Matteo Sonza Reorda is a Full Professor at the Polytechnic University of Turin, affiliated with the Department of Automatic Control and Computer Science (DAUIN). He holds roles such as Partnership Agreement Coordinator with SMAT and has served as Vice-Rector for Research (2021–2024). His research focuses on fault tolerance, hardware reliability, and AI acceleration, with contributions to GPU/CNN reliability, self-test libraries, and defect-oriented testing. Education: M.S. in Electronics Engineering, Politecnico di Torino (1986) Ph.D. in Computer Engineering, Politecnico di Torino (1990) Research Interests: His work spans automatic test equipment, circuit reliability, GPU fault tolerance, neural network hardening, and embedded systems safety . He leads the Electronic CAD & Reliability Group and collaborates with institutions like the ISI Foundation and the National Research Council (CNR). Publications & Impact: With over 180 publications, his recent work emphasizes AI accelerator reliability, fault injection techniques, and safety-critical systems. Key trends include GPU resilience for CNNs, self-test library optimization, and radiation fault modeling in neural networks. Awards & Recognition: IEEE Fellow (2016–) Multiple best-paper awards at IEEE conferences (DDECS, DATE, etc.) Grants & Collaborations: Coordinator of EU projects (RESCUE, TUTORIAL) Partnerships with EDF, Marelli Europe, and SMAT Lead on National HPC/Quantum Computing Spoke 1 (2022–2025) Labs/Teams: Leads the CAD Group and collaborates with the French-Italian LIA LAFISI lab on hardware-software integration.
Anand V Natarajan is the ITT Career Development Professor in Computer Technology and Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, with affiliation to the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds a PhD in Physics from MIT (2018), advised by Aram Harrow, and previously served as a postdoc at Caltech's Institute for Quantum Information and Matter. His research focuses on theoretical quantum information, emphasizing quantum complexity theory (e.g., MIP* and QMA(2) classes), nonlocality (Bell inequalities, nonlocal games), and semidefinite programming hierarchies. He teaches courses such as Quantum Cryptography, Quantum Systems Engineering, and Introduction to Algorithms at MIT. Notable contributions include resolving the MIP* = RE problem and the FOCS 2019 Best Paper Award for NEEXP ⊆ MIP*. He advises PhD students in quantum computing topics and collaborates extensively on quantum verification protocols and entanglement testing.
Dr. Saqib Khursheed is an Assistant Professor in the Department of Electrical Engineering and Electronics at the University of Liverpool, UK. He holds a PhD in Electronics and Electrical Engineering from the University of Southampton (2010), where he later worked as a Senior Research Fellow on EPSRC-funded projects. His research focuses on reliability, testability, and hardware security of low-power/high-performance systems and 3D integrated circuits. He has served in leadership roles at major conferences such as IEEE DFT (General Co-Chair 2018) and ETS (Program Co-Chair 2017). Dr. Khursheed is a Senior Member of IEEE and Fellow of the Higher Education Academy. His professional activities include roles as Guest Editor for IEEE Design & Test (2016) and IET Computers & Digital Techniques (2018). He currently chairs the Examinations Officer committee in his department and serves on multiple university-level committees (Senate Progress, Quality Assurance). He has organized workshops like the Friday Workshop on 3D Integration (2012-2015) and reviews for top-tier journals/conferences in his field. Dr. Khursheed’s funded projects include the eFutures Sandpit Award (2018) for secure microelectronics design. His teaching responsibilities include coordinating modules like Advanced Low Power Computer Architecture (ELEC470) and Digital Electronics & Microprocessor Systems (ELEC211). His research has led to innovations in hardware security (e.g., PCB Trojan detection via machine learning), age estimation of ICs, and fault tolerance in 3D ICs.
Dr. Karthika Mohan is an Assistant Professor of Computer Science in the College of Engineering at Oregon State University, affiliated with the School of Electrical Engineering and Computer Science. Her research bridges artificial intelligence and causal inference, focusing on graphical models, missing data, and non-IID data challenges. Her work has been recognized with the Google Outstanding Graduate Research Award. She serves as an associate editor for the Journal of Causal Inference and has secured NSF funding for research on incomplete data. Dr. Mohan mentors students in causal inference methods and maintains collaborations with institutions like UC Berkeley and UCLA. Her laboratory develops innovative approaches for causal reasoning in AI systems.
Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.
Georges Gielen is Full Professor in the Department of Electrical Engineering (ESAT) at KU Leuven, Belgium, and part-time Research Director at imec. He has held multiple leadership roles including Chair of ESAT Department (2012-2013, 2020-2024) and Vice-Rector for Science, Engineering & Technology (2013-2017). His academic career spans over 30 years at KU Leuven, progressing from Assistant to Full Professor. His research focuses on analog and mixed-signal integrated circuit design automation , with expertise in CAD tools, design optimization, sensor interfaces, and neuromorphic systems. His work bridges hardware design with machine learning, particularly in hardware-efficient AI implementations and biomedical applications. He has pioneered techniques for automated analog circuit sizing, topology synthesis, and reliability-aware design in nanometer CMOS. Gielen has received numerous accolades including the IEEE CAS Mac Van Valkenburg Award (2015), IEEE CAS Charles Desoer Award (2020), and EDAA Achievement Award (2021). He holds an ERC Advanced Grant AnalogCreate and is an IEEE Fellow since 2002. As a prolific scholar, he has chaired major conferences including DATE (2006), ICCAD (2007), and ESSCIRC (2017). He has graduated over 55 PhD students through the MICAS research group at KU Leuven, currently supervising 13 doctoral candidates. His research team collaborates extensively with imec and industry partners on cutting-edge projects in carbon-aware AI accelerators, uncertainty-aware design, and neuromorphic sensor interfaces.
Irith Pomeranz is the Cadence Professor of Electrical and Computer Engineering at Purdue University's College of Engineering. Her research focuses on advanced testing methodologies for VLSI circuits, including functional test compaction, fault diagnosis, and built-in self-test (BIST) techniques. She is affiliated with the Department of Electrical and Computer Engineering and has contributed extensively to improving test efficiency and fault coverage in digital circuits. Her work addresses challenges such as aging effects, transition faults, and path delay faults, with a particular emphasis on practical implementations for industrial applications. Key areas of interest include modular test sequences, configuration-based compaction, and dynamic testing strategies for in-field environments. She has developed algorithms for dual-target diagnostic testing and synchronization mechanisms for online fault detection in logic blocks. Research Trends in her publications emphasize innovations like storage-based BIST schemes, adaptive test scheduling, and shared test data architectures. These advancements aim to reduce test data volume, improve fault coverage, and enhance reliability in modern integrated circuits. Her work often bridges theoretical foundations and practical hardware implementations. Grants & Advising : While specific grants or student advisees are not listed, her prolific publication record indicates active involvement in research projects and graduate supervision within Purdue's ECE department. Labs & Teams : Her contributions are likely tied to Purdue's VLSI and testing research groups, though specific lab affiliations are not detailed in the provided text.
Marta Halina is a University Associate Professor in the Philosophy of Cognitive Science at the University of Cambridge, affiliated with the Department of History and Philosophy of Science. She serves as a Senior Research Fellow at the Leverhulme Centre for the Future of Intelligence and is a Fellow of Selwyn College. Her academic journey began with a PhD in Philosophy and Science Studies from the University of California, San Diego in 2013, followed by a McDonnell Postdoctoral Fellowship in the Philosophy-Neuroscience-Psychology Program at Washington University in St. Louis before joining Cambridge in 2014. Halina's educational background includes a PhD from UC San Diego (2013) and postdoctoral training at Washington University in St. Louis. Her academic trajectory reflects a strong interdisciplinary foundation bridging philosophy, cognitive science, and neuroscience. Her research focuses on nonhuman animal cognition, mechanistic explanation, and artificial intelligence, with particular emphasis on comparative cognition and the philosophical foundations of cognitive science. Halina investigates how researchers design studies to address complex questions about animal minds, arguing that current methods in comparative cognition often face challenges with hypothesis underdetermination by empirical evidence. She advocates for additional behavioral constraints on theorizing, known as 'signature testing,' while emphasizing the need to incorporate neuroscience and biology more substantially into animal cognition research. Her work on major transitions in cognitive evolution proposes treating the evolution of cognition as a series of major evolutionary transitions to better comprehend cognitive complexity across species. Analysis of Halina's recent publications reveals a clear trajectory toward computational comparative cognition. Her work increasingly integrates AI and machine learning techniques with traditional comparative cognition approaches, exemplified by her development of the Animal-AI Testbed. This platform allows for direct comparison between AI systems, humans, and animals on cognitive tasks, revealing that while AI and children perform similarly on basic navigational tasks, children outperform AI on more complex cognitive tests requiring object permanence. Her research demonstrates how computational modeling can generate novel hypotheses about animal behavior that generate precise, testable predictions beyond what traditional experimental methods alone can achieve. McDonnell Postdoctoral Fellowship Halina directs research initiatives at the Leverhulme Centre for the Future of Intelligence, particularly focusing on the intersection of AI and animal cognition. Her work on the Animal-AI Environment has received significant funding and collaborative support, enabling interdisciplinary research that bridges computer science, cognitive science, and biology. She actively collaborates with researchers across multiple institutions to develop computational frameworks for understanding nonhuman animal cognition. Halina leads significant research initiatives through the Leverhulme Centre for the Future of Intelligence, where she develops the Animal-AI Environment—a research platform for conducting cognitive experiments with artificial agents, humans, and nonhuman animals in directly comparable, ecologically valid contexts. This environment facilitates interdisciplinary collaboration between computer scientists, engineers, biologists, and cognitive scientists, reducing the 'language barrier' between these fields and enabling cross-pollination of ideas and methodologies.
Professor Jacob Savir is a distinguished academic in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology . He specializes in testability, built-in self-test (BIST), and fault detection for digital and analog circuits. His work focuses on improving integrated circuit testing methodologies, including BIST pretesting, defect level analysis, and reliability optimization in safety-critical systems. His research interests span BIST architectures, delay fault analysis, analog circuit testing, and power-constrained test synthesis . He has contributed extensively to the development of testing algorithms for embedded memories, scan designs, and fault diagnosis in complex systems. Notable contributions include studies on the impact of BIST pretesting on IC defect levels and yield optimization. His work bridges theoretical advancements with practical applications in aerospace and industrial electronics. Despite no awards listed, his substantial citation count (1,897) and h-index (22) reflect his influential contributions to the field. Prof. Savir has collaborated on 114 research outputs since 1977, with a focus on enhancing circuit testability and reliability across domains like FPGA-based systems and embedded memory diagnostics. His research emphasizes both academic rigor and industrial relevance, addressing challenges in modern VLSI design and testing.
Chr. Kavousianos is an Assistant Professor in the Department of Informatics at the University of Ioannina, Greece. He is actively engaged in research and teaching in the fields of VLSI design, testability, and low-power testing. He has been involved in major national and international research programs such as Heracleitus II, Pythagoras, and NSF-SRC (USA). Research Interests: His research focuses on advanced techniques in built-in self-test (BIST), test data compression, scan-based testing, fault tolerance, and embedded control architectures. He explores methods to reduce test data volume, power consumption during testing, and improve defect coverage in integrated circuits. Publication Trends: His recent publications (2004–2011) show a strong trend toward defect-aware testing, power-efficient test compression, and multicore SoC testing. He frequently collaborates with leading researchers, including Prof. Krishnendu Chakrabarty (Duke University). His work on multilevel Huffman coding and reseeding techniques has been highly influential, with one paper among the most accessed in 2004. Special Distinction for Excellent Academic Performance from TEE, 1996 Doctoral Scholarship 'In Memory of Professor Maritsa', 1999 Postdoctoral Scholarship from IKY, 2002 Advising and Grants: He has supervised multiple postdoctoral researchers, PhD candidates (e.g., Vasilis Tenentes, Emmanouil Kalligeros), and master’s students. He has led research projects such as 'Embedded Control Architectures' (Heracleitus II) and 'Design Techniques for Embedded Self-Control Circuits' (Pythagoras II). His international collaboration with Duke University included a postdoctoral research role in 2009. Labs and Teams: He leads a research group at the University of Ioannina with postdocs, PhD students, and visiting professors, including Prof. Krishnendu Chakrabarty. His team works on cutting-edge VLSI testing and design methodologies.
Professor Gordon Hush serves as Head of the Innovation School and has led the Product Design department at the Glasgow School of Art since 2007. He is a sociologist specializing in how design and innovation drive social change, emphasizing tangible, testable, and desirable outcomes through collaborative design practices. His work integrates Service Design, Citizenship, and Environmental Design with academic programs like the Master of Research in Design Innovation & Citizenship. Collaborations include the University of Glasgow for International Management initiatives. Research focuses on the societal impact of design innovations, particularly through projects like establishing the new School of Innovation and Technology. His academic contributions bridge theoretical sociology with practical design methodologies to address contemporary challenges.
Krishnendu Chakrabarty is the John Cocke Distinguished Professor of Engineering and Professor of Computer Science at Duke University, chairing the Department of Electrical and Computer Engineering. His research focuses on integrated circuits testing, microfluidics, biochips, and cyberphysical systems. He holds honorary titles from institutions globally and has received over a dozen best-paper awards. Education: B.Tech from IIT Kharagpur (1990), M.S.E. and Ph.D. from University of Michigan (1992, 1995). Awards include Humboldt Research Award (2013), IEEE Computer Society Technical Achievement Award (2015), and AAAS Fellowship (2018). Research explores fault diagnosis in hardware, smart manufacturing, and biochip automation. His cyberphysical systems work includes hybrid microfluidic platforms for single-cell analysis and droplet-based bioassays. Collaborations with TUM-IAS focus on Microfluidic Design Automation (MDA). Editor-in-Chief of IEEE Transactions on VLSI Systems and former Editor of ACM Journal on Emerging Technologies. Active in academic leadership and interdisciplinary projects, emphasizing sustainability and advanced manufacturing.
Professor Vincent Beroulle at Grenoble Institute of Technology's Esisar campus leads the LCIS laboratory, focusing on security and safety of complex integrated circuits and systems. His work bridges fault modeling, fault injection, and security for IoT and RFID technologies. Education: Engineer Degree (INPG, 1996), Master (University of Montpellier II, 1999), Ph.D. (University of Montpellier II, 2002) Current Role: Director of LCIS Laboratory, Professor in Embedded System Security & Safety Research spans hardware security, fault injection attacks, and vulnerability analysis of embedded systems. Recent work includes clock glitch fault injection studies, lightweight ECC countermeasures for RFID, and aging effects on PUF architectures. His team explores cross-layer methodologies to enhance fault models and security protocols. Supervision includes 12 current and former PhD students tackling topics like secure RFID tags, fault injection countermeasures, and PUF-based security. Projects under ANR, PEPR Cyber, and ERASMUS+ funding drive his lab's innovation in secure embedded design.
Lei Wang is the F. L. Castleman Associate Professor in Engineering Innovation at the University of Connecticut's School of Engineering, Department of Electrical and Computer Engineering. He holds a PhD from the University of Illinois at Urbana-Champaign (2001), an MS (1996) and BS (1992) from Tsinghua University, China. His research focuses on cyber-physical systems , embedded computing with renewable energy , and nanoscale integrated circuit design . Key areas include microbial fuel cells , memristor-based hardware security , and low-power signal processing architectures . Recent work trends involve Quantum-dot transistor applications for in-memory computing Adaptive LDPC decoder optimization Hardware security leveraging memristor properties Energy-efficient power management systems for underwater sensors Scientific recognition includes National Science Foundation CAREER Award (2010) F. L. Castleman Term Professorship in Engineering Innovation Professional roles encompass editorial and committee positions at IEEE and ACM journals. His work spans interdisciplinary domains in renewable energy integration , VLSI design , and bio-inspired computing systems .
Shubhangi Saraf is an Associate Professor in the Department of Computer Science and Mathematics at the University of Toronto. Previously, she held faculty positions at Rutgers University and was a postdoctoral researcher at the Institute for Advanced Study in Princeton. She earned her Ph.D. in EECS from MIT under the supervision of Madhu Sudan. Her research focuses on theoretical computer science and discrete mathematics, with an emphasis on complexity theory, algebraic computation, error-correcting codes, and discrete geometry. Her work has been supported by prestigious grants including the Sloan Research Fellowship, NSF CAREER Award, and Simons Collaboration on Algorithms and Geometry. Teaching highlights include courses such as Computational Complexity and Computability , Algebraic Complexity Theory , and Introduction to Combinatorics . She has advised current students like Deepanshu Kush and Devansh Shringi, and past students including Mrinal Kumar and Ben Lund. Awards and honors include recognition for her contributions to coding theory and complexity analysis. Her research frequently bridges algebraic techniques with computational challenges, yielding impactful results in areas like polynomial identity testing and arithmetic circuit lower bounds. She has led multiple academic initiatives, including special topics courses exploring algebraic gems in theoretical computer science and discrete mathematics. Her interdisciplinary work intersects with cryptography, combinatorics, and algorithm design, reflecting her broad scholarly contributions.
