جست‌وجوی استاد و پژوهشگر

Paul Wachمشاهده پروفایل

استادیار

• Systems Engineering
• Digital Transformation
• Artificial Intelligence
• +۲ مورد دیگر

Paul Wach serves as a Research Assistant Professor within the Intelligent Systems Division of the Virginia Tech National Security Institute. He holds a PhD in Systems Engineering from Virginia Tech, an M.S. in Mechanical Engineering from the University of South Carolina, and a B.S. in Biomedical Engineering from Georgia Tech. His career spans leadership roles at the Department of Energy managing $1-12B engineering projects, experience at two National Laboratories, and medical industry work. Dr. Wach's research centers on the theoretical foundations of systems engineering intersecting with digital transformation and artificial intelligence. As a subject matter expert for The Aerospace Corporation, he focuses on digital transformation applications. His scholarly contributions include founding Virginia Tech's student chapter of the International Council on Systems Engineering (INCOSE) and pioneering work in model-based systems engineering. Analysis of his 15 most recent publications (2022-2025) reveals three dominant research thrusts: (1) Application of large language models for generating systems engineering artifacts, (2) Mathematical formalization of model-based systems engineering verification, and (3) Digital engineering testbed development for military test and evaluation. His work consistently bridges theoretical frameworks with practical national security applications. Dr. Wach actively contributes to digital engineering education through curriculum development initiatives and academic case studies. His industry partnerships with The Aerospace Corporation and prior Department of Energy experience demonstrate strong government-industry collaboration in advancing digital transformation. He operates within Virginia Tech's National Security Institute ecosystem, specifically through the Intelligent Systems Division, where his work supports national security technology development. Current projects include Operation Safe Passage digital testbeds and frameworks for verifying AI-generated engineering artifacts.

Adrien Leitoldمشاهده پروفایل

دانشیار

• Process Diagnosis
• Colored Petri Nets
• Manufacturing Systems
• +۲ مورد دیگر

Dr. Adrien Leitold is an Associate Professor at the University of Pannonia. His office is located at Building I, Room 309, 8200 Veszprém, Egyetem str 10. He can be contacted via phone (+36 88 624000/6108) or email ( leitolda@almos.uni-pannon.hu ). His academic homepage is accessible at http://math.uni-pannon.hu/~leitolda/ . He holds a PhD degree, with research spanning: Model-based diagnosis of industrial and technological systems Application of Colored Petri Nets for fault detection Structural analysis and decomposition of process models Monitoring frameworks for manufacturing systems His publications demonstrate a consistent focus on Petri Net-based methodologies , structural analysis of dynamic systems, and real-time diagnosis solutions for manufacturing and batch processes. Research integrates computer science, systems engineering, and applied mathematics to address industrial automation challenges. No scientific awards, student advisements, or grant activities are detailed in the available information.

Carolyn MacDonaldمشاهده پروفایل

استاد

• X-ray Optics
• Medical Imaging
• Materials Science
• +۲ مورد دیگر

Carolyn MacDonald is Professor and Director of the Center for X-ray Optics at the University at Albany's Physics Department. With over $4M in research funding from NIH, NASA, and breast cancer programs, her work revolutionizes medical and materials imaging through advanced x-ray technologies. Research spans: X-ray phase imaging and microfocus therapy Polycapillary/crystal optics for monochromatic beams Gamma cameras for nuclear medicine Diffraction systems for crystallography Her 100+ publications show consistent focus on translating x-ray optics to clinical applications, with recent advances in mesh-based phase imaging, nanoparticle-enhanced radiotherapy, and small-animal neuromodulation techniques. Major themes include dose optimization for ocular diseases and developing practical alternatives to synchrotron imaging.

• Joint biomechanics
• Sensor development and fabrication
• Implant design
• +۷ مورد دیگر

Josh Roth is an Assistant Professor at the University of Wisconsin-Madison, with dual appointments in the Department of Mechanical Engineering and the Department of Orthopedics & Rehabilitation. His research bridges engineering and clinical domains to advance personalized treatments for musculoskeletal injuries and diseases through translational innovations. Education: PhD in Biomedical Engineering (2016), University of California, Davis MS in Biomedical Engineering (2014), University of California, Davis BS in Biomedical Engineering (2009), California Polytechnic State University His research focuses on joint biomechanics , sensor development , and implant design , leveraging computational modeling and experimental methodologies to address patient variability. Recent articles highlight advancements in shear wave tensiometry , knee arthroplasty , and dynamic tissue analysis . Scientific awards include NIH NRSA F32 Fellowship (2021), multiple Orthopedic Research Society NIRA Finalist recognitions (2017-2023), and the WARF Accelerator Grant (2024). His teaching portfolio spans graduate and undergraduate courses in biomedical and mechanical engineering, including Orthopaedic Biomechanics and independent research mentorship.

Xiangru Xuمشاهده پروفایل

استاد

• Control Theory
• Autonomous Systems
• Cyber-physical Systems
• +۳ مورد دیگر

Dr. Xiangru Xu is a Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison, leading the Autonomous & Resilient Controls Laboratory (ARC Lab). His research focuses on developing control methodologies for trustworthy autonomous systems, emphasizing safety-critical control, cyber-physical systems, and artificial intelligence applications. He holds affiliations with both the Mechanical Engineering Department and the Electrical & Computer Engineering Department within the College of Engineering. Dr. Xu earned his PhD (2014) from the Institute of Systems Science, Chinese Academy of Sciences, and a BS (2009) in Mathematics from Beijing Normal University. His work has been recognized with prestigious awards, including the NSF CAREER Award (2023) and the IEEE Best New Application Paper Award (2019). His research explores adaptive safety protocols for nonlinear systems, neural feedback systems verification, and robust control strategies. Recent publications highlight advancements in hybrid zonotope-based reachability analysis, safety-critical control barrier functions, and autonomous vehicle development. His contributions bridge theory and practice, with applications in robotics, autonomous vehicles, and control system design. Awards: NSF CAREER Award, IEEE Best Paper Award, and multiple scholarships from Beijing Normal University. Teaching: Courses include Advanced Control Systems, Dynamic Systems, and interdisciplinary design projects. Labs: ARC Lab focuses on resilient control algorithms for autonomous systems, emphasizing simulation-to-reality validation.

Prahalada Raoمشاهده پروفایل

دانشیار

• Manufacturing
• Additive Manufacturing Processes
• Sensing Technologies
• +۵ مورد دیگر

Prahalada Rao is an Associate Professor in the Department of Industrial and Systems Engineering at Virginia Polytechnic Institute and State University (Virginia Tech). He specializes in manufacturing, sensing technologies, and data science with a focus on additive manufacturing processes. His research emphasizes sensor-based monitoring, thermal physics, and quality assurance in advanced manufacturing. Education: Ph.D., Industrial Engineering, Oklahoma State University (2013) M.S., Industrial Engineering, Oklahoma State University (2006) B.Eng., Production Engineering, Victoria Jubilee Technical Institute, Bombay University (2003) His research interests include additive manufacturing (e.g., laser powder bed fusion, directed energy deposition), real-time process monitoring using heterogeneous sensors, and data-driven quality control. He has pioneered methods combining spectral graph theory and machine learning for defect detection in 3D printing. Key contributions span thermal modeling of additive processes, predictive analytics for part quality, and bio-manufacturing applications. His work has attracted over $2M in federal funding from NSF, DOE, and ONR. Awards: 2018 NSF CAREER Award for smart additive manufacturing research 2017 SME Outstanding Young Manufacturing Engineer Award 2019 UNL College of Engineering Research Award Grants & Funding: NSF CAREER: Smart Additive Manufacturing (2018-2023) DOE: Thermal Physics in Metal Additive Manufacturing (2019-2024) NSF AI Institute Planning: Secure Smart Manufacturing (2020-2023) He advises on advanced manufacturing projects and collaborates with industry on ceramic/metal 3D printing and bioprinting applications. His lab focuses on bridging sensor data analytics with manufacturing physics to achieve zero-defect production systems.

• Computer architecture
• Computing with novel technologies
• Brain-inspired computing

Georgios Tzimpragos is a Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison, with a focus on computer architecture and novel computing technologies. Previously, he was an Assistant Professor at the University of Michigan (2022–2025) and earned his PhD from UC Santa Barbara (2022), focusing on neuro-inspired temporal paradigms for superconducting computing. His research explores overcoming technology limitations in superconductors, quantum computing, and biological computing systems, aiming to bridge computation and verification for improved correctness and security. Education: PhD in Computer Science, University of California-Santa Barbara (2022) MS in Computer Science, University of California-Davis (2016) BS in Computer Science, National Technical University of Athens (2012) Research Interests: His work spans computer architecture, novel computing technologies (e.g., superconductors, flexible electronics), and brain-inspired computing. Recent projects include developing energy-efficient superconducting circuits, quantum computing hardware, and neuromorphic systems that integrate biological principles. His group emphasizes formal verification and semantic gap reduction in computing systems. Recent Publications Trends: His articles address topics like superconducting logic design, quantum circuit simulation, and neuromorphic hardware acceleration. Recent work highlights include clockless superconducting logic, evolutionary neural network optimization, and formal methods for circuit validation. Awards & Honors: James E. Smith Professorship (UW-Madison, 2025) ACM SIGARCH & IEEE CS TCCA Outstanding Dissertation Award (2023) Quantum Research Institute Fellowship (U-Michigan, 2024) Grants & Teaching: Teaches E C E 554 (Digital Engineering Laboratory) and has received grants supporting superconducting and quantum computing research. His lab collaborates with industry partners on low-power and scalable architectures.

Harald Königمشاهده پروفایل

استاد مدعو

• Software Engineering
• Formal Methods
• Model Transformations

Harald König serves as Adjunct Professor at Fachhochschule für die Wirtschaft Hannover (FHDW) in Germany. His research focuses on formal methods in software engineering, particularly model transformations and theoretical foundations of computer science. König has industry experience from SAP AG where he worked on financial services software including claims management systems. His current research examines: Multi-model consistency management Formal foundations of object-oriented modeling Graph transformations Category theory applications König collaborates on developing formal frameworks for model repair and consistency checking in software engineering, with recent publications focusing on colored Petri nets and verification techniques.

• Formal Methods
• Parallel and Distributed Computing
• Symbolic Computation
• +۳ مورد دیگر

Wolfgang Schreiner is an A.Univ.-Prof. (Associate University Professor) at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz, Austria. His research focuses on formal methods, parallel and distributed computing, and symbolic computation. He leads projects in automated reasoning, programming language semantics, and educational software tools. His work emphasizes practical applications of formal methods, including the development of tools like RISCAL for model checking and the RISC ProgramExplorer for program reasoning. He collaborates on interdisciplinary projects, such as analyzing queueing systems using probabilistic model checking and designing grid computing frameworks for medical applications. Key research interests include semantics-based language design, theorem proving, and educational technology. He has authored textbooks like *Concrete Abstractions* and *Thinking Programs*, emphasizing foundational concepts in computer science. His contributions span theoretical advancements and practical implementations, with notable work in distributed systems, formal verification, and computational logic.

Kristian Amadoriمشاهده پروفایل

دانشیار مدعو

• Aeronautical Engineering
• Systems Engineering
• Multidisciplinary Optimization
• +۳ مورد دیگر

Kristian Amadori is an Adjunct Associate Professor and Docent at Linköping University's Department of Management and Engineering (IEI), affiliated with the Fluid and Mechatronic Systems (FLUMES) division. His research focuses on aeronautical systems engineering, multidisciplinary optimization, and conceptual aircraft design, with a particular emphasis on sustainable aviation and system-of-systems approaches. He collaborates extensively on projects involving unmanned aircraft systems, radar signature optimization, and technology portfolio analysis. His work bridges theoretical frameworks with practical applications in aerospace engineering, leveraging advanced modeling techniques such as multi-fidelity integration and ontology-based reasoning. Key research interests include systems integration challenges, stealth technology for UAVs, and automated design methodologies. His publications span topics from economic viability of regional aircraft to agent-based simulation for complex systems. Amadori's contributions emphasize holistic engineering approaches that address both technical and operational aspects of aeronautical product development. Recent articles highlight advancements in multidisciplinary optimization, technology prioritization frameworks, and parametric modeling for conceptual design. His work often involves collaboration across academia and industry to address challenges in aircraft design, surveillance systems, and sustainable aviation technologies. While no specific awards or grants are listed, his active publication record reflects sustained engagement with cutting-edge aerospace research.

• Control Systems
• IoT and Smart Grids
• Renewable Energy Systems
• +۳ مورد دیگر

Seyed Amir Alavi is a Teaching Fellow at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on control systems, IoT integration in energy grids, and fractional-order control methodologies. He specializes in microgrid design, distributed control strategies, and state estimation techniques for smart energy systems. His work addresses challenges in renewable energy integration, delay compensation in power systems, and battery health monitoring for electric vehicles. Key research areas include: IoT-based distributed control systems for microgrids Advanced control algorithms for DC and AC power systems Quantum computing testbed infrastructure development Application of machine learning in battery diagnostics Resilient communication networks for energy systems Recent publications highlight innovations in: Privacy-preserving IoT data collection methods Event-triggered control for distributed systems Forecast-based consensus control using deep learning No academic awards or grants are explicitly mentioned in the provided materials. His work contributes to both theoretical advancements and practical implementations in smart grid technologies and energy management systems.

• Formal Methods
• Model Checking
• Probabilistic Systems
• +۳ مورد دیگر

David N. Jansen is a Researcher at the University of Twente's Department of Formal Methods and Tools. His work focuses on formal methods, probabilistic model checking, and algorithmic tools for software verification. He has contributed to the development of the probabilistic model checker MRMC and conducted extensive research on binary decision diagrams (BDDs) for symbolic model checking. His research interests include formal verification techniques, probabilistic systems analysis, and the optimization of computational methods in software engineering. He has published on topics such as BDD package comparisons, scheduling algorithms under uncertainty, and the performance evaluation of model checking tools. Jansen's work aligns with advancements in formal methods and their applications in ensuring software reliability and efficiency. His publications span conference proceedings, journals, and technical reports, reflecting a strong emphasis on both theoretical foundations and practical tool development.

Graeme Kellyمشاهده پروفایل

پژوهشگر

• Numerical Weather Prediction (NWP)
• Data Assimilation
• Satellite Meteorology
• +۳ مورد دیگر

Graeme Kelly is a Researcher at the Department of Meteorology, University of Reading, and an Expert Scientist in Data Assimilation at the Met Office. His work focuses on advancing numerical weather prediction (NWP) through the use of satellite data, particularly in variational analysis and data assimilation systems. He has expertise in integrating SEVIRI and IASI radiance data into operational models, improving forecast accuracy, and optimizing observation systems. His career spans roles at ECMWF (1983–2007) and the Met Office (2007–present), where he contributed to projects like the ERA-40 reanalysis and the RTTOV radiative transfer model. His research interests include satellite data assimilation, cloudy radiance processing, and observation error characterization. Notable contributions involve enhancing the use of geostationary satellite observations (e.g., SEVIRI, IASI) in high-resolution models and developing infrastructure for cloudy radiance assimilation. He has also advised on reanalysis projects and satellite bias correction initiatives. Key technical achievements include improving the Met Office’s UKV model through 4D-Var techniques and advancing understanding of atmospheric motion vector (AMV) errors. His work bridges satellite remote sensing and operational NWP, emphasizing the value of targeted observations and interdisciplinary collaborations with space agencies.

Norhan Bayomiمشاهده پروفایل

پژوهشگر ارشد

• Climate Change Science
• Urban Metabolism
• Built Environment
• +۳ مورد دیگر

Norhan Bayomi is a Research Fellow at the MIT Environmental Solutions Initiative and a Research Assistant in the Urban Metabolism Group. She holds a BSc in Architecture from Cairo University, an MSc in Building Technology from MIT (2017), and a PhD from the same program (2021). Her research integrates climate science, urban design, and technology to address climate change impacts in developing regions. Her work focuses on assessing health risks from urban heat exposure and leveraging aerial technology and AI for adaptive building and urban design strategies. Research domains include remote sensing, thermal anomaly detection, and climate vulnerability quantification. Recent publications demonstrate strong emphasis on drone-based thermal imaging, machine learning applications for infrastructure diagnostics, and climate risk modeling. Her work consistently bridges computational methods with urban sustainability challenges. She is affiliated with the Urban Metabolism Group, which investigates resource flows in urban systems to enhance resilience under climate stressors.

Fabio Gratlمشاهده پروفایل

پژوهشگر

• Molecular Dynamics
• High-Performance Computing
• Node-Level Performance Optimization
• +۳ مورد دیگر

Dr. Fabio Gratl is a former Researcher (Wissenschaftlicher Mitarbeiter) at the Chair of Scientific Computing in Computer Science (SCCS) at the Technical University of Munich (TUM). He holds a M.Sc. in Informatics from TUM (2017) and completed his doctoral studies (Dr. rer. nat.) in 2020. His research focuses on high-performance computing, molecular dynamics simulations, and auto-tuning algorithms for particle systems. He contributed to the development of the AutoPas library, which enables node-level auto-tuning for optimal performance in molecular dynamics. Key research interests include parallel computing (shared memory/task-based/mpi), vectorization, and algorithm selection strategies. He has advised numerous theses on topics like GPU acceleration, auto-tuning with machine learning, and space debris modeling. His work integrates software development (C++, Python) with computational engineering challenges. Teaching activities included courses on scientific computing, molecular dynamics, and numerical programming at TUM. He also organized seminars on leadership and future HPC trends. Notable software contributions include AutoPas, ls1 mardyn extensions, and tools for polyhedral gravity modeling. Publications span computational science journals and conferences, emphasizing algorithm optimization and cross-domain applications. Despite leaving the SCCS chair, his research legacy persists through ongoing AutoPas development and collaborations.