Trond Ytterdal is a Professor at the Department of Electronics and Telecommunications, Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. His research focuses on analog integrated circuits, mixed-signal systems, and nanoscale transistor modeling for THz applications. Fields of Interest: Analog circuit design, THz technology, and semiconductor device modeling. Affiliation: Centre for Innovative Ultrasound Solutions (CIUS), NTNU. Research Trends: Over the past five years, Ytterdal’s work has centered on ultra-low-voltage circuits (e.g., sub-100mV designs), THz spectrometers using TeraFET and GNRFET technologies, and noise/power optimization in nanoscale devices. His publications also explore memristor crossbar arrays, Josephson junction integration, and energy-efficient memory systems. Scientific Recognition: Senior Member of IEEE Member of The Norwegian Academy of Technological Sciences Collaborations: Regularly co-authors with researchers at Rensselaer Polytechnic Institute and industry experts in semiconductor modeling.
Felix Kaule is a Laboratory Engineer at the Institute for Development-Oriented Mechanical Engineering , Faculty of Engineering, HTWK Leipzig. He works under the supervision of Prof. Dr.-Ing. Anke Bucher and Prof. Dr.-Ing. Stephan Schönfelder, focusing on mechanical strength analysis of silicon wafers and solar cells. Current research in PV production technologies Project manager for BMWi-funded NextTec project Expertise in finite element modeling and experimental validation His work bridges computational simulation with practical testing in laboratories like bikelab , where mechanical properties of components are evaluated. Recent publications highlight innovations in half-cell module manufacturing, thermal laser separation, and microcrack mitigation in photovoltaic materials. Scientific contributions include: Optimizing diamond wire sawing processes Analyzing residual stress effects in solar cells Advancing kerfless cutting technologies Improving mechanical strength through plasma etching
Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments
Dr. Amir Koushyar Ziabari is a Senior R&D Staff Data Scientist at Oak Ridge National Laboratory (ORNL), working in the Multimodal Sensor Analytics group under the Electrification and Energy Infrastructure Division. His career spans advanced research in physics-informed computational imaging, signal processing, and machine learning applications for scientific imaging. Education: PhD in Electrical and Computer Engineering, Purdue University (2012-2016) MS in Electrical and Computer Engineering, University of California Santa Cruz (2009-2012) MS in Electrical and Computer Engineering, Sharif University of Technology (2006-2008) BS in Electrical and Computer Engineering, Amirkabir University of Technology (2001-2005) Dr. Ziabari's research focuses on "data science for science", combining data-driven and physics-based methodologies to develop computational imaging and machine learning algorithms. His work addresses image reconstruction, segmentation, and classification challenges across domains like advanced manufacturing, medical imaging, nuclear materials, and materials science. Key innovations include the SIMURGH software for X-ray CT reconstruction and diffusiveINR for energy-efficient foundation models. Recent publication trends show expertise in multi-modal imaging for additive manufacturing, thermal transport analysis at nanoscale, and physics-informed neural networks. His scientific awards include the R&D 100 Finalist, IEEE Senior Member (2022), IEEE Computational Imaging Technical Committee Member (2023), and multiple best paper recognitions. He has secured $3.5M+ in funding as PI/Co-PI and holds patents in tomographic reconstruction and thermal imaging. Dr. Ziabari contributes to professional societies (IEEE, ASTM, OSA), organizes symposia on additive manufacturing imaging, and mentors postdocs to build inclusive research environments. His collaborations with ZEISS, INL, and NIST demonstrate his ability to bridge academic research with industrial applications.
Jan-Frederik Pietschmann is a Professor for Inverse Problems at the University of Augsburg's Faculty of Mathematics, Natural Sciences and Technology. He previously held the same position at TU Chemnitz (2018–2023) and served as a Research Associate at institutions including the University of Münster and TU Darmstadt. University of Cambridge (PhD, Mathematics, 2008–2011) University of Münster (Diploma, Mathematics and Physics, 2008) Habilitation in Mathematics, University of Münster, 2018 His research focuses on the analysis of nonlinear PDEs , gradient flows , and parameter identification in PDEs . Recent work explores applications to cell invasion , pedestrian dynamics , neurite growth , and organic semiconductors . Publications highlight his expertise in optimal transport , cross-diffusion systems , and uncertainty quantification . Notable trends in his 15 most recent articles include Nonlinear PDEs for biological and physical systems Optimal transport on networks and metric graphs Computational methods for inverse problems His current team includes Gianna Götzmann, Carmen Tretmans, and Team Assistant Juliane Krautz. Contact details: jan-f.pietschmann@uni-a.de , +49 821 598-3926.
Joyce Poon is a Professor in the Electrical and Computer Engineering Department at the University of Toronto , with affiliations as Director of the Max Planck Institute for Microstructure Physics and Honorary Professor at the Technical University of Berlin. Her research focuses on integrated photonic devices for communications and neurotechnology , leveraging silicon photonics for applications in visible light systems and neural interfacing. Education: PhD and M.S. in Electrical Engineering from Caltech (2007, 2003); BASc in Engineering Science (Physics) from the University of Toronto (2002) Her work spans visible-light silicon photonics , optical phased arrays , and implantable neural probes , with recent advancements in 3D-printed scaffolds for neural tissue engineering and thermally tunable photonic devices. She has pioneered programmable photonic circuits and hybrid integration techniques for high-efficiency systems. Key trends in her publications include visible-light silicon nitride waveguides , MEMS-based optical switching , and neurophotonic probes for deep brain optogenetics. Collaborative efforts extend to AI-assisted photonic design and biomedical applications of photonic integrated circuits. Scientific Honors : IEEE Fellow (2022) Fellow of Optica (2018) Mit TR35 (2012) Canada Research Chair in Integrated Photonic Devices (Tier 2, 2012–present) Milton and Francis Clauser Doctoral Thesis Prize (Caltech, 2007) She contributes to professional communities through editorial roles (e.g., Optics Express) and leadership in conferences like OFC and IEEE Group IV Photonics. Her lab develops nanophotonic neural probes for brain imaging and stimulation, and she is affiliated with the Krembil Research Institute (University Health Network).
Massimo Messori is a Full Professor at the Department of Applied Science and Technology (DISAT) of Politecnico di Torino. He serves as Coordinator of the Doctoral School in Materials Science and Technology and Deputy Coordinator of the College of Chemical and Materials Engineering. His affiliations include membership in the Interdepartmental Center IAM@PoliTo and the National Interuniversity Consortium for Materials Science and Technology (INSTM). Laurea in Industrial Chemistry (1992), University of Bologna Ph.D. in Industrial Chemistry (1997), University of Bologna Research Assistant (1999-2002), University of Bologna Associate Professor (2002-2017), University of Modena and Reggio Emilia Full Professor (2017-2021), University of Modena and Reggio Emilia Full Professor (2021-present), Politecnico di Torino Messori's research focuses on polymer synthesis, modification, and characterization with emphasis on additive manufacturing and sustainable materials. His current work includes photopolymerizable resins for 3D/4D printing and valorization of agro-industrial wastes as biopolymer additives. He leads projects like FIBRA (sustainable brake pads) and MUSTANG (polymer sustainability). His article trends show expertise in photopolymerization , bio-based composites , shape-memory polymers , environmental remediation materials , and sustainable manufacturing . He actively supervises PhD students in these domains. He co-founded spinoffs MAT3D (special resins) and Agromateriae (bio-composites from waste). His teaching spans Materials Engineering, Additive Manufacturing, and Sustainable Design.
Chang-Yong Nam is a Senior Scientist and Group Leader of the Electronic Nanomaterials Group at Brookhaven National Laboratory's Center for Functional Nanomaterials. He also holds Adjunct Professor positions at Stony Brook University (Department of Materials Science and Chemical Engineering) and University of Texas at Dallas (Department of Materials Science and Engineering). His research focuses on semiconductor materials processing, atomic layer deposition, and hybrid organic-inorganic materials for microelectronics and energy applications. Ph.D. in Materials Science and Engineering (University of Pennsylvania, 2007) M.S. in Materials Science and Engineering (KAIST, 2001) B.E. in Metallurgical Engineering (Korea University, 1999) Research interests include advanced patterning technologies for next-generation microelectronics, EUV lithography, and low-dimensional semiconductor devices. His work bridges fundamental material science with practical applications in nanotechnology and energy conversion systems. Scientific achievements include the DOE Accelerate Initiative Award (2023), Battelle Inventor of the Year recognition (2022), and multiple Spotlight Awards from Brookhaven National Laboratory. He has secured significant funding for projects like the Angstrom Era Semiconductor Patterning Material Development Accelerator. As an inventor of multiple patents and author of over 100 scientific publications, his work has direct implications for semiconductor manufacturing innovation. He actively mentors graduate students and contributes to international scientific collaborations, including projects like the Electron-Ion Collider and Lunar Surface Electromagnetics Experiment-Night.
Ramesh Harjani is the Edgar F. Johnson Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. He maintains an active research program with numerous ongoing projects and publications, and is currently accepting PhD students. His office is located in Kenneth H. Keller Hall on the University of Minnesota campus in Minneapolis. Dr. Harjani received his educational training at prestigious institutions globally: B.Tech in Electrical and Electronics Engineering from Birla Institute of Technology, Pilani, India (1982) MTech in Electrical Engineering from Indian Institute of Technology, New Delhi (1984) PhD in Electrical and Computer Engineering from Carnegie Mellon University (1989) Professor Harjani's research focuses on analog and RF circuit design for communication systems with particular emphasis on CMOS technologies. His work spans both wireless and wired communication domains, with current efforts directed toward high-performance front-end components for broadband and multi-standard wireless systems, as well as next-generation high-speed wired communication systems. His research group also investigates specialized areas including data converters, sensor interface circuits, analog circuit synthesis, and micro-power analog design. Recent publication trends show continued innovation in analog circuit design methodologies, with emphasis on improving performance metrics while addressing modern semiconductor challenges like FinFET self-heating effects. His work bridges theoretical circuit design with practical implementation challenges in advanced technology nodes, particularly 65nm CMOS processes. The research demonstrates strong industry relevance with applications in wireless communications, signal processing, and high-speed data conversion. Professor Harjani leads multiple significant research projects: Scalable Ultra-Low Variation Analog-Time Neural Network (S-UATN) Accelerator (2025-2025) Passive Tile Architecture for MAX (2023-2024) ALIGN Analog Layout Design Suite (2023-2024) Automated Layout Of Analog Arrays In Advanced Technology Nodes (2022-2024) ALIGN: Analog Layout, Intelligently Generated from Netlists (2018-2023) He maintains the Harjani Research Group, which collaborates extensively with industry partners including BAE SYSTEMS, NORTHWESTERN UNIVERSITY, SPAWAR, and THE NATIONAL SCIENCE FOUNDATION, demonstrating strong connections between academic research and practical engineering applications.
Chris Kim serves as a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota's College of Science and Engineering. He holds the prestigious McKnight Presidential Endowed Chair and was named a Distinguished McKnight University Professor in 2022, one of the highest honors at the university. His research focuses on designing energy-efficient, robust, and intelligent integrated circuits and systems with expertise in building chips and collaborating across materials, devices, algorithms, systems, and signal processing. Professor Kim's research interests span quantum-inspired computing, cryogenic computing, machine learning hardware, neuromorphic computing, hardware security, internet-of-things, medical devices, sensor networks, and radiation hardened chips. His group has transferred key technologies to the semiconductor industry, including silicon odometer circuits for measuring circuit wear out, radiation monitoring circuits, and SPICE models for magnetic tunnel junctions. Analysis of Professor Kim's recent publications reveals a strong trend toward quantum-inspired computing and combinatorial optimization using coupled oscillator-based Ising chips. His work bridges theoretical computer science with practical circuit implementation, with significant contributions in electromigration characterization, aging sensors, and compute-in-memory architectures. The research demonstrates a clear trajectory from fundamental circuit design to application-specific implementations for real-world problems. Intel Outstanding Researcher Award (2024) for contributions to coupled oscillator based Ising chip research Semiconductor Research Corporation (SRC) Sustainable Future award (2024) for quantum-inspired computing chips McKnight Presidential Endowed Chair (2024) Distinguished McKnight University Professor (2022) Louis John Schnell Professor in Electrical and Computer Engineering (2021) Professor Kim actively mentors numerous Ph.D. and Master's students, with over 50 graduates who now work at leading technology companies including Intel, Apple, Samsung, and NVIDIA. His research is supported by significant grants from the National Science Foundation, Semiconductor Research Corporation, Samsung Electronics, and the Department of Defense. Current projects include electromigration lifetime characterization, energy-efficient circuits for cryogenic operation, characterization of single event effects in DRAM chips, and development of CMOS oscillator-based Ising computers. The VLSI Research Group at the University of Minnesota, led by Professor Kim, focuses on developing core circuit technologies for smart and energy-efficient integrated systems. The group has produced notable achievements including a 48-spin all-to-all connected Ising solver chip published in Nature Electronics (featured on the cover), and a quantum-inspired Ising chip with nearly 2,000 coupled ring oscillators. The group maintains strong industry connections and has transferred multiple technologies to semiconductor companies.
Jongse Park is currently an Associate Professor at the School of Computing (SoC), KAIST , and a core member of the Computer Architecture and Systems Laboratory (CASYS) . He holds co-affiliations with the School of Electrical Engineering , Graduate School of AI Semiconductor , Graduate School of System Architect , and Department of Semiconductor System Engineering at KAIST. Since 2025, he has been serving as a Visiting Associate Professor at Stanford University's Pervasive Parallelism Lab within the EECS department. PhD in Computer Science, Georgia Institute of Technology (2018), advised by Prof. Hadi Esmaeilzadeh MS in Computer Science, KAIST (2012), advised by Prof. Seungryoul Maeng BS in Computer Science and Engineering, Sogang University (2010) His research focuses on accelerating AI serving systems , enabling on-device AI , processing-in-memory (PIM) architectures , and flexible AI compiler frameworks . Recent work explores transformer optimization, heterogeneous AI semiconductors, and efficient video-language processing. Recent publications include MICRO 2025 work on PIM for LLMs, VLDB 2025 research on video-language engines, and ISCA 2025 contributions to LLM quantization. His team's projects have received funding from the K-Cloud Project , NRF Young Researcher Program , and IITP Core Technology Development grants . Teaching Innovation Award Excellence Prize, KAIST (2025) Samsung Humantech Paper Award Gold Prize (2025) IEEE Senior Member (2024) Best Paper & Distinguished Artifact Awards at IISWC (2024) and ISCA (2024) He actively supervises PhD and MS students in AI systems research and serves on program committees for top conferences like ASPLOS , ISCA , and MICRO , including organizing roles as Sponsorship Chair for MICRO 2025.
Eric Giacomo Armando is a Full Professor at the Department of Energy (DENERG) within the College of Electrical and Energy Engineering at Politecnico di Torino. He serves as a member of the Power Electronics Innovation Center (PEIC) and leads the PEEMD research group. Research Interests: Electrical Machines Power Electronics Electric Drives Energy Conversion Sustainable Motor Drive Systems Wide Bandgap Semiconductors Recent Publications focus on GaN converter technologies, flux control algorithms, thermal management via 3D-printed heatsinks, and fault tolerance in multi-phase motors. His work appears in IEEE ECCE, APEC, and industry applications journals. Supervised Students: Stefano Savio (PhD, ongoing, researching GaN multilevel inverters for motorsport) Enrico Vico (completed PhD, focusing on overcurrent protection for GaN semiconductors) Projects: Includes EU-funded E-motors, PNRR SEMDY, and commercial contracts with HBM Italia Srl and Servotecnica SpA. He holds multiple national/international patents in traction inverters and battery chargers.
Eckhard Hennig is a Professor in the Department of Electronics & Drives at Reutlingen University, specializing in digital circuit design, low-power systems, and power electronics. His work bridges academic research and industrial application, with a focus on robust multisensor systems and technical origami for engineering. Education: Dipl.-Ing. in Electrical Engineering (1994, TU Braunschweig), Dr.-Ing. (2000, TU Kaiserslautern) Professional History: Researcher at University of Kaiserslautern (1995), Fraunhofer ITWM (1996–2000), Infineon Technologies AG (2000–2008), IMMS Institute (2008–2015) His research interests include low-power CMOS technology , delta-sigma modulation , and heterogeneous system modeling , with applications in energy-efficient systems and sensor networks. Recent publications emphasize adaptive modulation techniques , GaN-based power devices , and temporal decoupling for circuit stability. He leads projects like RoMulus (robust multisensor systems) and ZEBRA (technical origami) and contributes to interdisciplinary initiatives in hearing diagnostics and energy systems. His teaching includes courses on digital electronics, mixed-signal circuit design , and system modeling , with hands-on labs in FPGA/ASIC design and GaN device optimization. He works in the Digital Circuit Technology Lab, focusing on verification, heterogeneous simulation, and industry 4.0 sensor systems.
Ferenc Simon is a full professor and deputy director at the Institute of Physics, Faculty of Natural Sciences, Budapest University of Technology and Economics (BME). He also holds a venia docendi (Privat-dozent) position at the University of Vienna. He leads the MTA-BME Spintronics Research Group (PROSPIN) and was PI on the ERC Starting Grant “SYLO”. Education & Qualifications PhD (Hungarian Academy of Sciences) Doctor of the Hungarian Academy of Sciences Habilitation at TU Budapest Habilitation (venia docendi) at Universität Wien Research Interests Professor Simon’s work spans spintronics , carbon-based nanostructures (nanotubes, graphene, fullerene peapods), superconductivity , and low-dimensional quantum systems . He employs electron spin resonance, NMR, optical and microwave spectroscopy, and isotope engineering to explore spin relaxation, Tomonaga–Luttinger liquids, and high-frequency response of superconductors. Recent projects extend into biomedical applications such as magnetic nanoparticle hyperthermia. Scientific Output & Trends His publication record (hundreds of papers, book chapters, reviews) is dominated by Physical Review Letters and Scientific Reports articles that collectively advance the understanding of spin and electronic correlations in carbon nanostructures, superconducting anisotropy, and advanced instrumentation. The 2017–2018 burst of papers on microwave absorption in superconductors and non-calorimetric hyperthermia power measurements signifies an expansion toward applied physics and medical physics. Awards & Honors MTA Talentum Award (2006) Editorial Board Member, Scientific Reports (since 2017) Editor, Fizikai Szemle (since 2018) Referee for Nature, PRL, and >10 other journals Students, Grants & Labs Simon has supervised ~85 BSc, MSc and PhD students (listed explicitly on his homepage). Current PhD students include L. Szolnoki, B. Gyüre, B. Márkus, I. Gresits, and others. He leads the PROSPIN group (MTA-BME Spintronics Research Group) and participates in the EnsembleQC consortium under Hungary’s Quantum Technology National Excellence Program.
Lisandra Flach is Professor of Economics, especially Economics of Globalization, at Ludwig Maximilian University of Munich and Head of the ifo Center for International Economics. She previously served as Temporary Academic Councillor at LMU (2014-2020) and held postdoctoral positions at institutions including the University of California, San Diego. PhD in Economics, University of Mannheim (2012) Economics Degree, Federal University of Santa Catarina (2006) Business Administration Degree, Santa Catarina State University (2006) Her research focuses on international trade, globalization economics, and trade policy. She examines global value chains, corporate taxation, multi-product exporters, and geopolitical trade challenges. Recent projects analyze EU services market integration, semiconductor industry economics, and raw material substitution. Her publications show expertise in trade gravity models, quality upgrading, WTO/GATT impacts, and supply chain resilience. Key trends include analyzing US trade policies' effects on Germany, EU-China dependencies, and Brexit implications. Capital Top 40 unter 40 (2022, 2021) Handelsblatt 100 Frauen, die Deutschland bewegen (2021) Modigliani Research Grant (2018) DFG Research Grant (2016-2018) Brazilian Prize in Economics (2007) Flach serves on editorial boards (Journal of Comparative Economics) and advisory committees (RIEF Network). She advises on EU trade policy and German economic resilience through media commentary and institutional reports.
