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Sabre KaisView profile

Professor

• Quantum Computing
• Quantum Information Science
• Quantum Chemistry
• +5 more

Sabre Kais is the Goodnight Distinguished Chair in Quantum Computing at North Carolina State University's Department of Electrical and Computer Engineering. He previously held roles at Purdue University, including Distinguished Professor of Chemistry and courtesy professorships in Computer Science and Physics. His research focuses on quantum information science, quantum computing for complex systems, and quantum chemistry simulations. Education: BSc, MSc, and PhD in Chemical Physics, Theoretical Chemistry, and Chemistry from the Hebrew University of Jerusalem (1983–1989). Postdoctoral research at Harvard University (1989–1994). Research interests include quantum algorithms, open quantum systems, quantum machine learning, and quantum simulation. He directs the NSF-funded Center for Quantum Technology and has led major initiatives like the Quantum Information for Quantum Chemistry center. Notable awards: Fellow of the American Physical Society (2006), AAAS Fellow (2007), Guggenheim Fellow (2005), and the 2019 Herbert Newby McCoy Award (Purdue’s highest natural science honor). He also serves on editorial boards and is a visiting professor at Berkeley (2024–2025). His work bridges quantum computing with applications in chemistry, finance, and healthcare, emphasizing interdisciplinary collaboration. Recent publications explore quantum federated learning, energy teleportation protocols, and defect-induced information scrambling in quantum systems.

Christian B. MendlView profile

Assistant Professor

• Quantum Computing
• Tensor Network Methods
• Computational Physics
• +3 more

Christian B. Mendl is an Assistant Professor (Rudolf Mößbauer Tenure Track) at Technische Universität München's Department of Computer Science. His research focuses on quantum computing, tensor network methods, computational physics/chemistry, high-performance computing, and theoretical condensed matter physics. He holds a PhD in Physics from LMU München and dual diplomas in Physics and Mathematics from TU München, with postdoctoral experience at Stanford University and TU Dresden. His work bridges quantum algorithms, numerical methods, and interdisciplinary applications. Notable contributions include Riemannian quantum circuit optimization, tensor network simulations, and quantum-classical computing frameworks. He collaborates with institutions globally and actively develops open-source tools like PyTreeNet for tensor networks. Recent research emphasizes scalable quantum algorithms, optimization techniques, and hybrid quantum-classical systems. His work addresses challenges in quantum computing, many-body systems, and high-performance simulation frameworks.

• Quantum Materials
• Condensed Matter Physics
• Moiré Physics
• +3 more

Adolfo Otero Fumega serves as an Academy Postdoctoral Researcher in the Department of Applied Physics at Aalto University, Finland, and is an active member of the Correlated Quantum Materials (CQM) research group. His work bridges theoretical modeling and computational analysis to explore emergent quantum phenomena in low-dimensional systems. His research centers on quantum materials, with emphasis on magnetic order, superconductivity, and electronic correlations in 2D materials and moiré superlattices. Key interests include spin-1/2 systems, charge density waves, and topological states in van der Waals heterostructures, aiming to decode fundamental mechanisms for next-generation quantum technologies. The CQM group employs advanced computational frameworks to simulate atomic-scale behavior in novel materials. Publications from 2024-2025 reveal a cohesive focus on atomic-scale quantum phenomena, particularly in monolayer and twisted bilayer systems. Dominant themes include magneto-orbital coupling in VCl 3 , frustration-induced degeneracy in molecular rings, and electric-field manipulation of skyrmions in NiI 2 . His collaborative approach integrates theoretical predictions with experimental validation through partnerships with scanning probe microscopy and materials synthesis teams. Scientific Awards: No awards were documented in the source material. Advising and Grants: No information regarding student mentorship or research funding was provided. Labs and Teams: Dr. Otero Fumega contributes to the Correlated Quantum Materials group, which specializes in computational investigations of quantum phases using kernel polynomial methods, tensor networks, and first-principles simulations to model correlated electron behavior in engineered nanostructures.

• Quantum Physics
• Condensed Matter Physics
• Computational Physics
• +3 more

Marcel Niedermeier is a Doctoral Researcher at the Department of Applied Physics at Aalto University. His research focuses on quantum computing and the theoretical investigation of two-dimensional quantum matter. Institution: Aalto University Department: Applied Physics Academic Rank: Researcher His work involves computational methods to study quantum systems, including tensor networks and quantum algorithms. Recent publications highlight advances in simulating quantum algorithms with limited entanglement, analyzing correlated states in super-moiré materials, and computing topological invariants. These works span Quantum Computing , Condensed Matter Physics , and Computational Physics , emphasizing Tensor Networks , Quantum Simulation , and Moiré Materials .

Archontis GiannakidisView profile

Senior Lecturer

• Machine Learning
• Deep Learning
• Biomedical Image Analysis
• +8 more

Archontis Giannakidis is a Senior Lecturer in Data Science at the School of Science and Technology , Nottingham Trent University (NTU). He leads modules in Discrete Mathematics & Computational Complexity (Year 2) and Convex Optimisation (Year 3), while supervising undergraduate and Masters dissertations. Education: PhD in Electronic Engineering (Inverse Problems), University of Surrey (2010) Research Interests focus on applying Deep Learning and Machine Learning to biomedical data processing, particularly in Biomedical Image Analysis , Convolutional Networks , and Diffusion MRI . His work emphasizes automating intellectual tasks, efficient data representation, hidden pattern discovery, and decision optimization in healthcare and environmental contexts. Recent Article Trends highlight his expertise in 2D echocardiography-based cardiac quantification , MRI-driven ACL tear diagnosis , and landslide-tsunami prediction via geometry-invariant machine learning. These studies often integrate uncertainty modeling, attention mechanisms, and lightweight architectures for clinical and environmental applications. Scientific Awards : Fellow of the Higher Education Academy (FHEA) Advisory Roles include mentoring PhD students Tuan Aqeel Bohoran (Marie Skłodowska-Curie-funded) and David Gwillym Jenkins (internal funding), alongside visiting/external PhD candidates Michael Lystbaek (Aarhus University) and Athanasios Siouras (University of Thessaly). He has received grants from HORIZON 2020 , EPSRC , and Innovate UK , and collaborates with institutions like the Archimedes Research Unit (Greece) and National Heart and Lung Institute , Imperial College London. Professional Activity includes editorial board membership for Frontiers in Physiology , peer reviewing for journals like IEEE Transactions on Medical Imaging , and external examining for University of Strathclyde’s MSc programs. He also contributes to conference organization and Portuguese grant evaluation panels.

Erhai ZhaoView profile

Professor

• Quantum Many-Body Physics
• Ultracold Quantum Gases
• Topological Phases of Matter
• +5 more

Erhai Zhao is a Professor in the Department of Physics & Astronomy at George Mason University. His research spans both fundamental and applied aspects of quantum many-body physics, with a focus on ultracold quantum gases and topological phases of matter. He leads projects developing advanced numerical methods for strongly correlated systems, including tensor networks and neural network quantum states. His work on non-Hermitian Hamiltonians and Floquet systems has revealed novel insights into entanglement and topological properties of open quantum systems. Current projects include functional renormalization group algorithms for competing orders and quantum phase transitions in spin and Fermi systems. Research trends from his publications include: Topological phases in non-Hermitian systems Floquet engineering of quantum states Machine learning applications in condensed matter Strongly correlated Fermi gases Quantum criticality and Fisher zeros Spin-orbit coupling in 2D materials He is supported by grants from the Air Force Office of Scientific Research and the National Science Foundation, with prior funding from the National Institute of Standards and Technology and Office of Naval Research.

• Computer vision
• Image processing
• Pattern recognition
• +2 more

George Bebis is a Foundation Professor at the Department of Computer Science & Engineering, University of Nevada, Reno, affiliated with the College of Engineering. He has published extensively in computer vision, machine learning, and image processing. Research Interests: Computer vision, Image processing, Pattern recognition, Machine learning, Evolutionary computing Email: bebis@unr.edu Recent research trends include crater detection using convolutional neural networks, horizon/sky line detection via semantic segmentation, low-resolution face recognition, Gabor feature optimization, and dynamic programming integration with machine learning. His work spans applications in remote sensing, transportation safety, medical imaging, and biometrics.

• Machine Learning
• Signal Processing
• Computer Vision
• +4 more

Yuan Liao is an active researcher with a focus on interdisciplinary areas spanning computer science, electrical engineering, and applied mathematics. His work emphasizes innovative solutions in signal processing, machine learning, robotics, and human mobility studies. Notably, he has contributed to advancements in wearable biomedical devices, remote sensing image analysis, and next-generation wireless communication systems like 6G. Collaborations with institutions like Academia Sinica and co-authors such as Vasilis Friderikos highlight his global academic network. Liao's research often bridges theoretical frameworks with practical applications, addressing challenges in healthcare, transportation, and urban planning. Key contributions include developing novel neural network architectures for tasks like image inpainting and emotion recognition from EEG signals. His work on robotic aerial base stations for mmWave backhauling demonstrates expertise in cutting-edge wireless systems. While specific institutional affiliations are not explicitly stated in the provided data, his prolific publication record across top-tier journals and conferences indicates an established academic role, likely as a researcher or faculty member in a technical discipline.

• Health Disparities
• Cancer Care
• Health Equity
• +3 more

Do Lee is a Researcher at the COPPER Center within the Yale School of Medicine at Yale University. She holds a B.S. in Elementary Education from the University of Maryland, College Park, and an MPH in Biostatistics from George Washington University. Her research focuses on addressing racial and socio-economic disparities in cancer care to advance equitable healthcare. She contributes to interdisciplinary efforts in health equity, biostatistics, and public health, leveraging her expertise to improve patient outcomes through data-driven strategies. Affiliated with both the COPPER Center and the Department of Internal Medicine, her work integrates statistical methodologies with clinical and translational research. While no awards are explicitly noted, her contributions to health disparities research reflect a commitment to impactful translational science. Her scholarly publications span neuromorphic computing, artificial synapse electronics, and efficient machine learning techniques, demonstrating a blend of computational innovation and applied health research. Collaborations likely bridge engineering and medical domains to address complex healthcare challenges.

Dirk EnglundView profile

Professor

• Quantum Technologies
• Semiconductor Systems
• Nanophotonics
• +5 more

Dirk Englund is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, leading the QP-Group focused on quantum technologies, nanophotonics, and optical systems. His research spans quantum computing, quantum sensing, and photonic integration, with emphasis on silicon photonics and 2D materials. Englund joined MIT in 2013 after roles at Columbia University and Harvard. He has pioneered advancements in quantum networks, photonic neural networks, and optical AI accelerators. Education: BS in Physics from Caltech (2002), MS in Electrical Engineering and PhD in Applied Physics from Stanford (2008). Postdoctoral research at Harvard (2008–2010). Research Interests: Quantum-enhanced sensing, solid-state quantum memories, spin-photon interfaces, and photonic integrated circuits. Key areas include silicon photonics for quantum information processing, 2D material-based optoelectronics, and optical quantum networks. Awards: 2011 PECASE, 2017 Adolph Lomb Medal, 2018 Bose Fellowship, 2020 Humboldt Fellowship. Active in developing MIT’s quantum engineering curriculum, including the 6-5 Electrical Engineering with Computing track. Labs/Teams: QP-Group collaborates with researchers like Dr. Ryan Hamerly and Dr. Matt Trusheim. Current projects include high-definition quantum-limited optical bolometry and photonic AI processors. Grants/Positions: Recipient of DARPA Young Faculty Award (2012), NSF grants, and industry partnerships with companies like Q Tools. Oversees research positions in quantum networks, photonics for machine learning, and 2D quantum devices.

Tom BraeckeveltView profile

Research Fellow

• Materials Science
• Perovskite Solar Cells
• Computational Chemistry
• +3 more

Tom Braeckevelt is a Research Fellow at Ghent University, Belgium, working within the computational materials science group led by Prof. Veronique Van Speybroeck. Based at Tech Lane Ghent Science Park (Technologiepark 46, Zwijnaarde), he collaborates extensively with experimental teams including Prof. Johan Hofkens (photophysics) and Prof. Sara Bals (electron microscopy), bridging theoretical modeling with advanced characterization techniques to solve stability challenges in next-generation photovoltaics. Education: PhD in Materials Science, Ghent University (2018). Dissertation: Designing 2D hybrid organic-inorganic perovskites for game-changing photovoltaics , supervised by Prof. Veronique Van Speybroeck and Dr. Kurt Lejaeghere. His research centers on perovskite stability mechanisms through multiscale computational modeling (DFT, machine learning potentials, molecular dynamics) integrated with experimental validation (TEM, GIWAXS, spectroscopy). Key focus areas include phase transition kinetics, strain engineering, doping strategies, and interfacial design for cesium lead halide perovskites. This work addresses critical barriers to commercial solar cell deployment, particularly ambient-condition stability and efficiency retention. From 2019-2025, Dr. Braeckevelt co-authored 13 high-impact publications including Science (2019), Nature Communications (2022), and ACS Nano (2025), demonstrating progression from fundamental phase transition studies to machine learning-enhanced stability solutions. Recent work expands into covalent organic frameworks and rare-event sampling algorithms, reflecting methodological diversification while maintaining photovoltaic applications as the core driver. No scientific awards or fellowships were documented in the source materials. Supported by institutional research grants at Ghent University, Dr. Braeckevelt has presented findings at 8+ international conferences including PSCO19 (Lausanne), ICAMM (Rennes), and DFT2022 (Brussels). His invited talk at the 2025 Eindhoven Psiflow workshop highlights growing recognition in machine learning for perovskites. No student supervision roles were indicated in current position. He operates within Ghent's integrated materials research ecosystem, contributing to cross-disciplinary projects that combine computational prediction with nanoscale characterization to accelerate renewable energy technology development.

Stephen R ClarkView profile

Professor

• Non-Equilibrium Physics
• Many-Body Systems
• Quantum Entanglement
• +4 more

Professor Stephen R Clark is a faculty member at the University of Bristol's School of Physics, holding the Professor title. His research focuses on non-equilibrium phenomena in many-body systems, including ultra-cold atoms and strongly correlated electron materials. He specializes in tensor network theory, quantum entanglement, and foundational quantum mechanics. Ultra-cold atomic systems Strongly correlated electron materials Quantum entanglement and correlations Tensor network algorithms (DMRG, TEBD) Quantum-classical simulation interfaces Clark has developed the open-source Tensor Network Theory Library , advancing classical simulability of quantum systems. His work connects tensor networks to variational Monte Carlo and dynamical mean-field theory, with applications to light-driven quantum systems and thermodynamics of small systems. Current projects include QuamNESS (2020-2024) and EPSRC-funded research on strong driving correlations. He actively supervises research and has produced 77 research outputs including datasets and software tools. Article trends show a focus on quantum transport , non-Markovian dynamics , machine learning for quantum states , and nonequilibrium quantum thermal machines . Clark's tensor network innovations span 1D to 2D systems, with applications in superconductivity, polarons, and photonic lattices.