جست‌وجوی استاد و پژوهشگر

Christine Bauerمشاهده پروفایل

استاد

• Recommender Systems
• Interactive Intelligent Systems
• Fairness in AI
• +۴ مورد دیگر

Christine Bauer is a Professor of Interactive Intelligent Systems at the Department of Artificial Intelligence and Human Interfaces (AIHI) at Paris Lodron University of Salzburg, Austria. She co-leads the focus area 'InterMediation. Music—Effect—Analysis' at Wissenschaft & Kunst. Her research integrates human-centered computing with intelligent technologies, specializing in fair recommendation systems. Education: Doctoral degree in Social and Economic Sciences (Business Informatics), University of Vienna (2009) MSc in Business Informatics, TU Wien (2011) Diploma in International Business Administration, University of Vienna (2002) Jazz Saxophone studies, Konservatorium der Stadt Wien Research Focus: Bauer's work centers on context-aware recommender systems with emphasis on music/media applications, fairness in algorithmic decision-making, and multi-method evaluation frameworks. She advocates human-centered AI approaches where technology adapts to societal needs. Publication Trends: Her recent work focuses on algorithmic fairness in music recommendation, gender imbalance mitigation, and evaluation frameworks for recommender systems, frequently employing cross-disciplinary approaches blending computer science with social sciences. Awards: Elise Richter Laureate (FWF) 6 best paper awards with 5 nominations Women in RecSys Journal Paper Awards (2023, 2024) 5 reviewing awards with 6 special recognitions Leadership: Principal Investigator of the FWF-funded 'Fine-grained Culture-aware Music Recommender Systems' project (€257k). Supervised 70+ theses. Serves as Associate Editor for TORS, TOIS, and JITT. Initiatives: Co-founder of PERSPECTIVES workshop series, FairRecKit analysis toolkit, and advocate for women in STEM through WiMIR, LEA Role Model program, and CHI Allyship initiatives.

• Formal Concept Analysis
• Machine Learning
• Semantic Web
• +۳ مورد دیگر

Professor Dr. Tom Hanika is affiliated with the University of Hildesheim , working in the Intelligent Information Systems (IIS) division within the Institute of Computer Science. His research bridges formal concept analysis , machine learning , and knowledge representation , focusing on geometric interpretations of data and explainable AI systems. Research Themes: Intrinsic dimensionality, lattice structures, and hybrid human-AI collaboration Teaching: Offers courses in databases, C++ programming, and semantic technologies Contact: Office (SC.C. 2.03), Phone +49 5121 883-40312, Email via contact form Recent publications highlight his work on geometric data analysis and formal context manipulation , including applications in graph neural networks, ordinal pattern recognition, and conceptual lattice visualization. His Collaborative Hybrid Human AI Learning framework demonstrates practical implementations of these theories. Current projects explore dimensionality resilience in machine learning models and topic flow visualization in academic networks, reflecting his dual focus on theoretical foundations and applied knowledge systems.

Dmitriy (Tim) Kuniskyمشاهده پروفایل

استادیار

• Probability
• Statistics
• Mathematical Data Science
• +۷ مورد دیگر

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.

Luca Verginerمشاهده پروفایل

مدرس

• Network Science
• Empirical Economics
• Supply Chain Management
• +۴ مورد دیگر

Luca Verginer is a Lecturer at the Department of Management, Technology, and Economics within ETH Zurich. His work bridges empirical economics and network science, focusing on pharmaceutical supply chains, patent dynamics, and social network impacts. Current affiliation: ETH Zurich (School of Management and Social Sciences) Academic rank: Lecturer His research integrates causal inference, advanced econometrics, and deep learning methods like Graph Neural Networks (GNN) and Natural Language Processing (NLP) to address policy and strategic challenges in healthcare and innovation. Key trends in his publications include: Quantifying supply chain resilience through reroute flexibility Analyzing scientist mobility networks and global cities' dominance Studying social dynamics in online radicalization and migration Methodologically, he employs agent-based modeling, big data analytics, and empirical validation across diverse domains from opioid distribution to academic collaboration. His work demonstrates strong interdisciplinary focus, combining economics, computational methods, and policy analysis to tackle real-world challenges in healthcare systems and knowledge production networks.

Guanhua Chenمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Applied Machine Learning
• +۴ مورد دیگر

Dr. Guanhua Chen is an Assistant Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). His research focuses on Natural Language Processing (NLP), Large Language Models (LLMs), and Multimodal AI, with applications in drug discovery and urban planning. He previously earned his Ph.D. from the University of Hong Kong and B.S./M.S. from Tsinghua University, with internships at Microsoft Research Asia and Huawei Noah’s Ark Lab. Education: Ph.D. in Computer Science (University of Hong Kong, 2022), B.S. and M.S. in Computer Science (Tsinghua University, 2012 & 2014) Dr. Chen's research explores LLMs, data synthesis, multimodal systems, and model compression. He leads a lab equipped with 16 RTX 4090 GPUs, 16 L40 GPUs, and 4 A100 GPUs, providing resources for students to experiment with open-source and proprietary LLM APIs. His recent publications highlight innovations in LLM jailbreaking, parameter-efficient tuning (MiLoRA), cross-lingual transfer (mCLIP), and evaluation frameworks for robustness testing. Notably, he was awarded the Microsoft Research Asia StarTrack Scholar in 2025. Key Publications: mCLIP (ACL 2023), ImPart (ACL 2025), MiLoRA (NAACL 2025) Dr. Chen actively seeks motivated PostDoc, PhD, and Master's students. He serves as an Area Chair for ACL and EMNLP and contributes to advancing AI through open-source projects like mCLIP on GitHub.

• Machine Learning
• Artificial Intelligence
• Ethics
• +۷ مورد دیگر

Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.

Hedderik van Rijnمشاهده پروفایل

استاد

• Temporal Cognition
• Interval Timing
• Cognitive Control
• +۵ مورد دیگر

Hedderik van Rijn is a Professor at the Department of Psychology, University of Groningen, and chair of "Cognitive Science and Neurosciences." His research focuses on temporal cognition, exploring how time influences cognitive processing, memory, and decision-making through behavioral, neuroimaging (fMRI, EEG, MEG), and computational modeling approaches. He develops methods like the adaptive learning system SlimStampen / RuggedLearning , which personalizes fact-learning using cognitive models and is used by 800,000+ students in the Netherlands. University: University of Groningen Department: Department of Psychology Academic Rank: Professor Van Rijn's research spans computational models of interval timing, cognitive control mechanisms, and applications in education and public health. He integrates symbolic process models (ACT-R) with neurobiological data, particularly in Drosophila studies, to uncover genetic bases of temporal processing. His work on time perception has theoretical and practical implications, from understanding neural substrates to real-world applications in skill training and language processing. His recent publications highlight interdisciplinary collaborations in temporal cognition, memory dynamics, and educational technology. Key themes include the interplay between time perception and cognitive load, the development of adaptive algorithms for learning, and the neural correlates of timing and decision-making. His lab's focus on deconvolving pupillary responses and modeling multi-tasking scenarios underscores methodological innovation. Scientific Awards: VICI grant (NWO Netherlands Organization for Scientific Research) Van Rijn has supervised numerous PhD students whose work spans pronoun processing, interval timing, Bayesian optimization of learning systems, and clinical applications of timing research. He collaborates with institutions like Noordhoff Publishers and the University Medical Center Groningen, and is a founding director of SlimStampen B.V., a spin-off commercializing adaptive learning principles. His grants include NWO, Horizon 2020, and John Templeton Foundation support.

Aude Olivaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Generative Models
• +۵ مورد دیگر

Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

• Network Security
• Privacy in Wireless Networks
• Information-Centric Networking
• +۴ مورد دیگر

MARIAS IOANNIS is an Assistant Professor at the Department of Informatics , Athens University of Economics and Business . With a PhD in Informatics (2004) from the University of Athens and an engineering degree from the University of Patras (1995), he has focused on security and privacy in next-generation networks, including sensor networks, vehicular networks, and VoIP services. His research explores Privacy in information-centric networks Trust management in dynamic networks Anti-spam mechanisms for SIP/VoIP Location anonymity techniques Recent publications address content pollution in peer-to-peer systems, homomorphic encryption for privacy, and security in cognitive radio . He has received the Best Student Paper Award at NGI 2010 and contributes to European projects like PURSUIT (FP7) and PSIRP , where he co-designed security frameworks.

• Data Visualization
• Human-Centered AI
• Human-Computer Interaction
• +۷ مورد دیگر

Huamin Qu is a Chair Professor and founding Dean of the Academy of Interdisciplinary Studies at the Hong Kong University of Science and Technology (HKUST) . He leads the VisLab and coordinates the Human-Computer Interaction (HCI) group within the Department of Computer Science and Engineering. His academic journey began with a BS in Mathematics from Xi'an Jiaotong University , followed by an MS and PhD in Computer Science from Stony Brook University . Founding Head of HKUST's Division of Emerging Interdisciplinary Areas (EMIA) Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ) Director of IPO and senior administration team member As a pioneer in data visualization and human-computer interaction , his research bridges urban computing , explainable AI , social media analysis , and E-learning . Recent work focuses on human-AI teaming , multimodal communication , and augmented reality applications . His publications reveal a trajectory from foundational graph visualization and volume rendering to cutting-edge AI-integrated visual systems . Scientific awards include induction into the IEEE Visualization Academy , IEEE VGTC Technical Achievement Award , and multiple best paper/honorable mention awards at top conferences like IEEE VIS, ACM CHI, and IEEE VAST. His lab has graduated 48 PhDs and 21 MPhil students , with 21 PhDs now faculty members at institutions including UC Davis, University of Minnesota, and Zhejiang University. Technologies developed by his group have been adopted by Microsoft , IBM , and Google . His work on projects like Pulse of HKUST and ATMSeer has received global media coverage from MIT News , IEEE Spectrum , and NHK TV . He has served as Associate Editor of IEEE TVCG and held leadership roles in major conferences including IEEE VIS , PacificVis , and VINCI .

Haluk Bingölمشاهده پروفایل

استاد

• Complex Networks
• Evolutionary Game Theory
• Social Network Analysis
• +۵ مورد دیگر

Prof. Haluk Bingöl serves as a Professor at Yeditepe University's Faculty of Computer and Information Sciences, Department of Computer Science and Information Technologies since 2018. Previously, he held academic positions at Boğaziçi University from 2001 to 2018, progressing from Assistant Professor to full Professor in the Department of Computer Engineering. His educational background includes a PhD (1987) and MS (1985) in Electrical and Computer Engineering from Syracuse University, with doctoral research on Boolean Comparison by Simulation , and a BS in Electrical and Electronics Engineering from Middle East Technical University (1982). His research centers on Complex Networks and Evolutionary Game Theory , with significant contributions to understanding cooperation dynamics in the Iterated Prisoner's Dilemma, community detection algorithms, and epidemic modeling in urban networks. His work integrates computational modeling with real-world applications in social dynamics and information systems, often employing agent-based simulations to explore emergent phenomena. Recent publications demonstrate consistent focus on game-theoretic models of cooperation, network structure analysis, and applications in social media and epidemiology. He maintains strong publication output in high-impact journals including Physical Review E , Advances in Complex Systems , and Journal of Theoretical Biology , with increasing interdisciplinary reach into mental health informatics and computational linguistics. Multiple Publication Incentive Awards from Boğaziçi University and TÜBİTAK Interpro IT Award for industry-academia collaboration Best Poster Award at COST MP0801 United Nations Fellowship (1984-1985) and TEV International Scholarship National competition awards for student thesis supervision Prof. Bingöl has supervised 23 graduate students across master's and PhD programs, with research spanning community detection, evolutionary dynamics, and attention economics. His grant portfolio includes leadership of the TURKCELL m-learning Project ($130,000), participation in EU-funded initiatives (EUMedGrid, ASSYST), and COST actions (TD1210, MP0801) focusing on complex systems research infrastructure. He actively contributes to COST actions and collaborative networks, developing methodologies for complex systems analysis while mentoring next-generation researchers through structured academic programs and industry partnerships.

• Machine Learning
• Natural Language Processing
• Network Analysis
• +۳ مورد دیگر

Dr. Derek Greene is an Assistant Professor at the School of Computer Science, University College Dublin, and a Funded Investigator at the Insight Centre for Data Analytics and the VistaMilk Research Centre. His research spans machine learning, natural language processing, and network analysis, with a focus on interdisciplinary applications in cultural analytics, smart agriculture, and political science. Dr. Greene has published over 60 research papers at international conferences and journals. His work includes developing methods for natural language processing , network analysis , and deep learning applied to diverse domains such as literary text mining, dairy industry monitoring, and political communication analysis. He leads projects integrating machine learning into cultural analytics, enhancing agricultural practices, and modeling policy agendas. The articles in his Google Scholar profile highlight a trend toward explainable AI , synthetic data generation , and network-based modeling . Key sub-fields include counterfactual explanations , transformer-based frameworks , temporal analysis of historical texts , and interdisciplinary knowledge transfer . His work bridges machine learning with applications in cultural studies , agriculture , and political science . Dr. Greene collaborates with institutions like the Insight Centre for Data Analytics and the VistaMilk Research Centre , integrating academic research with industry and policy needs. His funded investigator roles reflect ongoing support for applied research in data analytics and agricultural technology.

Suchana Dattaمشاهده پروفایل

پژوهشگر ارشد

• Information Retrieval
• Natural Language Processing
• Digital Humanities
• +۳ مورد دیگر

Suchana Datta is a Research Fellow at the Insight Centre for Data Analytics . Her work bridges Information Retrieval and Artificial Intelligence , with a focus on query performance prediction (QPP) and causality-driven retrieval . She has contributed extensively to improving neural ranking models through relevance feedback and reproducibility studies. Research Highlights Developing novel frameworks for supervised and unsupervised QPP using deep learning and hybrid feature integration Advancing causality modeling in information retrieval systems Exploring temporal trends in 19th-century literature through computational analysis Pioneering work in cloud forensics with dynamic forensic frameworks Publication Trends Her recent work (2022-2025) emphasizes neural QPP models , reproducibility in IR experiments, and causality detection in query events. Earlier contributions (2016-2020) focus on cloud forensic frameworks like DCF and causal analysis in retrieval systems.

Marlene Lutzمشاهده پروفایل

پژوهشگر

• Language Model Compression
• Bias Mitigation
• Responsible Natural Language Processing
• +۲ مورد دیگر

Marlene Lutz is a Researcher at the Chair of Data Science in the Economic and Social Sciences within the Business School at the University of Mannheim, Germany. She works under the supervision of Prof. Dr. Strohmaier and has been active in academic research since 2018. Her contact details include room 323 on the 3rd floor at L 15, 1–6, 68161 Mannheim, and her email: marlene.lutz@uni-mannheim.de. Her research focuses on responsible AI, fairness in algorithmic systems, and interpretability in natural language processing. M.Sc. Computer Science, RWTH Aachen University (2018–2021) Foreign Studies, Seoul National University, South Korea (2020) B.Sc. Computer Science, RWTH Aachen University (2014–2018) Her research interests span several critical areas in modern data science, including language model compression for resource efficiency, bias mitigation techniques in NLP systems, and the development of fair algorithmic ranking frameworks. She also specializes in interpretable word embeddings that enhance transparency in language models, aligning with the broader goals of responsible AI and ethical computational methods. Marlene has contributed to publications in both computational social science and health informatics domains. Her 2025 paper on group fairness measures for rankings demonstrates her focus on fairness evaluation metrics, while her 2022 work on clinical guideline visualization showcases interdisciplinary applications of data science. She has taught multiple Master's level courses at the University of Mannheim since 2022, including Text Analytics, Network Science, and Data Science seminars. These courses emphasize practical implementation of computational methods in information systems and social computing contexts.