Find Academic Supervisors

• Mathematical physics
• Quantum field theory
• Quantum gravity
• +3 more

Pedro Vieira is a Portuguese theoretical physicist affiliated with the Perimeter Institute and ICTP-SAIFR . He specializes in mathematical physics , quantum field theory , and quantum gravity , with a focus on AdS/CFT correspondence and integrability . His groundbreaking work includes the exact solution for the spectrum of planar N=4 Super Yang-Mills theory and advancements in finite coupling proposals for polygonal Wilson loops and three-point functions. His research has established critical connections between quantum field theory and string theory, particularly in analyzing non-perturbative effects in supersymmetric systems. Key contributions involve Bethe ansatz techniques, flux tube S-matrices, and mathematical structures underlying integrability. Pedro Vieira has received numerous prestigious awards, including: Sloan Fellowship (2015) Gribov Medal (2015) Raymond and Beverly Sackler International Prize in Physics (2018) New Horizons in Physics Prize (2020) The articles in his bibliography highlight trends in quantum field theory, particularly in supersymmetric Yang-Mills theories , non-perturbative methods , and finite coupling phenomena , reflecting his expertise in mathematical structures of theoretical physics.

• Theoretical Physics
• Cosmology
• String Theory
• +3 more

Professor Ivo Sachs is a distinguished theoretical physicist at the Ludwig-Maximilians-Universität München (LMU), where he holds a position at the Arnold Sommerfeld Center for Theoretical Physics with a Chair on Cosmology. His research spans multiple areas of theoretical physics with a particular focus on string theory, quantum field theory, and cosmological applications. He maintains an active research program with numerous recent publications in prestigious journals. Professor Sachs' research interests center around fundamental theoretical physics, with significant contributions to string field theory, cosmological perturbation theory, and the mathematical structures underlying quantum gravity. His work often bridges abstract mathematical concepts with physical applications, particularly in understanding the early universe and quantum aspects of gravity. He has developed innovative approaches to studying cosmological correlators, spinning particles, and the relationship between quantum field theory and gravitational physics. Analysis of his recent publications reveals a strong focus on the intersection of cosmology and string theory, with particular attention to mathematical structures in quantum gravity. His work demonstrates consistent exploration of how quantum field theory techniques can be applied to cosmological problems, especially regarding correlation functions in the early universe. He frequently collaborates with researchers across Europe, indicating an active international research network. Martín Enríquez Rojo (PhD, 2022): Asymptotic symmetries in FLRW and deformations of gravitational symmetry algebras

• Signal Processing
• Wireless Communications
• Networking
• +3 more

Philippe Ciblat is a Professor at TELECOM Paris Tech, affiliated with the Department of Signal Processing and Communications. His research spans signal processing, wireless communications, and machine learning applications in networking. He has collaborated extensively with institutions like the University of Paris-Saclay and international researchers in areas such as cooperative communication protocols, resource allocation, and coding theory. Research Interests: Machine learning for signal processing, wireless channel modeling (Rician fading), lattice decoding, caching strategies, and distributed optimization. Notable Work: Pioneered transformer-based packet scheduling, neural network approaches to lattice decoding, and effective capacity analysis in fading channels. His contributions include over 170 publications in top venues (IEEE Trans. Signal Process., IEEE Trans. Wireless Commun.) and collaborations with industry partners on practical implementations like cache-aided polar coding. He has advised multiple researchers in distributed systems and wireless resource management.

Heiner GiefersView profile

Professor

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +7 more

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

• Mathematical Data Science
• Machine Learning
• Compressed Sensing
• +5 more

Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.

Detlev MarpeView profile

Researcher

• Video Coding
• Image Compression
• Signal Processing
• +3 more

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

• Computer Architecture
• Parallel Processing
• FPGA Design
• +3 more

Frank Hannig is a Professor at the University of Erlangen-Nuremberg, Germany, specializing in computer architecture and high-performance computing. His research focuses on FPGA acceleration, hardware-software co-design, neural network optimization, and embedded systems. He has collaborated extensively with co-authors such as Jürgen Teich and Oliver Reiche, producing over 200 publications since 2001. His work emphasizes domain-specific languages (DSLs) for image processing (e.g., Hipacc) and compiler optimizations for FPGAs. Key contributions include techniques for quantized neural networks on microcontrollers, CGRA toolchain evaluation, and efficient mapping of CNNs onto processor arrays. Hannig also explores energy-efficient architectures and reconfigurable computing for emerging applications like edge AI and automotive systems. Publications span conferences like ASAP, FPL, and ARC, reflecting his interdisciplinary approach to bridging algorithm design and hardware implementation. His research often addresses practical challenges in deploying machine learning models on resource-constrained devices while maintaining performance and energy efficiency.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +7 more

Irina Rish is a Research Professor at Mila - Quebec AI Institute, affiliated with Université de Montréal, and maintains a significant research affiliation with IBM Research. With an extensive publication record spanning decades and continuing through 2025, she is an active leader in artificial intelligence research. Primary Affiliation: Mila - Quebec AI Institute, Université de Montréal Secondary Affiliation: IBM Research Research Focus Areas: Machine Learning, AI Systems, and Applications Dr. Rish's research interests encompass machine learning theory and applications, with particular expertise in neural networks, language models, reinforcement learning, and efficient AI systems. Her work bridges theoretical understanding with practical implementations across multiple domains including healthcare, time series analysis, and multimodal systems. Analysis of her recent publications (2023-2025) reveals a strong trend toward efficient AI architectures, with significant contributions to model quantization (particularly ternary models), scaling laws in language models, and novel techniques for continual learning. Her research demonstrates how theoretical insights can translate into practical improvements in model efficiency and performance. Key Publication Trends: Model efficiency, scaling laws, continual learning Application Areas: Healthcare (EEG analysis), time series forecasting, vision-language systems Dr. Rish collaborates extensively with researchers across academia and industry, working with notable colleagues including Guillermo A. Cecchi, Djallel Bouneffouf, Eugene Belilovsky, and Matthew Riemer. Her work appears regularly in top-tier conferences (NeurIPS, ICML, ICLR, AAAI) and journals like Transactions of Machine Learning Research, demonstrating both the quality and impact of her research contributions. She leads research in federated learning techniques, reinforcement learning systems, and multimodal AI architectures, with recent work focusing on making AI systems more robust, efficient, and applicable across diverse real-world scenarios. Her research program demonstrates consistent growth in impact and scope, with increasing publication output in recent years.

Urs SchreiberView profile

Professor

• Mathematical Physics
• Quantum Physics
• Algebraic Topology
• +7 more

Urs Schreiber is a Professor at New York University in Abu Dhabi (NYU AD), working in the Mathematics Division of Science. He leads the Research Center for Quantum and Topological Systems (CQTS) under the group of Prof. Hisham Sati. His work bridges mathematical physics, quantum theory, and topology, with significant contributions to understanding strongly-coupled quantum systems and their applications to topological quantum technology. His research focuses on applying tools from algebraic topology and geometric homotopy theory to fundamental problems in quantum physics. His major projects include Hypothesis H on flux quantization in M-theory, geometric engineering of anyons on M5-branes, and developing quantum language via linear homotopy types. His work represents a unique synthesis of advanced mathematical techniques with cutting-edge theoretical physics, particularly in the areas of M-theory, topological quantum computing, and differential cohomology. Schreiber has established himself as a leading figure in the application of higher categorical and homotopical methods to physics. His research demonstrates a clear trajectory toward developing mathematical frameworks for next-generation quantum technologies, with particular emphasis on topological approaches that could provide fault-tolerant quantum computing platforms. His work shows strong connections between abstract mathematical structures and practical quantum computing applications, particularly through homotopy type theory implementations. Monograph: The Character Map in Non-Abelian Cohomology (with D. Fiorenza & H. Sati, 2023) Monograph: Equivariant Principal ∞-Bundles (with H. Sati, 2025) Contributor: Mathematical Foundations of Quantum Field and Perturbative String Theory (2011) Schreiber has advised numerous graduate students in areas spanning homotopy theory, quantum field theory, and mathematical physics. His teaching portfolio includes courses on Mathematical Quantum Field Theory, Topological K-Theory, and Stable Homotopy Theory. He is also the founder of the nLab research wiki project (started in November 2008), which has become an essential resource for researchers working at the intersection of mathematics and physics. His research group actively develops mathematical foundations for quantum and topological systems with practical implications for quantum technology development.

• Digital Signal Processing
• Speech Communication
• Acoustic Signal Processing
• +3 more

Peter Vary is a Professor at the Faculty of Electrical Engineering and Information Technology of RWTH Aachen University, serving as Director of the Institute for Communication Systems. His work focuses on speech and audio signal processing for communication systems. Digital Signal Processing Speech Enhancement Acoustic Echo Control Microphone Array Beamforming Communication Systems Audio Compression His recent publications (2023–2024) emphasize speech coding, noise reduction, and bandwidth extension for hearing aids and mobile devices, with technical innovations in Kalman filters, hybrid digital-analog transmission, and wind noise detection. He holds a leadership role in the Institute for Communication Systems and serves as Ombudsperson for teaching in his faculty. Contact: vary@iks.rwth-aachen.de

• Mathematical Physics
• Quantum Field Theory
• Spectral Geometry
• +3 more

Prof. Dr. Alexander Strohmaier is a Professor at the Institute of Analysis, part of the Faculty of Mathematics and Physics at Leibniz University Hannover. He serves on the Executive Board of the university, the Riemann Center for Geometry and Physics, and various committees such as the Faculty Council and Admissions Board for Mathematics. His research focuses on mathematical physics, quantum field theory, spectral geometry, and partial differential equations, with a particular emphasis on scattering theory and geometric analysis. He has contributed significantly to topics like Casimir energy computation, microlocal analysis, and the application of spectral methods in curved spacetimes. Prof. Strohmaier’s work bridges theoretical physics and mathematics, addressing foundational questions in quantum field theory on curved spacetimes and the interplay between geometry and analysis. His recent articles explore trace formulas in electromagnetic scattering, Lorentzian manifolds, and numerical methods for spectral problems. While no specific awards are listed, his prolific publication record reflects his influence in these fields. He is actively involved in academic governance and mentorship within the university.

• Tactile Sensors
• FPGA-Based Systems
• Robotics
• +3 more

Óscar Oballe-Peinado is a professor at the Department of Electronics, School of Engineering, University of Málaga. His work focuses on tactile sensors, FPGA-based embedded systems, and robotics, particularly addressing crosstalk elimination, sensor calibration, and smart preprocessing techniques. He contributes to both academic research and educational innovations, including remote digital electronics laboratories. PhD in Electronics (University of Málaga, 2016) Active researcher in tactile sensor technology and FPGA implementations Supervisor of Raúl Lora Rivera's research on texture detection and compliance recognition His research explores advanced methods for tactile sensor optimization, including hysteresis correction, quantization error reduction, and compliance recognition algorithms. Recent publications (2023-2024) focus on smart tactile preprocessing and embedded FPGA implementations for robotics. The 2023-2024 articles emphasize his contributions to tactile sensor applications in robotic manipulation, texture detection, and compliance recognition using FPGA-based embedded systems. These works align with trends in smart sensor interfaces and real-time processing for robotics. Collaborated on improving remote digital electronics laboratories using Raspberry Pi 4 (2022) Developed FPGA-based tactile sensor suite electronics (2017)

A. Lee SwindlehurstView profile

Professor

• Signal Processing
• Wireless Communications
• Biomedical Applications
• +3 more

A. Lee Swindlehurst is a Professor and Associate Dean for Research and Graduate Studies at the Henry Samueli School of Engineering of the University of California, Irvine . He is also a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study , where he collaborates on next-generation wireless communications research. Ph.D., Stanford University (Electrical Engineering, 1991) M.S., Brigham Young University (Electrical Engineering, 1986) B.S., Brigham Young University (Electrical Engineering, 1985) Swindlehurst's research focuses on sensor array signal processing for radar, wireless communications, and biomedical applications. His work addresses detection and estimation theory , system identification, and channel modeling, with recent emphasis on massive MIMO systems , low-resolution hardware, and physical layer security . His 2018-2017 publications demonstrate technical advances in precoding algorithms , channel estimation , and ADC/DAC optimization for emerging wireless architectures. IEEE Fellow (2004) IEEE Signal Processing Society Best Paper Awards (2010, 2006) Stephen O. Rice Prize in Communications Theory (2006) IEEE W.R.G. Baker Prize Paper Award (2000) Karl G. Maeser Research Award (2004) His research has produced foundational work on space-time block codes , vector-perturbation techniques , and blind equalization methods that continue influencing modern wireless system design.

Marcel CampenView profile

Professor

• Computer Graphics
• Geometry Processing
• Surface Parametrization
• +4 more

Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.

Rajmadan LakshmananView profile

Research Fellow

• Optimal Transport Theory
• Stochastic Optimization
• Financial Mathematics
• +3 more

Rajmadan Lakshmanan is a Research Fellow at the Faculty of Mathematics , Technische Universität Chemnitz , specializing in optimal transport theory, stochastic optimization, and computational finance. Since 2021, he has contributed to academic development through teaching assistant roles and active participation in research seminars and workshops.