جست‌وجوی استاد و پژوهشگر

Blake Millerمشاهده پروفایل

استادیار

• Computational Social Science
• Text Analysis
• Social Media Politics
• +۷ مورد دیگر

Blake Miller is an Assistant Professor of Computational Social Science in the Department of Methodology at the London School of Economics (LSE), affiliated with the Data Science Institute. Their research focuses on computational methods applied to political communication in authoritarian regimes, particularly China, and the intersection of social media with political violence and identity politics. They hold a PhD from the University of Michigan (2018) and conducted postdoctoral research at Dartmouth College. Key substantive areas include: China's surveillance-driven security state and information control mechanisms Political mobilization through moral outrage and outgroup targeting Technological adaptations in authoritarian governance Methodological expertise spans machine learning, text analysis, and fairness in AI applications. Their book project Platforms and Power examines how authoritarian states delegate censorship to private platforms. Teaching focuses on quantitative text analysis and machine learning in political contexts. Research outputs include influential work on: Censorship patterns during China's zero-COVID protests Moral-emotional triggers for violence support Evaluation of active learning algorithms for text labeling Blake's work has been featured in The Washington Post , China File , and the CSIS Pekingology Podcast. They maintain an active presence in interdisciplinary research communities.

Md Noor-A-Rahimمشاهده پروفایل

استادیار

• Intelligent Transportation Systems
• Machine Learning
• Internet of Things
• +۴ مورد دیگر

Dr. Md Noor-A-Rahim is an Assistant Professor (Lecturer-Above the Bar) at the School of Computer Science and Information Technology, University College Cork (Ireland). He previously served as a Senior Researcher and Marie Curie Fellow at the same institution. His academic journey includes a PhD from the University of South Australia (2015) and the prestigious Michael Miller Medal for his outstanding thesis on wireless communication systems. His research focuses on Intelligent Transportation Systems, Machine Learning, IoT, Wireless Networks, and DNA-based data storage. He has published extensively on topics like 6G-V2X systems, time-sensitive networking, and error characterization in DNA storage. His work integrates cutting-edge technologies such as intelligent reflecting surfaces (IRS), federated learning, and ultra-reliable low-latency communication (URLLC). Research Interests : Dr. Rahim's research bridges theoretical advancements and real-world applications in vehicular networks, smart manufacturing, and next-generation communication systems. He explores challenges in autonomous driving, edge computing, and bio-constrained data storage. His contributions include novel coding schemes for anytime transmission and frameworks for mitigating big vehicle shadowing in V2X communications. Key Publications : His recent work includes a comprehensive survey on wireless TSN (2025), analysis of 6G-V2X systems (2022), and breakthrough studies on DNA data storage error modeling (2023). These publications highlight his expertise in both foundational research and industry-relevant solutions. Awards : Recipient of the Michael Miller Medal (2015) for doctoral research excellence. Grants & Labs : While specific grants are not listed in the text, his research portfolio suggests involvement in collaborative projects with industry partners and funding bodies. He leads interdisciplinary efforts in smart manufacturing and vehicular communication systems.

• Control Theory
• Partial Differential Equations
• Drilling Engineering
• +۶ مورد دیگر

Ole Morten Aamo is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. His office is located at Elektro D/B2, D344, Gløshaugen, O. S. Bragstads plass 2, and he can be reached at aamo@ntnu.no or by phone at 73594386. Professor Aamo's research focuses on control theory with particular emphasis on partial differential equations (PDEs) and their applications in drilling engineering and the petroleum industry. His work spans multiple areas including boundary control of hyperbolic systems, adaptive control methodologies, vibration control in drilling operations, and leak detection systems for pipe networks. His research combines theoretical control developments with practical applications in the oil and gas sector, particularly addressing challenges related to stick-slip phenomena, torsional vibrations, and pressure oscillations in drilling operations. His publication record reveals a consistent trajectory of high-impact research in control systems, with a notable shift toward integrating machine learning approaches with traditional control theory in recent years. The majority of his work centers around hyperbolic PDE systems, with applications primarily in drilling engineering and fluid dynamics. His research demonstrates a strong connection between theoretical control developments and practical implementations in the petroleum industry. Professor Aamo has actively supervised multiple graduate students, as evidenced by the master's theses listed in his publication record. His work often appears in top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters, as well as petroleum engineering venues like SPE Journal and ASME publications.

Duarte Guerreiro Tomé Antunesمشاهده پروفایل

استادیار

• Control Theory
• Optimal Control
• Stochastic Control
• +۳ مورد دیگر

Dr. Duarte Guerreiro Tomé Antunes is an Assistant Professor at the Department of Mechanical Engineering , Eindhoven University of Technology (TU/e), specializing in control theory. He is affiliated with the Control Systems Technology Group and focuses on optimal/stochastic control and networked control systems. Research emphasizes overcoming the curse of dimensionality in large-dimensional systems through approximate control strategies. Explores event-triggered control for networked systems with latency and computational constraints. Teaches Optimal Control and Dynamic Programming , Training Project 4 , and Robotic Seminars . Publications highlight advancements in: Event-triggered methods for linear quadratic control Stability analysis of networked systems with asynchronous links Frequency-domain modeling of control loops with data losses Switched system regulation via informed policies

Liu Derongمشاهده پروفایل

استاد

• Neural Networks
• Reinforcement Learning
• Intelligent Control
• +۵ مورد دیگر

Liu Derong is a distinguished academic holding the position of Chair Professor at Southern University of Science and Technology (Shenzhen, China) since 2022. He is also a Full Professor at the University of Illinois at Chicago (UIC) since 2006. His academic journey includes roles as Professor at Guangdong University of Technology (2017–2022) and the Chinese Academy of Sciences' Institute of Automation (2008–2016). He earned his Ph.D. in Electrical Engineering from the University of Notre Dame (1994), M.S. from the Chinese Academy of Sciences (1987), and B.S. from Nanjing University of Science and Technology (1982). His research focuses on neural networks, reinforcement learning, intelligent control, and adaptive dynamic programming. He has authored 19 books and 260+ journal papers, contributing significantly to computational intelligence and control systems. Notable works include Adaptive Dynamic Programming with Applications in Optimal Control (2017) and co-editing volumes like Frontiers of Intelligent Control and Information Processing (2014). Liu Derong has held leadership roles in professional societies, including Editor-in-Chief of Artificial Intelligence Review , Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica , and President of the Asia Pacific Neural Network Society (2018). He has organized major conferences such as the IEEE World Congress on Computational Intelligence (2014) and received prestigious awards like the Dennis Gabor Award (2018) and membership in Academia Europaea (2021). His contributions span theoretical advancements and practical applications in control systems, with a focus on optimization, robotics, and energy systems. He has also served on editorial boards of leading journals and as a keynote speaker at 30+ international conferences.

Xiaowei Chenمشاهده پروفایل

دانشیار

• Earthquake Rupture
• Induced Seismicity
• Subsurface Structure
• +۳ مورد دیگر

Xiaowei Chen is an Associate Professor in the Department of Geology and Geophysics at Texas A&M University. His research focuses on observational seismology, with an emphasis on earthquake rupture processes, induced seismicity, subsurface structure analysis, and applications of distributed acoustic sensing (DAS). He holds a PhD from the University of California, San Diego (2013), and has held prior academic positions including the Stubbeman-Drace Presidential Professorship at the University of Oklahoma (2020). His work integrates field observations, dense seismic arrays, and advanced computational methods to address critical questions in crustal dynamics and seismic hazard assessment. **Education:** PhD, University of California, San Diego, 2013 MS, University of California, San Diego, 2010 BS, University of Science and Technology of China, 2007 **Research Interests:** Chen investigates how anthropogenic activities influence fault behavior, interactions between seismic and aseismic slip, and factors controlling earthquake rupture characteristics. He studies these phenomena in tectonically active regions (e.g., western US, Japan) and intraplate settings (e.g., Oklahoma), leveraging DAS technology for high-resolution subsurface imaging. Recent projects include forecasting induced seismicity via machine learning and analyzing pore-pressure diffusion mechanisms in Oklahoma. **Awards:** Stubbeman-Drace Presidential Professor, University of Oklahoma (2020) Editor's citation for excellence in refereeing, JGR-Solid Earth (2018) **Advising & Grants:** While no formal advisees are listed, Chen’s collaborative research involves multidisciplinary teams addressing induced seismicity, fault dynamics, and crustal structure. His work is supported by grants from agencies like the USGS and SCEC. **Labs & Teams:** Engages with the Texas A&M Seismology Group and collaborates with institutions like the University of Oklahoma and the University of California system on projects involving seismic array deployments and DAS applications.

Massimo Franceschettiمشاهده پروفایل

استاد

• Mathematical Engineering
• Networked Control Systems
• Information Theory
• +۷ مورد دیگر

Massimo Franceschetti is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), with faculty affiliation at Calit2. His research spans mathematical engineering, focusing on control, communication, computation, and sensing, particularly in complex networks and systems. He integrates tools from statistical physics, wave propagation, and information theory to analyze and design networked systems. Born in Naples, Italy, he studied at the University of Naples Federico II and the University of Edinburgh (European exchange program), graduating in 1997. He earned his M.Sc. (1999) and PhD (2003) from Caltech, where he received the Walker von Brimer Award and the C.H. Wiltz Prize for outstanding research and thesis. After postdoctoral work at UC Berkeley (2003-2004), he joined UCSD as faculty and held visiting positions at Vrije Universiteit Amsterdam, EPFL (Switzerland), and the University of Trento (Italy). He became an IEEE Fellow in 2018 and was nominated a Guggenheim Fellow in 2019. His research includes networked control systems , stochastic geometry , electromagnetic information theory , and social dynamical systems . Recent work explores non-invasive emotional contagion in social networks, quantum limits on information entropy, and the physics of wave propagation. His publications bridge information theory , machine learning , and network science , often applying percolation theory and random walks to explain scaling laws and wireless signal behavior. Scientific accolades include the S.A. Schelkunoff Transactions Prize , IEEE Communications Society Best Tutorial Paper Award , and the IEEE Ruberti Young Researcher Prize . He co-authored two books: Random Networks for Communication (2007) and Wave Theory of Information (2018). His students have pursued careers in academia (e.g., IIT-Bombay, Notre Dame) and industry (e.g., Google, IBM, Tesla). He teaches courses on network science , information theory , and control systems , emphasizing data-driven analysis and the physical foundations of communication. His group’s work impacts cyber-physical systems , quantum network coding , and epidemic modeling on networks .

• Control Theory
• Digital Signal Processing
• Adaptive Signal Processing and Control
• +۷ مورد دیگر

Dr. Allahyar Montazeri is a Senior Lecturer in Control and Electronics Engineering at Lancaster University's School of Engineering, specializing in advanced control systems and signal processing. His research focuses on adaptive signal processing, robust control, system identification, and applications in robotics, active noise/vibration control, and wave energy conversion. He has over 110 publications and serves on editorial boards such as Frontiers in Robotics and AI, and IFAC Technical Committees. Montazeri holds a Humboldt Research Fellowship (2011) and ERCIM Fellowship (2010). His industrial collaborations include Bosch for automotive noise control and Fraunhofer Institute. He has supervised PhD students in acoustic signal processing and leads projects on autonomous robotics and environmental monitoring. Notable awards include 'Outstanding Associate Editor' (2023) and 'Fellow of The Higher Education Academy.' He actively participates in conferences like IEEE CDC and chairs sessions on mechatronics systems. His work bridges theoretical control advancements with practical applications in extreme environments, including nuclear robotics and underwater systems.

• Multibody dynamics
• Control of nonlinear systems
• Model predictive control

Michael Mühlebach is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zurich, affiliated with the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He received Bachelor (2010) and Master (2013) degrees from ETH Zurich, awarded the Outstanding D-MAVT Bachelor Award and Willi-Studer Prize for top performance. His research specializes in multibody dynamics , control of nonlinear systems , and model predictive control , with applications in robotics and aerospace systems. Publications demonstrate consistent focus on theoretical control frameworks validated through experimental platforms like ducted fans and reaction-wheel systems. Awards: Outstanding D-MAVT Bachelor Award Willi-Studer Prize (Best Master in Robotics, Systems, and Control)

Damir Isovicمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۳ مورد دیگر

Damir Isovic is an Associate Professor and Vice-Chancellor for Internationalization at Mälardalen University's Academy of Innovation, Design and Technology. Previously, he served as Dean of the School of Innovation, Design and Engineering. His roles include leadership in academic administration and participation in national boards. He holds a PhD and has extensive international teaching experience. Research focuses on real-time systems, embedded systems design, and scheduling algorithms. Notable contributions include seminal work in real-time scheduling recognized by the IEEE Technical Community on Real-Time Systems. He has organized major conferences and delivered keynotes globally. His publications emphasize hybrid scheduling approaches, real-time operating systems (RTOS), media processing in resource-constrained systems, and MPEG standards. Recent work integrates memetic algorithms with fuzzy controllers and explores multi-core scheduling fairness. His research bridges theoretical scheduling models with practical embedded system implementations. No scientific awards explicitly listed in the text. Advising activities include supervising PhD students, though specific names are not provided. Lab affiliations include the Division of Networked and Embedded Systems, where he develops frameworks like GENESIS for embedded system engineering. His work emphasizes cross-disciplinary collaboration and industry partnerships in education and technology development.

• Machine Learning
• Control Systems
• Robotics
• +۳ مورد دیگر

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

Jia Diمشاهده پروفایل

استاد

• Asynchronous Integrated Circuit Design
• Hardware Security
• Extreme Environment Electronics
• +۳ مورد دیگر

Jia Di serves as Professor and Department Head of the Department of Electrical Engineering and Computer Science at the University of Arkansas, holding the Rodger S. Kline Endowed Leadership Chair. He has been with the institution since 2004, progressing from Assistant Professor to his current leadership position within the College of Engineering. Education: B.S. in Automatic Control, Tsinghua University (1997) M.S. in Automatic Control, Tsinghua University (2000) Ph.D. in Electrical and Computer Engineering, University of Central Florida (2004) Research Focus: Dr. Di's work centers on asynchronous integrated circuit design and hardware security , with emphasis on Multi-threshold Null Convention Logic (MTNCL) for ultra-low-power secure systems. His research spans hardware Trojan detection, polymorphic logic gates, extreme environment electronics, and security solutions for IoT infrastructure. His Trustable Logic Circuit Design Lab has pioneered techniques for side-channel attack mitigation and cold boot attack prevention through self-destructive memory mechanisms. Publication Trends: Recent publications reveal a strategic shift toward hardware security applications for renewable energy systems and IoT edge devices, while maintaining core expertise in asynchronous circuit design. His work increasingly integrates machine learning (e.g., graph neural networks for hardware Trojan detection) and cross-platform verification frameworks, demonstrating evolution from pure circuit design to holistic cybersecurity solutions for critical infrastructure. Scientific Recognition: Senior Member of IEEE Eminent Member of Tau Beta Pi Elected Member of the National Academy of Inventors Research Leadership: Dr. Di has secured over $23 million in research funding for his Trustable Logic Circuit Design Lab, supporting development of 6 U.S. patents and two authoritative books. His lab collaborates with federal agencies and industry partners on hardware security challenges, with recent grants focusing on photovoltaic system protection and extreme-environment electronics. While specific student names aren't documented here, his extensive publication record indicates significant graduate mentorship in hardware security and asynchronous design. Lab Infrastructure: The Trustable Logic Circuit Design Lab maintains specialized capabilities for testing circuits in extreme environments (high temperature/radiation) and developing polymorphic security mechanisms. Current projects include RF aperture security, hardware-based IoT verification systems, and digital twin implementations for power electronics with integrated trust verification.

Adrian Filipescuمشاهده پروفایل

استاد

• Control Systems
• Robotics
• Mechatronics
• +۵ مورد دیگر

Prof. Adrian Filipescu is a full professor and PhD supervisor at the Department of Automation and Electrical Engineering, Dunărea de Jos University of Galati. He holds a PhD in Control Systems (1993) and has over 40 years of academic and research experience. His expertise spans numerical calculus, adaptive control, robotics, and Industry 4.0/5.0 applications. He has authored 12 books and over 110 indexed articles with an h-index of 11 (SCOPUS), and his work earned two UEFISCDI awards (2014, 2019). Education: Engineer diploma in Automatic Control (Polytechnic University of Bucharest, 1981) and PhD in Control Systems (Dunărea de Jos University, 1993). His research focuses on mechatronics systems integration, robotic control, digital twins, and industrial automation. Notable contributions include developing a mechatronics line with integrated autonomous robots, wheelchair systems for disabled users, and multidirectional autonomous vehicles. Research Themes: Digital twin frameworks, IoT-cloud robotics control, Industry 4.0/5.0 systems, inverse kinematics modeling, and autonomous robotic navigation. His recent work emphasizes cloud-based remote control, HIL simulation, and SCADA integration. Awards: Recipient of the 2014 and 2019 UEFISCDI 'Award for Research Results' for highly commended papers. He has led 10+ research projects, endowing labs with advanced robotic infrastructure and PLC-controlled systems. Advising & Grants: Supervised 7 completed PhD theses (including 1 international co-supervision). Projects focused on robotic-assisted manufacturing, autonomous systems, and mechatronics line optimization. Current lab facilities include mobile robots, industrial manipulators, and PLC-based control systems. Labs/Teams: Director of a research lab developing Industry 5.0-ready systems, including robotic assistants for elderly/disabled users and flexible assembly/disassembly technologies. Collaborations include NATO internships at Politecnico di Torino (Italy), INPG Grenoble (France), and ISR Coimbra (Portugal).

Jo Leonardi-Beeمشاهده پروفایل

استاد

• Evidence Synthesis
• Meta-Analysis
• Systematic Reviews
• +۵ مورد دیگر

Professor Jo Leonardi-Bee is a faculty member at the University of Nottingham within the School of Medicine . As Professor of Evidence Synthesis and Co-Director of the Nottingham Centre for Evidence-Based Healthcare (JBI Centre of Excellence), her work focuses on quantitative evidence synthesis, Cochrane reviews, and clinical guideline development. Education: BSc in Mathematics & Chemistry, Nottingham Trent University MSc in Medical Statistics, University of Leicester PhD and PGCHE, University of Nottingham Her research spans tobacco control (smoking cessation, legislation impact, maternal exposure) and dermatology (skin cancer, corticosteroid safety). She specializes in meta-analysis techniques, systematic reviews, and health policy evaluation, with over 50 peer-reviewed publications. Recent publications highlight trends in respiratory health (alcohol/smoking effects), pregnancy outcomes (smoking cessation interventions), and allergy prevention (infant dietary guidelines). She serves as Statistical Editor for the British Journal of Dermatology and former Cochrane Skin Group editor. Teaching includes postgraduate modules in systematic reviews and developing reusable learning objects for evidence-based practice. Current funded projects include maternal tobacco control initiatives, familial hypercholesterolemia management, and infant feeding guidelines with the Health Technology Assessment (HTA) program.

Victor Gabriel Lopez Mejiaمشاهده پروفایل

پژوهشگر

• Control Theory
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Victor Gabriel Lopez Mejia is a postdoctoral researcher at the Institute of Automatic Control Engineering, Faculty of Electrical Engineering and Computer Science, Leibniz University Hannover. His work focuses on data-driven control systems, reinforcement learning, and multi-agent system synchronization. PhD in Electrical Engineering (2019), University of Texas at Arlington M.Sc. in Electrical Engineering (2013), CINVESTAV, Mexico B.Sc. in Telecommunications and Electronics (2010), Universidad Autonoma de Campeche Research interests include: Neural network applications in control systems Reinforcement learning for cooperative control Game-theoretic analysis of multi-agent systems Data-based modeling of nonlinear dynamics Publications highlight advancements in data-driven predictive control, Gaussian process-based estimation, and nonlinear system analysis, with recent work in IEEE Transactions on Automatic Control and conferences like CDC and ECC. He has held academic roles including Adjunct Professor at the University of Texas at Arlington and Research Assistant at UTARI (2016-2019).