جست‌وجوی استاد و پژوهشگر

• Competition Law
• Procedural Law
• Judicial Decision-Making
• +۳ مورد دیگر

Johannes Kruse is a Senior Research Fellow at the Max Planck Institute for Research on Collective Goods , where he focuses on Competition Law , Procedural Law , Judicial Decision-Making , Computational Legal Studies , and Behavioural Law and Economics . His work bridges legal theory with empirical and computational approaches. He earned his Dr. iur. (Doctor in Law) from the University of Münster in 2023 and passed the Second State Exam in Law in 2022 after a Legal Clerkship (2020–2022) . He studied Law and Economics at the University of Münster, University of Turin, and FernUniversität in Hagen. Kruse’s research integrates computational methods with legal analysis , particularly in competition law and procedural law. His recent work includes AI-driven legal commentary, empirical studies on judicial behavior, and neuroscientific perspectives on fundamental rights. His publications emphasize quantitative legal analysis , AI applications , and evidence-based legal frameworks . Key trends include computational antitrust , economic evidence in court , and neurojurisprudence . Scientific recognition includes the WuW Award „Future of Competition” (2024) for his computational analysis of Article 102 TFEU. He contributes to legal education through empirical case studies and digital legal tools , with a focus on interdisciplinary collaboration.

Olaf Wolkenhauerمشاهده پروفایل

استاد

• Systems Biology
• Bioinformatics
• Machine Learning
• +۵ مورد دیگر

Olaf Wolkenhauer is a Full Professor and Chair in Systems Biology & Bioinformatics at the Faculty of Computer Science & Electrical Engineering, University of Rostock, a position he has held since 2003. Additionally, he serves as Guest Professor and Interim Head of Section III at the Leibniz Institute for Food Systems Biology, Technical University of Munich, until 2025. He maintains adjunct professorships at Chhattisgarh Swami Vivekanand Technical University (India) and Case Western Reserve University (USA). His research integrates data science and mathematical modeling to decipher emergent properties in cellular systems, combining data-driven and model-driven approaches. Utilizing machine learning, statistics, systems theory, and stochastic processes, he investigates how interactions between cellular components produce system-level functions that constrain part behaviors. His work bridges computational sciences with life sciences applications in biomedical research, pharmaceutical development, and clinical decision support. Recent publications reveal dominant themes in cancer systems biology (melanoma, metastasis), network analysis of high-throughput data, drug repositioning, and computational methods for imbalanced datasets. Key interdisciplinary applications include SARS-CoV-2 virus-host interaction mapping, inflammation resolution mechanisms, and E2F1 transcription factor networks in therapeutic resistance. Scientific recognition includes: SPIE Pioneer Award (2009) IBM Computing Prize (1994) Wolkenhauer leads collaborative projects with biomedical researchers, pharmaceutical companies, and IT firms, developing machine learning workflows for wet-lab data analysis, molecular network modulation studies, and clinical decision support systems. His teaching includes graduate courses in BioSystems Modelling, Life Sciences Simulation, and Python-based Data Science. He directs the Research Group Network Regulation & Modeling / Machine Learning at the Leibniz Institute, contributing to major initiatives like the COVID-19 Disease Map and Atlas of Inflammation Resolution, fostering international networks in systems medicine and computational biology.

Mirjam Knörnschildمشاهده پروفایل

استاد

• Bioacoustics
• Animal Communication
• Ethology
• +۴ مورد دیگر

Mirjam Knörnschild is Professor of Evolutionary Ethology at Humboldt-Universität zu Berlin and Head of the Department of Evolutionary Diversity Dynamics at the Museum für Naturkunde (Leibniz Institute for Evolution and Biodiversity Research) in Berlin. She serves as Co-Lead for the Science Programme "Dynamics of Nature" and conducts interdisciplinary research at the intersection of behavioral biology, evolutionary theory, and acoustic communication. Her research program centers on bioacoustic mechanisms in mammalian communication, with bats as primary model organisms. Key interests include vocal production learning, babbling behavior as evolutionary precursor to language, social decision-making through acoustic signals, and the relationship between social complexity and vocal complexity. Her work integrates neuroethological approaches with ecological field studies to examine how communication systems evolve under selective pressures. Knörnschild's publication record reveals a consistent focus on vocal learning mechanisms across species, with recent work establishing bats as critical models for understanding human language evolution. Her studies frequently bridge computational biology, sensory ecology, and evolutionary neuroscience, demonstrating how acoustic signals function as both social coordinators and evolutionary drivers in natural populations. Her scientific contributions have been recognized through major competitive grants: ERC Starting Grant CULTSONG (2019-2024) investigating whether song learning accelerates speciation in bat ring species Leibniz Competition Best Minds Professorship (2023-2027) establishing her academic position at HU Berlin GIF NEXUS Grant (2023-2026) exploring the transition from social signals to active acoustic sensing As department head and principal investigator, Knörnschild leads research teams examining evolutionary dynamics through bioacoustic lenses. Her current projects involve longitudinal field studies of bat colonies, cross-species comparative analyses, and experimental paradigms testing vocal learning capabilities. The ERC-funded CULTSONG project represents a flagship initiative examining cultural transmission in natural populations, positioning her work at the forefront of evolutionary communication research.

Philipp Wiedemannمشاهده پروفایل

استاد

• Biotechnology
• Cell Culture Technology
• Process Monitoring
• +۴ مورد دیگر

Prof. Philipp Wiedemann serves as a Professor at Mannheim University of Applied Sciences (Technische Hochschule Mannheim) within the Faculty of Biotechnology. His research program centers on advanced monitoring techniques for biotechnological processes, with particular expertise in developing and implementing in situ microscopy systems across diverse applications. His research spans multiple domains of biotechnology: Development and application of in situ microscopes for cell density and viability assessment Image analysis using Artificial Intelligence for bioprocess characterization Cell stress detection via mass spectrometry Glucose monitoring systems for animal cell cultures Applications in pharmaceutical production, wastewater treatment, blood banking, and bioethanol manufacturing Wiedemann's recent publications (2021-2025) reveal a strong interdisciplinary approach, integrating bioprocess engineering with medical diagnostics and environmental science. His work demonstrates consistent innovation in monitoring technologies, with increasing incorporation of AI and machine learning techniques for real-time process control. The publications show applications ranging from pharmaceutical manufacturing to wastewater treatment plants, highlighting the versatility of his monitoring approaches. He teaches several specialized courses: Applied Cell Biology (covering animal cell culture techniques) Quality Assurance in the Pharmaceutical Industry (GMP requirements and compliance) Advanced Animal Cell Technology (process development and practical applications) Prof. Wiedemann maintains active collaborations with industry partners including KVE Rhein-Neckar and Bilfinger Industrial Services Salzburg, as well as research institutions. His work on in situ microscopy has been implemented commercially for wastewater treatment monitoring (ismanalytics.com), demonstrating successful translation of academic research to industrial applications.

Nataša Živićمشاهده پروفایل

استاد

• Information and Coding Theory
• Cryptography
• Information Security
• +۱۱ مورد دیگر

Prof. Dr. Nataša Živić is a Professor of Information and Coding Theory and Real-Time Image Processing at the Faculty of Digital Transformation , HTWK Leipzig . Her work bridges theoretical foundations in coding, cryptography, and signal processing with practical applications in IoT, smart grids, automotive systems, and blockchain technologies. Academic background: Dr.-Ing. habil. and Dr.-Ing. in Electrical Engineering, University of Siegen; Magister and Dipl.-Ing. from University of Belgrade. Professional journey: Founder of Secutanta GmbH; Senior Technical Security Engineer at Volkswagen (Cariad); academic roles at University of Siegen. Her research focuses on secure and robust communication , including noise-tolerant data authentication, joint channel coding and cryptography, and real-time image processing. She explores probabilistic AI algorithms and distributed ledger technologies for industrial applications in mobility and energy systems. The 15 most recent publications highlight her leadership in applying blockchain and DLT to automotive and IoT contexts, advancing secure communication in smart metering and charging infrastructure, and integrating machine learning for environmental and financial prediction. Her work spans IEEE, MDPI, and Springer venues, reflecting strong academic impact. Scientific Awards: Best Paper and Innovation Award, IEEE SEGE 2015 Best Paper Award, UBICOMM 2010 She has led DAAD-funded international projects with universities in the USA, Korea, and China, and contributes to standardization as a member of DIN and ISO committees on security (SC 27) and AI (SC 42). She is also an IEEE Senior Member and active reviewer for top journals. Her research addresses real-world challenges in automotive security, smart energy, and digital transformation.

Sami Daviesمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Combinatorial Optimization
• Algorithm Design
• +۵ مورد دیگر

Sami Davies is a Research Scientist in Theoretical Computer Science at the Simons Institute and the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He earned his PhD in Mathematics from the University of Washington in 2021 under the supervision of Thomas Rothvoss, with work spanning the Department of Mathematics and the Allen School theory group. Prior to Berkeley, he was a postdoctoral researcher in the theory group at Northwestern University. His research lies at the intersection of theoretical computer science and combinatorial optimization. Key interests include scheduling algorithms, clustering, hypergraph matchings, approximation algorithms, and algorithmic applications in machine learning. His work often addresses fundamental problems in algorithm design with practical implications in resource allocation, data analysis, and network optimization. The most recent publications reflect a strong trend in developing efficient combinatorial algorithms for clustering and scheduling problems, particularly under complex constraints such as communication delays or fairness objectives. There is a growing integration of machine learning concepts into traditional algorithmic frameworks, as seen in works on predictive flows and trace reconstruction. His research is consistently published in top-tier venues including NeurIPS, ICML, SODA, FOCS, and COLT, indicating high impact and recognition within the theoretical CS community. Sami is actively engaged in academic service and outreach. He has served as a mentor in the Washington Directed Reading Program and co-taught the 'Math in Society' course through FEPPS, which provides education to incarcerated individuals. He has also taught yoga at the University of Washington. He enjoys bouldering, running with his dog, vegan cooking and baking, and practicing yoga. He completed a 200-hour yoga teacher training in 2019. Contact: Email: samidavies@berkeley.edu , davies@berkeley.edu

• Optical Networking
• Elastic Optical Networks
• Machine Learning for Network Management
• +۳ مورد دیگر

Georgios Ellinas is a Professor at the University of Cyprus, specializing in optical networking, machine learning for network management, and UAV swarm coordination. His research spans critical areas such as Elastic Optical Networks (EONs) , Physical Layer Security , and Multi-Agent Reinforcement Learning for autonomous systems. Recent work includes quantile regression models for handling uncertainty in network traffic, edge-assisted collision warning systems for urban mobility, and multi-task learning architectures for drone state identification. He applies distributed estimation techniques to jamming aerial targets and explores probabilistically robust trajectory planning for autonomous vehicles. His contributions extend to fair resource allocation in optical networks, UAV swarm coordination using ROS-LoRa integration, and quantum key distribution optimization. Collaborations with researchers like Tania Panayiotou and Panayiotis Kolios highlight his interdisciplinary approach.

Mario Fritzمشاهده پروفایل

استاد

• Trustworthy Artificial Intelligence
• Machine Learning
• Information Security
• +۴ مورد دیگر

Mario Fritz is a faculty member at the CISPA Helmholtz Center for Information Security , an honorary professor at Saarland University , and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS) . Previously, he led a research group at the Max Planck Institute for Informatics (2011-2018) and conducted postdoctoral work at UC Berkeley. His research focuses on trustworthy artificial intelligence at the intersection of information security and machine learning , with recent projects including the EU-funded ELSA - European Lighthouse on Secure and Safe AI . Current roles: CISPA faculty (2018-present), Saarland University professor (2019-present) Past roles: Max Planck senior researcher (2011-2018), ICSI/UC Berkeley postdoc (2008-2010) Education: PhD from TU Darmstadt, computer science studies at FAU Erlangen-Nuremberg His research spans AI security , privacy-preserving machine learning , computer vision , and deep generative models . Recent work includes LLM-based code deobfuscation , certified malware detectors , and membership inference attacks on DocVQA models . He serves as Associate Editor for the journal IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and leads projects like Trustworthy Federated Data Analytics , PriSyn (synthetic health data with privacy), and ImageTox (automated toxicity detection). As of 2025, he has published over 100 scientific articles , including 80 in top venues like ICML, ICLR, and CVPR.

Fernando Magno Quintão Pereiraمشاهده پروفایل

دانشیار

• Compilers
• Code Generation
• Compiler Optimizations
• +۳ مورد دیگر

Fernando Magno Quintão Pereira is an Associate Professor in the Department of Computer Science at the Federal University of Minas Gerais (UFMG), Brazil. He earned his Ph.D. from the University of California, Los Angeles (UCLA) in 2008 under the supervision of Jens Palsberg. His research focuses on compiler technologies, including code generation, optimization techniques, and static program analysis. Pereira's work spans: Binary analysis and optimization Program transformation and synthesis Performance profiling and code size reduction Security-oriented compiler techniques Machine learning applications in compilers His recent publications demonstrate a strong emphasis on practical compiler tools, theoretical frameworks for program analysis, and optimization algorithms for modern computing systems. He actively contributes to the programming languages community through service on program committees for major conferences including PLDI, CGO, ECOOP, and OOPSLA. He has also held organizational roles such as Diversity and Inclusion Co-Chair for PLDI 2023 and Finance Chair for CGO 2025.

• Concurrency and Parallelism
• Quantum Computing
• Self-adjusting computation
• +۴ مورد دیگر

Umut A. Acar is a Professor at the Department of Computer Science, Carnegie Mellon University, and an Amazon Scholar. His research bridges formal methods, systems, algorithms, and AI, focusing on concurrency, quantum computing, self-adjusting computation, and dynamic algorithms. 2025: Promoted to Full Professor 2025: PC Chair for SPAA 2025 His research group includes current PhD students like Pengyu Liu, Colin McDonald, and Mingkuan Xu (jointly advised with Zhihao Jia). Alumni include notable researchers like Sam Westrick (now at NYU) and Stefan Muller (now at IIT Chicago). Recent publications span quantum computing (e.g., Atlas for GPU-based simulation, GraFeyn for sparse circuits), parallel functional programming (e.g., Quartz, DePa), and self-adjusting computation (e.g., dynamic trees, mesh refinement). Key themes: Bridging safety and performance in parallel systems Quantum circuit optimization and simulation Incremental algorithms for dynamic data Provenance tracking in functional programs Scientific awards include: Best paper (QCE 2024) Distinguished paper (POPL 2024, ICFP 2022, POPL 2021) Intel Award (2022), JP Morgan Chase AI Award (2021) ACM SIGPLAN Research Highlight (2019) CMU Teaching Innovation Award (2019) He has supervised numerous projects (Diderot, MPL, Quartz) and advised students on PhD theses in parallel and quantum computing. His work emphasizes practical implementations of theoretical principles.

Jens Müllerمشاهده پروفایل

پژوهشگر

• Fluid Mechanics
• Aerospace Engineering
• Turbulence Analysis
• +۷ مورد دیگر

Jens Müller is a Researcher at the Technical University of Berlin , affiliated with the Faculty of Transportation and Mechanical Systems and the Institute of Fluid Mechanics and Technical Acoustics (ISTA) . He completed his M.Sc. in Aerospace Engineering at TU Berlin in 2018 and is currently pursuing his doctoral research as a Research Assistant in the Dynamics of Unstable Flows group. His research focuses on theoretical and data-driven analysis methods for flow control applications, including: Suppression of global flow instabilities in gas turbines and Francis turbines Reduction of trailing edge noise in wind turbine rotor blades Development of linear stability theory and adjoint-based optimization tools Dr. Müller has contributed to 15+ publications across fluid mechanics, turbulence modeling, and renewable energy applications, with a particular emphasis on: Bayesian optimization and resolvent analysis (2024) PIV-based modeling of combustor flows (2021) Multi-physics CFD solvers like FELiCS (2023) Vortex breakdown modes in jet flows (2020) He serves as a supervisor and teaching assistant for the course 'Methods of Data Analysis in Thermofluid Dynamics' at TU Berlin. His work bridges fundamental fluid dynamics with industrial applications in: Wind energy (noise reduction) Hydroelectric power (vortex precession control) Aerospace engineering (combustion instability mitigation)

Dirk Reiserمشاهده پروفایل

پژوهشگر

• Plasma Physics
• Numerical Methods
• Plasma Transport
• +۱۰ مورد دیگر

Dr. Dirk Reiser serves as Team Leader for "Theory and Numerical Simulations" at Forschungszentrum Jülich, specifically within the Plasma Physics (IFN-1) group of the Institute of Fusion Energy and Nuclear Waste Management. His research focuses on advancing computational methods for plasma physics and fusion energy applications, with particular emphasis on plasma edge physics and divertor operation in tokamak devices. Dr. Reiser's expertise spans multiple critical areas of computational plasma physics: Numerical Methods and Partial Differential Equations for plasma modeling Plasma Transport and Turbulence analysis using advanced computational techniques Statistical approaches including Bayesian Inference and Monte Carlo Methods Machine learning applications for plasma modeling and data analysis Plasma-material interactions and surface physics His research bridges theoretical plasma physics with practical computational approaches to solve complex fusion energy problems, particularly related to impurity transport, plasma edge physics, and divertor operation. He has made significant contributions to the EMC3-EIRENE simulation framework, which is widely used in the international fusion community for modeling plasma edge and divertor physics. Analysis of his recent publications (2021-2025) reveals a strong focus on applying advanced computational methods to fusion energy challenges, with increasing integration of artificial intelligence techniques alongside traditional physics-based modeling. His work demonstrates consistent contributions to major international fusion projects including ITER, ASDEX Upgrade, and JET, addressing critical challenges in plasma exhaust, impurity control, and plasma-material interactions essential for future fusion reactors. As a team leader at one of Europe's largest research institutions, Dr. Reiser oversees research efforts that contribute significantly to the international fusion energy research community. His work helps advance the physics basis for ITER operation and future fusion power plants, with particular emphasis on solving the complex plasma physics challenges at the boundary between hot plasma and material surfaces.

Henrik Walterمشاهده پروفایل

استاد

• Psychiatry
• Neuroscience
• Psychology
• +۸ مورد دیگر

Henrik Walter is a Full Professor (W3) of Psychiatry with a focus on Psychiatric Neuroscience and Neurophilosophy at Charité – Universitätsmedizin Berlin. He serves as Director of the Mind and Brain Research Division and Deputy Medical Director (Research) at the Department of Psychiatry and Psychotherapy, Charité Campus Mitte. Walter is also a faculty member at the Berlin School of Mind and Brain , a faculty member of the Bernstein Computational Center Berlin , and a principal investigator at the Berlin Center for Advanced Neuroimaging . Clinical expertise: Schizophrenia and affective disorders Empirical research: Working memory, volition, reward mechanisms, emotion regulation, mentalization, imaging genetics, connectomics Philosophical research: Philosophy of mind, neurophilosophy, neuroethics, philosophy of psychiatry Research trends in his 15 most recent publications (2009-2024) emphasize connectome-based machine learning , dynamic network reconfiguration , self-control mechanisms , and predictive models for psychiatric relapse . His work explores the intersection of neural network organization , emotional processing , and philosophical frameworks in mental health. Walter oversees major projects including environMENTAL (data harmonization in large cohorts) and FOR5187 PREACT (personalized psychotherapy). His team actively trains students and researchers through internships, theses supervision, and doctoral programs.

• Telemedicine
• Clinical Informatics
• Chronic Disease Management
• +۳ مورد دیگر

Prof. Dr. med. Henning Schneider serves as a Professor at the Technical University of Central Hesse (THM), School of Health, with his office located at Ostanlage 45, Building B14, Room 2.07. His research spans telemedicine systems for chronic disease management, rare disease monitoring, and clinical informatics, with significant publications in emergency medicine technology and respiratory care. His primary research interests include: Telemedicine implementation for COPD and rare diseases Clinical data interoperability and EHR optimization Digital health solutions for pre-hospital emergency care Predictive modeling in chronic disease management IT-supported clinical trial recruitment systems Analysis of his 15 most recent publications (2015-2024) reveals consistent focus on telemonitoring technologies for respiratory diseases, evolving into rare disease awareness systems (RD-MON) and drone-based emergency response tools. His work emphasizes practical clinical integration, with methodologies centered on semantic interoperability (LOINC/FHIR), user acceptance studies, and health economic evaluations. Regarding academic mentorship, no doctoral students or formal advisees are documented in the provided materials. His collaborative research involves institutions across Germany, with frequent co-authorship with Sohrabi, Blasini, and Marquardt, though specific grant details remain unreported. Current projects appear centered on VR tolerability assessment and IT-enhanced trial recruitment, reflecting ongoing engagement with emerging health technologies.

Sandipan Mohantyمشاهده پروفایل

پژوهشگر

• Computational Biophysics
• Protein Folding
• Quantum Computing Applications
• +۵ مورد دیگر

Dr. Sandipan Mohanty is a computational biophysicist at Forschungszentrum Jülich's Jülich Supercomputing Center (JSC), where he conducts research at the intersection of high-performance computing and molecular biology. His work leverages advanced computational techniques to address complex problems in protein folding, biomolecular simulation, and quantum computing applications to biological systems. Dr. Mohanty's research focuses primarily on computational approaches to protein folding and biomolecular dynamics, with particular emphasis on Monte Carlo simulations, molecular dynamics, and increasingly, quantum computing applications. His work investigates protein behavior in crowded cellular environments, examining how molecular crowders affect folding, stability, and aggregation processes. He has developed and applied specialized simulation techniques to study lattice proteins, peptide folding, and protein aggregation phenomena, contributing significantly to our understanding of biomolecular behavior in realistic cellular conditions. Analysis of Dr. Mohanty's recent publications reveals a clear trajectory toward quantum computing applications in protein science. While maintaining his strong foundation in classical computational biophysics and Monte Carlo methods, he has increasingly focused on quantum annealing and variational quantum algorithms for protein design and folding problems. His work bridges traditional high-performance computing with emerging quantum technologies, demonstrating how these novel computational paradigms can address longstanding challenges in computational biology. Dr. Mohanty has made substantial contributions to computational methodology, including the development of simulation frameworks like PROFASI (PROtein Folding and Aggregation Simulation Interface) and SMMP (Simple Molecular Mechanics for Proteins). His work spans from fundamental algorithm development to specific biological applications, with publications covering everything from programming techniques in C++ to detailed biophysical analyses of protein behavior.