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Lihong ZhangView profile

Professor

• VLSI Design Automation
• MEMS Design and Optimization
• Analog and Mixed-Signal Circuit Design
• +7 more

Dr. Lihong Zhang is a Full Professor in the Department of Electrical and Computer Engineering at Memorial University of Newfoundland. He holds a PhD in Electrical Engineering from Otto-von-Guericke University (Germany) and has held post-doctoral positions at Concordia University, Dalhousie University, and the University of Washington. His research focuses on VLSI design automation, analog/mixed-signal circuits, MEMS, energy harvesting, and biomedical instrumentation. Dr. Zhang leads the CADLAMS lab, supported by NSERC, CFI, and industry partners. Education: B.E. (H.U.S.T. Wuhan), M.Sc. (Eng.) (H.U.S.T. Wuhan), Ph.D. (Otto-von-Guericke University, Germany). Research Interests: VLSI design automation, MEMS design, renewable energy harvesting, microfluidics, wireless sensor networks, and EDA tools. His work addresses challenges in nanometer-scale technologies, including layout-dependent effects and manufacturability. Awards: 2008 CFI Leaders Opportunity Fund, 2016 Memorial University Research Excellence Award. Grants include funding from NSERC, CFI, and industry collaborations. Students and Collaborations: Supervises over 50 graduate students and collaborates with industry partners like Cadence, Synopsys, and IBM. Active in editorial roles for IEEE journals and conference organization. Lab Infrastructure: CADLAMS lab includes advanced computing resources, EDA software, and lab equipment for circuit prototyping and testing.

Mats Erling HøvinView profile

Associate Professor

• Generative Design
• CAD
• CAM
• +6 more

Mats Erling Høvin is an Associate Professor at the University of Oslo's Department of Informatics, affiliated with the Robotics and Intelligent Systems (ROBIN) research group. His academic work focuses on generative design, CAD/CAM systems, rapid prototyping, and evolutionary robotics. Previously, he conducted significant research in Delta-Sigma noise shaping for analog-to-digital converters. Research Interests: Generative design methodologies for manufacturing Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems Rapid prototyping and additive manufacturing techniques Evolutionary algorithms applied to robotics and optimization problems Delta-Sigma modulation for analog/digital signal conversion Robotic locomotion and control systems His publications demonstrate a consistent focus on optimization techniques applied to robotics, signal processing, and manufacturing systems. Recent work explores physics-based simulations for medical robotics and generative design for structural optimization. Earlier contributions established foundations in evolutionary robotics, motion capture filtering, and analog circuit design. Høvin teaches courses including IN5590 and IN1080, and leads research in the ROBIN group focusing on intelligent systems development. His work bridges theoretical computer science with practical engineering applications in robotics and manufacturing.

Jürgen ScheibleView profile

Professor

• Electronic Design Automation
• Analog Integrated Circuit Design
• Automation of Circuit and Layout Design
• +5 more

Jürgen Scheible is a Professor at the Faculty of Technology, Reutlingen University, specializing in Electronic Design Automation (EDA). He leads the Electronics & Drives Lab and the Reutlingen Energy Center (REZ), focusing on analog integrated circuit design automation, machine learning integration, and power electronics methodologies. His career spans academic and industrial roles, including 18 years at Robert Bosch GmbH before joining Reutlingen University in 2010. 1987: Dipl.-Ing. in Electrical Engineering, Karlsruhe Institute of Technology (KIT) 1991: Dr.-Ing. in Electrical Engineering, KIT His research bridges procedural automation with artificial intelligence in analog IC design, addressing challenges in performance optimization, constraint handling, and thermal analysis of bond wires. Key projects include SWARM (self-organizing layout automation) and Bond Calculator (thermoelectrical simulation tool). Recent publications emphasize differentiable surrogate models, reinforcement learning applications, and knowledge-based methodologies in EDA. He received the Südwestmetall Award for Scientific Talent in 1992 and has authored 2+ textbooks on analog layout fundamentals. 2024: Keynote on analog/digital design divergence 2023: Co-authored foundational EDA textbook 2022: Reinforcement learning integration Labs and teams under his leadership conduct cutting-edge research in MEMS sensor parasitics, hybrid layout techniques, and full-custom IC design. His work establishes systematic frameworks for next-generation EDA tools.

Tarek M. TahaView profile

Professor

• Artificial Intelligence
• Neuromorphic Computing
• High Performance Computing
• +4 more

Dr. Tarek M. Taha is a Professor in the Department of Electrical and Computer Engineering at the University of Dayton. He leads the Parallel Cognitive Systems Laboratory, focusing on AI algorithms, neuromorphic computing, and high-performance systems. His research spans deep learning applications (e.g., medical imaging, cybersecurity), memristor-based architectures, and FPGA acceleration. Education: Ph.D., M.S., and B.S. in Electrical Engineering from Georgia Institute of Technology (2002, 1998, 1996), and a B.A. in Pre-Engineering from DePauw University (1996). Active collaborations include AFRL, NSA, Sandia National Labs, and multiple universities. Research Interests: Neuromorphic computing, memristor circuits, AI hardware acceleration, parallel algorithms, and cybersecurity. Key contributions include neuromorphic spiking processors (e.g., Intel Loihi), memristor device modeling, and low-power neuromorphic systems for real-time applications. Awards: NSF CAREER Award (2007), IEEE Dayton Bio-Engineering Award (2018), and multiple best-paper recognitions. Over 150 publications in top-tier journals/conferences. Advising: Supervised over 30 graduate students (listed in full description) and collaborates on grants from AFOSR, NSA, and industry. Labs include the Parallel Cognitive Systems Lab with state-of-the-art neuromorphic hardware and memristor fabrication capabilities.

• Electronic design automation
• Embedded systems
• Energy efficient electronic systems
• +16 more

Daniele Jahier Pagliari is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is also a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center. He is actively involved in teaching and research, focusing on embedded systems, electronic design automation, and machine learning for edge computing. He teaches courses such as Optimized Execution of Neural Networks at the Edge, Machine Learning for IoT, and Hardware/Software Codesign of Flexible Computing Systems for Edge AI across various engineering programs including Computer Science and Systems Engineering, Data Science, and Automotive Engineering. His research interests span electronic design automation, embedded systems, energy-efficient computing, low-power design, and machine learning. He is particularly engaged in applying machine learning techniques to improve the design and performance of digital and analog circuits, with a focus on edge AI applications. His work aligns with key scientific areas including computer architecture, cyber-physical systems, and scientific computing. The recent publications highlight a strong trend in optimizing deep learning models for resource-constrained environments, accelerating neural network inference on ultra-low-power devices, and integrating physics-based models with AI for battery state estimation. There is also significant focus on using machine learning to enhance electronic design automation, particularly for analog and mixed-signal circuits, reflecting a convergence of AI and hardware design. He leads and participates in several high-impact research projects, including EU-funded initiatives like HAL4SDV, ISOLDE, TRISTAN, and AMBEATion, as well as commercial projects such as MASAI and software platform development for production support. He serves as the Scientific Responsible or Director in multiple projects, demonstrating leadership in both academic and industrial research contexts. He supervises multiple PhD students in the Computer and Systems Engineering program, including Luca Benfenati, Mohamed Amine Hamdi, Beatrice Alessandra Motetti, Giovanni Pollo, and Matteo Risso, whose research topics include latency-optimized inference, compiler optimization for edge devices, and hardware-aware deep learning design. He is also involved in patent development, notably for an instrumentation method to dynamically modify circuit precision. He is a member of the College of Computer, Film and Mechatronics Engineering and contributes to various degree programs. His work supports UN Sustainable Development Goals related to good health, affordable and clean energy, industry innovation, and sustainable cities.

Andreas KrinkeView profile

Researcher

• Analog Circuit Design
• Mixed-Signal Integrated Circuits
• Constraint Propagation
• +3 more

Andreas Krinke is a Researcher at the Institute of Microengineering and Electronic Design, TU Dresden, specializing in constraint-guided design of integrated analog circuits. He actively participates in BMBF-funded projects including HyPerStripes, MANNHEIM-KI4BoardNet, and Technology for Trust, focusing on electronic design automation and reliability engineering. His research centers on analog/mixed-signal IC design with emphasis on constraint propagation, physical design automation, and reliability analysis. Key contributions include methodologies for constraint handling from system-level to tape-out, open-source EDA tool development, and applications in flexible electronics and automotive systems. His work bridges theoretical constraints with practical implementation challenges in nanoscale and automotive electronics. Publication trends since 2011 reveal consistent focus on constraint propagation techniques, evolving toward flexible electronics (2024), open-source layout verification (2024), and automotive mission profile analysis (2021-2022). Recent work integrates AI for trustworthy systems while maintaining core expertise in constraint-driven design flows for analog circuits. Krinke teaches Fundamentals of Electrical Engineering seminars and design automation exercises. His project portfolio includes: HyPerStripes, MANNHEIM-KI4BoardNet, Technology for Trust (BMBF) ELDA-MP (BMWi) autoSWIFT, RESCAR 2.0 (BMBF) These initiatives target automotive electronics, design automation, and trustworthy computing systems. He collaborates within TU Dresden's Institute of Microengineering and Electronic Design, working closely with Prof. J. Lienig's research group on constraint-driven methodologies and open-source EDA ecosystems for next-generation circuit design.

Thaddeus RoppelView profile

Associate Professor

• Robotics
• Sensor Fusion
• Artificial Intelligence
• +7 more

Thaddeus Roppel is an Associate Professor in the Department of Electrical and Computer Engineering at Auburn University’s College of Engineering. He conducts research in robotics, sensor fusion, artificial intelligence, and MEMS sensors, and actively mentors graduate students in the Sensor Fusion Laboratory. He teaches courses in electronics, IC fabrication, wireless design, and advanced mobile robotics, and contributes to outreach programs including K-12 robotics and Engineers Without Borders. Ph.D., M.S., B.S. in Electrical Engineering — Michigan State University Dr. Roppel’s research focuses on integrating data from heterogeneous sensors to enhance robotic perception and decision-making. His work spans cooperative robotics, neural network-based odor classification, and real-time sensor fusion inspired by biological systems. He emphasizes hands-on learning and innovation in both research and teaching. His recent scholarly output centers on intelligent systems, including neural processing of sensor data, robotics for space and agriculture, and educational technologies. These works reflect a strong interdisciplinary focus on AI, embedded systems, and practical engineering solutions for real-world challenges. Recurrent neural networks for odor recognition Cooperative and mobile robotics Technology Readiness Level (TRL) frameworks in academic projects MEMS and sensor integration Capstone design and international collaborations Educational tools for electronics and simulation Dr. Roppel has advised multiple graduate students, including Ph.D. and Master’s candidates, and leads the Sensor Fusion Laboratory at Auburn. He has secured academic and project-based funding for student research in robotics and sensor systems. His outreach includes organizing robotics competitions and promoting engineering education through digital and physical initiatives. The Sensor Fusion Laboratory, under his direction, explores advanced integration of sensor data for applications in environmental monitoring, defense, and autonomous systems. The lab supports graduate research, senior design projects, and collaborative initiatives with national and international institutions.

Dinesh K BhatiaView profile

Professor

• Reconfigurable Computing
• Medical Devices and Systems
• Power Conversion Methods
• +4 more

**Dinesh K. Bhatia** is a **Professor and Department Head** of Electrical and Computer Engineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds joint professorships in Computer Science and Bioengineering. His research focuses on **Reconfigurable Computing**, **Medical Devices**, and **Domain-Specific Architectures for AI/High-Performance Computing**. Dr. Bhatia leads the **IDEA Lab**, pioneering advancements in FPGA-based systems, power conversion, and healthcare technologies. Notably, he co-founded **Cirasys Corporation**, a startup funded by NSF and Texas Emerging Technology Fund, commercializing breakthroughs in power electronics. **Education**: PhD (1990), M.S. (1987), and B.E. (1985) in Electrical Engineering from UTD and NIT Suratkal, India. **Research Interests**: Reconfigurable computing, medical electronics (e.g., wearable sensors for sleep apnea, bladder monitoring), AI-driven medical systems, and energy-efficient embedded systems. He has led projects like **REACT** (DARPA-funded adaptive computing) and **RACE** (reconfigurable hardware-software environments). **Awards**: IEEE CAS Society Distinguished Lecturer (2007–2008), Senior Member of IEEE/ACM, Fellow of Phi Kappa Phi. His work spans over 100 peer-reviewed publications and 15+ patents. **Grants & Funding**: NSF, UT System, Texas Emerging Technology Fund, and AFRL grants. He has advised numerous graduate students and led initiatives in graduate education and industry partnerships. **Labs/Teams**: IDEA Lab (core research hub), collaborations with industry (e.g., Texas Instruments) and healthcare institutions (e.g., UT Southwestern).

Ricardo Miguel Ferreira MartinsView profile

Assistant Professor

• Analog Integrated Circuit Design Automation
• Machine Learning in EDA
• Ultra-Low Power Circuits
• +3 more

Ricardo Miguel Ferreira Martins is an Assistant Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), University of Lisbon. His research focuses on analog/mixed-signal integrated circuit design automation, leveraging machine learning for layout optimization, sizing, and post-layout performance prediction. He leads multiple projects funded by institutions like FCT and Sony, emphasizing AI-driven EDA solutions for 5G/6G and biomedical applications. Education: Doctoral Degree in Electrical and Computer Engineering (IST/UL) Research Interests: Deep learning for analog IC placement and routing Ultra-low power biomedical circuits RF/mmWave IC design automation Yield-aware synthesis methodologies Article Trends: Recent work emphasizes integrating machine learning into EDA workflows, particularly for layout-aware optimization , human-AI collaboration , and autonomous design tools . Key contributions include PACOSYT (passive component synthesis) and DeepPlacer (AI-driven placement). Grants & Projects: ACTON Project (2025-2026): 5G/6G mmWave IC design via deep learning GENERALISE Project (2023-2026): Generative AI for analog chip design Student Supervision: Mentors 21+ graduate students in AI-driven circuit design and EDA automation. Ongoing works explore generative AI for layout generation, reinforcement learning in design space exploration, and cloud-based EDA tools. Conference Roles: Holds leadership positions in major EDA conferences like SMACD and DATE, serving as Technical Program Chair and Awards Chair.

Margit MakóView profile

Lecturer

• Electronics Engineering
• Embedded Systems
• Circuit Design
• +3 more

Margit Makó is a Lecturer at Óbuda University, affiliated with the BUDAi Road Campus (8000 Székesfehérvár, Budai út 45). Her research focuses on electronics engineering, embedded systems, and circuit design, with a particular emphasis on Field-Programmable Analog Arrays (FPAA), control systems, and educational technology. She has published extensively on topics including analog circuit robustness, sensorless motor control, and hybrid feedback circuits. Her work often bridges theoretical concepts with practical applications, such as developing educational measurement laboratories and optimizing pneumatic cylinder control systems. Recent contributions include innovative FPAA-based solutions for circuit testers and hybrid systems. While her primary expertise lies in electronics, she has also explored interdisciplinary areas like mathematical modeling of germination kinetics and comparative pedagogy analysis between Hungary and France. Makó’s articles reflect a strong trend toward real-time systems, fault-tolerant design, and reconfigurable hardware. Her research highlights the integration of microcontrollers with FPAA for enhanced system performance. No scientific awards are explicitly mentioned in the provided materials. Consultation hours are held weekly on Mondays from 10:40 to 11:25 in Room 24 of Building K at the BUDAi Campus. She can be reached at mako.margit@amk.uni-obuda.hu for academic inquiries.

• Analog IC Design
• RF Circuits
• Machine Learning Applications
• +2 more

Nuno Lourenço is a researcher at Instituto de Telecomunicações, University of Lisbon specializing in analog/RF integrated circuit design. His work focuses on machine learning approaches for circuit optimization and CMOS-based energy harvesting systems. Recent publications develop neural network models for analog IC placement, millimeter-wave system synthesis, and ultra-low-power circuits for wearable devices. Collaborative projects include radiation-hardened designs for space applications and ANN-based modeling of transformers. He co-authored the book 'Analog IC Placement Generation via Neural Networks' and serves on technical committees for IEEE CAS and SMACD conferences.

• Operations Strategy
• Supply Chain Strategy
• Sustainable Operations
• +4 more

Lieven Demeester is an Associate Professor of Operations Management (Practice) at the Lee Kong Chian School of Business (LKCSB), Singapore Management University. His academic journey includes a Ph.D. in Operations and Technology Management from the University of California, Los Angeles (UCLA Anderson School of Management) in 1995 and a Diploma in Electrical Engineering from the University of Gent, Belgium in 1990. His research interests focus on operations strategy, sustainable operations, supply chain strategy, and sustainable business innovation. Notable contributions include studies on recycling networks, operations executives' strategic roles, and age-diverse innovation teams. Demeester has held leadership roles including Associate Dean of MBA Programs (2015–2017), Academic Director for MBA/EMBA programs (2014–2017), and Area Coordinator for Operations Management (2011–2015). Awards: Dean's Teaching Honour List (2009), Richard A. Freund Scholarship (1994) Key Research Themes: Sustainable logistics, circular economy, technology adoption in operations, and strategic leadership Recent Work: Explores AI applications in education and maritime transport optimization in recycling networks His publications span over three decades, addressing topics from semiconductor manufacturing cycle times (1996) to modern AI-driven educational systems. He remains active in shaping strategic priorities at LKCSB through teaching and executive education programs.

Manfred DietrichView profile

Researcher

• Design automation
• IC design
• System design
• +3 more

Manfred Dietrich is a Researcher at TU Dresden's Institute of Precision Engineering and Electronic Design, actively leading EU projects including HyPerStripes (2022-2025) and KI4BoardNet (2022-2025). His work focuses on design automation, IC design, and system design for advanced semiconductor integration. His educational background includes: 1970-1974: Dipl.-Ing in Information Technology, TU Dresden 1981: Promotion (PhD) on 'Fault localization in analog circuits' Dr. Dietrich's research centers on 3D/2.5D integration challenges, particularly interposer-based memory interfaces, thermal management of stacked systems, and Wide IO implementations. His methodologies address critical bottlenecks in signal integrity, power delivery, and design verification for next-generation semiconductor packaging. Publications from 2014-2016 demonstrate consistent focus on thermal optimization of 3D systems and XML-based design rule checking, establishing foundational work for current EU projects targeting high-performance computing architectures. Scientific awards: None documented. With extensive grant leadership including ECSEL's Microprince project (2017-2020) and current EU supervision roles, his portfolio reflects significant funding acquisition expertise. No student advisees are listed in available records. He actively contributes to industry standards through memberships in VDE's ITG/GMM groups, the GI/VDE 'Computer-aided circuit and system design' cooperation, and as an ECSEL Strategic Research Group expert.

Sara AchourView profile

Assistant Professor

• Programming Languages
• Compilers
• Runtime Systems
• +2 more

Sara Achour is an Assistant Professor jointly appointed to the Computer Science and Electrical Engineering departments at Stanford University. She earned her PhD in Computer Science from MIT in 2021. Her research develops programming languages, compilers, and runtime systems for emerging analog computing platforms. Dr. Achour leads research in hardware-aware optimization frameworks and novel analog compute paradigms, with applications spanning quantum computing, IoT devices, and neuromorphic systems. Her research focuses on: Bridging software abstractions with unconventional hardware capabilities Energy-efficient computing paradigms for edge devices Cross-layer optimization of analog and hybrid computing systems Publication analysis reveals consistent themes in analog computing architectures, hardware-aware optimizations, and emerging computing platforms. Recent work explores quantum compilation techniques, hyperdimensional computing optimizations, and hardware security metrics. Dr. Achour advises multiple graduate students including: 16 doctoral candidates across computer architecture and quantum computing 10 master's students in software-hardware co-design She maintains active research collaborations through the Stanford SystemX Alliance.

Ying XuView profile

Adjunct Associate Professor

• Architectural Engineering
• Building Energy and Environments
• Emissions of SVOCs from polymeric materials and their interaction with airborne particles and interior surfaces

Dr. Ying Xu is an Adjunct Associate Professor at the University of Texas at Austin, specializing in architectural engineering, building energy systems, and the environmental impact of materials. Her technical focus includes emissions from polymeric materials and their interactions with indoor environments. She holds a Ph.D. (Virginia Tech, 2009), M.E. (Tsinghua University, 2004), and B.E. (Tsinghua University, 2001). Research interests span sustainable building technologies, indoor air quality, and the mitigation of SVOC emissions. Her work also explores advanced analog IC design methodologies, including automation tools like MAGICAL and neural network-guided routing systems. She has contributed to innovations in low-power sensor circuits, VCO-based architectures, and optical logic synthesis. Publications emphasize automation in circuit layout, energy-efficient designs, and hybrid system architectures. These contributions reflect expertise in both environmental engineering and cutting-edge electronics research.