Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.
Caglar Oskay is the Cornelius Vanderbilt Professor of Engineering and Chair of the Department of Civil and Environmental Engineering at Vanderbilt University. He is also a Professor of Mechanical Engineering. His research focuses on multiscale computational modeling of material and structural systems under extreme conditions, with expertise in composite materials, failure mechanisms, and computational mechanics. Dr. Oskay earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute (2003) and has held academic roles there before joining Vanderbilt in 2006. He was honored as an ASME Fellow in 2017 and as a Chancellor Faculty Fellow in 2016. Key research areas include multiscale failure modeling, life prediction of heterogeneous materials, and computational methods for composites and multiphysics systems. His work integrates advanced simulation techniques with experimental validation, addressing challenges in infrastructure resilience, additive manufacturing defects, and quantum computing applications in engineering. Education: Ph.D., Civil Engineering, Rensselaer Polytechnic Institute (2003) M.S., Civil Engineering, Rensselaer Polytechnic Institute M.S., Applied Mathematics, Rensselaer Polytechnic Institute B.S., Civil Engineering, Middle East Technical University Recent research highlights include stochastic modeling of geotechnical infrastructure failures, quantum-enhanced finite element methods, and predictive analytics for additive manufacturing defects. He leads interdisciplinary efforts on backward erosion piping in flood protection systems and has secured grants for multiscale modeling of titanium alloys and composites. Awards: ASME Fellow (2017) Chancellor Faculty Fellow (2016) Advising and grants: Dr. Oskay’s grants include NSF-funded studies on backward erosion piping and quantum computing applications. His research group collaborates with industry partners on materials for aerospace and energy sectors, emphasizing computational tools for failure prediction and material design. His work bridges computational mechanics with practical engineering challenges, advancing methods for infrastructure resilience, advanced materials, and sustainable design through multiscale modeling innovations.
Prof. Juan Carlos Cuevas is a Professor of Theoretical Nanophysics at the Universidad Autónoma de Madrid (UAM). He holds a PhD in Physics from UAM (1999) and has led research groups in molecular electronics and nanoscience. His work focuses on superconductivity, quantum transport, and radiative heat transfer at the nanoscale. Key areas of research include: theoretical analysis of molecular junctions, superconducting nanostructures, and near-field thermal phenomena. Cuevas has co-authored influential books such as *Molecular Electronics* (World Scientific, 2017) and pioneered studies on single-molecule conductance and quantum interference effects. Recent contributions include discoveries on phonon interference in molecular junctions (2025), thermodynamic uncertainty relations in superconductors (2025), and advanced machine learning approaches for radiative heat optimization (2024). His work bridges theoretical models with experimental insights from scanning tunneling microscopy and nanofabrication techniques.
Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.
Jonathan Novak is an Associate Professor of Mathematics at the University of California San Diego (UCSD). His research focuses on the combinatorial structure and high-dimensional behavior of multivariate special functions in random matrix theory, representation theory, and mathematical physics. He serves on the editorial board of the open-access journal Algebraic Combinatorics . Key research interests include algebraic combinatorics, special functions, and their intersections with random matrices and mathematical physics. His work explores combinatorial structures in high-dimensional spaces, asymptotic analysis of integrals, and connections between probability theory and algebraic structures. Recent publications emphasize topics like Berezin-Karpelevich integrals, quasimodular asymptotics, and topological expansions in matrix models. These studies highlight advancements in understanding complex systems through combinatorial and probabilistic lenses. No scientific awards are explicitly mentioned in the provided information. His contributions extend to editorial work and advancing interdisciplinary research in algebraic combinatorics and mathematical physics.
Sukhpal Singh Gill is an Assistant Professor (Lecturer) in Cloud Computing at the School of Electronic Engineering and Computer Science, Queen Mary University of London (UK). He holds a PhD, ME, and BE in Computer Science and is a Fellow of the Higher Education Academy (FHEA). His roles include Programme Director for MSc Advanced Computer Science and MSc Business Analytics, as well as Deputy Chair of the Main Misconduct Panel. He leads the GillNet Research Lab and is the Editor-in-Chief of the International Journal of Applied Evolutionary Computation (IJAEC) , with editorial roles in IEEE IoT, Nature Scientific Reports, and other journals. Education: PhD in Computer Science ME in Computer Science BE in Engineering Research Interests: Focus on Cloud Computing, Edge AI, IoT, Energy Efficiency, and Quantum Computing. His work bridges theoretical advancements with practical applications in healthcare, smart cities, and sustainable computing. Notable projects include AI-driven frameworks for carbon-neutral cloud resource management, blockchain-empowered healthcare systems, and quantum cloud computing models. Teaching: Teaches modules such as Cloud Computing (Postgraduate), Fundamentals of Web Technology (Undergraduate), and Semi-structured Data and Advanced Data Modelling (Postgraduate/Undergraduate). He emphasizes inclusive curriculum design and innovative teaching tools like the Q-Module-Bot for AI-supported learning. Awards & Grants: Recipient of 12,500+ citations and an H-index of 54 (Google Scholar). Secured grants for projects on edge AI, federated learning, and sustainable cloud computing. Winner of awards including the IEEE IT Professional Magazine Outstanding Reviewer Award (2024) and Elsevier's Best Paper Award (2023). Labs & Teams: Leads the GillNet Research Lab , focusing on cutting-edge research in cloud-edge computing, AI, and quantum systems. Collaborates with industry partners on projects like HealthEdgeAI and AIoT-driven smart healthcare systems .
Yi-Zhuang You is an Associate Professor in the Department of Physics at the University of California, San Diego (UCSD). He holds a Ph.D. from Tsinghua University (2013). His research focuses on theoretical investigations of correlated topological phases, quantum entanglement dynamics, and machine learning applications in many-body systems. Key areas include deconfined quantum criticality, symmetry-protected topological (SPT) phases, and the interplay between topology and quantum matter. His work bridges condensed matter physics and high-energy physics, exploring topics such as topological responses in gauge theories, entanglement holography, and quantum machine learning. Recent studies involve machine learning-driven approaches to quantum state preparation, symmetry discovery, and tomographic reconstruction of quantum systems. His contributions span theoretical frameworks for understanding topological transitions, fractionalization in lattice models, and the role of symmetry in quantum critical phenomena. Notable research highlights include the study of symmetric mass generation as a deconfined quantum criticality mechanism, the application of classical shadow tomography for efficient quantum state estimation, and the development of algorithms for self-similar dynamics modeling. His publications frequently intersect with experimental proposals for observing topological phases in materials like graphene and iridates. Dr. You’s affiliations include the UCSD Physics Department, with collaborations extending to institutions globally. His research is supported by grants focusing on quantum information, topological materials, and machine learning applications in physics. While no specific awards are listed, his work has been widely cited in high-impact journals across condensed matter and theoretical physics.
Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)
Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.
Matthias S. Maier is an Associate Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on multiscale methods, computational fluid dynamics, and finite element software development, particularly with the deal.II library. He organizes an annual undergraduate summer school on PDE modeling and simulation. Research Interests: Multiscale effects in Maxwell’s equations, computational fluid dynamics, finite element methods, and numerical analysis. His work includes studies on surface plasmon-polaritons, homogenization theory, and high-performance computing for hyperbolic systems. Developed ryujin, a high-performance finite-element solver for compressible flows. Contributed to deal.II, a widely used open-source finite element library. Recipient of NSF awards (DMS 1912847, DMS 2045636) and AFOSR funding. Teaching includes courses on numerical methods (Math 417, 610), mathematical modeling (Math 442), and finite element methods (Math 676). He advises graduate students in applied mathematics and computational science. Labs/Teams: Core developer of deal.II and contributor to the ryujin framework. Collaborates with interdisciplinary teams in physics and engineering.
Robert D Nevels is a Professor in the Department of Electrical & Computer Engineering at Texas A&M University. He holds the rank of Fellow in both the Institute of Electrical and Electronics Engineers (IEEE) and the Electromagnetics Academy (EM). His research focuses on analytical and numerical electromagnetics, nanophotonics, electromagnetic scattering, and antenna design. He has served as President of the IEEE Antennas and Propagation Society (AP-S) in 2010 and has been a member of its Administrative Committee during 1998-2001 and 2011-2014. His teaching excellence has been recognized through multiple awards, including the Region 5 Outstanding Educator Award and the University-level Distinguished Teaching Award from the Association of Former Students. Dr. Nevels earned his Ph.D. in Electrical Engineering from the University of Mississippi, followed by an M.S. from Georgia Institute of Technology and a B.S. from the University of Kentucky. His research interests emphasize advanced computational methods for electromagnetics, including FDTD techniques for nonlinear optics and propagator methods for wave analysis. His work spans theoretical foundations (e.g., Coulomb gauge formulations) and practical applications (e.g., antenna design for high-power systems). Key honors include: Eugene E.Webb'43 Faculty Fellow (Texas A&M) Twice recipient of the Outstanding Professor Award from IEEE Texas A&M Student Chapter Amoco Foundation Award for Distinguished Teaching Nevels has collaborated on grants related to electromagnetic scattering, plasma-based devices, and terahertz technology. His lab focuses on numerical methods and experimental validation of electromagnetic phenomena.
Associate Professor Ke Deng is affiliated with the School of Computing Technologies at RMIT University. His research focuses on urban computing, spatiotemporal data analysis, and social networks, with expertise in data mining and artificial intelligence. He holds a PhD in Computer Science from The University of Queensland (2007), a Master's in Information and Communication Technology (2001), and a Bachelor's in Electrical Engineering (1994). Previously, he was a postdoctoral researcher at CSIRO ICT Centre and a researcher at Huawei Noah's Ark Lab. His work emphasizes practical applications of AI, such as fairness-aware recommendation systems, traffic scenario modeling, and energy-efficient protocols for IoT. Dr. Deng's career includes roles as an acting lecturer at The University of Queensland and co-supervisor of a PhD student. He has supervised numerous research projects, including advancements in quantum annealing for recommenders, fake news mitigation via reinforcement learning, and smart human sensing using millimeter-wave radar. His research outputs span journals like ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Education: PhD in Computer Science, The University of Queensland (2007) MSc in Information and Communication Technology (2001) BEng in Electrical Engineering (1994) Key Research Themes: Urban computing and smart cities Mechanisms for fair AI systems Energy-efficient IoT protocols Spatiotemporal data mining Grants and Collaborations: ARC Australian Postdoctoral Fellowship (2010–2012) Research at Huawei Noah's Ark Lab (2013) His recent projects highlight interdisciplinary innovation, such as quantum computing for feature selection and self-supervised networks for traffic scenario clustering. Dr. Deng is actively involved in mentoring PhD/Masters students and contributes to RMIT’s research initiatives in AI and urban informatics.
Paul Kockelman is a Professor of Anthropology at Yale University, specializing in linguistic anthropology, semiotics, and environmental studies. He holds undergraduate degrees in mathematics and physics, an MS in physics, and a PhD in anthropology from the University of Chicago. His research focuses on the intersection of human agency, environmental degradation, and computational systems, with extensive fieldwork among Q’eqchi’-Maya communities in Guatemala. He served as editor of the Journal of Linguistic Anthropology (2016–2018) and is the author of numerous books and essays. Key research areas include the pragmatics of possible worlds, the semiotic dynamics of machine learning, and the ontological coupling between humans and algorithmic models. His work bridges cultural theory, science studies, and linguistic analysis, often employing mathematical models to explore meaning-making processes. His publications span books like Last Words: Large Language Models and the AI Apocalypse (2024) and A Mathematical Model of Meaning (forthcoming MIT Press), alongside foundational works in linguistic anthropology. His articles address topics from Mayan grammatical structures to the epistemic implications of AI. Grants include an EPA-funded dissertation on commons management. He maintains a website at www.envorganism.org .
Christian Ikenmeyer is Professor in Computer Science and Mathematics at the University of Warwick. His research focuses on algebraic complexity theory, geometric complexity theory (GCT), and representation theory, with emphasis on tensor rank, Kronecker coefficients, and polynomial identity testing. Research Focus: Dr. Ikenmeyer develops mathematical frameworks to solve fundamental problems in computational complexity, including P vs NP. His work connects representation theory with algebraic geometry to establish complexity lower bounds and classify computational hardness. Leadership: He organizes workshops on algebraic complexity and GCT, including the 2023 Algebraic Complexity Theory Workshop at ICALP. His research is funded by EPSRC and DFG grants, supporting investigations into homogeneous complexity and branching programs. Teaching: Courses include 'Groups and Representations' and programming contest coaching. He has previously taught at MIT, Texas A&M, and Saarland University, developing lecture notes on GCT.
Christos Gagatsos is an Assistant Professor in the Department of Electrical and Computer Engineering and a member of the Wyant College of Optical Sciences at the University of Arizona. He joined the university in 2018 as a postdoctoral research associate, was promoted to Assistant Research Professor in 2020, and became an Assistant Professor in ECE in 2023. Prior to this, he was a postdoctoral research fellow at the University of Warwick, UK. His educational background includes: PhD in Engineering Sciences and Technology, Université Libre de Bruxelles and École Polytechnique, Belgium (2014) MSc in Physics of Elementary Particles, University of Athens, Greece (2010) BSc in Physics, University of Athens, Greece (2007) Christos Gagatsos's research lies at the intersection of quantum information, quantum sensing, and quantum communications, with a strong theoretical focus on bosonic systems. His work explores fundamental concepts such as entanglement, non-Gaussianity, and Bayesian estimation in quantum systems. He is particularly interested in pushing the limits of quantum-enhanced sensing, including optical phase and transmissivity estimation, and in developing theoretical frameworks for quantum detection and discrimination. His teaching interests include quantum information, quantum optics, probability theory, and applied mathematics. The recent trend in his publications reflects a deep engagement with Bayesian methods in quantum parameter estimation, quantum change point detection, and the characterization of quantum states through measures like Wigner entropy. His work spans both fundamental quantum theory and practical applications in sensing and communication, often bridging classical and quantum approaches. He advises several graduate students across departments, including Boyu Zhou (Physics), Ali Cox (Physics), Qipeng Qian (Mathematics), and Leo Bia (Optical Sciences). While no formal scientific awards are listed in the provided text, editorial recognition such as an Editor’s Pick in APL Quantum highlights the impact of his research. Christos Gagatsos leads a research group focused on theoretical quantum information, actively collaborating with quantum research groups across the University of Arizona, Arizona State University, and international institutions in the USA and Europe. His lab investigates quantum sensing, communications, and foundational aspects of quantum mechanics using bosonic platforms, fostering a collaborative and interdisciplinary research environment.
