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Ulle EndrissView profile

Professor

• Artificial Intelligence
• Collective Decision Making
• Computational Social Choice
• +5 more

Ulle Endriss is a Professor of Artificial Intelligence and Collective Decision Making at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam. He leads the COMSOC Group and is a member of both the Theoretical Computer Science research unit of the ILLC and the Centre for Explainable, Responsible and Theory-driven AI (CERTAIN). Endriss has held academic positions since 2005, progressing from Assistant Professor (2005-2011) to Associate Professor (2011-2019) and finally Full Professor since 2019. Endriss's research focuses on formal methods in AI, particularly in multiagent systems and knowledge representation, with special interest in computational social choice—the intersection of AI with economics and political science. His work spans voting theory, fair division, participatory budgeting, and game theory. He is actively involved in the ADDI project on digital democracy and regularly contributes to major AI conferences including AAMAS, IJCAI, and AAAI. His recent publications demonstrate a strong focus on computational social choice, with particular emphasis on voting systems, participatory budgeting mechanisms, and the theoretical foundations of collective decision making. His research combines theoretical rigor with practical applications in democratic processes and resource allocation problems. Runner-up for the Best Paper Award (2023) Best Paper Award (2016) Best Paper Award (2012) Best Paper Award (2005) Finalist for the Best Paper Award (2014) Finalist for the Best Paper Award (2008) IJCAI-JAIR Best Paper Prize 2016 Endriss regularly teaches courses on Computational Social Choice and Game Theory for MSc Logic and MSc AI programmes. He makes all his teaching materials openly available, consistent with his commitment to open access in both research and education. He has supervised numerous Master's theses that have led to published research. His work extends to outreach through platforms like Game-Academy.org and Pabuviz.org, which help explain complex voting concepts to broader audiences.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +3 more

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.

• Robotics
• Control Systems
• Predictive Control
• +6 more

Jan Swevers is a Full Professor at KU Leuven , affiliated with the MECO Research Team . His work focuses on predictive control, sensor-based robotics, and optimal motion planning, with applications in autonomous systems, industrial robotics, and aerospace engineering. Research Interests : Predictive control (Model Predictive Control, Iterative Learning Control), sensor-based robotics (surface following, cable shaping), optimal motion planning (Reeds-Shepp algorithms, time-optimal interception), and constraint handling in robotics. Publications : Recent work explores deformable object dynamics, autonomous surface vessel navigation, and computational efficiency in optimal control. Key trends include integrating predictive control with real-time estimation, simplifying complex environments via constraint reduction, and advancing human-like autonomous driving through imitation learning. Advising : Supervised PhD theses on topics like constraint-based robot programming, motion planning, and predictive control for sensor-based tasks. Labs : Leads the MECO Research Team at KU Leuven, focusing on control systems, robotics, and optimal planning.

Katharina BrediesView profile

Researcher

• Interaction Design
• Design Theory
• Electronic Textiles
• +4 more

Katharina Bredies is a Researcher at the Design Research Lab and the Berlin University of the Arts , where she explores the intersection of electronic textiles , interaction design , and design theory . Her work emphasizes user agency and the repurposing of digital objects in everyday contexts. Education : Integrated Design (Diploma), Hochschule für Künste Bremen (2006) Current Affiliation : Berlin University of the Arts, School of Textiles (Borås, Sweden) Her research focuses on electronic textiles and the integration of traditional textile techniques with digital functionality , promoting sustainable design and social inclusivity in technology. She has contributed to 15 publications spanning topics like smart textiles , user-driven innovation , and design philosophy . Key projects include Architextiles , Cushion Pressure Sensors , and MINODU (fostering local sustainable development). Her work has been featured in DRS , CHI , and AMBIENT conference proceedings.

Marten van DijkView profile

Professor

• Computer Security
• Cryptography
• Secure Processor Design
• +7 more

Dr. Marten van Dijk is a Full Professor in the Computer Security department at Vrije Universiteit Amsterdam (VU) since 2022 and a Group Leader for Computer Security at CWI since 2020. He also holds a Gratis Full Research Professor position at the University of Connecticut's ECE Department since 2020. Previously, he served as Associate and Full Professor at the University of Connecticut and held research roles at MIT CSAIL, RSA Laboratories, and Philips Research. PhD in Mathematics (1997, Eindhoven University of Technology) M.S. in Mathematics (Cum Laude, 1993) M.S. in Computer Science (Cum Laude, 1991) His research focuses on foundational computer security problems using cryptographic principles, including secure processor design, oblivious computation, and privacy-preserving machine learning. Notable contributions span Physical Unclonable Functions (PUFs), Aegis secure processor architecture, and oblivious RAM protocols. 15+ publications in 2023-2025 address topics like PUF cryptanalysis, differential privacy in federated learning, and Byzantine fault tolerance Key journals: IEEE Transactions on Computers, Journal of Cryptology, ACM CCS Conference Award highlights include: IEEE Fellow (2022) for secure processor design and encrypted computation IEEE Technical Achievement Award (2023) Intel Test of Time Award (2022) ACM CCS Best Paper (2013) A. Richard Newton Technical Impact Award (2015) His technical leadership spans hardware security (blu-ray error correction codes), cryptographic protocol design, and machine learning privacy frameworks. Current projects focus on secure processors with hardware-enforced isolation and differential privacy optimization.

• Computational Fluid Dynamics
• Turbulence Modeling
• Machine Learning
• +5 more

Olga Doronina serves as Researcher III in Data Science within the Computational Science group at the National Renewable Energy Laboratory (NREL), applying expertise in turbulence modeling, computational fluid dynamics, machine learning, and statistical methods to advance renewable energy research. Her academic foundation includes: PhD in Mechanical Engineering from the University of Colorado Master of Science in Mechanical Engineering from the University of Colorado Master of Science in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor of Science in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Her research centers on developing computational tools for energy applications, with current projects focused on surrogate modeling of non-ideal reacting flows in oxy-combustors and wind turbine blade representation. She integrates Bayesian analysis and machine learning with fluid dynamics to solve complex engineering challenges in aerodynamics and combustion systems. Analysis of her 2024-2025 publications reveals strong emphasis on multi-fidelity modeling techniques, uncertainty quantification in scale-up problems, and neural-network-enhanced control frameworks for building energy systems. Her work consistently bridges computational mathematics with practical renewable energy applications, particularly in aerodynamic shape optimization and combustion modeling. Dr. Doronina actively collaborates within NREL's Computational Science division on interdisciplinary projects involving wind energy systems, combustion technologies, and building efficiency solutions, contributing to the laboratory's mission through advanced computational methodologies.

• Computational Biology
• Systems Biology
• Computer Science
• +3 more

Andreas Wagner is a researcher affiliated with Helmholtz-Zentrum Dresden-Rossendorf , with a focus on interdisciplinary research spanning computational biology, systems biology, computer science, and materials science. His work explores genotype-phenotype mappings, evolutionary innovation, and robustness in biological systems, while also contributing to machine learning, numerical methods, and positron annihilation spectroscopy in physics. Wagner collaborates internationally, with co-authors from institutions in Germany, Austria, Finland, and beyond. Research Interests : Wagner's research bridges computational biology and systems biology, analyzing evolutionary processes through genotype networks, metabolic innovation, and gene regulatory circuits. He applies machine learning techniques to energy systems, such as solar power forecasting in federated learning frameworks. His physics work involves positron annihilation spectroscopy for material defect analysis, particularly in alloys and thin films. Publications & Data Science : He has published extensively on topics like robust numerical algorithms, adaptive cruise control optimization, and data-driven approaches for systematic reviews. His recent work includes matrix-free preconditioning methods and physics-regularized multi-modal image assimilation for medical imaging. Wagner contributes to open data initiatives, including datasets on radiation damage and material porosity via RODARE.

• Machine Learning
• Control Systems
• Reinforcement Learning
• +3 more

Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.

Alan ZameView profile

Professor

• Mathematics
• Statistics
• Probability Theory
• +4 more

Alan Zame is a Professor in the Department of Mathematics at the University of Miami , College of Arts and Sciences. His research spans diverse areas in mathematics, statistics, and probability theory. Education & Training: (Details not explicitly provided in the text) Research Interests: Dr. Zame's work includes combinatorics , probability theory , game theory , and number theory . He has contributed to understanding stochastic processes, tournament planning, urn schemes, and subgroup chains. Publication Trends: His publications from 1984–2010 focus on probabilistic modeling, strategic games, and mathematical structures. Key topics include random sequences, gambling strategies, string matching, and combinatorial fairness in tournaments. Advising & Grants: No specific students or grants are listed in the provided text.

Shuang AoView profile

Research Fellow

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +4 more

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au

Can Deha KarıksızView profile

Assistant Professor

• Functional Analysis
• Dynamics of Linear Operators
• Mean-Field Games
• +3 more

Can Deha Karıksız is an Assistant Professor of Mathematics at Özyeğin University's Faculty of Engineering. He holds a PhD in Mathematics from Sabancı University (2014), with prior degrees from Sabancı University (2007, MSc) and Middle East Technical University (2005, BSc). His academic journey includes a postdoctoral fellowship at Adam Mickiewicz University (2014-2015) supported by a TÜBİTAK grant and an instructorship at the University of Zurich (2019-2020). Assistant Professor, Özyeğin University (current) Postdoctoral Researcher, Adam Mickiewicz University Instructor, University of Zurich His research spans three primary domains: Applications of functional analysis to complex analysis Dynamics of linear operators Mean-field games and stochastic control Combining mathematical rigor with computational approaches, his work bridges theoretical analysis with practical implementation in multi-agent systems and reinforcement learning frameworks. Recent publications focus on mean-field games (2019-2024), particularly through algorithmic approaches like Q-learning, value iteration, and inverse reinforcement learning. Earlier work (2018) examines linear operator dynamics on real analytic function spaces. Key trends show increasing specialization in stochastic control and game theory applications to machine learning and engineering. Scientific achievements include: TÜBİTAK Postdoctoral Research Fellowship He has taught diverse courses across institutions and levels since 2005, including Linear Algebra, Calculus sequences for engineering/social sciences, Differential Equations, and graduate-level Numerical Linear Algebra and Analysis II. His teaching portfolio demonstrates expertise in both foundational mathematics and advanced computational methods.

Manuel Fernández DelgadoView profile

Associate Professor

• Machine Learning
• Pattern Recognition
• Computer Vision
• +3 more

Manuel Fernández Delgado is an Associate Professor at the University of Santiago de Compostela (manuel.fernandez.delgado@usc.es). His research spans Machine Learning , Pattern Recognition , and Computer Vision with applications in medical diagnostics, agricultural monitoring, and gender-inclusive education. PhD in Computer Science (1999) Developed software tools: Govocitos , CystAnalyser , STERapp Research highlights: Medical Imaging : Breast cancer and oral leukoplakia analysis via image segmentation/classification Agricultural AI : Nutrient deficiency detection in wheat, marbling analysis in ham Gender Equality : Pioneering computational thinking education with gender perspectives Robotics : Fault diagnosis systems for antenna arrays Key methodologies include Extreme Learning Machines, Support Vector Machines, and Deep Learning. He collaborates with teams in TELGalicia and TecAnDaLi networks.

• Process Automation Systems
• Control Engineering
• Numerical Optimization
• +7 more

Sergio Lucia is a Full Professor (W3) for Process Automation Systems at Technische Universität Dortmund within the Department of Biochemical and Chemical Engineering since 2023. He previously served as a W2/W3 Professor (2020-2023) and W1 Assistant Professor at TU Berlin (2017-2020). His research focuses on the intersection of control engineering, numerical optimization, and machine learning, with applications in chemical processes, biotechnology, and energy systems. He leads the Laboratory of Process Automation Systems (Building G2, North Campus) and has held prestigious roles including Vice Chair of IFAC Technical Committee on Optimal Control since 2020. Education: Dr.-Ing. (summa cum laude) in "Robust multi-stage nonlinear model predictive control" (2014) Postdoctoral: Massachusetts Institute of Technology (2016), Otto-von-Guericke University Magdeburg (2015-2017) Alumni: Research Assistant at TU Dortmund (2010-2014), Diploma in Electrical Engineering (2010) His research explores novel methods to bridge theory and applications in control engineering, particularly through model predictive control (MPC) innovations. Recent work emphasizes robustness under uncertainty , Bayesian optimization , deep learning integration , and privacy-preserving federated learning for industrial applications. His 2025 publications address challenges in chemical recycling networks, crystallization processes, and serverless computing triggers. Scientific recognition includes: Teaching award (2023) Best student paper awards (2022, 2021) VAA Dissertation Award (2015) Erasmus Scholarship (2010) M.Sc. Extraordinary Career Award (2011) As a dedicated educator, he refines courses to enhance learning outcomes and mentors PhD students Sarah Braun and Benjamin Karg. His laboratory at TU Dortmund's North Campus is strategically located near the H-Bahn monorail system for accessibility.

Kim van OorschotView profile

Professor

• Systems Thinking
• System Dynamics
• Operations Management
• +4 more

Kim van Oorschot is a Professor in the Department of Accounting and Operations Management at BI Norwegian Business School. Previously, she served as an Associate Professor at BI (2011-2016), Assistant Professor at Eindhoven University of Technology (2008-2011), Post-Doc at Tilburg University, and Research Fellow at INSEAD. She also worked as a consultant at Minase Consulting BV from 2002-2006, serving major clients like ASML, DSM, KPN, NXP, and Stork Fokker. Dr. van Oorschot holds a PhD (2001) and MSc (1996) from Eindhoven University of Technology. Her research focuses on decision-making, trade-offs, and tipping points in dynamically complex settings like supply chains and new product development projects. She specializes in identifying 'decision traps'—short-term beneficial decisions that have counterproductive long-term effects—using system dynamics modeling based on organizational data. Her work spans operations and supply chain management, project management, systems thinking, and product development. The analysis of her recent publications reveals a consistent focus on system dynamics approaches to complex organizational challenges. Her research demonstrates strong interdisciplinary connections between operations management, healthcare systems, sustainability reporting, and digital service delivery. A notable trend is her increasing focus on resilience in critical supply chains, particularly medicine supply systems during crises like Brexit and the COVID-19 pandemic. Her work often combines qualitative case studies with system dynamics modeling to reveal underlying patterns in complex systems. Dr. van Oorschot teaches system dynamics, operations and supply chain management, and project management to executive and master's students at BI Norwegian Business School. She has published in top-tier journals including Academy of Management Journal, Journal of Operations Management, Production and Operations Management, and Journal of Product Innovation Management. Her research has practical implications for organizations dealing with complex, dynamically evolving challenges where traditional analytical approaches fall short.

• Digital Controlling
• Management Accounting
• Business Analytics
• +5 more

Prof. Dr. Imke Keimer is a Professor at the Lucerne University of Applied Sciences and Arts (Hochschule Luzern) in the Department of Economics within the Business School. She is based at the Campus Zug-Rotkreuz and works at the Institute for Financial Services Zug (IFZ) in the CC Controlling (IFZ CO) division. Her work focuses on the intersection of digital transformation and financial management, particularly in the field of controlling. Her educational background includes: 2016: Appointed Professor at Lucerne University of Applied Sciences and Arts 2013: Certificate in University Teaching, Lucerne University of Applied Sciences and Arts 2011: Financial Risk Manager (FRM), Global Association of Risk Professionals (GARP) 2005-2009: Dissertation (Dr. rer. oec.), University of Bern, Topic: "Accounting Policy, IT Investments and the Strategic Orientation of Cost Management - Empirical Work in the Field of Accounting" (summa cum laude) 1999-2005: Studied Business Mathematics (M.Sc.), Technical University of Dortmund (overall grade: very good) Prof. Keimer's research primarily centers on digital transformation in controlling, business analytics, and agile finance. She has developed frameworks for assessing digital maturity in controlling functions and has pioneered approaches to integrate agile methodologies into financial management. Her work bridges theoretical research with practical applications, often through collaborations with industry partners. She has established herself as a thought leader in how digital technologies are reshaping the controller role from traditional accounting functions toward strategic business partnership. Her recent publications reveal a clear trajectory from foundational research on digital controlling frameworks toward more specific applications in sustainability, agile methodologies, and educational technology. The articles show increasing focus on practical implementation challenges, with notable attention to sustainable business practices and the evolving role of controllers in complex organizational environments. Her work consistently emphasizes the human dimension of digital transformation - how skills, mindsets, and organizational culture must evolve alongside technological changes. Scientific awards and recognitions include: Publication Award 2021 in the "Teaching" category for "PLAYMOBIL pro im Controlling: Mit Plastikfiguren das zukünftige Controlling-Rollenprofil gestalten" Financial Risk Manager (FRM) certification from GARP Prof. Keimer has been actively involved in advising and research projects. She served as Program Director for the MSc International Financial Management program and has led several significant research initiatives including the Innosuisse Project "Digital Transformation in Controlling" (2017) and the "Business Analytics in Controlling" project (2019). Her grant portfolio demonstrates strong industry-academia collaboration, with projects often co-developed with financial institutions and corporate partners seeking to modernize their controlling functions. She has also been instrumental in developing continuing education programs that bridge academic research with professional practice. She is closely associated with the Institute for Financial Services Zug (IFZ), where she contributes to research and teaching in the CC Controlling division. Her work often involves cross-disciplinary collaboration, particularly with colleagues in information systems and organizational behavior. She has developed a distinctive approach to teaching and research that combines academic rigor with practical applicability, frequently using case studies and industry partnerships to ground theoretical concepts in real-world challenges.