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Associate Professor Dario Poletti is the Head of the Science, Mathematics, and Technology (SMT) Cluster at Singapore University of Technology and Design (SUTD). A key contributor to SUTD’s pedagogy since 2012, he leads research and graduate programs in the SMT cluster. His work focuses on complex quantum systems, including quantum transport, thermodynamics, chaos, computing, and machine learning applications. He has over 100 publications in prestigious journals like Nature and Physical Review Letters. Education: PhD in Physics (joint NUS/Australian National University, 2010), Master of Engineering (Nuclear Engineering, Politecnico di Milano), and Master of Engineering (Applied Physics, École Centrale Paris). Research interests span theoretical and computational physics, with emphasis on quantum phenomena such as transport in open systems, thermalization, and algorithmic approaches to quantum problems. His work bridges quantum physics and machine learning for predictive modeling and system characterization. Awards: Asia Pacific MIT Technology Review Innovators under 35 (2015) Institute of Physics Outstanding Referee Award (2017) Medal of Material Research Society of Singapore (Best PhD Thesis, 2010) Advising and Grants: Despite no listed advisees, his research group drives cutting-edge projects in quantum computing and simulation. Collaborations focus on developing scalable algorithms and quantum devices. His work aligns with NISQ-era computing challenges and experimental implementations. Labs/Teams: Active in SUTD’s SMT cluster, contributing to interdisciplinary research on quantum technologies, energy-efficient computing, and theoretical frameworks for open quantum systems.

• Quantum Physics
• Quantum Transport
• Quantum Thermodynamics
• +4 more

Snorre Aunet is a Professor II in the Department of Informatics at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. He is a key member of the Nanoelectronics Systems Research Group and contributes to the 4DSpace project. His academic background includes a Dr. ing. from NTNU (2002) and a cand. scient. from the University of Oslo (1993). His research focuses on ultra-low-voltage and low-power mixed-signal circuits , particularly in subthreshold operation, with applications in IoT, space systems, and energy harvesting. He specializes in radiation-hardened (radhard) and defect-tolerant architectures, leveraging advanced semiconductor technologies like 22nm and 28nm FDSOI. His work integrates circuit design, microarchitecture, and reliability under extreme conditions such as radiation exposure and process variation. The recent trend in his publications shows a strong emphasis on energy-efficient VLSI design , including SRAM cells, adders, level shifters, and flip-flops operating in subthreshold regimes. He has also expanded into MEMS-based energy harvesting and hardware security, demonstrating a broad and evolving research portfolio. His collaborations span institutions and involve both theoretical modeling and practical implementation. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Snorre Aunet has supervised multiple students, including Somayeh Hossein Zadeh, Even Låte, and Ali Asghar Vatanjou, whose names appear as co-authors on several publications. He is involved in the 4DSpace research project, indicating active grant funding and leadership in space-related electronics research. His advising style appears collaborative, with students contributing to high-impact journals and conferences. Labs and Teams: He is a core member of the Nanoelectronics (NANO) research group at the Department of Informatics, which focuses on next-generation electronic systems. This group is engaged in cutting-edge research in nanoscale circuits, low-power design, and robust computing architectures.

• Ultra-low-voltage circuits
• Low-power CMOS design
• Subthreshold electronics
• +9 more

Professor Mingsheng Ying (University of Technology Sydney) is a Distinguished Professor specializing in quantum programming , quantum verification , and the foundations of artificial intelligence . He leads the Centre for Quantum Software and Information and co-founded the Quantum Lab . His work bridges quantum computation with formal methods and reasoning under uncertainty. Education : Mathematics, Fuzhou Teachers College (1981) Research Interests His research spans: Quantum programming languages and verification techniques Model checking quantum systems and cryptographic protocols Quantum machine learning robustness Entanglement theory and distributed quantum computation Recent Publications Key contributions include: Quantum error correction verification frameworks Quantum register machine architecture Hamiltonian simulation parallelization Symbolic execution for quantum debugging Robustness tools like VeriQR Awards & Editorial Roles NSF China Distinguished Young Scholar Award (1997) China National Science Award (2008) Co-Editor-in-Chief, ACM Transactions on Quantum Computing Vice President, International Fuzzy Systems Association (2005) Leadership & Grants He oversees 14 active grants (2010–2029) from: Australian Research Council (ARC Discovery Projects) National Natural Science Foundation of China Sydney Quantum Academy Baidu Contract Research His grants fund research in quantum program verification, entanglement classification, and distributed quantum protocols.

• Quantum Computation
• Quantum Programming
• Formal Methods
• +6 more

Giovanni Del Galdo is a full Professor at the Technische Universität Ilmenau, where he leads the Electronic Measurement Technology and Signal Processing (EMS) group within the Faculty of Electrical Engineering and Information Technology. He also maintains an affiliation with the Fraunhofer Institute for Integrated Circuits IIS. Since 2012, he has directed a research group comprising both a Fraunhofer department and a university chair, which expanded to approximately 60 staff members following a 2016 merger with another research group. Del Galdo received his Laurea degree in Telecommunications Engineering from Politecnico di Milano in 2002 and his Dr.-Ing. from TU Ilmenau in 2007 under Prof. Martin Haardt, with a dissertation titled "Geometry-based Channel Modeling for Multi-User MIMO Systems and Applications." Prior to his current position, he worked as a Senior Scientist at Fraunhofer IIS and was a member of the International Audio Laboratories Erlangen, where he focused on audio watermarking and spatial sound representation. His research spans multidimensional signal processing, wireless communications, and advanced measurement techniques. He specializes in channel modeling, MIMO systems, Over-The-Air testing, and high-resolution parameter estimation methods including compressed sensing. His work addresses challenges from sub-THz frequencies to practical industrial applications. Analysis of his recent publications reveals strong focus on wireless channel characterization, particularly for 5G/6G and V2X applications. His research integrates theoretical signal processing with practical measurement systems, often developing novel hardware implementations for real-world testing scenarios. Key application areas include automotive communications, industrial measurement systems, and next-generation wireless technologies. Del Galdo actively supervises numerous research projects and has extensive experience with laboratory testbed development for wireless systems validation. His group maintains advanced facilities for channel sounding, OTA testing, and signal processing algorithm development, supporting both academic research and industry collaboration.

• Wireless Communications
• Signal Processing
• Channel Modeling
• +6 more

Chun-Wei Yao serves as Associate Professor in the Department of Mechanical Engineering at Lamar University, directing the Advanced Materials and Surface Engineering Lab since 2021. His academic journey includes prior positions as Assistant Professor at Lamar University (2015-2022) and Georgia Southern University (2014-2015), plus research roles at Texas A&M University and the National Renewable Energy Laboratory. Education Ph.D. in Mechanical Engineering, Texas A&M University, College Station M.S. Engineering Science, National Taiwan University Research Focus Dr. Yao's work centers on surface engineering and nanotechnology , with specialized expertise in superhydrophobic coatings , nanomechanical characterization , and microdroplet dynamics . His research bridges fundamental material science with practical applications in corrosion resistance, water harvesting systems, and bio-inspired design through advanced nano/microfabrication techniques. Publication Trends Analysis of his 15 most recent publications (2020-2025) reveals three dominant research trajectories: (1) Nanomechanical properties of superhydrophobic coatings under extreme conditions, (2) Droplet dynamics on engineered surfaces for fluid management applications, and (3) Sustainable materials development for civil infrastructure. These works consistently integrate experimental characterization with practical engineering solutions. Scientific Recognition NASA Artemis Educator Award (2025) for aerospace education contributions Lamar University Merit Award (2022) Research Enhancement Grant (2018) George Bush Presidential Library Foundation Grant (2014) Mentorship and Funding Dr. Yao actively mentors undergraduate and doctoral researchers through his Advanced Materials Lab, with eight documented advisees since 2018. His research program is supported by competitive grants from NASA, NSF (where he serves as invited panelist), and Lamar University's internal funding mechanisms, focusing on surface engineering innovations with real-world industrial applications. Research Infrastructure As Director of the Advanced Materials and Surface Engineering Lab, he maintains high-end instrumentation for material characterization while facilitating collaborations with industry partners and educational outreach through lab tours and hands-on workshops for K-12 students.

• Surface Engineering
• Nanotechnology
• Materials Science
• +4 more

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University, with a courtesy appointment in Electrical and Computer Engineering. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Gdansk University of Technology (1980 and 1970, respectively). His career includes visiting roles at institutions such as McGill University, Stanford, and INRIA. He directs the NSF-funded Center for Science of Information, focusing on extending information theory to modern data challenges. Szpankowski is an IEEE Fellow, Erskine Fellow, and recipient of the Humboldt Research Award (2010). His research spans algorithms, information theory, bioinformatics, and analytic combinatorics. Key contributions include work on data compression, network analysis, and quantum computing. He authored *Average Case Analysis of Algorithms on Sequences* (2001) and co-launched the interdisciplinary Institute for Science of Information (2008). His recent work addresses fundamental limits in machine learning, quantum algorithms, and privacy-preserving systems. Research Interests: Bioinformatics, algorithm analysis, quantum computing, graph theory, and information-theoretic limits of learning. His work often bridges theory and applications, such as analyzing biological networks and developing efficient compression methods for dynamic data. Awards: IEEE Fellow Erskine Fellowship Humboldt Research Award (2010) Grants & Labs: Director of the NSF Science & Technology Center for Science of Information. Active in interdisciplinary projects, including temporal network analysis and quantum information theory.

• Bioinformatics and Computational Biology
• Theory of Computing
• Algorithms
• +5 more

Jinzhao Sun is a Lecturer (Assistant Professor) at Queen Mary University of London (QMUL) and holds a Schmidt AI in Science Fellowship at the University of Oxford. He is affiliated with the School of Physical and Chemical Sciences and the Department of Physics and Astronomy at QMUL. His research focuses on quantum computing, quantum information, and quantum many-body physics, with particular emphasis on quantum algorithms, quantum simulation, quantum error mitigation, and applications in computational chemistry and materials science. Education and Career: Sun completed his PhD at the University of Oxford under Vlatko Vedral and Andrew Boothroyd, following undergraduate studies at Peking University. He conducted postdoctoral research at Imperial College London, the University of Cambridge, and University College London (UCL). He has held roles including Associate Research Fellowship at Reuben College Oxford and Visiting Scholarships at Wolfson and Jesus Colleges, Oxford. Research Interests: His work explores quantum computing’s potential to solve classically intractable problems, such as quantum many-body systems, and develops algorithms for eigenstate problems, Hamiltonian simulation, and error mitigation. He also investigates quantum computational chemistry, quantum spectroscopy, and foundational questions in quantum information theory. Publications: Sun has authored over 30 papers in top journals like Nature Communications , Nature Physics , and Physical Review Letters , focusing on quantum algorithms, error mitigation, and applications in materials science. Notable contributions include perturbative quantum simulation, adaptive product formulas, and hybrid quantum-classical computing frameworks. Service and Collaboration: He serves as a referee for leading journals ( Nature , Physical Review , Quantum ) and conferences (QIP, TQC). He actively mentors PhD and postdoctoral researchers, offering supervision in quantum computing and related fields. His group at QMUL welcomes applications for fellowships and collaborative projects. Labs and Teams: Sun leads a research group at QMUL, collaborating with institutions like the University of Oxford and industrial partners. His work bridges theoretical quantum computing with experimental implementations, particularly in superconducting qubits and quantum embedding simulations.

• Quantum Computing
• Quantum Information
• Quantum Many-Body Physics
• +4 more

Sybille Hellebrand is a Professor and Head of the Data Technology (DATE) department at the University of Paderborn, within the Faculty of Electrical Engineering, Computer Science and Mathematics. She leads research in testing methodologies, fault tolerance, and reliability engineering. Her work focuses on advanced testing techniques for nanoelectronic systems, including BIST (Built-In Self-Test), delay fault analysis, and interconnect reliability under process variations. Key research areas include: Testing and diagnosis of electronic systems Robust testing under PVT (Process-Voltage-Temperature) variability Functional safety of interconnects Approximate communication for error-tolerant systems Hardware fault modeling and simulation Recent publications emphasize advancements in storage-based BIST optimization, defect vs. fault coverage analysis, and cross-talk delay modeling using polynomial regression. She has contributed to international conferences such as European Test Symposium (ETS), IEEE Latin American Test Symposium (LATS), and International Symposium of EDA (ISEDA). Teaching responsibilities include courses on systems engineering, quality assurance in microelectronics, and algorithms for test and diagnosis of systems-on-chip. Her work aligns with emerging challenges in nanoelectronics, including reliability under variations and fault-tolerant communication. No specific awards or grants are listed in the provided texts, but her extensive publication record underscores her leadership in testing and reliability research. The DATE lab at Paderborn University serves as a core research hub for her activities.

• Testing and Diagnosis of Electronic Systems
• Fault Tolerance
• VLSI Design and Testing
• +3 more

Zoltán Molnár is Professor of Developmental Neuroscience at the University of Oxford, holding positions at the Department of Physiology, Anatomy and Genetics and St John's College. His pioneering research focuses on cerebral cortical development, thalamocortical projections, and subplate neuron function, significantly advancing our understanding of neuronal migration, circuit formation, and developmental plasticity. His educational background includes an M.D. from Albert Szent-Györgyi Medical University (1982-1988) and a D.Phil. from the University of Oxford (1989-1994) under Sir Colin Blakemore. Additional training occurred at the University of Lausanne and Kyoto Prefectural School of Medicine. MD: Albert Szent-Györgyi Medical University, Szeged, Hungary (1982-1988) DPhil: University of Oxford, UK (1989-1994) Molnár's research examines how cortical neurons are born, migrate, differentiate, and form circuits, with particular focus on thalamocortical development and subplate neuron functions. His innovative work includes 3D-printed cerebral cortical tissue for brain injury repair and investigations into neuroprotective agents like neuroserpin. Current projects explore cortical circuit plasticity, sleep regulation mechanisms, and metabolic impacts on neurodevelopment. Recent publications (2022-2025) reveal strong thematic continuity in cortical development research while expanding into neuroprotection applications, advanced imaging techniques, and translational models for brain repair. Key trends include molecular mechanisms of neuronal migration, thalamocortical connectivity evolution, and metabolic influences on neurodevelopmental disorders. His scientific recognition includes: Elected Member of Academia Europaea (Physiology and Neuroscience, 2019) Fellow of the Royal Society of Biology (2022) Einstein Visiting Fellow at Charité-Universitätsmedizin Berlin (2020-2026) Fellow of the Anatomical Society, New Fellow of the Year Award (2018) Medical Sciences Teaching Excellence Award (2010) Molnár has secured major research funding from MRC, Wellcome Trust, and BBSRC totaling over £15M, supporting 15+ projects including thalamocortical connectivity studies and Zika virus neurodevelopmental impact research. His laboratory has trained numerous postdoctoral fellows through HFSP and EU programs while mentoring graduate students via Rhodes, Wellcome Trust, and Felix Scholarships. He serves on editorial boards for Cerebral Cortex and Journal of Comparative Neurology, and has chaired the Oxford Neuroscience Committee since 2009. The Molnár Laboratory at Oxford employs correlative microscopy, 3D bioprinting, and electrophysiological techniques to investigate cortical development. The lab maintains active collaborations with Charité-Universitätsmedizin Berlin and participates in the Oxford Cortex Club, which Molnár has sponsored since its 2009 inception. Current projects focus on neuroprotective strategies for neonatal brain injury and thalamocortical circuit evolution.

• cerebral cortical development and evolution
• thalamocortical projections
• subplate neurons
• +2 more

Prof. Simone Fiori is an Associate Professor at the University of Ancona and Marche Polytechnic (UNIVPM), affiliated with the Department of Information Engineering under the Faculty of Engineering. His research focuses on control systems, dynamical systems on manifolds, robotics, and applications of differential geometry to engineering problems. His work emphasizes the use of Lie groups, Riemannian manifolds, and geometric control theory to model and control complex systems such as spacecraft, submersibles, and drones. Recent studies include automated rendezvous for spacecraft, neuroevolutionary controllers for quadcopters, and synchronization of dynamical systems on manifolds. Publications highlight advancements in mathematical modeling, optimization techniques under constraints, and statistical methods for circuit identification. His research bridges theoretical foundations (e.g., Euler-Poincaré reduction) with practical applications like brain-computer interfaces and blind deconvolution algorithms. Prof. Fiori’s contact information includes his email s.fiori@univpm.it and office hours on Thursdays 15:00-18:00 at the Department of Information Engineering (Q165).

• Control Systems
• Dynamical Systems
• Applied Mathematics
• +4 more

Dr. Adrian Lupascu is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC) and the Department of Physics and Astronomy. He holds a cross-appointment in the Department of Electrical and Computer Engineering. His research focuses on superconducting quantum devices, including quantum annealing, quantum control, and strong light-matter interactions. Lupascu leads the Superconducting Quantum Devices Group, which explores novel architectures for quantum computing and sensing. Education: BSc in Physics (University of Bucharest, 2000); PhD in Physics (Delft University of Technology, 2005). Postdoctoral training included work at Delft University of Technology and École Normale Supérieure Paris under Professors Haroche and Raimond, supported by a Marie Curie Fellowship. Research Interests: Development of superconducting flux qubits for quantum information processing, quantum sensing, and ultrastrong light-matter interactions. Key projects include quantum annealing error suppression, qubit-qutrit gate optimization, and advanced flux qubit fabrication techniques. Awards: Alfred P. Sloan Fellowship (2011), Early Researcher Award (2011), Waterloo Nanotechnology Institute Research Leader Award (2018, 2022). His work has been funded by NSERC, MRI, and industry partnerships. Teaching: Courses include Quantum Nanophysics (PHYS 461), Nanoelectronics for Quantum Computing (QIC 880/ECE 770), and Quantum Optics Theory (QIC 895). Lab Activities: Active in developing scalable superconducting qubit systems, cryogenic microwave engineering, and quantum device fabrication. Collaborates with industry and academic partners on quantum computing hardware advancements.

• Quantum Computing
• Superconductivity
• Quantum Information Processing
• +5 more

Dr. José Miguel Simões is a Visiting Assistant Professor of Neurobiology in Reed College’s Biology Department within the Division of Mathematical and Natural Sciences. His academic journey includes a PhD from the University of Lisbon, where he studied animal behavior focusing on fish social dynamics, followed by postdoctoral research at Northwestern University exploring neural mechanisms of temperature perception in Drosophila. His interdisciplinary work combines neuroethology and neurobiology, employing optogenetics, calcium imaging, connectomics, and behavioral modeling. Current research investigates how environmental conditions like temperature influence social interaction and neural decision-making in animals. Key research interests include understanding neural circuit adaptations to environmental stimuli, social behavior modulation under thermal stress, and cross-species comparisons of thermoregulatory behaviors. His methodologies integrate advanced neuroimaging and genetic techniques to map neural pathways linked to sensory processing. Dr. Simões has contributed to studies on acoustic communication in toadfish and cichlids, zebrafish aggression dynamics, and transcriptional responses to social interactions. His work bridges evolutionary biology and neuroscience, with implications for understanding how organisms navigate environmental and social challenges. At Reed, he emphasizes collaborative research and mentoring students in neuroethological studies.

• Neuroethology
• Animal Behavior
• Neurobiology
• +3 more

Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.

• AI Hardware Acceleration
• Emerging Nanoelectronics
• Cryogenic Circuits
• +4 more

Sabine Paarmann is a Research Fellow in the Department of Mechanical Engineering at Imperial College London, part of the Electrochemical Science and Engineering group. Her research focuses on inhomogeneities in lithium-ion batteries, particularly thermal gradients and heat generation linked to performance and degradation. She joined Imperial in July 2023. Her work integrates experimental and analytical methods to address critical challenges in battery technology, including aging mechanisms, thermal management, and material stability. Key areas of exploration include lithium plating reversibility at subzero temperatures, accelerated aging characterization, and holistic battery system design. Paarmann’s publications (2020–2025) emphasize empirical correlations between thermal conditions and battery lifespan, with notable contributions to calendar aging studies, temperature-dependent entropy coefficients, and manufacturing process optimization. Her interdisciplinary approach bridges material science, electrochemistry, and engineering design. Her research is supported by collaborations within the Electrochemical Science and Engineering group, focusing on advancing battery technologies for sustainable energy storage solutions.

• Lithium-ion Batteries
• Thermal Management in Energy Storage
• Battery Degradation Mechanisms
• +3 more

Seenivasan Hariharan is a Researcher at the Department of Algorithms and Complexity, Universiteit van Amsterdam. His work focuses on advancing computational methods for heterogeneous catalysis and quantum computing applications. He holds a PhD in Chemical Engineering and has over a decade of experience in simulating molecule-surface interactions using density functional theory (DFT) and quantum dynamics approaches. His research interests bridge traditional computational chemistry with emerging quantum technologies. Key areas include quantum algorithm development for catalytic interfaces, optimization of alloy catalysts, and modeling surface reactions under extreme conditions. Current projects involve applying variational quantum eigensolvers (VQE) and quantum phase estimation (QPE) algorithms to address limitations of classical DFT methods in capturing spin-related phenomena and strong correlation effects. Publications highlight interdisciplinary contributions, including a 2024 review on quantum computing's role in catalysis modeling and foundational work on surface reaction dynamics since 2013. His work explores material systems ranging from graphene to bimetallic alloys, addressing topics like CO oxidation mechanisms, water dissociation pathways, and coating material characterization through electrodeposition methods. Active in both academic and industrial collaborations, his research emphasizes embedding quantum computing strategies within traditional chemical modeling frameworks to enable large-scale simulations. Recent efforts focus on qubit routing optimization for quantum hardware and developing fault-tolerant algorithms for future quantum systems.

• Heterogeneous Catalysis
• Quantum Computing
• Computational Chemistry
• +3 more