Başar Öztayşi is a Professor at the Department of Industrial Engineering , Istanbul Technical University , with expertise in fuzzy logic, multi-criteria decision making, and decision science. He has held administrative roles including Associate Professor (2017–present), Deputy Director of the Institute (2016–2017), and Assistant Professor (2013–2017). Fields of Study : Fuzzy Logic, Multi-criteria Decision Making, Decision Science Contact : oztaysib@itu.edu.tr , +90 212 293 1300 Research Interests focus on applying fuzzy set theory to complex decision problems, including financial management, risk assessment, and smart city energy systems. His work extends to industry 4.0 applications and process mining in e-commerce. Recent Publications (2024) analyze fuzzy approaches in financial management, risk assessment, and Industry 4.0, with subfields spanning bibliometric trends, allocation optimization, and sustainable energy planning. Earlier works explore AHP matrix consistency, file distribution models, and customer segmentation. Awards : Best Paper Award, FLINS 2018 Science - Art Awards
Martin Müller is a Professor in the Department of Computing Science at the University of Alberta, where he conducts research in artificial intelligence, game theory, and heuristic search. He holds the Canada CIFAR AI Chair at Amii and is an Amii Fellow, underscoring his leadership in AI. His research group focuses on Monte Carlo tree search, reinforcement learning, combinatorial game theory, and automated planning, with applications in games such as Go, Hex, and NoGo. His research interests span Monte Carlo and exact methods in game-tree search , exploration in heuristic search and machine learning , and algorithms in combinatorial game theory . He has developed open-source software like MCGS (Minimax-based Combinatorial Game Solver) and contributes to game-playing systems such as Fuego for Go. His work bridges theoretical foundations with practical implementations in AI-driven game solvers. Recent publications show a strong trend in reinforcement learning , particularly in deep Q-learning, policy gradient methods, and anomaly detection in deep RL. His team also explores combinatorial game solving , sparse reward environments , and imperfect information games . The research integrates machine learning with classical AI techniques, emphasizing empirical validation and algorithmic innovation. Canada CIFAR AI Chair Amii Fellow Best student paper award at IEEE Conference on Games 2024 Best paper award at IEEE COG 2021 Outstanding paper award at AAAI-18 Faculty of Science Dissertation Award (2016) Dissertation Award from the Canadian Artificial Intelligence Association (2013) Müller has supervised numerous PhD and MSc students, including Hongming Zhang, Henry Du, and Timo Bertram, many of whose theses focus on game AI, reinforcement learning, and combinatorial optimization. He is funded by NSERC, Mitacs, and Compute Canada. His group collaborates on projects involving neural networks for game playing, SAT solving, and planning algorithms. He is currently on sabbatical but remains academically active, teaching a graduate course on combinatorial games in 2025 and hosting visiting researchers. His lab is involved in the development of MCGS, a solver for sum games, and contributes to open-source AI software. The team publishes regularly in top venues such as NeurIPS, ICML, AAAI, and IEEE Transactions on Games. Future work includes advancing combinatorial game solvers, improving deep RL robustness, and exploring generalization in game representations.
Claudia Wagner is a full professor for Applied Computational Social Sciences at RWTH Aachen University and the Scientific Director of the Computational Social Science department at GESIS—Leibniz Institute for the Social Sciences. She is also an External Faculty member at the Complexity Science Hub Vienna. Her work bridges computer science and the social sciences to study algorithmic systems and their societal impacts. Her research focuses on socio-technical phenomena such as inequality, sexism, and perception bias in algorithmically infused societies. She investigates methodological challenges in using digital behavioral data to study human behavior, attitudes, and group dynamics. Her interests span computational social science, algorithmic fairness, network science, and AI ethics. The analysis of her recent publications reveals a strong emphasis on bias, fairness, and methodological rigor in digital data analysis. Her work spans AI psychometrics, gender inequality in online platforms, and validation frameworks for digital traces. She frequently publishes in top-tier venues such as Nature , Science , and AAAI conferences. DOC-fFORTE fellowship from the Austrian Academy of Sciences Four best paper awards at international conferences (ICWSM, CSCW, WWW, AAAI) Associate Editor, EPJ Data Science Steering Committee Member, International AAAI Conference on Web and Social Media Board Member, International Society for Computational Social Science Claudia Wagner has led and co-led substantial research projects funded by national and international agencies. She mentors a diverse group of PhD students working on topics like algorithmic bias, data quality, and dehumanization. She has organized training events such as the CSS Methods Summer School and delivered keynotes globally on inequality and computational social science. She leads the Computational Social Science department at GESIS and collaborates with interdisciplinary teams at RWTH Aachen and the Complexity Science Hub. Her group develops tools for measuring algorithmic impacts and visualizing disparities in socio-technical systems, such as the 'Planets of Disparity' dashboard.
Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .
Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.
Joss Wright is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute , University of Oxford. He co-directs the Oxford EPSRC Cybersecurity Doctoral Training Centre and the Oxford Martin Programme on the Wildlife Trade, focusing on computational approaches to social science questions about information control and privacy. Education : PhD in Computer Science from the University of York (research on anonymous communication systems), postdoctoral work at the University of Siegen (cloud computing security). His research spans internet censorship , privacy-enhancing technologies , and cyber-enabled crime (notably the online illegal wildlife trade ). He bridges technical analyses of security systems with their social and political implications, advising the European Commission and UK Parliamentary Science Committee on digital policy. Recent work includes machine learning applications to detect patent filing trends related to wildlife trade and analyzing Chinese smart city surveillance for human rights risks. He has contributed to media outlets like the Guardian and New Scientist. Notable projects include the Oxford Martin Programme on Wildlife Trade and studies on discriminatory effects of internet filtering . He supervises students like William Lugoloobi (DPhil in Social Data Science) and former advisee Samantha Bradshaw (now Assistant Professor at American University).
Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.
Joshua Ignatius is a Professor of Business Analytics at the Aston Business School, part of the College of Business and Social Sciences at Aston University. He focuses on research areas including supply chain analytics, prescriptive analytics, electronic commerce, and operations management. His work often addresses challenges such as information asymmetry in supply chains, user recommender systems, and logistics optimization. He is currently accepting PhD students in topics like Information Asymmetry in Supply Chains, User Recommender Systems, and Supply Chain Analytics. His research interests are centered on leveraging data-driven approaches to improve decision-making in supply chain and operational contexts. This includes studying disruption risk management, dynamic data modeling, and the integration of AI in cloud services. He also explores strategic decisions in e-commerce logistics, customer segmentation strategies, and environmental sustainability. Recent publications highlight his contributions to supply chain resilience, optimal security in cloud computing, and sustainable manufacturing processes. For instance, his 2025 work on supply chain network viability addresses disruption risks through dynamic data strategies. Another key area is the analysis of customer behavior in product upgrades, utilizing online review data to inform quality differentiation strategies. Dr. Ignatius has collaborated on projects involving platform information sharing, manufacturer encroachment, and logistics sourcing for e-commerce firms. His research often bridges theoretical frameworks with real-world applications, emphasizing practical solutions for operational challenges. He holds a strong record of supervising PhD students and guiding projects that combine academic rigor with industry relevance. His work frequently appears in leading journals such as the European Journal of Operational Research and Journal of Operations Management.
John F. Brady is the Chevron Professor of Chemical Engineering and Mechanical Engineering at the California Institute of Technology. He earned his B.S. from the University of Pennsylvania (1975), M.S. (1977) and Ph.D. (1981) from Stanford University, and has held academic roles at Caltech since 1985, including Executive Officer for Chemical Engineering (1993-99; 2013-19). His research focuses on fluid mechanics, transport processes, and complex/multiphase fluids. Elected to the National Academy of Sciences (20XX) Elected to the American Academy of Arts and Sciences (20XX) Brady's publications reveal expertise in active matter dynamics, microrheology, and non-equilibrium systems. His work spans fundamental fluid mechanics to applied biomedical device design, with a strong emphasis on computational modeling and experimental validation in colloidal and soft matter physics.
Prof. Dr. Ralf Merz serves as Head of the Department of Catchment Hydrology at the Helmholtz Centre for Environmental Research (UFZ) and holds a Full Professorship in Catchment Hydrology at Martin-Luther University Halle-Wittenberg since 2011. His career bridges hydrological modeling, flood risk assessment, and water quality analysis across diverse climates from Central Asia to Europe. MSc in Civil Engineering (Technical University of Karlsruhe, 1997) PhD in Hydrology (Vienna University of Technology, 2002) Habilitation in Hydrology (Vienna University of Technology, 2009) Research Interests span comparative hydrology, flood generation mechanisms, climate change impacts on water resources, and nitrate dynamics in river systems. His work emphasizes process-based understanding of runoff events and regional flood modeling through innovative approaches like the PHEV distribution framework. Scientific Contributions include over 100 publications (2003-2025) on: Flood frequency analysis in changing climates Groundwater recharge in arid regions Hydrochemical response to droughts Remote sensing applications for groundwater studies Multi-response calibration of hydrological models Key projects involve MOSES observatory development, TRACER research school, and Pamir Mountains glaciological studies. Recognitions : APART research grant (Austrian Academy of Sciences, 2006) Leadership extends to directing the Catchment Hydrology department and participating in European hydrological networks like the Bode Hydrological Observatory and TERENO infrastructure. His methodological advancements include flood time-scale analysis and event runoff coefficient regionalization.
Dr. Juha Kärkkäinen is a University Lecturer and Principal Investigator at the Department of Computer Science, University of Helsinki, specializing in Algorithmic Bioinformatics. He supervises PhD students in the Doctoral Programme in Computer Science and actively contributes to research outputs and academic events. Institution: University of Helsinki Department: Department of Computer Science Rank: Lecturer His research focuses on string processing , data structures , and combinatorial pattern matching , with recent work on bijective Burrows-Wheeler transforms, LCP arrays, and efficient string indexing. Google Scholar lists publications spanning 2024 to 1999, highlighting his expertise in algorithm design and compression techniques . Notably, he has participated in and organized events like the International Workshop on Combinatorial Algorithms and delivered a keynote speech at the 1st Summer School on Bioinformatics Algorithms. While no explicit awards are detailed, his editorial roles in scientific collections and conferences underscore his academic influence.
Jim Luedtke is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on operations research, integer programming, and stochastic optimization methods for solving discrete and uncertain decision problems. Educational Background: BS in Industrial Engineering from University of Wisconsin-Madison MS in Operations Research from Georgia Institute of Technology PhD in Industrial and Systems Engineering from Georgia Institute of Technology Postdoctoral Research at IBM T.J. Watson Research Center His work spans applications in power systems optimization, healthcare analytics, and network design, with particular emphasis on developing cutting-edge algorithms for chance-constrained and multistage stochastic programming problems. Recent publications demonstrate strong focus on Benders decomposition techniques, Lagrangian dual methods, and distributionally robust optimization frameworks. Scientific Awards: NSF CAREER Award (2010) for "Risk Management via Stochastic Programming: Models, Computation, and Applications"
Christian Coester is an Associate Professor of Computer Science at the University of Oxford and a Tutorial Fellow at St Anne's College. His research focuses on theoretical computer science, particularly in the design and analysis of algorithms for problems involving uncertainty and incomplete information. His primary research areas include: Online algorithms, with groundbreaking work on the k-server problem (including refuting the randomized k-server conjecture, which earned the STOC 2023 Best Paper Award) Learning-augmented algorithms (algorithms with predictions) that leverage machine learning predictions while maintaining robustness guarantees Fundamental problems such as the k-taxi problem, metrical task systems, and online shortest paths Coester's theoretical work aims to develop algorithms with provable performance guarantees, particularly focusing on competitive ratios that measure worst-case performance against optimal offline solutions. His research often addresses problems that are 'simple to state and hard to solve,' leading to techniques with broad applicability across theoretical computer science. His publications span top venues including STOC, FOCS, SODA, and ICML, showing consistent contributions to both classical online algorithms and the emerging field of learning-augmented algorithms. The publications reveal a strong focus on metric spaces, competitive analysis, and the integration of prediction models into traditional algorithmic frameworks. Coester has received significant recognition including the STOC 2023 Best Paper Award and a substantial ERC Starting Grant (EUR 1.5M) for 'Challenges in Competitive Online Optimisation' (2025-2029). He actively supervises PhD students and welcomes inquiries from mathematically skilled candidates interested in theoretical computer science.
Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.
Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.
