جست‌وجوی استاد و پژوهشگر

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.

Marco Platznerمشاهده پروفایل

استاد

• Reconfigurable Computing
• Approximate Computing
• Self-Aware Computing
• +۷ مورد دیگر

Marco Platzner is a Professor for Computer Engineering at Paderborn University , Germany. He serves as the Dean of Research for the Faculty of Computer Science, Electrical Engineering and Mathematics and heads the Department of Computer Science. Previously, he held research positions at ETH Zurich, Stanford University, GMD (now Fraunhofer IAIS), and Graz University of Technology. Education: Diploma and PhD in Telematics (Graz University of Technology, 1991 and 1996), Habilitation in Hardware-Software Co-Design (ETH Zurich, 2002) Research Interests focus on reconfigurable computing, approximate computing, self-* computing, and embedded systems. His work addresses hardware security, FPGA design, and sustainable AI in data centers. Current projects include energy-efficient AI through deep neural network approximation for FPGAs (EKI-App) and lifecycle sustainability of socio-technical systems (SAIL). Publication Trends show expertise in FPGA security, approximate circuit synthesis, robotics, and hardware acceleration. Collaborations span robotics (ROS 2 integration), AI (transformer optimization), and cybersecurity (Trojan detection). Scientific Awards: ACM SIGDA Hall of Fame (2020) Significant Paper Award (FPL 2015) Best Paper Awards at IEEE ISVLSI (2024), ARC (2018), IEEE ReConFig (2015), and others Weierstraß Prize for Teaching (2008) Leadership Roles include membership in the board of Paderborn Center for Parallel Computing (PC2) and the Jenny Aloni Centre for Early Career Researchers. He has contributed to EU FP7 FET project EPiCS and German priority programs on embedded systems and organic computing.

• 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.

• 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.

Michael Ulbrichمشاهده پروفایل

استاد

• Nonlinear Optimization
• Optimal Control
• Numerical Analysis
• +۳ مورد دیگر

Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.

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.

• Database Systems
• Data Management
• GPU Acceleration
• +۶ مورد دیگر

Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for her work in database systems and data management . Her research focuses on optimizing query processing for modern hardware, particularly GPUs and heterogeneous systems, and advancing cloud data analytics with serverless architectures like PixelDB . She has co-authored influential frameworks for adaptive query optimization , hardware-conscious database engines , and model-relational data management . Key research areas: GPU acceleration , HTAP , query approximation , spatial data processing , and cloud-native databases . Recent work emphasizes cross-task optimizations in distributed environments, efficient sampling , and context-aware joins integrating vector embeddings. In 2023, she contributed to adaptive recursive query optimization and speculative K-means clustering, while 2024 publications addressed proportional caching (HPCache) and model-relational systems . Her collaborations span institutions such as MIT, Microsoft, and ETH Zurich, with publications in top venues like SIGMOD , VLDB , and ICDE .

Oliver Hohlfeldمشاهده پروفایل

استاد

• Network Security
• Network Measurements
• Internet Architecture
• +۹ مورد دیگر

Oliver Hohlfeld is a Professor at the University of Kassel, where he leads the Distributed Systems group. He previously held academic positions at Brandenburg University of Technology and RWTH Aachen University, and was a visiting scholar at the University of Wisconsin–Madison. His research focuses on network security, Internet measurements, and Quality of Experience (QoE). He completed his Ph.D. in Computer Science at TU Berlin under Anja Feldmann and holds a B.Sc. and M.Sc. from Darmstadt University of Technology. He also worked at Fraunhofer IGD on telemedical network architectures. His research adopts a data-driven approach, combining large-scale Internet measurements, user studies, and machine learning to understand and improve Internet performance and security. Key areas include DDoS detection, QUIC, HTTP/2, TLS deployment, and BGP analysis. He investigates how protocols like QUIC and HTTP/2 impact user experience and network efficiency, often through empirical studies of real-world deployments. His recent publications highlight a strong trend in network security and protocol analysis, with significant work on DDoS attacks, traffic ingress detection, and web consolidation. He also explores social media dynamics and censorship circumvention through user reviews. 2022 IETF/IRTF Applied Networking Research Prize Best of CCR (2021) for TLS 1.3 deployment study ACM Senior Member (2020) IEEE QoMEX 2019 Best Reviewer Award ACM IMC Community Contribution Award (2018) He has advised numerous Master’s and Bachelor’s students, primarily at RWTH Aachen, and has been a principal investigator in major projects such as AIDOS (AI-based DDoS mitigation), DFG SFB MAKI, COMTEX, and the EU-funded SSICLOPS. He actively serves on the technical program committees of top-tier conferences including SIGCOMM, NSDI, IMC, and CoNEXT, and is a frequent reviewer for leading journals in networking and systems. He leads the Distributed Systems group at the University of Kassel, which conducts research on Internet observability, secure infrastructures, and scalable networking solutions.

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Günter Rote is a Professor in the Department of Computer Science at Freie Universität Berlin, specifically within the Theoretical Computer Science group (Arbeitsgruppe Theoretische Informatik). He holds a formal academic title of Professor Dr. and is affiliated with the Faculty of Mathematics and Computer Science. His research focuses on theoretical computer science, computational geometry, algorithms, and discrete mathematics. Key research interests include geometric algorithms, optimization problems (e.g., shortest paths, traveling salesman problems), and algorithm design for parallel computing systems. His work spans topics such as systolic arrays, convex hulls, and combinatorial optimization. Rote’s contributions include foundational studies on computational geometry problems, algorithmic complexity, and practical applications in energy equity and infrastructure design. Publications highlight contributions to solving extremal equations, polygon transformations, and the quadratic assignment problem. He has been active in academic leadership, mentoring students, and contributing to computational science communities. His email is rote@inf.fu-berlin.de, and his office is located at Takustraße 9 in Berlin.

Gert-Ludwig Ingoldمشاهده پروفایل

استاد

• Casimir effect
• Dissipative quantum systems
• Mesoscopic physics
• +۲ مورد دیگر

Prof. Dr. Gert-Ludwig Ingold is a Professor of Theoretical Physics at the Institute of Physics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg. His research spans multiple areas of theoretical physics with emphasis on quantum phenomena in nanoscale systems. His work on the Casimir effect explores interactions between various geometries including spheres, plates, and dielectric materials, with applications in nanotechnology and biophysics. His research on dissipative quantum systems investigates thermodynamic anomalies and quantum Brownian motion. In mesoscopic physics, he studies charge transport through nanoscale structures and quantum interference effects. His work on quantum systems in phase space connects classical and quantum dynamics, while his semiclassical research examines quantum revival patterns and phase-space trajectories. Prof. Ingold's publications reveal a strong focus on the Casimir effect, with numerous papers examining interactions between different geometries and materials. His work demonstrates expertise in both theoretical modeling and numerical methods, as evidenced by his development of the CaPS software for Casimir effect calculations. He frequently collaborates with international researchers, particularly with Paulo A. Maia Neto, Tanja Schoger, and Benjamin Spreng. Prof. Ingold has made significant contributions to physics education through textbooks and popular science books, including Quantentheorie: Grundlagen der modernen Physik and Die 101 wichtigsten Fragen: Moderne Physik . He has co-authored the Python-based educational resource Numerische Physik mit Python and maintains extensive online teaching materials including lecture notes and video tutorials.

Ignasi Sau Vallsمشاهده پروفایل

پژوهشگر

• Graph Theory
• Parameterized Complexity
• Algorithmic Graph Theory
• +۵ مورد دیگر

Ignasi Sau Valls is a Directeur de Recherche (DR2) at CNRS, affiliated with the LIRMM laboratory at Université de Montpellier, France. He is a member of the AlGCo team, focusing on algorithms for graphs and combinatorics. His academic background includes dual degrees in Mathematics and Telecommunications Engineering from UPC (Barcelona), a PhD in joint supervision between UPC and Projet Mascotte (Sophia Antipolis), and a postdoctoral position at the Technion (Israel). He has been with CNRS since 2010 and was promoted to his current role in October 2024. He also served as a Visiting Professor at UFC (Brazil) from 2016–2017. His research interests lie primarily in Graph Theory and Parameterized Complexity , with a focus on structural graph properties, kernelization, and algorithm design. He has made significant contributions to problems involving minor-closed graph classes, treewidth, and graph modification. His work bridges theoretical foundations with algorithmic applications, particularly in discrete optimization and network problems. The recent articles highlight a strong trend in parameterized algorithms, especially for graph modification, kernelization, and structural graph problems. Topics such as hitting minors, dynamic programming on tree decompositions, and edge contractions reflect a deep engagement with structural parameterizations and fixed-parameter tractability. His publications frequently appear in top-tier journals like SIAM Journal on Computing, Journal of Combinatorial Theory, and Algorithmica, as well as major conferences such as ICALP, SODA, and IPEC. Best paper award of Track C of ICALP'10 Best student paper award of WG'09 Ignasi Sau has been a principal investigator of the ANR JCJC project ELIT (ANR-20-CE48-0008-01), funded with 169k€ from 2021 to 2026. He serves as an editor for DMTCS and Information and Computation , and has held significant organizational roles, including PC member of numerous conferences (MFCS, WG, IPEC, COCOON) and as co-chair and main organizer of WG 2019, ICGT 2022, and JCALM 2023. He has delivered invited courses at international schools in France, Argentina, and Brazil. He is actively involved in the research community through editorial duties, conference organization, and collaborative research. His lab affiliation is the AlGCo team at LIRMM, a leading group in algorithmic graph theory and combinatorics.

• Managing discontinuous business model innovation
• Digital strategies for Industrie 4.0
• Additive Manufacturing
• +۱۱ مورد دیگر

Prof. Dr. Frank T. Piller is a University Professor and Co-Leader of the Institute for Technology and Innovation Management (TIM) at RWTH Aachen University, where he also serves as Academic Director of the Executive MBA program at RWTH Business School. He leads a research team of approximately 30 doctoral students, 5 postdocs, and over 20 student researchers within the TIME Research Area of the School of Business and Economics. His educational background includes a doctoral degree in Operations Management from the University of Würzburg (1999) and a Habilitation degree from TUM Business School (2004) on "Innovation and Value Co-Creation." Prior to joining RWTH Aachen in 2007, he was a Research Fellow at MIT Sloan School of Management and faculty at TUM Business School. Prof. Piller is recognized as one of the world's leading experts in customer-centered value creation, specializing in mass customization, personalization, and customer co-creation. His current research focuses on how established companies can transform in response to disruptive business model innovations, with particular emphasis on digital transformation (Industry 4.0), AI-augmented innovation, and sustainable business models. He is particularly known for his work on innovation ecosystems, platform-based business models, and stakeholder-oriented technology development. His recent publications demonstrate a clear trajectory toward integrating artificial intelligence with traditional innovation management frameworks, exploring how AI transforms manufacturing systems, innovation processes, and business models. His work increasingly addresses the challenges of digital transformation in established industries while maintaining focus on customer co-creation and mass customization principles. His scientific achievements have been recognized with numerous awards: Co-Creation Award of the PDMA Nomination for "Innovating Innovation" Prize by Harvard Business Review and McKinsey "Lecturer of the Year" by Executive MBA students at TU Munich RWTH Aachen Rector's Prize for Excellent Teaching (since 2010) Grant for innovative "Flipping the Classroom" teaching concept ERC Synergy Grant for SAFER Grid project (2025-2031) Prof. Piller maintains an extensive research network spanning academia and industry. He collaborates with numerous corporations including 3M, Adidas, BASF, EON, J&J, P&G, Siemens, and Vodafone, as well as many technology startups across Europe and North America. As a co-founder, supervisory board member, and investor in innovative startups, he actively transfers research into practice. His research has received significant funding, most notably the prestigious ERC Synergy Grant for the SAFER Grid project. He leads the Technology and Innovation Management Group (TIM) within the TIME Research Area at RWTH Aachen, which comprises over 100 senior and junior researchers working at the intersection of innovation, technology management, marketing, and entrepreneurship. The institute is a leading European research institution for strategic, behavioral, and computer-supported technology and innovation management.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +۳ مورد دیگر

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

Florian Brandlمشاهده پروفایل

دانشیار

• Microeconomics
• Social Choice Theory
• Decision Theory
• +۴ مورد دیگر

Florian Brandl is an Argelander Professor (associate professor with tenure) at the University of Bonn, holding positions in both the Department of Economics and the Hausdorff Center for Mathematics. Previously, he was a postdoctoral research scholar at Princeton University and Stanford University. His academic career demonstrates a strong foundation in mathematical economics and game theory, with affiliations spanning multiple prestigious institutions. Brandl earned his Doctoral degree in Mathematics (summa cum laude) from the Technical University of Munich in 2018, following a Master's degree (2013) and Bachelor's degree (2011) from the same institution. His doctoral work focused on "Zero-Sum Games in Social Choice and Game Theory" under the supervision of Felix Brandt, establishing the foundation for his research trajectory. Prof. Brandl's research spans microeconomic theory with a focus on social choice theory, decision theory, and game theory. He is particularly interested in decision-making under uncertainty, connections between social choice and game theory, and dynamic processes converging to equilibrium. His work employs mathematical tools to analyze interactions of multiple entities in economic contexts, often incorporating algorithmic approaches and methods from theoretical computer science. He has made significant contributions to fair division, mechanism design, and probabilistic social choice. His publication record shows consistent contributions across multiple subfields, with recent work focusing on patience effects in fair division, social learning barriers, and axiomatic characterizations of equilibrium concepts. Brandl's research demonstrates strong interdisciplinary connections between economics, mathematics, and computer science, with publications in top journals across all three disciplines. Best Student Paper Award at WINE 2021 for "Funding Public Projects: A Case for the Nash Product Rule" Associate Editor for Theoretical Economics Co-organizer of the COMSOC Video Seminar Prof. Brandl actively contributes to academic service and community building. He co-organizes the COMSOC Video Seminar and will host a Trimester Program on "Advances in Mechanism Design" in Bonn in summer 2026. He serves on program committees for major conferences including COMSOC 2023 and EC 2023. His research has been supported through his position as a Bonn Junior Fellow at the Hausdorff Center for Mathematics since 2021. Based at the Institute for Microeconomics and affiliated with the Hausdorff Center for Mathematics, Brandl collaborates with a broad network of researchers across economics and computer science. His work often involves interdisciplinary collaboration, as evidenced by his numerous co-authored publications with researchers from various institutions worldwide. He maintains strong connections with the University of Oxford's Global Priorities Institute as a Research Affiliate.

Nicole Megowمشاهده پروفایل

استاد

• Combinatorial optimization
• Discrete optimization
• Mathematical optimization
• +۱۱ مورد دیگر

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).