جست‌وجوی استاد و پژوهشگر

Thijs Janzenمشاهده پروفایل

پژوهشگر

• Phylogenetics
• Population Genetics
• Computational Biology
• +۳ مورد دیگر

Dr. Thijs Janzen is a Researcher and Scientific Programmer at the University of Groningen's Faculty of Science and Engineering, affiliated with the Theoretical Research in Evolutionary Life Sciences department. He holds a PhD in Biology from the University of Groningen (2015) and has extensive postdoctoral experience at institutions including the Max Planck Institute for Evolutionary Biology and Carl von Ossietzky University Oldenburg. Education: PhD in Biology, University of Groningen (2010–2014) MSc in Biology, University of Groningen (2006–2009) BSc in Biology, University of Groningen (2003–2006) Research Interests: Phylogenetics and phylogenetic reconstruction Population genetics and genomic admixture Individual-based computational modeling Software development in R, C++, and Python Support for MSc/PhD students in programming and modeling Research Trends: His work spans theoretical evolutionary biology, computational genomics, and phylogenetic analysis. Recent studies focus on hybridization dynamics, viral oncolytic therapy modeling, and tools for phylogenetic statistics (e.g., the 'treestats' R package). His contributions bridge ecological theory with genomic data, emphasizing quantitative methods in evolutionary processes. Advising & Grants: Collaborated on the MARM project (2018–2023) on adaptive responses in evolution, contributing technical expertise to PhD student projects. Maintains active roles in computational support and course development for biologists. Labs/Teams: Core member of the Theoretical Research in Evolutionary Life Sciences group, developing open-source tools (e.g., GenomeAdmixR, nLTT) and collaborating globally on biodiversity and evolutionary questions.

• Theoretical Community Ecology
• Evolutionary Biology
• Phylogenetic Community Ecology
• +۷ مورد دیگر

Prof. Rampal S. Etienne is a Full Professor in Theoretical and Evolutionary Community Ecology at the University of Groningen's Faculty of Science and Engineering. He leads the Etienne Group and serves as Scientific Director of the Groningen Institute for Evolutionary Life Sciences. His research focuses on understanding biodiversity processes through theoretical models and phylogenetic analysis. Education: PhD in Metapopulation Ecology, Wageningen University (2002) MSc degrees in Philosophy of Science, Environmental Science, and Foundations of Physics (cum laude), Utrecht and Nijmegen Universities (1996–1999) Research Interests: Theoretical ecology, evolutionary dynamics, phylogenetic community ecology, diversification modeling, and island biogeography. Collaborative projects include studies on African buffalo populations, fruit fly behavior, and microbial evolution. He develops frameworks like DAISIE for island biodiversity modeling and secsse for trait-dependent speciation analysis. Publications & Grants: Over 184 peer-reviewed articles, including work on phylogenetic color evolution and species diversification. Secured grants such as the VICI grant (2014) and Dutch Research Agenda funding (2024). Editor for Ecology Letters and Journal of Theoretical Biology . Awards: Descartes-Huygens Prize (2020), Honorable Mention for American Naturalist Student Paper Award (2023). Recognized for collaboration with CNRS and contributions to evolutionary theory. Labs/Teams: Leads experimental and computational studies on microsnails, microbes, and mites. Coordinates the Adaptive Life research initiative exploring eco-evolutionary dynamics under climate change.

Chaobo Zhangمشاهده پروفایل

پژوهشگر

• Building Physics
• Smart Building Energy Management
• Artificial Intelligence in Building Systems
• +۵ مورد دیگر

Chaobo Zhang is a postdoctoral researcher at the Eindhoven University of Technology, specializing in fast building performance simulation and smart building energy management. He belongs to the Building Performance Group within the Department of the Built Environment. Education: Bachelor in Heating, Ventilation and Air Conditioning, Harbin Institute of Technology (2017) PhD in Refrigeration and Cryogenic Engineering, Zhejiang University (2022) His research integrates artificial intelligence technologies into building systems to enhance energy efficiency and fault detection. Key areas include generative pre-trained transformers (GPT) for energy load prediction, convolutional neural networks for computational fluid dynamics, and domain-specific large language models for HVAC fault diagnosis. The 15 most recent publications highlight trends in AI-driven building energy management, focusing on technologies like graph convolutional networks for uncertainty quantification, hybrid data-driven physics-based cooling demand models, and self-attention variational autoencoders for digital twin parameter imputation. His work addresses challenges such as data imbalance, federated learning for data silos, and real-time anomaly detection. Scientific Awards: 2020 Best Paper Award, Energy and Built Environment Chaobo collaborates internationally and contributes to UN Sustainable Development Goals through energy-efficient building technologies. His expertise spans fault detection, machine learning, and computational optimization in building systems.

Bert Kappenمشاهده پروفایل

استاد

• Quantum Machine Learning
• Stochastic Control Theory
• Neural Networks
• +۴ مورد دیگر

Bert Kappen is a Professor of Physics at Radboud University Nijmegen, affiliated with the Department of Biophysics and the Donders Center for Neuroscience. His research focuses on the intersection of physics, machine learning, and neuroscience, with emphasis on quantum machine learning, stochastic control theory, and Bayesian inference methods applied to neural systems. His core research integrates statistical physics and quantum mechanics to develop computational methods for AI, exploring how intelligence emerges in biological systems. Key areas include: Quantum Machine Learning : Developing quantum algorithms for Boltzmann machines and exploring quantum advantage in optimization Path Integral Control : Creating efficient solutions for stochastic optimal control problems in robotics and neuroscience Bayesian Inference : Building probabilistic models for medical diagnosis and DNA identification (e.g., Bonaparte system used by Interpol) Atomic-scale Neural Networks : Implementing neuromorphic computing using nanoscale atomic switches Kappen leads an active research group with multiple PhD and Master's students, working on projects ranging from quantum perceptrons to multi-agent UAV control. His publication output shows strong recent focus on quantum computing applications (2020-2025), atomic-scale machine learning implementations, and advanced control theory. He teaches courses including Introduction to Machine Learning , Advanced Computational Neuroscience , and Statistical Machine Learning at Radboud University. Externally funded projects include EU FP7 initiatives (NETT, CompLACS), NWA Quantum Learning, and collaborations with Thales Nederland.

Christian Andersson Naessethمشاهده پروفایل

استادیار

• Approximate Statistical Inference
• Causality
• Generative Modeling
• +۶ مورد دیگر

Christian Andersson Naesseth is an Assistant Professor of Machine Learning at the University of Amsterdam , affiliated with the Informatics Institute , the Amsterdam Machine Learning Lab (AMLab) , and the UvA-Bosch Delta Lab 2 . A former postdoctoral researcher at Columbia University and PhD graduate in Electrical Engineering from Linköping University (advised by Fredrik Lindsten and Thomas Schön), his research bridges generative modeling , uncertainty quantification , and approximate Bayesian inference with applications in AI for Science . Research Interests Generative Models (diffusions, flows, flow networks) Approximate Inference (variational and Monte Carlo methods) Probabilistic Modeling (natural sciences, computer vision, health) Uncertainty Quantification (E-values, conformal prediction) Recent Scientific Contributions Developed SDE Matching for simulation-free training of latent stochastic differential equations Advanced controlled generation via equivariant variational flow matching Proposed risk monitoring frameworks for test-time adaptation under unknown data shifts Awards & Recognition Best Workshop Paper Award at AABI 2025 Advising & Collaborations Lab manager of UvA-Bosch Delta Lab 2 Collaborates with institutions like Mila Montreal , Microsoft Research , and ECMWF Mentors students: Grigory Bartosh (PhD), Hany Abdulsamad (postdoc), and visiting PhDs from Antwerp, Manchester, Bielefeld, and DTU

Nalan Bastürkمشاهده پروفایل

دانشیار

• Bayesian Inference
• Time Series Analysis
• Mixture Models
• +۳ مورد دیگر

Nalan Bastürk is an Associate Professor at Maastricht University within the School of Business and Economics , Department of QE Econometrics. Prior to this, she held roles as Assistant Professor at Maastricht University (2014-2019) and Erasmus University Rotterdam (2010-2014). Her research focuses on Bayesian inference, time series analysis, and mixture models, with applications in econometrics, healthcare, and financial modeling. Her key research project, funded by NWO, explores patient heterogeneity and time-variation in the course of depression . She contributes to methodological advancements in statistical modeling, including neural networks for density estimation and Bayesian causal analysis. Recent publications span diverse topics such as text analysis of tax news, portfolio optimization with genetic algorithms, and clinical phenotyping in schizophrenia. Her work bridges theoretical statistics and real-world applications, emphasizing interdisciplinary collaboration. Notably, she co-developed the R package BayesMultiMode for Bayesian mode inference. While no scientific awards are explicitly listed, her contributions to econometrics and computational methods reflect significant scholarly impact. Her advising and grant activities include the NWO-funded Vividi195187 project. No specific lab or team affiliations are mentioned, but her research often involves collaborations across disciplines within the School of Business and Economics.

Maryam Tavakolمشاهده پروفایل

استادیار

• Uncertainty in Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Maryam Tavakol is an Assistant Professor at Eindhoven University of Technology, affiliated with the Uncertainty in AI group within the Mathematics and Computer Science department. Her expertise bridges artificial intelligence, machine learning, and computational modeling for sequential systems. PhD in Machine Learning, TU Darmstadt B.Sc. & M.Sc. in Computer Science, University of Tehran Her research focuses on uncertainty estimation , reinforcement learning , and transformer architectures applied to domains like drug-target interaction prediction, soccer analytics, and music generation. Recent work explores activation sparsity in LLMs and robust VAE frameworks. Key trends in her publications (2012–2024) span reinforcement learning , neural network architectures , and applications in healthcare and creative domains . Notable themes include contextual bandits, variational inference, and energy-efficient computing. Contributed to UN Sustainable Development Goals (Education/Academic Qualification) She has supervised multiple research projects and served as corresponding author in peer-reviewed conferences. Collaborations include TU Dortmund, Leuphana University, and industry internships at Criteo.

Dmitry Bagaevمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Probabilistic Programming
• Reactive Message Passing
• +۲ مورد دیگر

Dmitry Bagaev is a Researcher at Eindhoven University of Technology's Signal Processing Systems department, specializing in Bayesian inference and probabilistic programming. His work develops computational tools like RxInfer.jl and ReactiveMP.jl for scalable probabilistic modeling. Research spans Bayesian inference frameworks, reactive programming architectures, and applications in computational immunology. Recent publications focus on probabilistic programming languages (GraphPPL.jl), multi-agent trajectory planning, and SARS-CoV-2 T-cell receptor databases. Articles demonstrate consistent innovation at the intersection of computational statistics, biological data analysis, and real-time systems engineering. Awards and advising roles are not documented in available records. Research occurs within the BIASLab group, focusing on signal processing systems and probabilistic AI.

• Information Retrieval
• Recommendation Systems
• Conversational AI
• +۷ مورد دیگر

Pengjie Ren is an academic lecturer specializing in information retrieval, recommendation systems, and conversational AI. He holds teaching roles in courses such as Introduction to Big Data Processing Technology, Probability Theory and Mathematical Statistics, Ph.D Professional English, and Statistical Natural Language Processing. As a supervisor, he has guided both PhD and Master's students in research-oriented projects. His research focuses on advancing conversational systems, generative models, and reinforcement learning applications in recommendation systems. He has contributed to top conferences like SIGIR, WWW, AAAI, and ACL, with notable work on knowledge-grounded dialogues, sequential recommenders, and debiasing techniques in AI. Pengjie actively serves the academic community as an organizing committee member (NLPCC 2021, YSSNLP 2021) and workshop co-chair (SIGIR AIIS 2020, WSDM NLP4REC 2020). He is also a program committee member for major conferences and a reviewer for journals like TOIS, TKDE, and IPM. His teaching and research emphasize practical applications of AI in dialogue systems, privacy-aware recommendation, and multi-domain learning, with a focus on bridging theory and real-world implementation.

Eric Nalisnickمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +۵ مورد دیگر

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.