Ulrik Pagh Schultz is a Professor at the University of Southern Denmark, specializing in generative programming, domain-specific languages, and embedded systems. His research focuses on automatic code generation, real-time systems, and reconfigurable robotics, with applications in autonomous drone operations and energy modeling for embedded systems. He has served as a committee member and session chair for numerous conferences including PEPM, GPCE, SPLASH, and POPL since 2011. Notable contributions include work on Idris programming language, reversible computing, and service-oriented architectures for drones. His career spans active involvement in programming language design and practical implementation challenges in robotics software engineering, particularly through collaborations at ACM SIGPLAN conferences. While specific students and awards aren't listed, his extensive service roles demonstrate significant academic engagement.
Francisco Jurado Melguizo is a Professor in the Department of Electrical Engineering at Universidad de Jaén. His research focuses on power systems, renewable energy integration, and optimization algorithms, with over 520 JCR-indexed publications and 260 conference papers. He has led projects funded by Spanish Ministries and the European Commission and authored 8 books. PhD: Universidad Nacional de Educación a Distancia (UNED), 1999 His work spans distributed generation, energy storage, smart grids, and techno-economic analysis, often incorporating AI and metaheuristic optimization techniques. Recent projects include hybrid renewable systems, EV charging infrastructure, and grid resilience under climate uncertainties. He has received recognition as one of the most influential researchers globally by Stanford University and contributes to research groups focused on electrical technology and energy systems.
Jonathan Cohen serves as the Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience at Princeton University's Princeton Neuroscience Institute, where he leads groundbreaking research on the neurobiological foundations of cognitive control and its disruption in psychiatric disorders. His work bridges computational modeling, cognitive science, and clinical neuroscience to unravel how distributed neural systems enable goal-directed behavior. Education: M.D. from the University of Pennsylvania Ph.D. from Carnegie Mellon University Cohen's research centers on cognitive control mechanisms involving prefrontal cortex, anterior cingulate cortex, basal ganglia, and neuromodulatory systems, with direct applications to schizophrenia and depression. His lab develops mechanistically explicit computational models to explain how billions of neurons coordinate purposeful behavior, seeking to establish a theoretical foundation for psychiatric research. This integrative approach combines empirical neuroimaging with rigorous mathematical frameworks to decode the neural basis of executive function. Analysis of his recent publications (2022-2025) reveals a compelling evolution toward interdisciplinary synthesis, where computational neuroscience increasingly intersects with artificial intelligence. His work now explores emergent symbolic reasoning in neural networks, cerebellar-cortical interactions in neurodevelopmental disorders, and the neural reconfiguration underlying task switching – all while maintaining clinical relevance to mental illness. Scientific Awards: Election to American Academy of Arts and Sciences APS William James Award Cohen mentors graduate students including Younes Strittmatter and directs the Neuroscience of Cognitive Control Lab, which has secured substantial funding such as a $16M Princeton-Rutgers Collaboration Grant for mental illness research. His lab actively investigates how disturbances in brain function manifest as psychiatric symptoms, with recent projects examining cognitive control allocation in depression and neural mechanisms of grief. The Neuroscience of Cognitive Control Lab employs a multidisciplinary framework combining computational modeling, fMRI, behavioral experiments, and machine learning to dissect the architecture of executive function. Current initiatives explore cerebellar contributions to cognitive aging, abstract reasoning mechanisms, and the development of theoretically grounded interventions for psychiatric disorders through collaborative neuroscience initiatives.
M. C. Frank Chang is a Distinguished Professor and Wintek Chair in Electrical Engineering at the University of California, Los Angeles (UCLA). He leads cutting-edge research in high-speed semiconductor devices and Terahertz electronics at the High Speed Electronics Lab. Department: Electrical and Computer Engineering Email: mfchang@ee.ucla.edu Website: Full publication list His research focuses on high-frequency mixed-signal semiconductor devices and CMOS integrated circuits for applications in: Radio and radar systems Space science instrumentation Edge-AI (AIoT) systems Wireless communication infrastructure Terahertz imaging and sensing Recent publications (2025) highlight trends in sub-Terahertz transceivers for 6G communication, on-chip antenna design, and advanced calibration techniques for high-frequency systems. Earlier works (2018-2022) explore AI accelerators for IoT, regenerative receivers for mm-Wave imaging, and noise-cancelling RF architectures. Scientific accolades include: 2024 AASF Asian American Pioneer Medal 2023 IEEE James Clerk Maxwell Medal 2022 IEEE Life Fellow 2017 J J Thomson Medal Multiple IEEE Best Paper Awards Honorary Doctorates from NTU and NCTU He has pioneered innovations in semiconductor device commercialization, including HBT power amplifiers for smartphones, and holds patents in advanced wireless interconnects. His work bridges fundamental research and practical applications in high-frequency electronics.
Brian J. German is the National Institute of Aerospace Langley Associate Professor in the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology. His research focuses on electric propulsion, urban air mobility (UAM), and the transformation of aviation through autonomy and distributed electric propulsion technologies. Education: Ph.D. in Aerospace Engineering, Georgia Tech (2007) M.S. in Aerospace Engineering, Georgia Tech (2000) B.S. in Aerospace Engineering, Georgia Tech (1999) German specializes in aircraft design optimization, battery modeling for electric aircraft, and operations research for emerging aviation markets. His work spans technical domains like aerodynamics, propulsion systems, and airspace utilization, with applications in UAM, thin-haul regional aviation, and cargo delivery via eVTOL aircraft. Recent research trends emphasize multidisciplinary optimization frameworks, propeller-wing interaction modeling, and economic feasibility studies for electric aircraft. He has led NASA-sponsored projects on vertiport placement, battery recharge economics, and blown wing performance analysis. Scientific Awards: NSF CAREER Award (2012) AIAA Associate Fellow (2017) Fulbright Scholarship (2002) Lockheed Martin Dean’s Excellence in Teaching Award (2012) National Defense Science and Engineering Graduate Fellowship (1999) German has advised students on eVTOL cargo delivery, blown wing wind tunnel studies, and optimization of reconfigurable aircraft. He teaches undergraduate and graduate courses on aerospace performance, design methods, and optimization. His lab conducts wind tunnel and flight tests of subscale CTOL/STOL/VTOL aircraft to validate aerodynamic and performance models.
Arijit Raychowdhury is a Professor and the Steve W. Chaddick School Chair at Georgia Tech's School of Electrical and Computer Engineering (ECE). He leads the Center for the Co-Design of Cognitive Systems (CoCoSys) and directs the DoD-sponsored SCALE Workforce Development Program in SoC Design. Current affiliations: Georgia Tech (ECE), Integrated Circuits and Systems Research Lab Prior affiliations: Intel Corporation (6 years), Texas Instruments (1.5 years) Research interests focus on low-power circuits, compute-in-memory (CIM) systems, cryogenic electronics, and neuro-symbolic AI. His work bridges circuit design with emerging device technologies and machine learning applications. Scientific contributions include: Over 250 publications and 27 patents Leadership in top conferences (ISSCC, VLSI Symposium, DAC, CICC) Pioneering 3D Gaussian Splatting acceleration, adaptive echo-cancellation networks, and CIM architectures Advancing GaN-based power converters and cryogenic CMOS Awards : IEEE Fellow (2022) SRC Technical Excellence Award (2021) Qualcomm Faculty Award (2021, 2020) IEEE/ACM Innovator under 40 (2018) Intel Young Faculty Award (2015) Dimitris N. Chorafas Award (2007) Leadership : • Director of CoCoSys Center • Site Director for SCALE Program • Distinguished Lecturer, IEEE Solid-State Circuits Society
Dr. Jonathan M. Aitken is a Senior Lecturer in Robotics at the University of Sheffield's School of Electrical and Electronic Engineering. Previously a Research Fellow at the Autonomous Control Laboratory (ACSE), his work focuses on autonomous robotic systems with emphasis on quadcopters, computer vision, spatial awareness, and multi-robot collaboration. Expert in safe autonomous drone deployment Specializes in collaborative robotics (cobots) Qualified UK commercial drone pilot Research spans: Autonomous reconfiguration of robotic systems Dynamic risk assessment for systems-of-systems Visual SLAM for feature-sparse environments Formal verification of UAS systems Human-robot co-working interfaces Recent publications examine: Robust localization in sewer pipes Visual saliency algorithms for mobile robots Cobotic safety controller synthesis Modular digital twinning frameworks Spatial awareness for multi-robot teams Major grants include EPSRC Programme Grants for buried pipe sensing and Lloyds Registry Foundation funding for cobot safety integration.
Xiumin Diao is an Associate Professor at Purdue University's School of Engineering Technology, specializing in robotics and mechatronic systems for healthcare and manufacturing applications. His work emphasizes human-robot interaction, control algorithms, and energy-efficient UAV designs. Education: Ph.D. in Mechanical Engineering (2007, New Mexico State University) M.S. in Measurement Technology and Automatic Device (2003, Beihang University) B.S. in Mechanical Design and Manufacturing (2000, Yantai University) Research focuses on cable-driven parallel robots , reinforcement learning , and intention prediction using neural networks. Recent publications explore stiffness analysis, UAV arm rotation efficiency, and hybrid control strategies. Key trends in his work include: Advancing adaptive control for robots with unknown dynamics Optimizing energy efficiency in aerial robotics Applying deep learning to human motion understanding His lab develops hardware-in-the-loop microgravity simulators and rehabilitation devices, with applications in disaster exploration and satellite docking.
Patricia Desgreys is a full professor at Institut Polytechnique de Paris , where she leads the Communication Circuits and Systems (C2S) research team within the Laboratory of Information Processing and Communication (LTCI) . Her work spans analog and mixed-signal (AMS) circuit design, cognitive radio systems, and digitally enhanced mixed-signal architectures for IoT and cyber-physical systems. Agrégation in Applied Physics, École Normale Supérieure de Cachan M.Sc. and Ph.D. in Microelectronics, University of Bordeaux (1995-1999) Her research focuses on AMS circuit design from transistor to architectural levels, including software-defined radio , cognitive radio , and neural-inspired analog-to-feature converters . She has contributed to digital predistortion techniques for power amplifiers, compressive sampling for astrophysical signals, and leadless pacemaker communication channels . Her 150+ publications highlight advancements in wireless systems, biomedical sensors, and 5G infrastructure. Recent work (2024) explores AI-driven analog design and 75 years of circuits innovation in IEEE Transactions. She has graduated 16 PhD students and co-authored the book Digitally Enhanced Mixed Signal Systems (IET, 2019). Her leadership includes Technical Program Chair roles at IEEE PRIME (2019), ICECS (2016), and NEWCAS (2012-2013), plus editorial work for IEEE TCAS-II special issues (2018-2019). She directs the ICS Master’s program (Institut Polytechnique de Paris/Paris-Saclay University) and teaches advanced electronics at SJTU-ParisTech in Shanghai . Her patents include signal sampling circuits and power amplifier linearization techniques.
Chao Qian, M.Sc., is a researcher and PhD student at the Department of Embedded Systems, University of Duisburg-Essen, since April 2020. He holds a bachelor's degree in Electrical Engineering from the University of Electronic Science and Technology of China (2015) and a master's degree in Embedded Systems from the University of Duisburg-Essen (2020). His research focuses on enabling artificial intelligence (AI) in reconfigurable hardware like FPGAs for energy-efficient and high-performance embedded systems. He investigates methods to deploy pre-trained neural networks on FPGAs, targeting traditionally resource-constrained embedded systems. Recent publications highlight his work on Configuration-aware FPGA power management Quantized transformers for time-series forecasting Soft sensor design for fluid flow estimation LSTM acceleration on embedded FPGAs In projects like "KI-Sprung: LUTNet" and "Elastic AI", he contributes to adaptive machine learning in pervasive computing. Contact: Email: chao.qian@uni-due.de Phone: +49 203 379 2390 Address: Bismarckstr. 90 (BC), Room BC 104, Duisburg
Armin Babaei is a Ph.D. researcher at the University of Duisburg-Essen (Uni DUE) within the Department of Computer Science, focusing on hardware security solutions for IoT devices. He holds an M.Sc. in Communications Engineering from RWTH Aachen and previously served as CTO in a startup for two years. Education: M.Sc. in Communications Engineering (RWTH Aachen) since 2015 His research integrates Physical Unclonable Functions (PUFs) with FPGA technology to develop secure, low-power authentication mechanisms for IoT systems. Publications highlight innovations in reconfigurable security architectures and spatial PUF implementations. Current research interests emphasize balancing security requirements with computational constraints in embedded systems. Key themes include: Hardware security for resource-constrained devices PUF-based authentication protocols Energy-efficient cryptographic solutions Contact: ababaei2000@gmail.com
Nagi N. Mekhiel is a Professor in the Department of Electrical and Computer Engineering at Toronto Metropolitan University (formerly Ryerson University), where he teaches courses including Digital Systems, Microprocessors, Advanced Computer Architecture, and Parallel Computing. His office is located in the George Vari Engineering and Computing Centre at 245 Church St., Toronto. Dr. Mekhiel received his B.Sc. in Electrical Engineering from Assiut University, Egypt (1973), M.A.Sc. from University of Toronto (1981), and Ph.D. in Computer Engineering from McMaster University (1995). Prior to academia, he worked as a Biomedical Engineer at Toronto's Hospital for Sick Children (1981-1987), Senior Hardware Engineer at Definicon Systems Corporation (1987-1990), and Senior Member of Technical Staff at Yarc System Corporation (1996-1998). His research focuses on solving fundamental computer industry challenges, particularly the processor/memory speed gap and scalability of parallel processors. His work spans computer architecture, parallel processing, high-performance memory systems, VLSI, and performance evaluation. Mekhiel's research has been cited in patents by major tech companies including IBM, Google, Apple, Intel, Microsoft, and others. Analysis of his recent publications reveals a strong trajectory toward quantum computing applications, orbital data processing architectures, and memory system innovations for big data applications. His work consistently addresses the processor/memory speed gap through novel architectural solutions, with increasing emphasis on quantum information representation and parallel time computing models. Intel Xeon+FPGA system access via Intel's Hardware Accelerator Research Program Senior Member of IEEE Dr. Mekhiel has supervised numerous graduate students who have co-authored publications with him in areas including facial recognition systems, quantum computing implementations, and parallel processing architectures. His research has received industry support through patent licensing agreements with Intellectual Ventures US. He leads a research group focused on advanced computer architecture solutions, with current projects involving quantum bit encoding, reconfigurable memory systems, and orbital network architectures for scalable multi-core processing.
Daniel Chillet is a Professor in the Electronics Department at the National School of Applied Sciences and Technology (ENSSAT) , part of the University of Rennes 1. He has held significant administrative roles, including Director of Studies at ENSSAT from 2012 to 2015 and scientific leadership in international collaborations such as the USTH master’s program in Hanoi. Education: Not explicitly detailed in the provided text. Research Interests focus on reconfigurable architectures, real-time scheduling, and energy-efficient embedded systems. His work addresses dynamic reconfiguration for memory hierarchy optimization, energy modeling of FPGAs, and spatio-temporal task scheduling in 3D heterogeneous multi-core systems. He has pioneered the integration of optical networks in chip-level communication and developed neural network-based scheduling mechanisms. Teaching Activities include digital electronics, VHDL language, processor architecture, and real-time operating systems. He has developed pedagogical tools (e.g., JSimVEM, JSimRISC) to illustrate advanced RISC techniques and real-time methodologies like SART. Responsibilities span academic leadership roles at ENSSAT, national committee memberships (CNU Section 61), and international coordination of the USTH master’s program in Hanoi. He chairs workshops like RAPIDO and participates in organizing conferences such as DASIP and PATMOS. Research Projects include collaborations with Thomson CSF, ANR-funded initiatives (Open-People, FosFor), and CNRS-supported efforts. His work involves system-level exploration of reconfigurable architectures, fault management, and optical interconnects for 3D MPSoC. Advising: Supervised 12 PhD theses (e.g., Hai Khuat, Robin Bonamy) and mentored numerous master’s students (e.g., Rim Abid, Gia-Tam Phan). Conference Participation: Member of program committees for DCIS, RAW, ARC, GRETSI, and others. Organizer and General Chair for RAPIDO workshops (2018–2021).
Günhan Dündar is a Professor in the Department of Electrical and Electronics Engineering at Bogazici University. His research specializes in analog/mixed-signal integrated circuit design and computer-aided tools for VLSI systems. Key contributions include aging-robust circuit design automation, low-power CMOS architectures, and security primitives like physically unclonable functions (PUFs). The Dündar Lab explores reconfigurable systems for reliability enhancement in nanoscale technologies. Collaborations address hardware vulnerabilities in IoT devices and optimization of ring oscillator networks for cryptographic applications. Teaching emphasizes design automation, semiconductor physics, and hardware implementation methodologies.
Heather Ligler is an Assistant Professor & Foundations Coordinator at the School of Architecture , Florida Atlantic University. Her work bridges computational and formal methods in architecture, particularly through shape grammars — visual algorithms enabling geometric rule-sets for design logic, criticism, and transformation. Education: Ph.D. & M.S. in Design Computation (Georgia Tech), B.Arch & B.Interior Arch (Auburn University) Her research focuses on shape computation to formalize design narratives, critique architectural history, and innovate future practices. She explores applications in adaptive reuse , refugee housing , and virtual reality . Recent publications address structural wall layouts in historic buildings, tile generation systems, and refugee shelter transformations. These align with interests in design automation , heritage conservation , and algorithmic aesthetics . Scientific Awards President’s Fellowship at Georgia Tech Hambidge Center for the Creative Arts & Sciences Fellowship Her work is supported by grants from the National Science Foundation , General Services Administration , and Stuckeman Center for Design Computing . She has taught at Penn State and Georgia Tech, and contributed to the Shape Machine software and CourtsWeb database .
