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Ira NeitzelView profile

Professor

• PDE-Constrained Optimization
• Numerical Analysis
• Optimal Control Theory
• +5 more

Professor Ira Neitzel leads the Research Group for Optimization with Partial Differential Equations at the Institute for Numerical Simulation, Faculty of Mathematics and Natural Sciences, University of Bonn. Her research focuses on PDE-constrained optimal control problems, numerical analysis, and phase-field fracture modeling. She maintains active collaborations with institutions including TU Darmstadt and Leibniz University Hannover. Her primary research interests include: PDE-Constrained Optimization Numerical Analysis of Optimal Control Problems Regularization Approaches for Nonsmooth Control Fracture Propagation Modeling using Phase-Field Methods Recent work emphasizes quasilinear parabolic PDEs, state constraints, and sparse control applications in fracture mechanics. Analysis of her 15 most recent publications (2019-2025) reveals consistent focus on: Mathematical foundations of phase-field fracture control Numerical methods for state-constrained problems Convergence analysis of SQP algorithms Error estimation in finite element discretization Key journals include SIAM Journal on Control and Optimization, ESAIM: COCV, and Numerische Mathematik. Her scientific contributions include leadership in DFG Priority Program SPP 1962 and organization of the "Women in Optimization" workshop series. Notable projects include "Optimizing Fracture Propagation Using Phase-Field Approach" (SPP 1962) and "Sparse Controls in Optimization of Quasilinear PDEs" (SFB 1060). Professor Neitzel advises doctoral researchers including Andreas Hehl and Konstantinos Dimitriou. She teaches advanced courses in Numerical Algorithms, Scientific Computing, and Graduate Seminars on Numerical Simulation. Her group maintains active participation in the International GAMM Activity Group on Optimization with PDEs.

Randal BurnsView profile

Professor

• Data Science
• Machine Learning
• Cloud Storage Systems
• +4 more

Randal Burns is a Professor and the Bill and Lisa Stromberg Head of the Department of Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also affiliated with the Data Science and AI Institute and co-founder of NeuroData. His research focuses on scalable data systems for scientific applications, spanning storage technologies, cloud infrastructure, and graph/sparse-matrix engines for machine learning. Key themes include data-intensive science, neuroscience imaging analysis, and adaptive performance management. Recent publications highlight trends in edge-parallel graph encoders masked matrix operations for sparsity decentralized foundation model training storage optimization for evolving AI models batch-parallel data structures turbulence data analysis These reflect his work on scalable systems for machine learning and scientific computing. Scientific awards include NSF CAREER Award DOE Early Career Principal Investigator Kavli Fellowship IBM Outstanding Innovation Award Advising: Mentors PhD students in scalable data systems and machine learning. Grants: Supported by NSF, DOE, and DARPA initiatives. Labs: Core member of the Johns Hopkins Turbulence Database Group and NeuroData team, developing open platforms for neuroscience and turbulence research.

Luitpold BabelView profile

Professor

• Discrete Mathematics
• Structural and Algorithmic Graph Theory
• Combinatorial Optimization
• +6 more

Luitpold Babel is a Professor of Mathematics and Computer Science at the Faculty of Business Administration, University of the Federal Armed Forces Munich. He has been serving in this position since 2007 and continues to be actively involved in teaching and research as evidenced by his Spring Term 2025 course offerings including Fundamentals of Computer Science, Scientific Computing with Matlab, and Engineering Mathematics tutorials. His educational background includes: 1982-1987: Studied mathematics at the Technical University of Munich 1987: Diploma (with distinction) 1990: Doctorate (with distinction) 1997: Habilitation Professor Babel's research has evolved significantly over his career, beginning with theoretical work in discrete mathematics and graph theory before transitioning to applied defense technology research. His current focus spans operations research applications in military technology, particularly UAV route planning, missile guidance systems, and logistics optimization. He has developed expertise in translating complex mathematical concepts into practical engineering solutions for defense applications, with particular emphasis on kinematic constraints in path planning and risk assessment in navigation. An analysis of his publication trends reveals a clear shift from pure graph theory (1990-2005) toward increasingly applied research in aerospace engineering and defense technology (2010-present). His recent work demonstrates sophisticated integration of mathematical optimization with real-world constraints in military applications, with growing emphasis on cooperative systems, real-time replanning, and decentralized decision-making for missile and UAV fleets. His contributions have been recognized with: Teaching Award of the University of the Bundeswehr Munich (2015), awarded by the Student Convention Study Award of the German Society for Defense Technology eV Professor Babel has supervised over 30 master's and bachelor's theses, primarily focusing on defense-related applications of mathematics and computer science. His research has been supported by substantial external funding from the Federal Ministry of Defense and through multiple industry partnerships with major defense contractors including MBDA Deutschland GmbH and RAM-System GmbH. Current projects include Decentralized Flight Path Planning (2023-2025) and Studies on Technical-Logistical Tasks (2021-2024), demonstrating his continued active engagement in cutting-edge defense research.

Luiz ChamonView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +3 more

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

Yong ChenView profile

Professor

• Industrial Engineering
• Reliability Engineering
• Maintenance Optimization
• +4 more

Yong Chen serves as a Professor in the Department of Industrial and Systems Engineering within the College of Engineering at the University of Iowa. His academic appointment centers on advancing methodologies in industrial engineering with emphasis on system reliability and optimization across diverse applications. Chen's research spans Industrial Engineering, Reliability Engineering, and Maintenance Optimization, with significant contributions to Statistical Process Control, Bayesian Statistics, and Machine Learning applications. His work develops novel frameworks for condition-based maintenance, multi-component system optimization, and IoT-enabled industrial analytics, addressing critical challenges in manufacturing quality control and system reliability. The integration of stochastic modeling and data-driven approaches characterizes his methodological innovations. Analysis of his 15 most recent publications (2016-2025) reveals a dominant research trajectory in Markov decision processes for maintenance optimization (35% of publications), Bayesian modeling for process monitoring (27%), and IoT/data analytics applications (13%). His work demonstrates increasing interdisciplinary expansion from traditional manufacturing systems into healthcare (dementia care analysis) and renewable energy sectors, while maintaining core focus on reliability engineering fundamentals. Scientific awards: No scientific awards were mentioned in the provided text. Advising and grants: The available documentation contains no information regarding doctoral students, postdoctoral researchers, or research funding sources. His academic profile focuses exclusively on research outputs and methodological contributions without reference to mentoring activities or sponsored projects.

• Vibroacoustics
• Hand-Arm Vibration
• Occupational Health
• +5 more

Dr.-Ing. Magdalena Scholz is a Research Associate at the Technical University of Munich (TUM) , affiliated with the School of Engineering and Design and the Department of Engineering Physics and Computation . She works in the Chair of Vibroacoustics of Vehicles and Machines under Prof. Steffen Marburg. Research Focus: Hand-arm vibrations, vibroacoustics, psychophysics, and computational modeling using COMSOL Multiphysics and Simulink. Teaching: Assistant in courses like Vibroacoustic Finite Element Simulations and Introduction to Vibroacoustics. Research Trends: Her publications emphasize occupational health impacts of vibration exposure, particularly vibration-induced white finger, with methodologies spanning experimental analysis, computational modeling, and epidemiological studies. She explores exposure-response relationships, prevalence prediction, and sensor placement challenges. Labs & Collaborations: Active in the Chair of Vibroacoustics of Vehicles and Machines, engaging in interdisciplinary projects linking acoustics, human factors, and engineering mechanics.

Thomas KamalakisView profile

Professor

• Optical Wireless Communications
• Photonic Crystal Waveguides
• Visible Light Communications
• +5 more

Thomas Kamalakis is a Professor at Harokopio University in Athens, Greece, specializing in optical communications and wireless networking technologies. His academic profile indicates a strong focus on cutting-edge research in photonic systems and telecommunications infrastructure. Dr. Kamalakis' research spans multiple domains within optical communications, with particular expertise in visible light communications, free space optics, photonic crystal waveguides, and IoT security applications. His work demonstrates a consistent trajectory toward integrating optical technologies with emerging computing paradigms including machine learning and neuromorphic systems. Recent publications show increasing attention to practical implementation challenges and economic considerations in telecommunications infrastructure. Analysis of his publication trends reveals a strategic evolution from fundamental optical waveguide research toward more applied systems integrating machine learning, security, and economic considerations. His scholarly output demonstrates strong technical depth in optical communications while increasingly addressing real-world implementation challenges across diverse contexts including satellite networks, railway communications, and pandemic response systems. Dr. Kamalakis maintains an active research program with consistent publication output across multiple high-impact venues in telecommunications and optical engineering. His recent work demonstrates growing interdisciplinary connections between optical communications, cybersecurity, and economic modeling. His professional activities include significant contributions to Greek telecommunications market analysis and emerging technologies for next-generation home and office networks. The breadth of his research suggests leadership in adapting optical communication technologies to address contemporary challenges in network infrastructure and security.

Robert BarthorpeView profile

Senior Lecturer

• Dynamics
• Structural Health Monitoring
• Energy Applications
• +3 more

Dr. Robert Barthorpe is a Senior Lecturer in the Department of Mechanical Engineering at the University of Sheffield, affiliated with the School of Mechanical, Aerospace and Civil Engineering. His research bridges structural dynamics and energy systems, focusing on developing advanced methodologies for data integration in engineering challenges. Education: MEng in Mechanical Engineering with German (University of Sheffield, 2005), PhD (University of Sheffield, 2010) Rob's research centers on two primary domains: Structural Health Monitoring (SHM): Pioneering techniques like model validation, uncertainty quantification, and machine learning to address data-scarcity challenges in damage identification. Energy Applications: Investigating distributed thermal/electrical storage systems and hierarchical control methodologies (incorporating model predictive control) for low-carbon energy grid optimization. Key publication trends reveal expertise in: Digital twins and real-time updating strategies Probabilistic risk-based SHM frameworks Hybrid physics-data modeling approaches Uncertainty quantification in composite structures Scientific contributions include: EPSRC/Wellcome Trust Doctoral Prize Fellowship Collaboration on £556k Horizon 2020 DyVirt project £4.15M EPSRC grant for structural dynamics lab verification £3.2M ERDF funding for Laboratory for Verification & Validation Teaching activities include Control Engineering (MEC321) for mechanical engineering undergraduates. His work has been published in premier journals like Mechanical Systems and Signal Processing and presented at major conferences including ISMA and EWSHM.

Iris TienView profile

Associate Professor

• Infrastructure Resilience
• Probabilistic Risk Assessment
• Data Analytics for Smart Cities
• +7 more

Iris Tien is an Associate Professor at the Georgia Institute of Technology in the School of Civil and Environmental Engineering , part of the College of Engineering. She joined Georgia Tech in 2014 after earning her Ph.D. in Civil Systems Engineering from UC Berkeley. M.S. in Civil and Environmental Engineering (2010) B.S. in Civil and Environmental Engineering with Minor in English (2008) Her research focuses on probabilistic methods for infrastructure reliability , integrating data analytics, machine learning, and risk analysis to improve civil systems under extreme events. She develops frameworks for seismic vulnerability assessment and community resilience through projects funded by NSF, DHS, and NIST. Key publication trends include: Applications of Bayesian networks and convolutional neural networks in infrastructure risk modeling Multi-hazard resilience strategies (earthquakes, floods, corrosion) Interdisciplinary work bridging engineering with environmental health and urban equity Integration of 5G technology impacts into infrastructure risk analysis Scientific recognition: Received the IASSAR Early Achievement Research Prize Twice awarded 1st Place Paper Awards in resilient infrastructure Editor’s Choice selections in leading ASCE journals Selected for National Academy of Engineering Frontiers of Engineering Symposia Her work addresses smart cities and interdependent systems through grants from the National Science Foundation , Department of Homeland Security , and U.S. Department of Transportation . She has developed tools for coastal flood sensor networks and corrosion detection in bridges .

• Biomechanics
• Human Movement Analysis
• Wearable Technology
• +5 more

Stephen A Antos is a Lecturer in the Department of Physical Therapy and Human Movement Sciences at Northwestern University Feinberg School of Medicine . He contributes to both academic research and clinical translational science through affiliations with the Northwestern University Clinical and Translational Sciences Institute (NUCATS) . Research Focus: Biomechanics, Wearable Technology Applications, and Neonatal Procedural Distress Assessment Key Collaborations: Partnerships with Shirley Ryan AbilityLab and Northwestern Memorial HealthCare His work spans wearable sensor technology validation, gait analysis, and clinical tool development for neonatal intensive care units. Publications highlight interdisciplinary approaches combining biomechanical modeling , machine learning , and clinical implementation . While no formal awards are listed, his industry disclosures reflect practical applications of his research through Antos Analytics, LLC . Current activities focus on advancing activity monitoring systems for geriatric and rehabilitation populations.

• Structural Control
• Consensus Methods
• Resilient Systems
• +4 more

Guilherme Henrique Caçador Ramos is an Assistant Professor at the Department of Computer Engineering, Instituto Superior Técnico, University of Lisbon, and affiliated with the Telecommunications Institute. His work spans structural control systems, resilient consensus methods, and ethical considerations in recommender and ranking systems. Structural control systems Resilient consensus algorithms Recommender systems fairness Ranking systems security Privacy in computational models His recent publications focus on robustness in networked systems, fairness in recommendation algorithms, and security against bribery in ranking systems. Research integrates mathematical frameworks with real-world applications in control theory and AI ethics. Scientific awards: None listed.

• Internet of Things
• Cybersecurity
• Network Optimization
• +5 more

Amine Brahmia is an Associate Professor in the Department of Computer Engineering at CESI's School of Engineering, specializing in Internet of Things, Cybersecurity, and Network Optimization with significant contributions to Smart Cities infrastructure. His research bridges theoretical algorithms with practical IoT applications in urban and healthcare environments. His academic credentials include a Doctorate in Computer Science (2012) from France Telecom & University of Haute Alsace, a Master's degree in Computer Science (2008) from University of Versailles Saint-Quentin-en-Yvelines, and a Computer Engineering Diploma (2006) from University of Guelma. Dr. Brahmia's research focuses on solving complex optimization problems in distributed systems through metaheuristic algorithms and AI techniques. He has pioneered work in federated learning for edge computing, blockchain consensus protocols, and wireless sensor network deployment, with publications demonstrating consistent innovation in security mechanisms and resource optimization for constrained environments. His 15 most recent publications (2013-2023) reveal three dominant research trajectories: 45% on IoT and sensor networks (focusing on deployment optimization and security), 35% on blockchain and distributed ledger technologies (particularly consensus protocols), and 20% on federated learning applications for edge computing. This interdisciplinary approach integrates operational research with cybersecurity to address real-world challenges in smart infrastructure. Dr. Brahmia currently supervises six doctoral students: Aimen KHIAR: Hybrid metaheuristics and federated learning for electric vehicle battery recharging (2024-2027) Ahmed BAAHMED: Computational efficiency of federated learning on edge-cloud infrastructure (2023-2026) Meriem ARBAOUI: Federated learning for IoMT networks with resource optimization (2023-2026) Mortada TERMOS: Security anomaly detection in IoT using complex networks (2022-2025) Kenza RIAHI: Optimal consensus protocols for private blockchains (2021-2024) Khoula ZAIMEN: Sensor deployment optimization in smart buildings (2021-2024) He leads the Engineering and Digital Tools research team at CESI LINEACT and previously directed the CESI Strasbourg Fablab (2016-2018), where he advanced IoT prototyping capabilities. As a certified CISCO Instructor since 2015, he teaches Systems and Protocols, Routing, Switching, and Cybersecurity across multiple engineering programs using project-based learning methodologies.

• Biomedical Engineering
• Medical Physics
• Computational Modeling
• +3 more

Vahid Badeli is a researcher at the Institute of Fundamentals and Theory in Electrical Engineering, Graz University of Technology. He specializes in biomedical engineering applications of electrical impedance and computational modeling, with a focus on cardiovascular diagnostics and aortic dissection detection. His work bridges electrical engineering, fluid dynamics, and clinical medicine. Key affiliations: Graz University of Technology, University of Oslo (visiting researcher) Research themes: Bioimpedance signal analysis, digital twin development for arterial assessment, medical device prototyping His publication trends (2014-2025) emphasize numerical simulation , Bayesian inference , and finite element modeling of bioimpedance phenomena. Recent projects include MedShapeNet (3D medical shape dataset) and BioImpedanceDigiTwin (digital twins for arterial assessment). Badeli actively collaborates across Europe, with presentations at IEEE and COMPEL journal. He has contributed to openCFS simulations and developed wearable biosensors like flexible electrode straps for arterial monitoring. His methodological innovations include nonconforming finite element formulations and multi-sensor Bayesian classification systems.

• Neuroscience
• Biomedical Engineering
• Magnetoencephalography
• +3 more

Joonas Iivanainen is a Postdoctoral Researcher in the Department of Neuroscience and Biomedical Engineering at Aalto University. His research focuses on magnetoencephalography (MEG), magnetic sensor design, and computational modeling of magnetic fields in biomedical contexts. He works with advanced magnetometer technologies, including induction coil magnetometers and optically pumped magnetometers, to improve neural signal acquisition and environmental magnetic sensing. Institution: Aalto University Department: Neuroscience and Biomedical Engineering Role: Postdoctoral Researcher His research explores novel approaches to MEG sensor array design, thermal magnetic noise computation, and magnetic field modeling. Recent publications highlight advancements in single-trial neural response classification, high-sensitivity rf detection, and calibration techniques for optically pumped magnetometers. His work bridges theoretical physics and practical biomedical engineering applications. Key trends in his publications include: Development of on-scalp MEG systems for improved spatial resolution Quantification of thermal noise in conducting materials Integration of active ambient-field cancellation in sensor arrays Comparative studies of OPM and SQUID-MEG technologies Optimization of electromagnetic coil configurations Advancements in generalized spatial-frequency analysis for neural signals

• Control Systems Theory
• Fractional-Order Systems
• Brain Dynamics
• +5 more

Sérgio Pequito is an Associate Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico, University of Lisbon, and a principal investigator at the Institute for Systems and Robotics. He previously held faculty positions at Uppsala University (2022-2024) and Technische Universiteit Delft (2020-2022), and was a faculty member at Rensselaer Polytechnic Institute (2017-2020). His academic journey includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University and a Doctorate in Electrical and Computer Engineering from Universidade de Lisboa. Pequito's research focuses on understanding the global qualitative behavior of large-scale systems through structural and parametric descriptions, with applications spanning neuroscience, biomedicine, and control theory. His work leverages dynamical systems, control theory, and artificial intelligence to develop new analysis tools for brain dynamics aimed at personalized medicine and improved bidirectional brain interfaces. His specific interests include fractional-order systems, structural systems theory, network controllability, and neurotechnology applications for epilepsy treatment and brain-computer interfaces. His recent publications reveal a strong emphasis on applying fractional calculus to model brain dynamics, developing control frameworks for neurotechnology, and advancing structural systems theory for large-scale networks. His work demonstrates a consistent trajectory toward bridging theoretical control systems with practical biomedical applications, particularly in understanding and controlling brain dynamics for therapeutic purposes. 2016 O. Hugo Schuck Award in the Theory Category by the American Automatic Control Council 2009 Best student paper finalist in the 48th IEEE Conference on Decision and Control 2023 Royal Swedish Academy of Engineering Sciences 100 List 2023 European Laboratory for Learning and Intelligent Systems (ELLiS) Research Scholar Multiple Trustees Faculty Achievement awards from Rensselaer Polytechnic Institute Pequito actively mentors numerous PhD and Master's students across multiple institutions, with research topics spanning cyber-neural systems, structural control theory, fractional-order optimization, and neurotechnology applications. His laboratory work focuses on developing theoretical frameworks for large-scale system control while maintaining strong connections to practical applications in biomedical engineering and neuroscience, particularly in developing new diagnostic tools and treatment strategies for neurological diseases through control systems theory.