جست‌وجوی استاد و پژوهشگر

Niki Kilbertusمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Ethics in AI
• +۵ مورد دیگر

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۱۱ مورد دیگر

Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.

Paul Gölzمشاهده پروفایل

استادیار

• Social Choice Theory
• Algorithmic Fairness
• AI Ethics
• +۳ مورد دیگر

Paul Gölz is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering (ORIE), with affiliations in Computer Science. His research focuses on computational social choice, algorithmic fairness, and AI ethics, addressing topics like democratic innovation, fair resource allocation, and AI systems for diverse users. Education: Undergraduate studies at Saarland University (Germany), PhD in Computer Science from Carnegie Mellon University, followed by postdoctoral research at Harvard University, UC Berkeley, and the Simons Laufer Mathematical Sciences Institute. His work has been recognized with awards including the JPMorgan Chase AI Research Fellowship (2021) and honorable mentions for prestigious dissertation awards (Dantzig and ACM SIGecom). Research Highlights : Developed Panelot, a tool for selecting citizens' assemblies using state-of-the-art algorithms. His work on fair refugee resettlement and apportionment methods has been featured in Operations Research and Nature . Recent projects include AI alignment distortion analysis and generative social choice mechanisms. Teaching : Teaches Optimized Democracy (Spring 2025) and Mathematical Programming (Fall 2024). Supervises PhD students in ORIE, focusing on interdisciplinary applications of optimization and social choice. Awards & Grants : Received a $40K Structural Democracy Fellowship (Crankstart) and an OpenAI grant for Democratic Inputs to AI. Active in policy briefs (e.g., mini-public selection strategies). Labs/Tools : Co-developed Panelot.org , a nonprofit platform for fair citizen assembly selection. Active in open-source tool development for social choice applications.

Nina Gantertمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Large Deviations
• +۴ مورد دیگر

Prof. Dr. Nina Gantert is a distinguished Professor of Probability Theory at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology . She has held faculty positions at Karlsruhe Institute of Technology and the University of Münster prior to joining TUM in 2011. Her research focuses on probability theory , particularly stochastic processes , large deviations , and random media . She investigates random walks in random environments as models for transport in disordered systems and explores applications in physics and biology . Recent publications highlight her work on branching random walks , mixing times , biased random walks , and large deviation principles for complex stochastic systems. She has co-authored studies on random walks in dynamical percolation , interacting edge-reinforced processes , and extremal point processes in branching models. Scientific Awards: Elected fellow of the IMS (2016) Her academic career spans institutions including ETH Zürich, University of Bonn, Technical University of Berlin, and TUM. She has supervised numerous Bachelor’s and Master’s theses on topics ranging from mixing time analysis to percolation theory , often collaborating with international co-authors.

Yin Tat Leeمشاهده پروفایل

دانشیار

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +۲ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.

Mirko Kremerمشاهده پروفایل

استاد

• Supply Chain Management
• Behavioral Operations Management
• Inventory Management
• +۶ مورد دیگر

Prof. Dr. Mirko Kremer is a Professor of Supply Chain Management at the Frankfurt School of Finance & Management since July 2014. Prior to this, he was an Assistant Professor at Pennsylvania State University (2008-2014) and held visiting roles at London Business School, Kellogg School of Management, and INSEAD. He earned a Ph.D. in Business Administration from the University of Mannheim (2008), with prior studies in Business Administration at Mannheim (1999-2003) and Economics at Ruhr-Universität Bochum (1997-1999). Research Focus: Micro-behavioral foundations of supply chains, inventory management, sales forecasting, and service systems. Editorial Roles: Department Editor for Production and Operations Management , Associate Editor for Operations Research . Teaching: Courses in Operations Management, Supply Chain Strategy, and quantitative decision modeling at Frankfurt School, INSEAD, London Business School, and Kellogg School of Management. Research Trends: His work bridges behavioral economics with supply chain operations, emphasizing how human decision-making (e.g., managerial biases, consumer behavior) impacts inventory systems, pricing strategies, and forecasting accuracy. Recent articles focus on diagnostic errors under congestion, strategic sourcing biases, and dynamic pricing experiments.

Ignacio Ojea Quintanaمشاهده پروفایل

استادیار

• Social Epistemology
• Philosophy of Data Science
• Ethics of Autonomous Decision Making
• +۴ مورد دیگر

Ignacio Ojea Quintana serves as Assistant Professor at Ludwig Maximilian University of Munich's Faculty of Philosophy, Philosophy of Science and Religious Studies within the Chair of Philosophy of Science. He joined LMU in 2022 after a three-year research fellowship at the Australian National University's School of Philosophy. His academic foundation includes a PhD in Philosophy from Columbia University (2019), supervised by Philip Kitcher with focus on formal social epistemology, and prior Masters/BA degrees in Philosophical Logic from the University of Buenos Aires where he collaborated with the Buenos Aires Logic Group. Ojea Quintana's research employs formal methodologies to examine digital technologies' impact on knowledge organization across scientific and public spheres. His work intersects Social Epistemology , Philosophy of Data Science , and Autonomous Systems Ethics , with significant contributions to social media network analysis of controversial movements. Current projects analyze Twitter dynamics in racial justice movements, ethical constraints in AI decision systems, and consensus formation in scientific modeling. His 2021-2022 publications reveal an interdisciplinary trajectory bridging philosophy, computer science, and social sciences. Key trends include computational analysis of online discourse, formal modeling of group rationality under uncertainty, and ethical frameworks for AI systems. Works appear in high-impact venues including Nature Humanities and Social Sciences Communications and the AAAI/ACM Conference on AI, Ethics, and Society. Ojea Quintana maintains active collaborations with the "Humanising Machine Intelligence" initiative (formerly at ANU) and the Buenos Aires Logic Group. His research program demonstrates sustained engagement with technology's societal implications through formal modeling approaches and empirical digital trace analysis.

Steffen Marburgمشاهده پروفایل

استاد

• Numerical Methods in Vibroacoustics
• Aeroacoustics
• Structural Optimization
• +۶ مورد دیگر

Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.

• Embedded Machine Learning
• High-Performance Computing
• Computer Architecture
• +۶ مورد دیگر

Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.

• Optimization for Machine Learning
• Collaborative Learning
• Distributed Learning
• +۷ مورد دیگر

Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ مورد دیگر

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

• Sports Science
• Football (Soccer) Analytics
• Cognitive Psychology in Sports
• +۶ مورد دیگر

Prof. Daniel Memmert is a Professor at the German Sport University Cologne, leading research in Sport Informatics and Sports Games within the Institute of Exercise Training and Sport Informatics. His work focuses on cognitive aspects of sports performance, decision-making, and data-driven analysis in football (soccer) and other sports. He has published extensively on topics like penalty kick strategies, home advantage dynamics, artificial intelligence applications in coaching, and route-setting in climbing. Memmert's research bridges sports science, computer science, and psychology, with over 550 publications and 41 projects to his name. He frequently engages with media, explaining complex sports phenomena to the public. Key Research Areas: Football analytics, cognitive psychology in sports, sports technology, decision-making under pressure Media Contributions: Over 50 media features discussing topics such as AI in coaching, referee bias, and athlete creativity Projects: Includes initiatives on sports data visualization, performance metrics, and prevention of sports betting addiction His work emphasizes translating academic findings into practical tools for athletes, coaches, and sports organizations, combining rigorous data analysis with real-world applications.

Susanne Predigerمشاهده پروفایل

استاد

• Mathematics Education
• Teacher Professional Development
• Educational Technology
• +۴ مورد دیگر

Susanne Prediger is a Professor at the Institute for Development and Research in Mathematics Education (IEEM) at Technische Universität Dortmund and Vice Director of the Department of Subject-Related Knowledge Transfer at the Leibniz Institute for Science and Mathematics Education (IPN). She directs the DZLM research network (German Center for Mathematics Teacher Education) and holds leadership roles in mathematics education research. Her research focuses on: Design and Research on Professional Development of Mathematics Teachers and Facilitators Topic-Specific Design Research in Mathematics Education for Secondary Schools Diversity in Classrooms (especially Language Diversity) Empirical Studies on Students' Conceptions in Arithmetic, Algebra, and Stochastics Networking of Theories in Mathematics Education Research Recent work explores AI-driven formative assessment, educational innovation implementation, and inclusive pedagogical strategies for teaching percentages. She leads projects like ALwAI, Startchancen-Kompetenzzentrum Mathematik, and DigiProMIN. Current roles include: Vice Director (half-time), Department of Subject-Related Knowledge Transfer, IPN Director of DZLM Research Network, IPN (since 2021) Full Professor at IEEM, TU Dortmund University (since 2006) Her academic background spans Mathematics and History studies at Technical University of Darmstadt and Université Bordeaux I, a PhD in Mathematical Logic and Universal Algebra (1998), and a Habilitation in Mathematics Education (2004).

Thomas S. Lontzekمشاهده پروفایل

استاد

• Economic Growth
• Quantitative Macroeconomics
• Resource and Energy Economics
• +۳ مورد دیگر

Thomas S. Lontzek is Professor of Economics at RWTH Aachen University, holding the Chair of Computational Economics within the School of Business and Economics since October 2016. His research develops numerical methods for economic decision-making with focus on climate change and environmental risks. His educational background includes economics studies at Maastricht University and UC San Diego (1999-2003), followed by PhD from University of Kiel (2009). Prior positions include Assistant/Senior Assistant at University of Zurich (2010-2016) and visiting scholar at Stanford's Hoover Institution (2012). Lontzek's research spans Economic Growth, Quantitative Macroeconomics, Resource and Energy Economics, Computational Economics, Climate Risk Management, and Decision Making under Uncertainty. His work emphasizes unconventional methods for analyzing economic processes through multidimensional, nonlinear stochastic optimization while incorporating ethical principles into economic analysis for sustainability challenges. His publication record reveals a consistent focus on climate economics, particularly the social cost of carbon, climate tipping points, and integrated assessment modeling. These works demonstrate how accounting for economic and climate risks, especially potential tipping points, necessitates more aggressive climate policies than traditional models suggest, often requiring sophisticated computational techniques to handle high-dimensional uncertainty. 2021 Erik Kempe Award for work on calculating an optimal CO2 tax Lontzek actively mentors students through research seminars and teaching, emphasizing methodological diversity and interdisciplinary approaches. He leads the Global Challenges Research Seminar through the Global Challenges Lab, providing students with opportunities to conduct innovative research using quantitative decision-making techniques for sustainable development challenges. His teaching portfolio for Summer Semester 2025 includes Quantitative Macroeconomics, Sustainable Finance, and specialized research seminars. He heads the Chair of Computational Economics, which includes scientific staff members Dr. Marco Thalhammer, Dr. Yifan Zhao, and Philipp Olivier, M.Sc., working collaboratively on climate economics and computational methods. The chair's research bridges theoretical economic modeling with practical policy applications, developing dynamic stochastic integrated assessment models capable of handling complex climate-economy interactions under uncertainty.