جست‌وجوی استاد و پژوهشگر

Ciarán Doneganمشاهده پروفایل

پژوهشگر ارشد

• Regulatory Genomics
• Explainable AI
• Geometric Deep Learning
• +۱ مورد دیگر

Ciarán Donegan is a Research Fellow in the Machine Learning group at the Technical University of Berlin and BIFOLD (Berlin Institute for the Foundations of Learning and Data), with a concurrent guest researcher position in the Ohler Lab at the Max Delbrück Center for Molecular Medicine. His primary research focuses on applying deep learning methodologies to elucidate gene regulation mechanisms within molecular biology. Education: M.A.I. in Electronic & Computer Engineering from Trinity College Dublin (2021) B.A.I. in Electronic & Computer Engineering from Trinity College Dublin (2020) His research program integrates Regulatory Genomics with cutting-edge machine learning techniques, emphasizing Explainable AI frameworks and Geometric Deep Learning architectures. This interdisciplinary approach targets complex biological systems, particularly in modeling transcriptional regulation and epigenetic modifications through neural network interpretability. Applications extend to disease mechanism analysis and therapeutic target identification in life sciences contexts. Publication trends reveal dual-domain expertise: the 2022 sports broadcasting paper demonstrates real-time computer vision capabilities for automated production systems, while the 2021 VPU-specific CNN research pioneers hardware-aware neural architecture search. These works collectively underscore a methodological thread in optimizing deep learning for constrained environments—whether computational (edge devices) or operational (live sports)—while maintaining biological research as his core focus area. No scientific awards were documented in the source materials. Advisory activities and grant funding details remain unspecified in available records. He operates within collaborative research ecosystems spanning TU Berlin's Machine Learning group, BIFOLD's interdisciplinary data science initiatives, and the Ohler Lab's genomic regulatory studies at the Max Delbrück Center, facilitating cross-institutional innovation in AI-driven life sciences.

• Wireless Communications
• Signal Processing
• Embedded Systems
• +۴ مورد دیگر

Ali Dziri is a permanent researcher at CentraleSupélec's Cedric Laboratory , focusing on wireless communications, signal processing, and embedded systems. His work spans 2004–2023 with key contributions in UWB communication, IoT networks, video/image transmission, and real-time tracking algorithms. His research interests include: Wireless Communications Signal Processing Embedded Systems Machine Learning IoT Networks Video/Image Compression Recent publications highlight trends in neural networks for channel equalization, MIMO relays for WSNs, and 5G D2D communication protocols. He has no listed scientific awards or advisees.

• Computational Mechanics
• Material Science
• Biomechanics
• +۵ مورد دیگر

Prof. Bernd Markert serves as Professor and Head of the Institute of General Mechanics at RWTH Aachen University's Faculty of Mechanical Engineering. He concurrently holds critical university-wide roles including Rector's Representative for Alumni and Rectorate Mandate for Mexico Cooperation, based in Aachen's Faculty of Mechanical Engineering building. His research spans computational mechanics with emphasis on phase-field modeling, molecular dynamics, and machine learning integration. Key focus areas include biodegradable magnesium alloys for biomedical implants, multiscale composite material behavior, and AI-enhanced structural failure prediction. Recent work bridges physics-based simulations with neural networks for constitutive modeling and corrosion prediction. Analysis of his 2022-2025 publications reveals accelerating convergence of mechanics and artificial intelligence, particularly in physics-informed neural networks for crystal plasticity and phase-field frameworks. His team demonstrates consistent innovation in modeling porous media, network materials, and biomechanical systems while maintaining strong industry-relevant applications in additive manufacturing and biomedical engineering. As Institute Head, Markert leads the Chair and Institute of General Mechanics (IAM), directing research on fundamental mechanics principles applied to advanced materials and structural systems through computational and experimental methodologies.

Terence Chi-Shen Taoمشاهده پروفایل

استاد

• Harmonic analysis
• Partial differential equations
• Algebraic combinatorics
• +۵ مورد دیگر

Terence Tao is an Australian-American mathematician and professor of mathematics at the University of California, Los Angeles (UCLA), where he holds the James and Carol Collins Chair in the College of Letters and Sciences. Widely regarded as one of the greatest living mathematicians, Tao has received numerous prestigious awards including the Fields Medal, the Breakthrough Prize in Mathematics, and the MacArthur Fellowship. Dr. Tao's educational background includes: Bachelor's and Master's degrees from Flinders University (1991) Ph.D. from Princeton University (1996) under Elias M. Stein Tao's research spans an extraordinary breadth of mathematical fields. He is particularly known for his work in harmonic analysis, partial differential equations, combinatorics, and analytic number theory. His research has included groundbreaking contributions to compressed sensing, the Green-Tao theorem on arithmetic progressions in prime numbers, and progress on the Navier-Stokes equations. Tao is renowned for his collaborative approach, having worked with over 60 co-authors throughout his career. Tao's publications demonstrate remarkable diversity across mathematical disciplines. His work shows strong trends in connecting seemingly disparate areas of mathematics, often bringing techniques from one field to solve problems in another. He has made significant contributions to both theoretical and applied mathematics, with applications ranging from signal processing to number theory. Among his numerous scientific achievements, Tao has received: Fields Medal (2006) Breakthrough Prize in Mathematics (2014) Royal Medal (2014) MacArthur Fellowship (2006) Crafoord Prize (2012) Princess of Asturias Award (2020) Tao has mentored numerous students throughout his career, with Monica Vișan among his doctoral students. He has secured significant research funding through prestigious awards including the Packard Fellowship, Sloan Fellowship, and Simons Investigator award. His collaborative research has been supported by multiple National Science Foundation grants. Tao maintains an active research group at UCLA and frequently collaborates with mathematicians worldwide. His blog and public lectures have made advanced mathematical concepts accessible to broader audiences, demonstrating his commitment to mathematical education and outreach.

Ofer Shayevitzمشاهده پروفایل

استاد

• Information Theory
• Statistical Inference
• Data Science
• +۳ مورد دیگر

Prof. Ofer Shayevitz is a faculty member at the School of Electrical Engineering , Tel Aviv University , holding the academic rank of Professor . He is affiliated with the Department of Systems and leads interdisciplinary research at the intersection of information theory , statistical inference , and data science . His research explores theoretical challenges in interactive communication , machine learning , and quantum information , with applications to communication complexity , graph analysis , and non-stationary environments . Notable work includes advances in high-dimensional regression , entropy estimation , and memory-constrained algorithms . The trends in his recent publications highlight information-theoretic bounds , statistical inference under constraints , and interactive protocols . His group has made significant contributions to quantum key distribution , planted graph detection , and guesswork analysis . Scientific awards include the Best Student Paper Award at ISIT 2020 . His research is supported by major grants from the Israel Science Foundation (ISF) , ERC Starting Grant , and Israel Innovation Authority . Prof. Shayevitz advises current PhD students Assaf Ben-Yishai , Uri Hadar , and Shahar Stein Ioushua , as well as M.Sc. students Inbar Pinsly and Oz Ben Hamo . Former advisees include faculty members at institutions like Kyushu University and University of British Columbia .

• Human Activity Understanding
• Computer Vision
• Sensor Fusion
• +۶ مورد دیگر

Rahul Chaudhari is a Senior Researcher at the Chair of Media Technology, Technical University of Munich (TUM), where he has been working since August 2019. He is affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI) and contributes to research in human-computer interaction, particularly in human activity understanding. Dr. Chaudhari earned his doctoral degree (Summa cum Laude) in Communications and Signal Processing from TUM in 2015, following a Master's degree in Communications Engineering from TUM in 2009 and a Bachelor's degree in Electronics and Telecommunications from the University of Pune, India. His academic journey reflects a strong foundation in communications engineering and signal processing that has evolved into interdisciplinary research spanning haptics, computer vision, and artificial intelligence. His research focuses on Human Activity Understanding using Computer Vision, Sensor Fusion, and AI techniques. His current work centers on understanding Human-Object Interactions using camera data (RGB and Depth) recorded in indoor environments, with potential integration of wearable sensors or environmental sensors. His work has significant applications in improving human well-being, comfort, and convenience through intelligent environments and ambient assisted living systems. Dr. Chaudhari's publication record shows a clear evolution from foundational work in haptic communications to cutting-edge research in human-object interaction and 3D pose estimation. His recent publications demonstrate expertise across multiple domains including computer vision for activity recognition, synthetic data generation for training models, and neurosymbolic approaches to human-AI collaboration. This interdisciplinary approach reflects the increasingly connected nature of modern AI research. Best student paper award for Yujun Wang (2025) LMT Spin-off Wins Third Place at euRobotics Technology Transfer Award 2025 LMT featured in Süddeutsche Zeitung (2025) Diego Fernandez Prado selected as finalist for IEEE CASE Best Paper Award (2024) Dr. Chaudhari actively supervises student research, including Master's theses on advanced topics such as 'Simulation and Optimization for 6G Network Planning using Digital Twins.' His supervision work involves guiding students through complex technical challenges in simulation software evaluation, digital twin implementation, and network optimization. He also serves on thesis committees and provides mentorship to students working on related research topics. His research group operates within the broader context of TUM's initiatives in robotics and machine intelligence, contributing to projects like the Centre for Tactile Internet with Human-in-the-Loop (CeTI) and 5G Testbed Bayern. The group maintains a strong focus on both theoretical foundations and practical implementations, with research that bridges the gap between academic innovation and real-world applications in intelligent environments.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Cem Eteke is a doctoral candidate at the Chair of Media Technology within the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich (TUM). His research focuses on semantic image and video synthesis, with an emphasis on explainable generative models, diffusion models, and their applications to semantic communication systems. Education: B.Sc. in Computer Engineering and Electrical & Electronics Engineering (2017), M.Sc. in same field (2019) from Koc University, Istanbul. Professional Experience: Former Data Scientist and AI Engineer, with brief research on high-dimensional non-convex optimization algorithms. Research Trends: His recent publications (2022-2025) highlight advancements in real-time semantic communication, diffusion models for video synthesis, and neural rendering techniques for indoor scene segmentation. Key themes include lossy coding strategies, spatially adaptive conditioning, and temporally consistent generative modeling. Projects: Involved in research initiatives like the Centre for Tactile Internet with Human-in-the-Loop (CeTI) , 5G Testbed Bayern , and KMU-Innovativ: KIMaps , focusing on haptic communication, indoor navigation, and semantic scene understanding.

• Haptic Communication
• Haptic Codecs
• Data Compression
• +۴ مورد دیگر

Daniel Rodriguez Guevara is a researcher at the Chair of Media Technology (TUM) within the Munich Institute of Robotics and Machine Intelligence (MIRMI). His work focuses on haptic communication, haptic codecs, and control systems. Education: Mechatronics Engineering (B.Sc., 2018), Predictive Control (M.Sc.) at Tecnologico de Monterrey His research explores haptic communication and data compression for tactile internet applications, with a specialization in predictive control for industrial systems. Recent work involves haptic codecs and vibrotactile actuator calibration . The three publications from 2020 demonstrate expertise spanning haptic feedback systems , deep learning for telepresence , and adaptive filtering in tactile displays. This reflects interdisciplinary work at the intersection of robotics , machine learning , and human-computer interaction . Labs: Works in the Chair of Media Technology under Prof. Eckehard Steinbach

Michael Zaiserمشاهده پروفایل

استاد

• Materials Simulation
• Dislocation Dynamics
• Plasticity
• +۵ مورد دیگر

Professor Michael Zaiser is a distinguished academic at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he holds the Chair of Materials Simulation within the Department of Materials Science. Since 2012, he has led research in computational materials science, with prior appointments at the University of Edinburgh where he served as Professor of Mechanics of Materials (2008-2012), Reader (2005-2007), and Lecturer (2001-2005). He maintains significant international collaborations as a Visiting Professor at Imperial College London since 2014 and previously served as Adjunct Professor of Physics at Michigan Technological University (2006-2012). Professor Zaiser's research spans computational materials science with particular expertise in dislocation dynamics, plasticity, fracture mechanics, and hierarchical materials. His work bridges theoretical physics and practical materials engineering, developing innovative computational approaches to understand material behavior at multiple scales. His research group employs advanced simulation techniques including continuum dislocation dynamics, peridynamics, and phase field modeling to investigate fundamental mechanisms of material deformation and failure. Recent work has focused on disordered mechanical metamaterials, hierarchical structures, and the relationship between microstructure and mechanical properties. Analysis of Professor Zaiser's recent publications reveals a strong focus on multiscale modeling approaches that connect atomic-level phenomena with macroscopic material behavior. His work demonstrates increasing integration of machine learning techniques with traditional computational methods, particularly in predicting material failure. The research spans diverse material systems including metals, ceramics, foams, and composites, with consistent emphasis on understanding how microstructural features govern mechanical properties. A notable trend is the investigation of hierarchical and disordered structures to achieve superior mechanical performance. Professor Zaiser leads an active research group at FAU's Department of Materials Science, supervising numerous doctoral students and postdoctoral researchers. His work has been supported by various research grants enabling extensive computational resources and collaborative opportunities with international institutions. His research group maintains strong connections with the Max Planck Society, Fraunhofer Institutes, and Helmholtz Association, reflecting FAU's position as one of Germany's most research-intensive universities. The research laboratory under Professor Zaiser's leadership focuses on computational materials science, with particular emphasis on developing and applying advanced simulation methodologies. The group maintains close collaborations with experimental researchers to validate computational predictions and guide new experimental investigations. Recent work has increasingly incorporated machine learning approaches alongside traditional physics-based modeling to address complex materials challenges.

Frank Meyer zu Heringdorfمشاهده پروفایل

استاد

• Condensed Matter Physics
• Materials Science
• Ultrafast Dynamics
• +۵ مورد دیگر

Prof. Dr. Frank Meyer zu Heringdorf is a Principal Investigator at the Collaborative Research Centre 1242 (University of Duisburg-Essen) and holds the Professor rank. His work focuses on ultrafast electron dynamics , spin-state switching , and X-ray spectroscopy in condensed matter systems. Affiliation: University of Duisburg-Essen, Germany Research Focus: Electron correlations, magnetic materials, and nanoscale physics Research Interests: Meyer zu Heringdorf explores collective electronic excitations , non-equilibrium energy transfer , and photoinduced magnetism using advanced techniques like femtosecond laser spectroscopy and soft X-ray absorption . His group investigates spin crossover complexes , magnetocaloric materials , and electron-phonon interactions in heterostructures. Article Trends: Recent publications highlight table-top X-ray sources , renormalized charge transfer in oxides, graphene-mediated magnetic anisotropy , and attosecond pulse synthesis . These works span ultrafast dynamics , nonlinear absorption , and quantum material design . Labs & Collaborations: He leads Project B06 in CRC 1242, collaborating with researchers across European XFEL , nickel and iron compounds , and multiferroic systems .

Matthias Hieberمشاهده پروفایل

استاد

• Partial Differential Equations
• Fluid Dynamics
• Evolution Equations
• +۳ مورد دیگر

Prof. Dr. Matthias Hieber is a Professor of Applied Analysis at the Department of Mathematics, Technical University of Darmstadt, and serves as Vice Director at the Mathematical Research Institute of Oberwolfach (MFO). His primary research focuses on partial differential equations, fluid dynamics, evolution equations, and harmonic analysis, with significant contributions to geophysical fluid models and liquid crystal theory. Hieber's research centers on the mathematical analysis of nonlinear PDEs arising in fluid mechanics, particularly the primitive equations for geophysical flows, Ericksen-Leslie equations for liquid crystals, and stochastic fluid models. His work establishes fundamental properties including well-posedness, regularity, and long-time behavior in diverse functional settings such as $L^p$ spaces and spaces of bounded functions. Key innovations include analyses of turbulent pressure effects, compressible liquid crystal dynamics, and hydrostatic Stokes operators. Recent publications reveal a concentrated focus on geophysical fluid dynamics and liquid crystal models, with increasing emphasis on stochastic formulations and non-isothermal effects. His work consistently bridges abstract harmonic analysis with concrete physical applications, demonstrating exceptional technical mastery in maximal regularity theory and operator calculus. Hieber's scientific recognition includes: Jean-Morlet Chair at CIRM, 2022 He leads the DFG Research Group FOR 5528 on Mathematical Investigations of Geophysical Fluid Models and has supervised Humboldt Fellows like Dr. Arnab Roy on fluid-structure interaction. His projects include the Geophysical Flows retreat in Manigod, France (2025), and the Leipzig Summer School (2024). At TU Darmstadt, Hieber heads the Applied Analysis research group, which organizes international workshops including the Oberwolfach conference on Mathematical Advances in Geophysical Fluid Dynamics (2022) and the CIRM conference on Nonlinear PDEs in Fluid Dynamics (2022).

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.

• Model Order Reduction
• Nonlinear Model Order Reduction
• Parametric Model Order Reduction
• +۲ مورد دیگر

Nicodemus Banagaaya is a Researcher in the Computational Methods in Systems and Control Theory group at the Max Planck Institute for Dynamics of Complex Technical Systems, based in office S1.08. His work bridges computational mathematics and engineering applications through advanced model reduction techniques. His research specializes in developing efficient model order reduction frameworks for complex systems, with particular emphasis on: Nanoelectronics circuit simulation Nonlinear dynamical systems Parametric dependency handling Differential-algebraic equation (DAE) systems High-fidelity model compression Dr. Banagaaya contributes to the institute's mission of advancing computational methods for complex technical systems, focusing on mathematical rigor and practical engineering implementation within his research group.

• Partial Differential Equations
• Fluid Dynamics
• Semiconductor Modeling
• +۳ مورد دیگر

Prof. Dr. rer. nat. Michael Dreher is a full professor at the University of Rostock , affiliated with the Faculty of Mathematics and Natural Sciences and leading the Analysis-Differential Equations Working Group . He has held visiting professorships at TU Wien, Darmstadt University of Technology, and the University of Bonn, and previously served as a Lecturer at Heriot-Watt University Edinburgh and an Associate Professor at the University of Konstanz. Current Position : Professor at University of Rostock (since 2017) Visiting Roles : TU Wien, Darmstadt University of Technology, University of Bonn Prior Roles : Lecturer at Heriot-Watt University Edinburgh (2013–2017), Associate Professor at University of Konstanz (2011), Junior Professor for Mathematical Physics at University of Konstanz (2013–2014) His research spans partial differential equations , with a focus on fluid dynamics , quantum hydrodynamics , and microlocal analysis . He has contributed to models for charge transport in semiconductors and mixed-order elliptic systems (Douglis-Nirenberg type). His work intersects mathematical physics, applied analysis, and computational mathematics. Recent publications highlight trends in viscous quantum systems , boundary layer analysis , and generalizations of hyperbolic equations , with applications to semiconductor physics and fluid dynamics. His teaching includes courses on functional analysis , partial differential equations , and mathematical foundations , with a focus on connecting theory to practical physics problems. Scientific Awards 1991 : Silver Medal at the International Mathematical Olympiad in Sweden His educational background includes a doctorate in natural sciences from TU Bergakademie Freiberg (1999), a diploma in mathematics (minor in physics) from the University of Halle-Wittenberg (1996), and postdoctoral research at the University of Tsukuba, Japan (1999–2002).

• Machine Learning
• Computer Vision
• Autonomous Driving
• +۴ مورد دیگر

Ross Greer is an Assistant Professor in the Department of Computer Science & Engineering at the University of California Merced. He leads the Mi³ Lab, focusing on machine intelligence, human-agent interaction, and safe autonomous systems. Education : B.S. and B.A. in EECS, Engineering Physics, and Music from UC Berkeley (2015), M.S. in Electrical & Computer Engineering from UC San Diego (2018), Ph.D. in Electrical & Computer Engineering at UC San Diego (2021) under Mohan Trivedi and Shlomo Dubnov. His research explores computational intelligence for open-world adaptability, robustness to rare events, and safety in chaotic environments. Key applications include autonomous driving, driver state analysis, trajectory prediction, and AI-assisted musical creativity. Recent publications emphasize vision-language models, active learning for 3D object detection, and safety metrics. Awards include the 2024 Interdisciplinary Research Award, Henry Booker Award for Ethical Engineering, and multiple best poster/grand prizes. Scientific Awards : 2024 Interdisciplinary Research Award 2024 Henry Booker Award for Exemplary Ethical Engineering Postdoctoral Networking Fellowship (Germany's Academic Exchange Service) Grand Prize (AWS Automotive Day competition at IEEE Intelligent Vehicles Symposium, 2023) Best Poster Awards (2021/2023 Jacobs Research Expo) He also co-authored the textbook Deep and Shallow: Machine Learning in Music and Audio (Chapman & Hall, 2023) and serves as a music director for UCSD's Symphonic Student Association and UC Merced's marching band.