جست‌وجوی استاد و پژوهشگر

• combinatorial games
• online algorithms
• graph theory
• +۳ مورد دیگر

Bartłomiej Bosek is a Professor at Jagiellonian University's Faculty of Mathematics and Computer Science, Department of Algorithmics. He holds a habilitation in exact sciences (computer science) and has held academic roles since 2004. His research focuses on combinatorial games, online algorithms, graph theory, and poset theory. He actively participates in international conferences and has led multiple grants, including managing the 2020-2025 'Algorithmic Nullstellensatz' project. Notable collaborations include work with Jarosław Grytczuk, Grzegorz Gutowski, and Anna Zych-Pawlewicz. Education: Master's in Computer Science (Jagiellonian University, 2004) PhD in Mathematics (Computer Science, Jagiellonian University, 2008) Habilitation in Exact Sciences (Computer Science, Jagiellonian University, 2019) Research Interests: Combinatorial games and their algorithmic applications Online algorithms for graph coloring and poset structures Graph theory with a focus on coloring and dynamic algorithms Poset theory and dimension analysis Grants: Algorithmic Nullstellensatz (2020–2025, Principal Investigator) New Techniques for Matching Algorithms (2014–2018, Principal Investigator) Tracking Moving People in Monitored Zones (2012–2015, Researcher) Key Contributions: Recent publications emphasize graph coloring algorithms, online chain partitioning, and combinatorial game theory. His work on dynamic graph coloring and poset dimension analysis has been published in top-tier venues like Discrete Mathematics and SIAM Journal on Discrete Mathematics . Labs/Teams: Lead researcher in algorithmic and combinatorial studies at the Department of Algorithmics, collaborating with a network of international researchers including those at Georgia Tech and UPC Barcelona.

Huseyin Topalogluمشاهده پروفایل

استاد

• Revenue Management
• Pricing Analytics
• Operations Research
• +۴ مورد دیگر

Dr. Huseyin Topaloglu is the Howard and Eleanor Morgan Professor at the School of Operations Research and Information Engineering at Cornell University and Cornell Tech . He holds a B.S. in Industrial Engineering from Bogazici University (1997), an M.A. in Operations Research from Princeton University (1999), and a Ph.D. in Operations Research from Princeton University (2001). Education: B.S. Industrial Engineering (1997) – Bogazici University M.A. Operations Research (1999) – Princeton University Ph.D. Operations Research (2001) – Princeton University His research focuses on revenue management , pricing analytics , assortment optimization , and stochastic dynamic programming . Recent work includes advancements in multinomial logit models , network revenue management , and dynamic inventory allocation . He has published extensively in leading journals such as Operations Research , Management Science , and M&SOM , often collaborating with researchers like Y. Bai , P. Rusmevichientong , and O. El Housni . His 15 most recent publications (2024–2003) demonstrate expertise in revenue management using multinomial logit models , dynamic programming , and stochastic optimization . Key subfields include assortment planning , network revenue , pricing under uncertainty , and approximation algorithms for complex systems like ambulance redeployment and airline capacity control . He has also authored books like Revenue Management and Pricing Analytics (2019) and Fundamentals of Linear Optimization (2021).

• Neuroinformatics
• Brain-Computer Interfaces
• Causal Modeling
• +۳ مورد دیگر

Moritz Grosse-Wentrup is a Professor at the Faculty of Computer Science, University of Vienna , where he leads the Neuroinformatics research group. His work spans neuroscience, machine learning, and brain-computer interfaces , focusing on causal modeling and neurotechnology. Location: Kolingasse 14-16, Room 02.49, 1090 Wien Email: moritz.grosse-wentrup@univie.ac.at Phone: +43-1-4277-79610 Research interests include: Developing causal frameworks to bridge neuronal activity and cognition Designing interpretable machine learning algorithms for BCI applications Neurorehabilitation using virtual reality and EMG-BCI systems Advancing causal inference and feature importance in AI models Open-source tools like PyTES for neurostimulation research Recent publications highlight trends in auditory BCI, causal recourse, and Riemannian geometry-based classification . His work integrates neuroscience, computer science, and clinical applications to improve communication for impaired patients and motor rehabilitation. Students mentored include Alex Markham (2021), Gunnar König (2023), and Jiachen Xu (2023). He teaches foundational courses in data analysis, signal processing, and neuroinformatics at the University of Vienna.

• Mathematical Foundations of Machine Learning
• Online Convex Optimization
• Statistical Learning Theory
• +۵ مورد دیگر

Dr. Tim van Erven is an Associate Professor at the University of Amsterdam , affiliated with the Korteweg-de Vries Institute for Mathematics . His research focuses on the mathematical foundations of machine learning , particularly adaptive methods in online convex optimization, PAC-Bayesian concentration inequalities, and robust learning strategies. Research Interests : Mathematical machine learning, explainable AI, online learning, statistical learning theory, and adaptive optimization. Grants & Awards : VICI (2025), VIDI (2019), TOP grant (2016), NIPS Outstanding Reviewer (2014), Rubicon (2011). Teaching : Involved in graduate-level machine learning and statistical learning theory courses. His recent work explores explainable machine learning and robust optimization , with a focus on algorithmic recourse, gradient filtering, and theoretical guarantees. His publications span top venues like NeurIPS, COLT, and JMLR, often addressing the intersection of statistics and machine learning. He co-organized workshops such as the AI & Mathematics (AIM) initiative and contributed to the COLT program committee. His group develops MetaGrad and other adaptive algorithms for online learning.

Elliot Creagerمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Algorithmic Fairness
• +۳ مورد دیگر

Elliot Creager is an Assistant Professor at the University of Waterloo's Faculty of Engineering , Department of Electrical and Computer Engineering. He directs the Socially Embedded Machine Intelligence (SEMI) Lab , focusing on reliable and equitable AI systems that prioritize user data autonomy. His interdisciplinary research bridges technical machine learning with sociology and policy implications, and he actively mentors students across multiple institutions (University of Toronto, York University, Vector Institute). Recent research trends include algorithmic fairness in sequential decision-making, differential privacy applications, and LLM auditing frameworks . Collaborative projects span institutions like University of Toronto, York University, and Vector Institute. His work addresses fluid simulation generalization , invariant learning , and collective action algorithms . Scientific achievements include a CIFAR AI Catalyst Grant (2024) and recognition as NeurIPS 2022 Top Reviewer . As an active Area Chair for ICML 2025 and NeurIPS 2025, he contributes to advancing equitable AI standards. Current advisees include MASc and postdoc researchers, with former students from undergraduate programs.

Timo Freieslebenمشاهده پروفایل

پژوهشگر

• Explainable AI
• Interpretable Machine Learning
• Philosophy of Science
• +۶ مورد دیگر

Timo Freiesleben is a Postdoctoral Researcher at the Machine Learning in Science Cluster (University of Tübingen) and an external member of the Munich Center for Mathematical Philosophy (LMU Munich). His work bridges philosophy with machine learning, focusing on interpretable/explainable AI and algorithmic fairness. Education Mathematics Philosophy of Science Computer Science Neuroscience Research Interests Freiesleben explores the philosophical foundations of machine learning, emphasizing causality , ethical constraints , and scientific inference from opaque models. His work addresses the epistemological validity of recourse explanations, robustness in AI, and the interplay between human concepts and neural representations. Recent Publications His 2021-2025 work spans counterfactual explanations , model robustness , and XAI critique , with methodological innovations like adversarial random forests and improvement-focused causal recourse. Themes include ethical AI deployment in healthcare, feature importance analysis, and philosophical implications of black-box models. Collaborations & Labs He collaborates with Dr. Thomas Grote (Tübingen) and was supervised by Prof. Stephan Hartmann (Munich). Affiliated with the Machine Learning in Science Cluster and the Munich Center for Mathematical Philosophy .

• Programming Languages
• Program Verification
• Software Security
• +۵ مورد دیگر

Gilles Barthe is a scientific director at the Max Planck Institute for Security and Privacy in Bochum, Germany, and a part-time research professor at the IMDEA Software Institute in Madrid, Spain. His research focuses on programming languages, program verification, cryptography, privacy, and mathematical logic, with an emphasis on applying relational verification to cryptographic systems. He leads the Foundations of Security and Privacy Group, developing techniques to ensure cryptographic software is secure against timing and side-channel attacks, and verifies implementations of post-quantum algorithms like Kyber. Barthe has pioneered tools such as EasyCrypt for formal proofs, and contributed to standards like AWS Key Management Service verification. His work bridges theory (e.g., quantum verification) and practice (e.g., compiler optimization for constant-time code). Education: Ph.D. in 1993 from University of Manchester, followed by roles at INRIA, IMDEA, and others. Research Interests: Secure compilation, cryptographic protocol verification, differential privacy, and quantum programming semantics. Recent articles explore secure compilation against Spectre variants, quantum relational logics, and formal verification of lattice-based cryptography. He has advised over 20 PhD/Master’s students, many contributing to cryptographic verification frameworks. Barthe’s teams collaborate on high-assurance implementations, masking techniques, and mitigating modern microarchitecture threats. His lab’s work includes PanCast for epidemic risk mitigation and certified cryptographic libraries.

• Theoretical Computer Science
• Algorithms
• Online Algorithms
• +۳ مورد دیگر

Hans-Joachim Böckenhauer is a Lecturer at the Department of Computer Science at ETH Zürich, specializing in theoretical computer science with a focus on algorithms, online algorithms, and computational complexity. His research explores algorithmic optimization in problems like knapsack, graph exploration, and parameterized complexity. Affiliation: ETH Zürich, Department of Computer Science Research Interests: Online Algorithms, Graph Theory, Algorithm Design, Computational Complexity His work emphasizes the theoretical foundations of algorithms, including advice complexity and reoptimization strategies. Recent contributions address online knapsack variants, graph exploration with limited memory, and parameterized problem-solving.

Anne-Marie Georgeمشاهده پروفایل

دانشیار

• Preference Learning
• Preference Modeling
• Computational Social Choice
• +۴ مورد دیگر

Anne-Marie George is an Associate Professor at the University of Oslo, affiliated with the Scientific Computing and Machine Learning department. Her research focuses on handling user preferences through modeling, elicitation, inference, and fairness in group decision-making systems. Academic Rank: Associate Professor Department: Scientific Computing and Machine Learning Email: annemage@ifi.uio.no Her work spans computational social choice, preference learning, and algorithmic fairness, with recent publications addressing dynamic resource allocation, robust recourse in binary problems, and fair voting procedures. Collaborations with researchers like Christos Dimitrakakis highlight interdisciplinary efforts in AI ethics and decision theory. Recent trends in her publications include: Dynamic allocation fairness Preference-based decision systems Explainable AI for resource distribution Ontology engineering applications Robustness in allocation algorithms Multiwinner voting optimization

John P. Dickersonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence Ethics
• Economics of Complex Systems
• +۳ مورد دیگر

John P. Dickerson is a tenured Professor of Computer Science at the University of Maryland and Chief Scientist at Arthur AI. He holds a PhD from Carnegie Mellon University and is affiliated with UMIACS, AMSC, HCIL, and MTI. His work bridges machine learning and economics, focusing on fairness, robustness, and incentive design in complex systems. Research areas include algorithmic fairness, healthcare optimization (e.g., organ allocation), and AI ethics. Education: PhD in Computer Science, Carnegie Mellon University Affiliations: UMIACS, AMSC, HCIL, MTI His research is supported by NSF CAREER, NIST, DARPA, and industry grants. Notable contributions include fairness in clustering, robustness in face detection, and kidney exchange optimization. He advises startups like GovShop and has been recognized with Siebel Scholarship and Facebook Fellowship. Key projects include Arthur AI's enterprise AI monitoring systems and collaborations with UNOS on organ allocation. Current focus is growing Arthur and mentoring students in machine learning and operations research.

Jenny Julén Votiniusمشاهده پروفایل

استاد

• Labor Law
• EU Law
• Gender Equality
• +۵ مورد دیگر

Jenny Julén Votinius is a Professor and Research coordinator at the Department of Law, Lund University, affiliated with the Norma Research Programme. She specializes in labor law, EU law, gender equality, and workplace rights. Her research focuses on aging workers, discrimination prevention, collective bargaining, and social security frameworks. She actively contributes to policy consultations and international research projects like DIGILARE and COGENS. Key areas include strategic litigation strategies, EU legal transposition, and interdisciplinary approaches to elder law and work-life balance. Her work spans 90+ publications, including books on parental rights in labor markets and legal frameworks for active aging. She co-edited Older Workers, Active Ageing, and the Future of Work (2024) and led projects addressing gig economy challenges and digital labor markets. Notable collaborations include studies on algorithmic discrimination and gender equality in Sweden. Awards and recognitions include contributions to EU directive implementations and policy reforms. She advises on strategic legal matters related to discrimination law and work-life balance directives. Current projects explore digitalization's impact on labor relations and prolonged working lives. She supervises research on survivor benefits, intergenerational equity, and legal futurism.

Cynthia Liemمشاهده پروفایل

دانشیار

• Multimedia Computing
• Human-Centered AI
• Music Information Retrieval
• +۱۰ مورد دیگر

Cynthia Liem is an Associate Professor at the Multimedia Computing Group, part of the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft). Her work uniquely bridges computer science and music, leveraging her dual expertise in both domains. Education: BSc and MSc in Media and Knowledge Engineering (Computer Science), TU Delft (2007, 2009); PhD, TU Delft (2015); BMus and MMus in Classical Piano Performance, Royal Conservatoire, The Hague (2009, 2011). Her research centers on human-centered multimedia analysis, with two core themes. First, she investigates how to algorithmically surface information in large multimodal archives that users would not discover through conventional search or recommendation systems, aiming to broaden user perspectives and help uncover underutilized digital content. Second, she focuses on validation and validity in data science, particularly in contexts where human responses are implicit and hard to measure, drawing inspiration from psychometric validity in the social sciences. Her work emphasizes real-world applicability, especially in music and cultural heritage domains. The two recent publications highlight her engagement with cutting-edge AI challenges: one explores algorithmic recourse dynamics, while the other examines evolutionary algorithms in adversarial example generation, indicating strong interests in robustness, explainability, and human-aligned AI systems. Notable scientific recognitions include: Google Anita Borg Scholarship (2008) Google European Doctoral Fellowship (2010) Top-5 nominee, New Scientist Science Talent Prize (2016) Cynthia Liem has been involved in several significant research projects, including the H2020 TROMPA project on crowd-powered enrichment of public-domain music, an NWO-KIEM project on musician well-being, an NWO-Veni project on perspective-broadening in recommender systems, and an ERASMUS+ initiative on big data in psychological assessment. She collaborates with key institutions such as the National Library of The Netherlands and CDR/Muziekweb. Beyond academia, she remains active as a performing pianist, notably in the Magma Duo, which won first prize at the A Feast of Duos competition and was part of the Dutch Classical Talent Tour, leading to a national concert tour. There is no indication of lab or research team leadership beyond project involvement, though her role as an Associate Professor suggests supervision of students and researchers. Future work appears directed toward socially responsible AI, improved validation frameworks, and interdisciplinary applications of music and data science.

Jeffrey Chanمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Responsible AI
• +۶ مورد دیگر

Jeffrey Chan is a Professor and Associate Dean of Data Science & Artificial Intelligence at RMIT University's School of Computing Technologies. His research focuses on machine learning, recommender systems, responsible AI, and interdisciplinary applications in sustainability, healthcare, and transportation. He has held roles such as Assistant Associate Dean and led projects on fairness-aware systems, energy-efficient recommendations, and AI-driven circular economies. His work bridges theory and practice, collaborating with industry and non-profits. He teaches courses in algorithms, network analytics, and machine learning, and actively supervises PhD and Honours students. Notable contributions include grants from the Australian Research Council and a Best Paper Award at ACM International Conference on Web Science 2011. His research also addresses ethical challenges in AI, such as fairness and transparency, and explores mobility solutions like e-scooter dynamics and parking optimization. Research Interests: Machine Learning, Recommender Systems, Data-Driven Optimization, Social Network Analysis, Responsible AI, Cloud Security, and Sustainability. Awards: Best Paper Award at ACM International Conference on Web Science 2011 Grants: Australian Research Council Discovery Project (2017–2019), Early Career Researcher Grant (2015), and EU FP7 ROBUST project (2011–2013). Labs/Teams: Leads initiatives in AI ethics, mobility systems, and interdisciplinary data science collaborations.

Anupam Guptaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Approximation Algorithms
• +۴ مورد دیگر

Anupam Gupta is a Silver Professor at the Department of Computer Science, Courant Institute of Mathematical Sciences, New York University. Previously, he was affiliated with Carnegie Mellon University. His academic journey includes a Ph.D. from the University of California, Berkeley (2000) and a B.Tech. from the Indian Institute of Technology, Kanpur (1996). Education : Ph.D., University of California, Berkeley (2000) B.Tech., Indian Institute of Technology, Kanpur (1996) Research Interests : Anupam's research lies at the intersection of theoretical computer science and algorithm design. He focuses on approximation algorithms for stochastic and online environments, metric embeddings, and algorithmic problems in network design, clustering, and optimization. His work bridges theoretical insights with practical applications in uncertain and dynamic settings. Publications & Trends : His recent publications highlight advancements in online algorithms with adversarial corruptions, stochastic optimization, and approximation techniques for problems like k-server, set cover, and graph searching. Key trends include leveraging predictions, balancing adaptivity and robustness, and exploring the interplay between machine learning and classical algorithm design. Scientific Awards : ACM Fellow (2021) Alfred P. Sloan Research Fellowship NSF CAREER Award Herb Simon Award for Teaching Excellence (Carnegie Mellon University) Advising & Grants : He has mentored numerous Ph.D. students and postdoctoral fellows. His research is supported by grants from the National Science Foundation (NSF), including a CAREER Award, and collaborative funding for algorithmic initiatives like the Simons Institute program and the Joint Indo-US Networked Center for Algorithms under Uncertainty.

• 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.