmental exposures such as air quality and lead in dementia risk. Editorial Board Member for The Journals of Gerontology Series B Editorial Board Member for Alzheimer's & Dementia Associate Editor for Alzheimer's & Dementia: Translational Research & Clinical Interventions Her research has been widely recognized, with 445 publications that have garnered over 15,000 citations. Dr. Dodge leads innovative clinical trials and methodology development for dementia research, including decentralized trial approaches and synthetic control methods that address challenges in contemporary Alzheimer's disease therapeutic development. She leads "Hiroko Hayama Dodge's Lab" which focuses on innovative approaches to dementia research, particularly through the integration of technology and clinical assessment. Her I-CONECT project, which investigates the impact of frequent social interactions via webcam/internet on cognitive function in socially isolated older adults, represents a novel approach to dementia prevention. Dr. Dodge collaborates extensively with researchers across multiple institutions, with notable partnerships at Oregon Health & Science University and other leading research centers.