Find Academic Supervisors

• Quantum Artificial Intelligence
• Artificial Intelligence for Science
• Semiconductor Devices
• +4 more

Dr. Zeheng Wang is a CERC Fellow at CSIRO, specializing in quantum artificial intelligence, artificial intelligence for science (AI4Science), and semiconductor devices. With a PhD in Si quantum computing devices from the University of New South Wales (UNSW), he has published over 50 papers in top-tier venues including Nature , Advanced Materials (cover article), and IEEE transactions, establishing himself as a leading researcher in quantum-semiconductor integration. Education: PhD in Electrical Engineering and Telecommunications, University of New South Wales (UNSW), 2022 Research Interests: Dr. Wang's work bridges quantum computing and machine learning, focusing on quantum kernel methods for NISQ devices and AI4Science applications in materials discovery, electronic design, and clinical medicine. His semiconductor research leverages ML for fabrication optimization and device modeling, while his medical AI work develops deep learning frameworks for traditional Chinese medicine safety and pancreatic cancer prognosis. This interdisciplinary approach targets real-world problems from quantum chemistry to clinical decision support. Publication Trends: Recent publications reveal three dominant trajectories: (1) Quantum AI for semiconductor fabrication using kernel methods on small datasets, (2) Energy-efficient chemiresistive sensor arrays with ensemble learning, and (3) Clinical AI applications for TCM side-effect prediction and cancer survival analysis. His work consistently integrates quantum principles with practical engineering constraints, evidenced by multiple cover articles in advanced materials journals. Scientific Awards: CERC Fellowship (Impossible Without You campaign) Outstanding Reviewer Award (IOP Publishing, 2022) Experience Program Scholarship (Sydney Quantum Academy, 2019-2022) Tuition Fee + Stipend Scholarship (UNSW, 2019-2022) Top-up Scholarship (UNSW, 2019-2022) Grants and Editorial Roles: Currently holding the IWY Fellowship (2022-2025) for quantum AI and CAPEX funding (2023-2026) for high-performance computing infrastructure, Dr. Wang serves as Academic Editor for PLOS ONE , Guest Editor for Micromachines , and Advisory Board Member for Materials . His open-source semiconductor fabrication service has been adopted by 300+ researchers globally, demonstrating significant community impact beyond traditional academic metrics. Laboratory and Collaborations: While institutional lab details aren't explicitly stated, his Nature co-authorship with J.-K. Huang indicates active collaboration with UNSW's quantum materials groups. His research bridges CSIRO's national facilities with clinical partners for medical AI validation, and industry connections through semiconductor fabrication patents. The Impossible Without You campaign affiliation suggests strategic positioning within Australia's national quantum initiative.

Shuichi HiraharaView profile

Associate Professor

• Computational Complexity Theory
• Meta-complexity
• Average-case Complexity
• +4 more

Shuichi Hirahara is an Associate Professor at the National Institute of Informatics (NII), Japan, within the Principles of Informatics Research Division. He holds a Ph.D. from the University of Tokyo and has held research positions at the University of Warwick and Nagoya University. His work lies at the intersection of theoretical computer science and cryptography, focusing on foundational questions in computational complexity. Educational Background: Ph.D. in Computer Science, Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo (2016–2019) His research centers on meta-complexity , a field probing the complexity of problems about computational complexity itself, such as the Minimum Circuit Size Problem (MCSP). He investigates average-case complexity , Kolmogorov complexity , and circuit minimization , aiming to understand the limits of efficient computation and to establish secure cryptographic foundations. His work often reveals unexpected hardness results and bridges worst-case and average-case complexity. The recent publications highlight a strong trend toward using meta-complexity to understand cryptographic primitives such as one-way functions and to unify hardness conjectures like the Planted Clique problem. His results have significant implications for proving the security of cryptography based on average-case hardness. Scientific Awards: Young Scientists' Award from MEXT (2024) Yamato Scientific Award (2024) Microsoft Informatics Research Award (2024) Complexity Result of the Year 2022 Funai Research Encouragement Award (2022) Machtey Award (FOCS 2018) Academic Encouragement Award (IEICE, 2019) Hirahara has been actively involved in research funding and academic service. He is the principal investigator of a JSPS Challenging Research (Pioneering) grant on average-time NP-completeness and has participated in several JST and JSPS projects on complexity theory and quantum algorithms. He serves on the program committees of major conferences including STOC, FOCS, and CCC. He is a frequent invited speaker at international workshops and seminars, contributing to the global discourse on computational complexity and cryptography. While no formal advisees are listed, his leadership in research projects suggests an active mentoring role. Labs and Research Teams: He is affiliated with the Informatics Principles Research Division at NII, a leading center for theoretical informatics in Japan. His work is highly collaborative, involving researchers from institutions like JST, University of Tokyo, and international partners.

Hudson GolinoView profile

Associate Professor

• Quantitative Psychology
• Psychometrics
• Network Psychometrics
• +12 more

Hudson Golino is an Associate Professor in the Department of Psychology at the University of Virginia, within the College of Arts and Sciences. His academic work bridges quantitative methods, psychometrics, and machine learning with applications across psychology, health, education, and political science. Research Interests: Dr. Golino specializes in quantitative psychology and psychometrics, with a focus on developing new methodologies for dimensionality assessment and structural analysis. He is a pioneer in Network Psychometrics , particularly through Exploratory Graph Analysis (EGA) , integrating network science, information theory, quantum information theory, and computational modeling. His research goals include improving latent factor estimation, developing innovative fit indices, enhancing interpretability in longitudinal data, and creating new metrics for item analysis and reliability. He also works on NLP, topic modeling, and text mining with applications in cybersecurity and political discourse. Recent Publications: His recent work demonstrates a strong trend in combining psychometric rigor with computational innovation. Articles focus on stability assessment via bootstrap EGA, network analysis of schizotypy, dynamic topic modeling of social media trolls, and assistive communication technologies. These reflect a consistent interdisciplinary approach spanning psychology, data science, and social informatics. Scientific Awards: International Test Commission Young Scholar Scholarship (2012) Sanofi Innovation in Medical Services Award (2015) – for improving ICU outcome prediction using machine learning Grants and Advising: Dr. Golino leads three major grant-funded initiatives at UVA focused on (1) formalizing and testing language model properties using quantum information theory, (2) developing software combining dynamic systems, NLP, and psychometrics for text modeling, and (3) creating a novel toolkit for dimensionality reduction across data types. While no formal students are listed, he actively seeks collaborators and mentors graduate and undergraduate students in quantitative methods and applied machine learning. Labs and Teams: Though no specific lab name is mentioned, his research group at UVA is clearly engaged in cutting-edge work in data science and psychometrics, likely operating as part of the Psychology Department's quantitative program. His team develops open scientific tools and software, contributing to open scholarship through platforms like The Open Scholar.

Katia ObraczkaView profile

Professor

• Computer Networks
• Distributed Systems
• IoT
• +3 more

Katia Obraczka is a Professor of Computer Science and Engineering at UC Santa Cruz and the director of the Center for Information Technology Research in the Interest of Society (CITRIS) and the Banatao Institute. She leads interdisciplinary research focused on societal and economic impacts of IT, particularly in IoT, edge computing, and cyber-physical systems. Her work emphasizes technology for social good, including environmental monitoring and healthcare applications. Education: B.S. and M.S. in Electrical and Computer Engineering from the Federal University of Rio de Janeiro M.S. and Ph.D. in Computer Science from the University of Southern California Research Interests: IoT and Edge Computing – Developing energy-efficient, decentralized systems for smart agriculture and healthcare monitoring. Cyber-Physical Systems – Designing resilient networks for autonomous vehicles and industrial control. Machine Learning – Applying ML to improve network performance and user mobility modeling. Healthcare Technology – Innovations in pressure injury prevention and real-time patient monitoring. Articles Trends: Recent publications emphasize IoT multipath routing, edge intelligence architectures, and energy-harvesting smart systems. Over 80% of her 2023-2025 work addresses healthcare and environmental applications, with notable contributions to autonomous vehicle platooning and QoS-aware quantum networks. Labs & Initiatives: Directs CITRIS UCSC programs like the CITRIS Initiative for Drone Education and Research and oversees the Internet Research Group , which explores edge computing and network security. Grants & Collaborations: Active in cross-campus and industry partnerships, including drone-based wildfire monitoring and solar-powered IoT deployments with UC Berkeley and Merced.

• Quantum Computing
• Cyber-Physical Systems
• Programming Languages
• +3 more

Renato Neves is an Assistant Professor at the University of Minho's Department of Informatics. His research focuses on Quantum Computing, Cyber-Physical Systems, and Programming Languages, with an emphasis on formal semantics and hybrid systems. He has contributed to foundational work in category theory applied to computational models and has led multiple research projects in quantum software engineering and formal methods. Education: Ph.D. in Computer Science (not explicitly stated but inferred from research output) His teaching includes courses on Quantum Computing and Cyber-Physical Systems at the undergraduate and doctoral levels. He has been principal investigator (PI) or co-investigator (co-I) on projects like Ibex (2022-2024) and CTRL-F (2022-2025), advancing computational methods in quantum and hybrid systems. Key research themes include hybrid automata, coalgebraic semantics, and formal verification. His work bridges theoretical computer science with practical applications in programming languages and quantum software engineering.

Sven RoggeView profile

Associate Professor

• Strain engineering
• Computational materials science
• Metal-organic frameworks
• +5 more

Sven Rogge is an Associate Professor at the Faculty of Engineering and Architecture at Ghent University, where he leads computational research on nanostructured materials. His work bridges theoretical modeling and practical applications in materials science, with particular focus on strain engineering and mechanical properties of framework materials. Rogge earned his Master of Science in Engineering Physics in 2014, followed by a Ph.D. in 2018 under supervision of Prof. Veronique Van Speybroeck and Prof. Toon Verstraelen. His doctoral research developed simulation algorithms for interrogating mechanical behavior of metal-organic frameworks under temperature and pressure. He subsequently held two postdoctoral fellowships from the Research Foundation Flanders before becoming an associate professor in 2023. Rogge's research centers on computational materials science with emphasis on strain engineering of nanostructured materials. His work spans molecular dynamics simulations, multiscale modeling, and machine learning approaches to understand and design materials with tailored mechanical properties. Key research themes include solid-state phase transformations, flexibility mechanisms in porous frameworks, and computational design of materials for energy applications. His group has pioneered techniques such as the micromechanical model (MicMec) for investigating cooperative phenomena in reticular materials. His publication record shows a strong trend toward integrating computational methods with experimental validation across diverse materials systems. Recent work demonstrates growing emphasis on machine learning applications in materials discovery, particularly for metal-organic and covalent organic frameworks. His research bridges fundamental understanding of material behavior with practical applications in energy storage, gas separation, and perovskite stability. ERC Starting Grant (2023) for establishing strain engineering as an in silico technique Member of the Young Academy of the Royal Flemish Academy of Belgium for Science and the Arts (2023) Member of the Federal Board of Research Policy (FRWB) (2023) Two postdoctoral fellowships from the Research Foundation Flanders Ph.D. fellowship from the Research Foundation Flanders Rogge co-teaches Bachelor and Master courses in Engineering Physics, including Quantum Mechanics I and Modelling and Engineering of Nanoscale Materials. His research group maintains active collaborations across European institutions and contributes to several large-scale projects focused on computational materials design. The group has developed several computational tools and databases, including MicMec for mechanical modeling and ReDD-COFFEE for covalent organic framework screening, which have gained international recognition in the computational materials community.

Georgi GaydadjievView profile

Professor

• Reconfigurable Computing
• Advanced Computer Architectures
• Parallel Memory Systems
• +7 more

Georgi Gaydadjiev is a Professor in Innovative Computer Architecture at the University of Groningen's Faculty of Science and Engineering. Previously, he held roles including Chair Professor at TU Delft and Chalmers University of Technology, and served as VP of Dataflow Software Engineering at Maxeler Technologies. He holds a PhD from TU Delft and has over 35 years of industry and academic experience, focusing on reconfigurable computing, high-performance systems, and energy-efficient architectures. His research spans embedded systems, fault tolerance, and scalable architectures. Education: MSc Electrical Engineering (TU Delft), PhD (TU Delft), studies at Voenmeh (Baltic State Technical University). Research interests include reconfigurable computing, advanced architectures, parallel systems, and HPC. He leads projects funded by EU, Google, and Swedish Research Councils, addressing exascale computing and customized hardware. His work has been recognized with awards like the CES Design Showcase (1999) and best paper awards at ICS'10 and WiSTP'07. He advises PhD students and oversees labs like Maxeler IoT-Labs BV, focusing on deploying dataflow technology beyond data centers.

Stacey JefferyView profile

Professor

• Quantum Cryptography
• Quantum Algorithms
• Quantum Walks
• +2 more

Roles & Affiliations: Senior Researcher at Centrum Wiskunde & Informatica (CWI), member of QuSoft Professor of Quantum Information at the Korteweg-de Vries Institute for Mathematics, University of Amsterdam Education: PhD in Computer Science (2014), University of Waterloo, supervised by Prof. Michele Mosca Postdoctoral Scholar at Caltech's IQIM (2014–2016) Research Interests: Focuses on quantum algorithms and quantum cryptography, including secure delegation of quantum computation, quantum algorithm frameworks, quantum walks, and quantum complexity theory. Her work emphasizes practical applications such as verifiable delegated computation and quantum time-space tradeoffs. Recent Trends in Articles: Recent publications explore verifiable quantum computation (e.g., quasilinear resource schemes), quantum time complexity (e.g., taming quantum algorithms), and multidimensional quantum walks (e.g., recursion and divide-and-conquer strategies). These contributions highlight advancements in quantum efficiency, security, and algorithmic design. Awards & Grants: ERC Starting Grant (2022) Quantum Delta NL Award (2023) NWO Veni Grant (2017) CIFAR Fellowship in Quantum Information Science (2019) Advising & Grants: Active in grant-funded projects like Quantum time-space tradeoff lower bounds and Algorithms, Security and Complexity for Quantum Computers (ASC-Q) . Teaches advanced courses on quantum algorithms at the University of Amsterdam. Labs & Collaborations: Affiliated with QuSoft, a Dutch research center for quantum software, and collaborates on international initiatives like the Lorentz Center Informatics Advisory board.

• Cybersecurity
• Artificial Intelligence
• Computer Networks
• +7 more

Pere Barlet-Ros is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Barcelona Neural Networking Center (BNN-UPC). He leads research in AI-driven network technologies and is a key member of the UPC CBA and IDEAI-UPC research groups. Ph.D. in Computer Science, UPC (2008) M.Sc. in Computer Science, UPC (2003) His research focuses on applying artificial intelligence, particularly graph neural networks, to solve critical challenges in network monitoring, traffic classification, congestion control, and cybersecurity. His work bridges theoretical innovation with practical deployment, as evidenced by integration into commercial tools like Auvik TrafficInsights and Talaia Polygraph. He explores digital twins for network optimization, privacy-preserving technologies, and next-generation web tracking detection. The recent publications highlight a strong trend toward leveraging graph-based machine learning for network modeling, anomaly detection, and traffic compression. His work spans top-tier journals like IEEE/ACM Transactions on Networking and conferences such as IEEE EuroS&P and ACM SIGCOMM workshops, emphasizing robust, real-world AI/ML applications in networking. 2nd VALORTEC prize for best business plan (2014) Fiber Entrepreneurs award, best entrepreneur at FIB (2015) Pere Barlet-Ros has served as Principal Investigator on seven competitive research projects and has advised at least 14 PhD students, including Paul Almasan, Miquel Ferriol, and Ismael Castell. His lab, BNN-UPC, fosters interdisciplinary collaboration in neural networking and AI for infrastructure. His entrepreneurial experience with Talaia Networks underscores a commitment to translating research into impactful technology.

• Condensed Matter Physics
• Quantum Physics
• Complex Systems
• +3 more

Elvira Guardia Manuel is a researcher affiliated with the Polytechnic University of Catalonia (UPC) , specifically within the Department of Physics under the School of Informatics of Barcelona (FIB) . She is a member of the Condensed, Complex and Quantum Matter Group (CCQM) , a research group associated with the UPC's Center for Molecular Biotechnology (CEBIM). Her research focuses on fundamental and applied aspects of condensed matter physics, quantum systems, and complex materials. She maintains a scientific profile via her ORCID (0000-0002-4569-534X) and has contributed to 208 academic activities, including peer-reviewed publications, book chapters, and competitive R&D projects.

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

Fernando Pablo Mazzanti Castrillejo is a Professor and Coordinator of the PhD Program in Computational and Applied Physics at the Universitat Politècnica de Catalunya (UPC). He is affiliated with the Department of Physics and the Barcelona School of Informatics (FIB). His research is conducted within the Condensed, Complex and Quantum Matter Group (CCQM), focusing on advanced computational methods and interdisciplinary physics applications. Research interests include condensed matter systems, quantum phenomena, complex systems modeling, and computational techniques in physics. With over 115 documented academic activities, his work spans journal articles, doctoral theses supervision, competitive research projects, and conference presentations. He has received 5 academic recognitions and awards. Mazzanti Castrillejo actively collaborates in research networks and contributes to the development of scientific infrastructure at UPC. His contributions include 70 indexed journal articles, 18 doctoral theses supervised, and participation in 16 competitive R+D+I projects. His work bridges theoretical physics with computational methodologies, addressing challenges in materials science and quantum systems.

Gemma SeséView profile

Professor

• Condensed Matter Physics
• Quantum Matter
• Complex Systems
• +2 more

Gemma Sesé is a Professor in the Department of Physics at the Facultat d'Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya · BarcelonaTech. She currently serves as Vice-Dean of Academic Affairs (Head of Studies), reflecting her leadership in academic management and education innovation. Her work bridges physics with informatics, contributing significantly to research and teaching at FIB. Academic Rank: Professor Administrative Role: Vice-Dean of Academic Affairs Institution: Universitat Politècnica de Catalunya · BarcelonaTech School: Facultat d'Informàtica de Barcelona Department: Department of Physics Research Group: CCQM - Condensed, Complex and Quantum Matter Group Her research focuses on fundamental and applied aspects of condensed matter and quantum systems, with emphasis on quantum materials, electronic and magnetic properties, low-dimensional systems, and computational modeling. The integration of physics into informatics education and research is a key aspect of her interdisciplinary approach. The 15 most recent articles (inferred from activity chart) span from 2011 to 2025 and are predominantly published in indexed journals, with consistent output over time. These works reflect a strong focus on theoretical and computational condensed matter physics, quantum phenomena in nanostructures, and the electronic properties of advanced materials. Trends indicate sustained contributions in quantum materials, topological systems, and electronic transport, often employing first-principles and numerical methods. She has been recognized with the FIB Mention at the 22nd Festibity for her outstanding contributions to teaching, research, and academic leadership. FIB Mention at the 22nd Festibity Gemma Sesé has led multiple competitive R&D+i projects and teaching innovation initiatives, demonstrating her active role in securing research funding and improving pedagogy. While specific student names are not listed, her role as a research group member and professor implies mentorship of graduate students and early-career researchers within the CCQM group. She is affiliated with the CCQM (Condensed, Complex and Quantum Matter Group), a research team focused on theoretical and computational studies of quantum and complex systems, contributing to the physics foundation within an informatics school context.

• Computational Chemistry
• Materials Science
• Catalysis
• +6 more

Professor Richard Catlow is a distinguished Research Professor in the Department of Chemistry at University College London (UCL), where he has held various leadership roles including Dean of the Faculty of Mathematical and Physical Sciences (2007-2014) and Head of the Chemistry Department (2002-2007). His career spans over four decades, with significant contributions to computational and experimental chemistry across multiple institutions including The Royal Institution and Keele University. Professor Catlow's educational background includes: Doctor of Philosophy from the University of Oxford (1974) Bachelor of Arts (Honours) from the University of Oxford (1970) Professor Catlow's research focuses on computational and experimental studies of complex inorganic materials, with particular expertise in microporous and oxide catalysts, ionic conductors, electronic ceramics, and silicate minerals. He has pioneered embedded cluster methodologies for studying catalytic reactions and has made significant contributions to understanding material properties at the atomic level through the integration of computational techniques with experimental methods including neutron scattering and synchrotron radiation. Analysis of Professor Catlow's recent publications reveals a strong focus on sustainable chemistry, catalysis for energy applications, and the integration of computational and experimental approaches. His work spans methane conversion to ethanol, CO2 reduction to methanol, and deNOx catalysis, reflecting his commitment to addressing global energy and environmental challenges through fundamental chemical research. Professor Catlow has received numerous prestigious awards for his contributions to chemistry: Fellowship of the Royal Society (2004) Liversidge Medal (2008) Royal Society of Chemistry Interdisciplinary Medal (1998) Royal Society of Chemistry Medal (1992) Fellow of the Royal Society of Chemistry (1990) Fellow of the Institute of Physics UK (1995) Honorary Member of the Materials Research Society of India (1996) Throughout his career, Professor Catlow has secured substantial research funding for projects related to computational chemistry, materials science, and catalysis, supporting both experimental and theoretical work in his research group. He has led major research initiatives including his role as Director of the Davy Faraday Research Laboratory at The Royal Institution (1998-2007) and has maintained extensive collaborations across the UK and internationally. Professor Catlow has led several prominent research teams throughout his career, including his long-standing group at The Royal Institution and his current research team at UCL. His work aligns with Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action), demonstrating his commitment to applying fundamental chemical research to address pressing global challenges. With over 1,080 publications, he remains an active and influential researcher in the field of computational and experimental chemistry.

Michael MartinsView profile

Researcher

• Soft X-ray spectroscopy
• Quantum systems
• Molecular physics
• +7 more

Dr. Michael Martins is a working group leader at the Institute of Experimental Physics within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. His research focuses on soft X-ray spectroscopy of small quantum systems, including studies of molecular ions, photocatalytic materials, and dynamic processes in molecules using facilities like FLASH and PETRA III. Develops instruments for spectroscopy at Free-Electron Lasers (FELs) and Synchrotrons Engaged in teaching and mentoring students in physics Part of the DFG-funded Collaborative Research Center (SFB) 925 "Light-induced dynamics and control of correlated quantum systems" His work spans experimental physics, quantum mechanics, and materials science, with applications in energy infrastructure (e.g., hydrogen production) and fundamental understanding of molecular interactions under extreme conditions. The group collaborates with universities in Giessen and Frankfurt, operates ion experiments like PIPE at PETRA III, and seeks students to contribute to projects on molecular ion sources, photokatalysis, high-resolution mass spectrometers, and simulation of photofragmentation processes.

• Machine Learning
• Materials Science
• Nanotechnology
• +3 more

Dr. Xiaohui Qu is a Researcher at Brookhaven National Laboratory's Center for Functional Nanomaterials (CFN). He specializes in integrating deep learning and data analytics with computational chemistry, focusing on energy storage systems, X-ray absorption spectroscopy, and nanomaterials. Education : B.S. in Environmental Engineering (Shandong University, 2004), Ph.D. in Environmental Science (Shandong University, 2009), Postdoc in Physics (Nanyang Technological University, 2011), Postdoc in Chemistry (Universidade Nova de Lisboa, 2013), Postdoc in Materials (Lawrence Berkeley National Lab, 2015). His research develops intelligent data pipelines for large-scale nanoscience challenges, leveraging deep learning, density functional theory, and in-situ experimental facilities. He explores structure-spectrum relationships, phase evolution in thin films, and kinetic modeling for materials synthesis. Recent publications emphasize machine learning applications in materials discovery, energy storage, and spectroscopy. Themes include predictive synthesis, quantum computing materials, and high-throughput characterization methodologies. Dr. Qu leads computational efforts at CFN's Theory/Computation Group, utilizing AI to address frontier problems in nanotechnology and advancing collaborations with state-of-the-art experimental facilities.