جست‌وجوی استاد و پژوهشگر

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.

Roza Kamiloğluمشاهده پروفایل

استادیار

• Emotional Expression
• Evolution of Emotions
• Cross-Cultural Psychology
• +۳ مورد دیگر

Dr. Roza Kamiloğlu is an Assistant Professor at Vrije Universiteit Amsterdam, holding dual affiliations with the Faculty of Behavioural and Movement Sciences and the Social Psychology department. She specializes in the study of emotional expression, including its evolutionary origins, cultural variations, and nonverbal communication mechanisms. Her research employs comparative studies with non-human species, computational methods, and open science practices. Education: Earned dual degrees in Physics and Psychology from Koç University (Turkey), followed by a Master’s in Social Psychology. Completed her PhD at the University of Amsterdam (2023), awarded the Best Dissertation Prize by the International Society for Experimental Social Psychology. Postdoctoral positions included roles at UvA and VU Amsterdam. Research Interests: Focus on emotional expression functions, cultural norms influencing vocalizations, and the application of machine learning to decode emotions. Active in public engagement through media collaborations to disseminate findings. Key Contributions: Pioneered studies on tickling-induced laughter, cross-cultural vocal display rules, and pandemic-era emotional wellbeing. Awarded grants such as the Amsterdam Brain and Cognition Support Grant and ISRE Public Engagement Award. Grants/Awards Timeline: Dancker Daamen Award (2017), Best Dissertation (2024), ISRE Engagement Prize (2024), and multiple research grants (2023–2024).

• Philosophy of Language
• Cognitive Science
• Natural Language Processing
• +۳ مورد دیگر

Sonia de Jager is a Researcher at the Erasmus School of Philosophy, Erasmus University Rotterdam. She is completing a PhD on the philosophy of language-modulation grounded in the Active Inference framework, exploring cognition's variational free energy-minimization principles. Her work manifests as a 'living book' at www.n-o.ooo and involves the EUR Sectorplan SSH-Breed digitalization initiative. She co-founded the Noise Research Union (NRU), focusing on noise's evolving interpretations through workshops and collective publications. She also teaches at the Design Academy Eindhoven and Willem de Kooning Academy, integrating philosophy with art/design. Her research interests span the philosophy of language, cognitive science, and interdisciplinary methodologies. Current projects bridge social dynamics with cognitive processing via Active Inference, examining how constraints shape human self-perception. She investigates semantic noise in NLP contexts, particularly pronoun disambiguation challenges. Her teaching emphasizes philosophical exploration through creative practices. Recent research outputs (2023–2025) address social power theories, linguistic ambiguity, and conceptual stagnation in AI systems. Collaborations include cross-disciplinary dialogues on allostasis, epistemic pitfalls, and complexity theory. Professional activities include invited talks on topics like negation's mathematical implications and neuroendocrinological philosophy.

• 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.

• Open Data
• Digital Government
• Smart Cities
• +۴ مورد دیگر

Marijn Janssen is a full Professor at Delft University of Technology with extensive research output (689 publications) spanning digital government, open data ecosystems, and smart city technologies. His work bridges computer science, public administration, and urban policy through interdisciplinary collaboration across European institutions. His research focuses on Open Data governance, AI implementation in public services, and inclusive smart city development. Recent work examines sociotechnical implications of AI in government transformation, bias mitigation in transportation datasets, and responsible web technologies for urban environments. This reflects a consistent emphasis on ethical technology deployment and citizen-centric digital services. Analysis of his 2025 publications reveals converging trends: integration of AI ethics frameworks in public sector transformation, data-driven inclusion strategies for energy transitions, and technical solutions for equitable smart infrastructure. His work consistently addresses the tension between technological innovation and societal values in digital governance. Scientific recognition includes: Best Management Paper Award at dg.o 2022 (23rd International Conference on Digital Government Research) GIQ Top Author Award (Government Information Quarterly, 2021) Janssen has supervised 21 students and maintains significant academic service through editorial roles for major conferences (EGOV 2017-2020, I3E 2017) and peer review activities. His current ODECO project (2021-2025) develops sustainable open data ecosystems with European partners, building on 12 publicly archived datasets he co-created. His research network spans multiple countries, demonstrating strong international collaboration in digital governance research.

• Machine Learning
• Natural Language Processing
• Language Modelling
• +۷ مورد دیگر

Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.

Merel Scholmanمشاهده پروفایل

استادیار

• Discourse Analysis
• Psycholinguistics
• Computational Linguistics
• +۳ مورد دیگر

Dr. Merel Scholman is an Assistant Professor at Utrecht University within the Department of Languages, Literature and Communication and affiliated with the Institute for Language Sciences . Her research focuses on how people use language to create meaning through discourse relations , investigating cognitive processing of coherence markers in written, spoken, and visual communication. PhD in Language Science and Technology from Saarland University (summa cum laude, 2019) Research Master in Linguistics from Utrecht University (cum laude, 2015) Visiting researcher at Lancaster University and University of Edinburgh Her work combines theoretical psycholinguistics with computational modeling , using quantitative empirical methods like corpus analysis and crowdsourcing experiments. Recent research examines cross-modal discourse signals across Nigerian Pidgin , English , and European languages , while methodological contributions explore annotation reliability and experimental design. Publications demonstrate expertise in discourse markers , coherence relations , and low-resource NLP , with applications to gesture synthesis , spoken language analysis, and educational tools . She received the Veni grant for her project on natural discourse structure signals across modalities. As an educator, she contributes to the Communication and Information Sciences (BA) and Linguistics (RMA) programs, teaching courses like Writing Style as a Choice . Her methodological innovations in crowdsourcing and annotation frameworks have shaped discourse analysis practices in computational linguistics.

• Genetics
• Statistics
• Psychology
• +۶ مورد دیگر

Stéphanie M. van den Berg is an Associate Professor at the University of Twente , affiliated with the Digital Society Institute and TechMed Centre . Her research spans interdisciplinary domains including Psychology , Genetics , Statistics , and Artificial Intelligence , with a focus on educational achievement, mental health assessment, and data-driven methodologies. Key Themes : Heritability analysis, machine learning applications, educational technology, longitudinal data modeling, and personalized medicine. Recent Work : In 2025, she investigated measurement invariance in child behavior phenotypes, while 2024 saw her contribute to personalized medicine through intensive longitudinal data. Methodologies : Expertise in item response theory, text mining, and statistical model selection, as demonstrated in her 2017 publications. She actively collaborates across disciplines, with recent work involving biomedical informatics and behavioral genetics. Her research outputs (97 total) reflect a sustained focus on integrating data science with psychological and health-related applications.