Dr. Erma Perenda serves as Professor and Chair of Distributed Signal Processing at RWTH Aachen University, Germany, leading research within the Department of Distributed Signal Processing. Her contact details include email perenda@dsp.rwth-aachen.de and phone +49 241 80-27879, with office location at Kopernikusstraße 16, 52074 Aachen in the ICT Cubes facility. Her research spans: Distributed Signal Processing Wireless Communications Machine Learning (Deep Reinforcement Learning, Federated Learning) Modulation Classification AI-driven Network Optimization She focuses on solving real-world challenges in wireless systems including hardware impairments, channel variations, and energy efficiency through advanced AI techniques. Analysis of her 2018-2024 publications reveals consistent innovation in applying multi-agent deep reinforcement learning to wireless power allocation, developing robust modulation classification methods resilient to channel impairments, and implementing federated learning for industrial edge computing. Her work bridges theoretical machine learning with practical wireless communication constraints. Scientific Awards: No awards documented in available sources Advising and Grants: No student advisees or grant information provided Labs and Teams: Leads Distributed Signal Processing research group at RWTH Aachen University Based in ICT Cubes building focusing on wireless AI systems
Surajit Chaudhuri is a Researcher at Microsoft , with a career spanning decades in database systems and data management . He has received the prestigious SIGMOD Edgar F. Codd Innovations Award (2011) for his contributions to query optimization , index tuning , and data lakes . Research Interests : His work focuses on database tuning , approximate query processing , fuzzy similarity joins , automated data transformations , and machine learning integration for scalable data systems. Recent Publications : In 2025, his research includes Auto-Test for unsupervised error detection in tables, Esc for budget-aware index tuning, and MMTU for multi-task table understanding benchmarks. Earlier works in 2024–2023 address spreadsheet formula recommendation , low-overhead index filtering , and time-series pattern recognition . Scientific Impact : He has co-authored influential papers in SIGMOD , VLDB , and IEEE Transactions , shaping practices in cloud databases , query optimization , and self-service BI . His collaborations span institutions like Microsoft, MIT, and ETH Zurich.
Mikel Sanz is a Ramón y Cajal Researcher and Ikerbasque Fellow at the University of the Basque Country (UPV/EHU) in Bilbao, Spain. His research focuses on quantum computing, quantum algorithms, quantum technologies, and quantum metrology. His research interests include: Quantum Computing and Quantum Algorithms Quantum Metrology and Quantum Sensing Digital-Analog Quantum Computing Quantum Machine Learning Quantum Simulation Quantum Error Correction and Mitigation Dr. Sanz's recent publications demonstrate a strong focus on practical applications of quantum computing across various domains. His work spans quantum hardware design, quantum algorithm development, quantum machine learning applications, and quantum metrology techniques. He has made significant contributions to digital-analog quantum computing approaches, quantum kernel methods, and quantum-enhanced sensing technologies. His scientific awards include being selected as a Ramón y Cajal Researcher, a prestigious research position in Spain for experienced researchers, and an Ikerbasque Fellow, which is awarded by the Basque Foundation for Science to attract top researchers to the Basque Country. Dr. Sanz has collaborated extensively with researchers across multiple institutions, contributing to a wide range of quantum information science projects. His work often bridges theoretical quantum information concepts with practical implementations, particularly in superconducting quantum computing platforms. He is actively involved in advancing quantum technologies through his research group at UPV/EHU, focusing on developing novel quantum algorithms and exploring applications of quantum computing in various scientific and industrial domains.
Prof. Dr. rer. nat. habil. Detlef Hauke Mache is a full Professor of Mathematics and Applied Mathematics at the TH Georg Agricola University of Applied Sciences in Bochum, Germany, within the Faculty of Electrical Engineering, Information Technology, and Industrial Engineering. Since 2003, he has held the chair for Applied Mathematics with a focus on Constructive Approximation. Education: Diploma in Mathematics and Computer Science, University of Dortmund (1988) Doctoral degree (Dr. rer. nat.) in Mathematics, University of Dortmund (1991) Habilitation (Dr. habil.) in Mathematics, University of Dortmund (1997) Research Interests: Prof. Mache's research spans Constructive Approximation Theory , emphasizing the development and analysis of approximation methods. He explores Radial Basis Function (RBF) Networks and their applications in neural networks and fuzzy logic. His work integrates theoretical foundations with practical algorithms, particularly in numerical analysis and approximation techniques. Key areas include: Neural Networks and Fuzzy Logic Systems Quasi-Interpolation Methods Orthogonal Polynomial Expansions Integral Transforms and Convolution Structures Publications and Editorial Contributions: Prof. Mache has authored over 30 peer-reviewed papers and edited several volumes in approximation theory. His research trends indicate a focus on advancing approximation methods through theoretical insights and practical applications in neural networks and computational intelligence. Scientific Awards and Honors: While no specific awards are listed, his extensive editorial roles and habilitation qualification signify recognition in his field. Teaching and Advising: He teaches courses in Higher Mathematics, Applied Mathematics, Differential Equations, and Numerical Analysis. While no specific students are named, his long-standing academic positions suggest significant contributions to graduate education. Laboratories and Teams: As a professor in the Faculty of Electrical Engineering and Information Technology, he likely collaborates with interdisciplinary teams, though no specific labs are mentioned.
Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.
Wing Lam is an Assistant Professor at George Mason University specializing in software engineering with a focus on software testing methodologies. His academic service includes program committee membership for major conferences including ASE, ICSE, ISSTA, and ESEC/FSE, as well as session chair roles across multiple tracks. His research interests center on flaky tests , mobile application testing , and continuous development optimization . Lam's work addresses critical challenges in test reliability, particularly order-dependent flaky tests and resource-related flakiness. His research bridges theoretical foundations with practical applications in modern software development pipelines, with recent expansion into AI-assisted testing methodologies. Lam's publication record shows a clear trajectory focusing on flaky test detection and mitigation, with approximately 60% of his recent work dedicated to various aspects of this problem. His research increasingly incorporates machine learning techniques for UI testing and test optimization, reflecting broader trends in the field. The consistent publication venue pattern across top software engineering conferences indicates strong recognition within the academic community. Lam has served in multiple leadership roles including Program Co-Chair for MOBILESoft Research Track and Committee Member for ASE's New Ideas and Emerging Results (NIER) Track. His involvement in workshops focused on flaky testing demonstrates his specialization in this niche area of software testing.
Despina Kontos, PhD is the Herbert and Florence Irving Professor of Radiological Sciences at Columbia University Irving Medical Center (CUIMC), with appointments in the Department of Radiology and the Herbert Irving Comprehensive Cancer Center. She serves as the Chief Research Information Officer for CUIMC, Vice Chair of Artificial Intelligence and Data Science Research in the Department of Radiology, and Director of Biomarker Imaging at NewYork-Presbyterian Hospital. Additionally, she holds appointments in the Departments of Biomedical Informatics and Biomedical Engineering. Dr. Kontos received her educational training from prestigious institutions: BS in Engineering from the University of Patras, Greece MSc and PhD in Computer and Information Sciences from Temple University Postdoctoral training in Radiology at the University of Pennsylvania Certificates in Biostatistics and Epidemiology from UPenn, Cancer Biology from Harvard, and AI for Decision Making from Wharton As a computer scientist with expertise in artificial intelligence and machine learning, Dr. Kontos focuses on developing computational methodologies to leverage imaging as quantitative biomarkers for personalized disease prediction, particularly in cancer. Her research program investigates how imaging data can be mined to extract sophisticated phenotypic signatures with diagnostic, prognostic, and predictive value. While her primary focus has been on breast cancer, her lab also pursues related research in lung cancers, evaluating the integration of CT radiomic features with liquid biopsy data to characterize tumor heterogeneity. Dr. Kontos founded and directs Columbia University's Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), a multidisciplinary center dedicated to developing and integrating quantitative imaging and non-imaging biomarkers for personalized disease prediction. Through CIMBID, she has built a vibrant scientific ecosystem that brings together expertise across Columbia's campuses, linking basic science, engineering, clinical medicine, public health, and health services research. Analysis of Dr. Kontos's publication record reveals a strong focus on applying AI and machine learning to biomedical imaging, particularly for cancer risk prediction and personalized treatment. Her work demonstrates a progression from foundational methodological development to clinical translation, with increasing emphasis on multi-modal biomarker integration. Recent publications show expansion into new disease areas including Alzheimer's disease prediction, while maintaining her strong focus on breast and lung cancer applications. Dr. Kontos has received significant recognition for her contributions to the field: Academy for Radiology and Biomedical Imaging Research Distinguished Investigator Award (2020) Eastern Cooperative Oncology Group - American College of Radiology Imaging Network ECOG-ACRIN Young Investigator Award of Distinction for Translational Research (2014) Dr. Kontos has been highly successful in securing research funding, with numerous grants from federal agencies including the National Institutes of Health (NIH) and the Department of Defense (DOD), as well as private foundations such as the American Cancer Society (ACS) and the Radiological Society of North America (RSNA). Her leadership extends to mentoring students and postdoctoral researchers through her roles at CIMBID and the Department of Radiology. As the founding director of CIMBID, Dr. Kontos leads a multidisciplinary team that includes the Computational Imaging Biomarker Group (CBIG), the Laboratory of AI and Biomedical Science (LABS), and several other affiliated research labs. The center leverages Columbia's institutional strengths in engineering, data science, and clinical medicine to advance personalized healthcare through AI and imaging technologies.
Willem Leterme is a Professor of High Voltage Technology at RWTH Aachen University, specializing in advanced power systems engineering. His research focuses on high-voltage direct current (HVDC) grids, fault protection mechanisms, and grid integration challenges. His work addresses critical issues such as DC fault mitigation, converter control strategies, and system resilience under fault conditions. He leads projects on HVDC grid protection algorithms, cable aging analysis, and interoperability solutions for multi-vendor systems. Key research themes include: DC grid protection and fault detection Modular multilevel converter (MMC) control High-frequency insulation testing Renewable energy grid integration Recent studies (2023-2025) emphasize: Advanced DC fault response modeling Hybrid AC/DC grid stability Multi-terminal HVDC interoperability Transformer insulation under harmonic stresses Publications highlight contributions to protection system design, DC cable testing methodologies, and grid-forming wind turbine applications. He collaborates on EU-funded initiatives for HVDC infrastructure development and standardization efforts.
Prof. Dirk Schneider is a Full Professor (W3) of Biochemistry at Johannes Gutenberg University Mainz since 2010, with previous appointments at the University of Freiburg (2003-2009) and postdoctoral training at Yale University. His research spans membrane biochemistry, biophysics, and transmembrane protein folding/assembly, focusing on thylakoid membrane biogenesis and protein-lipid interactions in cyanobacteria and chloroplasts. Current roles: Full Professor, University Mainz Previous roles: Assistant Professor (W1), University of Freiburg Education: PhD (summa cum laude) from Ruhr-University Bochum His research interests include: Membrane protein folding and stability ESCRT-III/Vipp1/PspA family structural dynamics ABC transporter activity regulation (e.g., BmrA) Protein-lipid interaction mechanisms Thylakoid membrane remodeling Comparative membrane biology between prokaryotes and eukaryotes Development of spectroscopic and computational methods Recent publications reveal trends in bacterial membrane remodeling (SynDLP, PspA), lipid effects on transporter activity (BmrA), and IM30/Vipp1-mediated membrane fusion. His work combines structural biology, biophysics, and functional assays to elucidate membrane dynamics. Awarded the Dr. Heinrich Kost Award (2001) and Leopoldina Fellowship (2001) , he has held leadership roles including Study Section Speaker (2010-2014) , Director of Institute of Pharmacy and Biochemistry (2013-2015) , and Dean of Faculty of Chemistry (2015-2020) . His scientific advisory roles include editorial board memberships and study section leadership.
Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.
Prof. Dr.-Ing. Stefan Schulte is a Full Professor at Hamburg University of Technology, leading the Institute for Data Engineering and the Christian Doppler Laboratory Blockchain Technologies for the Internet of Things (CDL-BOT). He holds a diploma in Economics and a Bachelor's in Computer Science from the University of Oldenburg, followed by a Master's in Information Technology (with Merit) from the University of Newcastle. After completing his PhD at TU Darmstadt in 2010, he held roles as Postdoctoral Researcher at TU Wien, Assistant Professor (tenure-track), and eventually Associate Professor before joining TU Hamburg in 2021. His research focuses on data engineering, blockchain technologies applied to IoT, elastic computing, and quality-of-service (QoS) aspects in smart systems. Notable contributions include work on fog computing, federated learning, and cross-blockchain interoperability. He has published over 140 papers in top-tier venues like IEEE Transactions on Services Computing and ACM Computing Surveys. Key awards include Best Paper Awards at the IEEE International Conference on Blockchain (2020) and the European Conference on Service-Oriented and Cloud Computing (2023). Prof. Schulte chairs major conferences such as the IEEE International Conference on Fog and Edge Computing (ICFEC 2025) and serves on editorial boards for journals like IEEE Transactions on Services Computing. He leads CDL-BOT, a lab exploring blockchain applications in IoT and manufacturing. His industrial collaborations include projects like SIMPLI-CITY (smart mobility) and CREMA (cloud-based manufacturing). Current research emphasizes blockchain interoperability, federated learning frameworks, and edge-AI systems. He actively reviews proposals for the German Research Foundation, EU programs, and industry initiatives.
Prof. Dr. Carolin Wienrich is a Professor of Psychology of Intelligent Interactive Systems at Julius-Maximilians-University Würzburg, Faculty of Human Sciences, and Co-director of XR HUB Würzburg since 2020. Her work bridges psychology, virtual reality, and human-computer interaction to understand human experiences in digital environments. Her educational background includes: 2010: Psychology Degree from Martin Luther University Halle/Wittenberg 2015: Interdisciplinary PhD from TU Berlin | Faculty of Traffic and Machine Systems Prof. Wienrich's research explores psychological aspects of presence, embodiment, and social interaction in XR systems. She investigates how device characteristics affect user experience, with applications ranging from workplace collaboration to therapeutic interventions. Her systematic review on psychological ownership of virtual objects has provided foundational insights into how users form emotional connections with digital assets. She has made significant contributions to understanding avatar embodiment effects on body image and self-esteem, as well as the impact of immersion levels on social presence and task performance. Analysis of her recent publications reveals several key research trends: Investigating cross-device collaboration and asymmetric interaction in virtual environments Exploring psychological ownership of virtual objects and environments Developing VR applications for therapeutic interventions in mental health Studying human-AI interaction dynamics in spatial computing environments Examining privacy, safety, and harassment issues in social VR Developing training approaches to improve user competence with intelligent systems Her notable awards include: 2020 Research Prize of the Faculty of Human Sciences (JMU Würzburg) 2019 Prize for Good Teaching Bavaria (Free State of Bavaria) 2018 Best Impact German Institute for Virtual Reality Best Poster award at IEEE VRW 2025 IDEATExR Best Paper award at IEEE VRW 2025 Prof. Wienrich actively engages with policy makers and the public, having presented to the Federal Commissioner for Data Protection and Information Security, participated in discussions at the German Ethics Council, and demonstrated her research to members of the German parliament. Her presentations cover critical topics such as the psychological consequences of the metaverse and human-centered AI interaction in virtual environments. As Co-director of XR HUB Würzburg, she leads an interdisciplinary initiative that connects researchers across psychology, computer science, and medicine to advance XR technologies and applications. The hub serves as a central platform for academic research, industry collaboration, and public engagement with extended reality technologies.
Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.
Maurits Haverkort is a Professor at the Institute for Theoretical Physics, Heidelberg University (Germany). His research focuses on quantum many-body systems , strongly correlated electrons , and X-ray spectroscopy of complex materials under strong fields. University of Cologne (PhD in Physics, 2005) University of Groningen (M.Sc. in Physics, 2002) Research Interests : He investigates orbital and magnetic properties in heavy fermion systems , actinide materials , and correlated oxides using resonant inelastic X-ray scattering (RIXS) , ARPES , and computational tools like Quanty . His work spans crystal field theory , spin-orbit coupling , and ultrafast electron dynamics . Scientific Awards & Activities : 2018 – Editorial Board Member, Physical Review Letters 2017 – Beam Time Allocation Panel, ESRF Grenoble 2016–2018 – Swedish Research Council Panel NT-4 2012–2016 – Scientific Selection Panel, Helmholtz-Zentrum Berlin Recent Publications highlight 5f electron counting , photon-modulated bonding , and precision neutrino mass experiments , reflecting his expertise in quantum materials and advanced spectroscopy .
Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.
