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Nicolas PerkowskiView profile

Professor

• Stochastic Analysis
• Probability Theory
• Stochastic Partial Differential Equations
• +3 more

Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.

• Microelectronics
• Nanotechnology
• RF Engineering
• +3 more

Prof. Dr. Amelie Hagelauer holds a professorship in Micro- and Nanosystem Technology at the TUM School of Computation, Information and Technology, Technical University of Munich. Her work focuses on advanced electronics and systems integration across quantum computing hardware, resistive memory technologies, and high-frequency RF systems. She has contributed to innovations in superconducting qubit readout architectures, multi-level RRAM designs, and 3D-integrated CMOS-compatible quantum devices. Research interests span quantum hardware design, nanoelectronic devices, RF front-end systems, and emerging memory technologies. Her work emphasizes practical implementation challenges such as low-power operation, high-voltage handling in RF switches, and wafer-scale fabrication processes. Recent projects include D-band radar systems, energy-efficient 60 GHz transceivers, and antenna tuning solutions for 5G applications. Publications from 2023-2025 showcase advancements in resistive switching device characterization, mitigation of TLS losses in superconducting qubits, and reconfigurable AI accelerators using RRAM-based digital twins. Her work bridges theoretical device physics with practical integrated circuit design, addressing scalability and reliability in next-gen electronics. Awards and grants: None explicitly listed in provided texts. Active collaborations include EU-funded projects on quantum computing platforms and TUM's Electronic Photonic Integration initiatives. Leads research teams in microsystem technology with emphasis on cross-disciplinary approaches combining CMOS processes, MEMS, and quantum engineering.

• Database Systems
• Data Management
• GPU Acceleration
• +6 more

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 .

Massimo FornasierView profile

Professor

• Mathematical Modeling
• Data Analysis
• Image/Signal Processing
• +5 more

Prof. Massimo Fornasier holds the Chair of Applied Numerical Analysis at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His research focuses on mathematical modeling, numerical analysis, and data-driven methods, particularly in areas like compression, sparse recovery, and optimization. He has made significant contributions to consensus-based optimization, control of multiagent systems, and applications in image/signal processing. Education: PhD in Computational Mathematics, University of Padua (2003) Postdoctoral fellowships at University of Vienna, Sapienza University of Rome, and Princeton University Awards: ERC Starting Grant (2012) START Prize (2011) Prix de Boelpaepe (2009) His work bridges theoretical analysis and computational methods, with applications ranging from compressive sensing to machine learning. Recent research emphasizes consensus-based optimization frameworks and their global convergence properties. Editorial roles include journals like Networks and Heterogeneous Media and Calcolo . He leads research groups in areas such as Data Science and Numerical Analysis at TUM.

Aleksandar BojchevskiView profile

Professor

• Trustworthy Artificial Intelligence
• Graph Neural Networks
• Adversarial Robustness
• +3 more

Aleksandar Bojchevski is a full professor of Computer Science at the University of Cologne, leading the Trustworthy Artificial Intelligence Lab (TAIL). His research focuses on developing robust, interpretable, and privacy-preserving machine learning models, particularly graph neural networks (GNNs). The lab emphasizes trustworthiness in high-stakes applications through methods that handle noisy, adversarial, or anomalous data. Bojchevski holds a PhD and PostDoc from the Technical University of Munich, advised by Stephan Günnemann. Previously, he was faculty at CISPA Helmholtz Center for Information Security. His work bridges theory and practice, addressing adversarial robustness, uncertainty quantification, and scalable GNN techniques. Research interests include: robustness certification for GNNs, conformal prediction, adversarial attack analysis, and privacy-aware machine learning. His lab actively collaborates with RWTH Aachen and organizes events like the Learning on Graphs Meet Up. Recent achievements include a teaching award for the Machine Learning lecture (SS 24) and NeurIPS 2024 acceptance of SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors . Open positions are available in his group focusing on trustworthy ML topics. Key labs/teams: Trustworthy Artificial Intelligence Lab (TAIL), Center for Data and Simulation Science (CDS) as a core scientist.

• Finite Model Theory
• Graph Structure Theory
• Computational Complexity
• +3 more

Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +5 more

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Oliver HohlfeldView profile

Professor

• Network Security
• Network Measurements
• Internet Architecture
• +9 more

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.

Dominik SchnausView profile

Academic

• Computer Vision
• Deep Learning
• 3D/4D Reconstruction
• +3 more

Dominik Schnaus is a PhD Student at the Computer Vision Group within the School of Computation, Information and Technology at the Technical University of Munich. His research focuses on computer vision and deep learning, particularly in vision-language correspondence and uncertainty estimation in neural networks. Research Interests: 3D/4D reconstruction, vision-language models, neural network uncertainty, robotics Contact: dominik.schnaus@tum.de

Markus BachmayrView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +4 more

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

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 IngoldView profile

Professor

• Casimir effect
• Dissipative quantum systems
• Mesoscopic physics
• +2 more

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.

Carlo D'EramoView profile

Professor

• Reinforcement Learning
• Decision-Making
• Multi-task Reinforcement Learning
• +3 more

Carlo D'Eramo is a Professor of Reinforcement Learning and Computational Decision-Making at the University of Würzburg. He leads the LiteRL group at hessian.AI until 2025 and is affiliated with the Intelligent Autonomous Systems group at TU Darmstadt's Computer Science Department, as well as the Hessian Centre for Artificial Intelligence. Ph.D. : Information Technology, Politecnico di Milano (2019) Double MSc : Computer Engineering, Politecnico di Milano (2015) and University of Illinois at Chicago (2015) BSc : Computer Engineering, Politecnico di Milano (2011) His research focuses on lightweight reinforcement learning methods for adaptive autonomous agents, spanning multi-task/curriculum RL, multi-agent RL, deep RL, uncertainty quantification, residual learning, and planning. He developed MushroomRL, a widely adopted RL library, and investigates how agents can acquire real-world expert skills efficiently. The 15 most recent publications highlight trends in deep reinforcement learning architectures, adversarial and multi-agent systems, domain randomization, and curriculum design. Key subfields include optimal transport applications, entropy maximization, neural network distillation, and bounded rationality frameworks for robust learning. He has contributed to top venues like ICML, NeurIPS, AAAI, ICLR, JMLR, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with a focus on advancing scalable and adaptive RL methodologies.

• Database Systems
• Graph Databases
• Data Management
• +7 more

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

• Gravitational wave detection
• Gravitational wave data analysis
• Early universe cosmology
• +6 more

Bruce Allen is the Director of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Hannover, Germany, where he also heads the Observational Relativity and Cosmology department. He holds dual academic appointments as Honorary Professor of Physics at Leibniz Universität Hannover and Adjunct Professor of Physics at the University of Wisconsin-Milwaukee, USA. His career spans over three decades in gravitational physics research, with a leadership role in the LIGO Scientific Collaboration from 1997 to 2018. Dr. Allen's research focuses on gravitational wave detection and data analysis, early universe cosmology, de Sitter space, curved-space quantum field theory, cosmic strings, inflationary models of the early universe, and gravitational radiation emission by cosmic strings. His work extends to large-scale cluster computing and public distributed computing projects like Einstein@Home, which has led to significant discoveries in gravitational wave astronomy. His recent publications demonstrate expertise in pulsar timing arrays, Hellings-Downs correlation analysis, and optimization of computational methods for gravitational wave detection. Allen's scientific contributions have been recognized with numerous prestigious awards including the Richard A. Isaacson Award (2020), the Bruno Rossi Prize (2017), the Princess of Asturias Award (2017), and the Special Breakthrough Prize (2016), all shared with the LIGO team for groundbreaking gravitational wave discoveries. He is also an Elected Fellow of both the American Physical Society and the Institute of Physics, UK. As a research leader, Allen has secured approximately $10 million in research funding from the National Science Foundation (1987-2018) and has mentored numerous students and researchers in gravitational physics. His work on Einstein@Home has engaged the public in scientific discovery through distributed computing, leading to several important astrophysical findings including gamma-ray pulsar discoveries.