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Andrew JordanView profile

Professor

Chapman UniversityUnited States

Andrew Jordan is a Professor and Co-Director of the Institute for Quantum Studies at Chapman University's Schmid College of Science and Technology . Previously, he was a Professor of Physics at the University of Rochester (2006-2021) after earning his PhD in Theoretical Physics (2002) from University of California, Santa Barbara and BS in Physics and Mathematics (1997) from Texas A&M University . NSF CAREER Award (2009) Simons Fellow in Theoretical Physics (2017) Research Focus His work spans quantum measurement theory , superconducting qubit design , and quantum thermoelectric phenomena . Recent publications examine Kerr-cat qubit stabilization , quantum refrigeration cycles , and supergrowth optical field generation for subwavelength imaging. Key technical innovations include on-chip band-block filters for reduced Purcell decay and phase-modulated quantum gates in cat qubit systems. Scientific Impact 2024 Nature Physics paper on programmable Heisenberg interactions between Floquet qubits 2024 Physical Review X demonstration of 2D Kerr-cat qubit with 99.6% QND readout 2025 arXiv preprint introducing STS-based Kerr-cat architecture with 10ms coherence times His research group investigates noise-biased quantum error correction and quantum control protocols that leverage nonlinear quantum systems and parametric Hamiltonian engineering . Current projects involve integrated quantum sensing devices and thermodynamically optimized measurement engines .

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Panagiotis ToulisView profile

Associate Professor

Pompeu Fabra UniversitySpain

Panagiotis Toulis (Panos) serves as Associate Professor of Econometrics and Statistics and John E. Jeuck Faculty Fellow at the University of Chicago Booth School of Business, where his research focuses on model-agnostic causal inference methods for complex networked environments using randomization techniques that provide robustness beyond classical statistical approaches. His academic credentials include: Ph.D. in Statistics from Harvard University (2016) under Edo Airoldi, David Parkes, and Don Rubin Master’s in Computer Science from Harvard University (2011) under David Parkes M.Eng. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece (2006) Toulis pioneers randomization and permutation tests for causal inference under network interference, developing methods applicable to experimental design in complex domains where traditional model-based approaches fail. His work bridges statistics , optimization , and economics , with significant contributions to invariance-based inference for observational studies and machine learning integration through stochastic gradient descent frameworks. This research has practical applications in business analytics, vaccine distribution, and economic policy. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in extending randomization tests to handle network spillover effects, two-sided market experiments, and temporal dynamics with treatment habituation. A growing emphasis on machine learning assistance for complex treatment effect detection demonstrates his innovative fusion of statistical rigor with computational scalability, consistently providing finite-sample theoretical guarantees uncommon in asymptotic-focused literature. Toulis has earned recognition through prestigious awards: Arthur P. Dempster Award from Harvard University’s Department of Statistics LinkedIn Economic Graph Challenge award 2012 Google United States/Canada PhD Fellowship in statistics He teaches core Booth courses including Applied Regression Analysis (41100), Causal Inference for Business Applications (41207), and the Econometrics and Statistics Colloquium (41600), while maintaining active industry connections through prior software engineering roles at Google and Greek startups. His research appears in top journals including Journal of the Royal Statistical Society and Annals of Statistics, addressing critical methodological challenges in modern data science. No dedicated laboratory structure is documented, but his work inherently involves collaborative research networks across statistics, economics, and computer science disciplines through the Booth School’s academic infrastructure.

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Federico CastagnaView profile

Lecturer

Brunel University LondonUnited Kingdom

Dr Federico Castagna serves as a Lecturer in Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences. His academic journey spans philosophy and informatics, featuring a Ph.D. from King's College London and prior degrees from the University of Milan. Education: Ph.D. in Informatics, King's College London, UK Master's in Philosophical Sciences, University of Milan, Italy Bachelor's in Philosophy, University of Milan, Italy Associate Fellowship of the Higher Education Academy Castagna's research pioneers argumentation-based Explainable AI (XAI) , developing frameworks like the Explanation-Question-Response (EQR) dialogue system to demystify AI decisions. His work in Computational Argumentation tackles inconsistent information handling through non-monotonic reasoning, with applications in medical AI where transparency is critical. Recent publications reveal a strategic shift toward user-centered XAI tools —particularly chatbots—that bridge formal logic with human cognition, addressing ethical gaps in black-box systems while optimizing for resource-bounded agents. Scientific Recognition: Associate Fellowship of the Higher Education Academy Castagna actively supervises PhD candidates in AI ethics, XAI methodology, and computational argumentation applications. His EPSRC-funded CONSULT project at the University of Lincoln established foundational work in argumentative explainability. As co-founder and editor-in-chief of the Online Handbook of Argumentation for AI (OHAAI) , he drives community knowledge integration. Current collaborations with researchers like Sassoon, Parsons, and Sklar focus on medical-domain implementations where argumentative explanations enhance clinician trust and decision quality.

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Siddhartha ChatterjeeView profile

Teaching Professor

University of Texas at AustinUnited States

Siddhartha Chatterjee serves as a Professor of Instruction in the Department of Computer Science at the University of Texas at Austin. With 25+ years of cross-sector experience spanning major corporations, government research laboratories, and academic institutions, he leverages a distinctive fusion of technical expertise, business acumen, and executive leadership capabilities. His faculty role emphasizes translating complex technological concepts for non-specialist audiences while maintaining active research engagement. His scholarly work centers on three interconnected domains: computer architecture (particularly cache-efficient systems and SIMD acceleration), programming languages and compilers (focusing on array expression optimization), and scientific computing (with emphasis on parallel algorithms). Key contributions include foundational research on recursive memory layouts for matrix operations, theoretical frameworks for cache-conscious algorithms, and hardware-software co-design for supercomputing platforms like IBM BlueGene/L. His publications consistently address performance bottlenecks in high-performance computing through algorithmic innovation and architectural insights. Chatterjee's recent publications reveal an evolving trajectory toward hardware-aware software design, where compiler techniques increasingly intersect with processor microarchitecture. His work demonstrates consistent focus on bridging theoretical computer science with practical engineering constraints, particularly in memory hierarchy optimization and parallel execution models. While maintaining core expertise in classical architecture domains, his research shows growing relevance to emerging challenges in exascale computing and energy-efficient design.

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Michael MavrosView profile

Lecturer

Wellesley CollegeUnited States

Michael Mavros is a Lecturer in the Department of Chemistry at Wellesley College. His academic journey includes a Ph.D. from the Massachusetts Institute of Technology and a B.S. from the University of Florida. Dr. Mavros specializes in theoretical and computational chemistry, with a focus on electron transfer reactions and catalytic material design using density functional theory. His research interests span: Quantum mechanical modeling of redox reactions Computational catalyst development Condensed-phase non-adiabatic dynamics Surface chemistry of silicon materials Chemical education innovation Recent work explores: Electronic processes in extraterrestrial environments Non-adiabatic dynamics in transition metal oxides Carbene-silicon surface interactions Memory kernel approaches for quantum dynamics Currently engaged in transforming chemistry laboratory education through active learning strategies that emphasize student autonomy and research-like experimentation.

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Arne MeierView profile

Professor

Leibniz University HannoverGermany

Arne Meier is a Professor at Leibniz Universität Hannover, affiliated with the Faculty of Electrical Engineering and Computer Science and the Institute of Theoretical Computer Science. He heads the Algorithms research group, focusing on theoretical aspects of computer science with applications to artificial intelligence and database systems. Meier obtained all his academic degrees—Bachelor's, Master's, PhD, and Habilitation—at Leibniz Universität Hannover, establishing a strong foundation in theoretical computer science. His academic journey at the same institution reflects his deep commitment to advancing research in computational theory. Meier's research spans several interconnected areas in theoretical computer science. His primary focus is on complexity theory, particularly the parameterized complexity of problems in non-classical logics with applications to AI. He also investigates enumeration algorithms and the logical foundations of artificial intelligence. His work bridges theoretical computer science with practical applications in knowledge representation and reasoning systems. He has a notable interest in LaTeX and typography, having developed the 'timeline' package for creating timelines in LaTeX documents. His recent publications (2023-2025) demonstrate a consistent focus on the intersection of logic, complexity, and artificial intelligence. Meier's work shows progression from foundational research in dependence and team logics toward more applied areas in argumentation theory and database systems. His research increasingly addresses computational challenges in AI systems, particularly in reasoning under uncertainty and handling inconsistent information. Meier actively contributes to the academic community through extensive program committee service for major conferences including AAAI (2021, 2023, 2024, 2025), IJCAI (2021-2025), and FoIKS (2024 as Co-Chair, 2026). He has also served as a reviewer for numerous conferences and journals in theoretical computer science and artificial intelligence. His current research projects include the DAAD-funded 'Applications and Complexity of Logics in Semiring-Team-Semantics' (2024-2025) and the DFG project 'Team Logics: New Bridges to Database Repairs' (2023-2026). Previously, he led the DFG project 'Nonclassical logics: parametrised and enumeration complexity' (2013-2022) and the MWK project 'Innovation Plus: Komplexität von Algorithmen' (2020-2022). Meier leads the Algorithms research group at Leibniz Universität Hannover, which focuses on theoretical aspects of algorithms with applications to logic and artificial intelligence. The group's work spans complexity theory, logical formalisms, and their applications to computational problems in knowledge representation and database systems.

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Pascale TremblayView profile

Professor

Laval UniversityCanada

Pascale Tremblay is a Full Professor in the Department of Rehabilitation within the Faculty of Medicine at Université Laval. She holds the prestigious Canada Research Chair in Neurobiology of Speech and Hearing and serves as Director of the Speech and Hearing Neuroscience Laboratory ( https://speechneurolab.ca/en/ ). Her research program integrates cognitive neuroscience, linguistics, and rehabilitation science to advance understanding of human communication processes. Dr. Tremblay's research focuses on the cognitive neuroscience of human communication, with particular emphasis on speech and voice perception and production across the lifespan. Her work investigates how communication mechanisms evolve with aging, including the effects of cognitive and sensorimotor changes on communication quality in social contexts. She employs a multidisciplinary approach using state-of-the-art methods including functional and anatomical brain imaging (fMRI), non-invasive brain stimulation (TMS), behavioral assessments, and physiological measurements of speech production. Her recent publications reveal a strong trajectory examining how musical activities, particularly singing, may protect or enhance speech perception and production in aging populations. Her research demonstrates expertise in understanding neural networks involved in language processing, moving beyond classical models to reveal complex brain connectivity underlying speech functions. This work has important implications for developing interventions to maintain communication abilities in older adults. 2018 Early Career Award from the Society for the Neurobiology of Language Career award from the Fonds de Recherche en Santé du Québec (FRQS) As founder of the Society for the Neurobiology of Language and director of her neuroscience laboratory, Dr. Tremblay actively mentors students and collaborators while pursuing innovative research on aging and communication. Her team studies speech mechanisms through multiple approaches including acoustical analyses, speech error patterns, facial electromyography, and respiratory patterns to understand how these processes evolve across the lifespan.

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Paolo SerenaView profile

Associate Professor

University of ParmaItaly

Paolo Serena is Associate Professor of Telecommunications at the University of Parma , Department of Engineering and Architecture. Since 2018 he has held this academic rank after serving as a researcher at the same university from 2005. He teaches Optical Communications in the Master’s program in Communication Engineering and Telecommunication Systems in the Master’s program in Computer Science, Electronics and Telecommunications. In addition to his university duties, he maintains active third-mission collaborations with Nokia Bell-Labs and Alcatel Submarine Networks in Paris, France. Education Ph.D. in Information Technology, University of Parma, 2000-2003 Laurea (M.Sc.) in Electronic Engineering, University of Parma, 1999 Thesis: “Theoretical Study of the Interaction between Chromatic Dispersion and Nonlinear Effects in Fiber Optic Systems” Research Interests Prof. Serena’s research spans the full spectrum of fiber-optic communication systems . His work focuses on understanding and mitigating nonlinear effects in both single-mode and space-division multiplexed fibers, developing numerical models for signal propagation, exploiting digital signal processing techniques for coherent receivers, and creating performance monitoring tools. He is the original author of Optilux , an open-source simulation platform widely used in the optics community. Recent investigations include modal dispersion in few-mode and multi-core fibers, machine-learning-assisted optimization of launch powers, and quantum-classical co-propagation in field-deployed multi-core links. These studies converge toward increasing the capacity, reach, and energy efficiency of next-generation optical networks. Scientific Service & Editorial Activity Associate Editor, Optics Express (OSA Publishing) Technical Committee Member, European Conference on Optical Communications (ECOC) , since 2018 Technical Committee Member, Fotonica (Italian National Conference on Photonic Technologies), since 2013 Collaborations & Tools Within the framework of university–industry collaboration, Prof. Serena works closely with Nokia Bell-Labs and Alcatel Submarine Networks on modeling and performance optimization of long-haul and submarine links. His open-source simulator Optilux is continuously extended to include the latest advances in nonlinear propagation modeling and machine-learning-enhanced system design.

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Pasquero StefanoView profile

Researcher

University of ParmaItaly

Prof. Stefano Pasquero is a confirmed University Researcher in the Department of Mathematical, Physical and Computer Sciences at the University of Parma since 1993. He teaches Rational Mechanics in the Mathematics Bachelor's program. Degree in Mathematics (1987, cum laude) PhD in Mathematics (1995, Pisa-Parma-Ferrara-Bari-Lecce consortium) His research focuses on geometric methods in classical mechanics, impulsive constraints, and second-order differential equations. Publications highlight applications of fibered manifolds, algorithmic collision modeling, and symmetry analysis in mechanical systems. Recent works emphasize educational tools for mechanics, including exam problem design and theoretical frameworks. His office is located in the Mathematical Complex at Science Park Area 53/a, Parma, and can be contacted at stefano.pasquero@unipr.it .

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Hubert de GuiseView profile

Professor

Lakehead UniversityCanada

Hubert de Guise is a Professor in the Department of Physics at Lakehead University. His research focuses on Theoretical Physics and Mathematical Physics , particularly SU(n) symmetries, quantum interferometry, and Wigner functions. University: Lakehead University Email: hdeguise@lakeheadu.ca, hubert.deguise@lakeheadu.ca Contact: Office CB4033, Phone: +1 (807) 343-8010 ext. 8468 His work spans quantum mechanics, phase space methods, and symmetry-based approaches in quantum systems. Key themes include SU(3) and SU(2) group applications, finite-dimensional quantum systems, and polarization coherence. Notable collaborations include researchers from the Canadian Association of Physics, Canadian Prairie Theoretical Physics Network, and Fields Institute. His publications address topics such as quantum tomography, squeezing transformations, and discrete Fourier methods.

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Ioakeim AmpatzoglouView profile

Assistant Professor

City University of New YorkUnited States

Ioakeim Ampatzoglou serves as an Assistant Professor in the Department of Mathematics at the Weissman School of Arts and Sciences, City University of New York (CUNY). His academic profile centers on advanced mathematical research with significant contributions to kinetic theory and partial differential equations. His educational trajectory includes a Ph.D. in Mathematics from The University of Texas at Austin (United States) and a B.Eng in Electrical and Computer Engineering from the National Technical University of Athens (Greece). These foundational qualifications underpin his rigorous analytical approach to complex mathematical problems. Dr. Ampatzoglou's research program focuses on evolutionary partial differential equations, mathematical physics, and kinetic theory. He specializes in deriving and analyzing fundamental equations like the Boltzmann equation and kinetic wave equations, with particular emphasis on well-posedness, scattering theory, and moment estimates for collisional processes. His work bridges abstract mathematical analysis with physical applications in statistical mechanics. His 14 publications from 2020-2024 reveal a cohesive research trajectory centered on kinetic theory. Key themes include rigorous derivations of higher-order Boltzmann equations for hard spheres and dense gases, global well-posedness for binary-ternary collision models, and scattering theory for inhomogeneous kinetic wave equations. These contributions demonstrate increasing mathematical sophistication in handling nonlinear phenomena across fluid dynamics and statistical physics. Dr. Ampatzoglou actively serves the academic community as a reviewer for prestigious journals including Nonlinearity , SIAM Journal on Mathematical Analysis , and Journal of Statistical Physics , as well as for National Science Foundation grant proposals. He also contributes institutionally through committee service at Baruch College, including the Calculus Committee and Finals Committee.

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Robert BrownView profile

Senior Lecturer

Duke UniversityUnited States

Robert Brown is a Senior Lecturer of Physics at Duke University's Trinity College of Arts & Sciences since 2023. His research focuses on applying algebraic and statistical methods to study equilibrium and nonequilibrium phenomena in quantum optics and magnetism, particularly through Monte Carlo simulations of critical behavior in Heisenberg models. Ph.D., Duke University (1982) His work bridges computational physics with theoretical models, including development of Langevin equation-based techniques for dynamic systems analysis. Research spans quantum mechanics, magnetism, and critical phenomena in condensed matter systems. Key publication trends include: classical Heisenberg model critical behavior studies (2006), finite size scaling (2005), and foundational contributions to non-muffin-tin band theory (1983-1988). His computational physics expertise extends to Beowulf cluster optimization (2000). He received a Monte Carlo Studies of Continuous Hamiltonian Systems Coupled to Dissipative Mechanisms grant from the Army Research Office (2001-2005). Current appointments include Senior Lecturer (2023-present), while previous roles include Lecturer (2010-2023) and Visiting Professor (2001-2010).

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Robert T. KönigView profile

Associate Professor

Technical University of MunichGermany

Prof. Dr. Robert T. König is an Associate Professor at the Technical University of Munich (TUM) , holding the Professorship of Theory of Complex Quantum Systems in the TUM School of Computation, Information and Technology and Department of Mathematics. His research focuses on quantum information theory, with particular emphasis on mathematical methods for quantum communication, fault-tolerant quantum computing, and quantum many-body systems. Education: Diploma in Theoretical Physics, ETH Zurich (1998–2003) PhD in Applied Mathematics and Theoretical Physics, University of Cambridge (2005–2007) Key research areas include quantum communication theory , fault-tolerant quantum information processing , and quantum computation , with significant contributions to topological quantum computing, quantum error correction, and quantum channel capacity analysis. His recent work explores hybrid quantum-classical algorithms, non-abelian anyon manipulation, and bosonic code optimizations. Scientific awards include Swiss National Science Foundation Fellowship (2010) Smith/Rayleigh-Knight Prize, Cambridge (2006) ETH Medal and Willi-Studer Prize (2003) Pólya Prize, ETH Zurich (2003) He co-leads the Quantum Information Theory research group with Prof. Michael Wolf at TUM, supported by grants like the ERC Consolidator Grant 'Enhanced quantum information processing targeting the near term (EQUIPTNT)' and the Munich Quantum Valley initiative . His work has applications in quantum hardware design, noise resilience strategies, and fundamental limits of quantum communication.

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Luca CapognaView profile

Professor

Smith CollegeUnited States

Luca Capogna is the Mary Augusta Jordan Professor of Mathematical Sciences at Smith College. Prior to joining Smith, he held positions as a Courant Instructor at NYU, a Rademacher Instructor at the University of Pennsylvania, and tenured positions at the University of Arkansas and Worcester Polytechnic Institute (where he served as department head from 2013-2020). From 2011-2013, he was a visitor at the University of Minnesota, serving as associate director of the Institute for Mathematics and its Applications (IMA). Capogna earned his Ph.D. from Purdue University and his B.S. from the Second University of Rome, Tor Vergata (Italy). His research spans analysis and partial differential equations, with specific interests in quasiconformal mappings, sub-Riemannian geometry, and geometric flows. In recent years, he has expanded his research to include machine learning applications, particularly using neural networks for system identification problems. His work demonstrates a strong connection between classical mathematical analysis and modern computational techniques. Capogna's publication record shows a clear evolution from pure mathematical analysis (particularly in sub-Riemannian geometry and PDE theory) to more applied work incorporating machine learning. His recent publications (2021-2024) increasingly focus on neural networks, system identification, and deep learning applications, while maintaining strong connections to his foundational work in analysis. Mary Augusta Jordan Professorship at Smith College Capogna has collaborated extensively with researchers including Giovanna Citti, Mario Bonk, and Enrico Le Donne. His work has been supported by various research grants, including an NSF RUI grant titled 'PDE and Geometry in non-smooth spaces' (2024). He has served in leadership roles, including as department head at Worcester Polytechnic Institute and associate director of the IMA at the University of Minnesota. His research has significant implications for both theoretical mathematics and practical applications in system modeling and identification, bridging the gap between pure mathematical analysis and modern computational approaches.

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Gaurav MahajanView profile

Lecturer

University of RochesterUnited States

Gaurav Mahajan serves as a Postdoctoral Associate in Yale University's Department of Computer Science under Dan Spielman's mentorship, concurrently holding lecturer appointments for specialized courses including CPSC 648: Quantum Codes (Fall 2024) and the ICTS Reinforcement Learning Theory Bootcamp (Fall 2025). His academic journey includes a PhD from UCSD's Theory Group advised by Sanjoy Dasgupta and Shachar Lovett, with research summers at Microsoft Research, Institute for Advanced Study, and Simons Institute. His research spans machine learning theory with dual emphases on quantum learning (quantum error correction applications to complexity theory) and reinforcement learning (computational-statistical gaps, sample complexity). Recent publications in COLT, ALT, and NeurIPS reveal a trajectory from foundational PAC learning theory toward quantum-enhanced complexity analysis, featuring collaborations with leading theorists including Sham Kakade, Shachar Lovett, and Daniel Kane. Teaching activities demonstrate specialized expertise: his Quantum Codes course progresses from basic stabilizer codes to quantum Tanner codes using chain complex formalisms, while the RL Bootcamp bridges PyTorch implementations with theoretical complexity analysis. Though not leading a formal lab, his work connects Yale's quantum initiative with theoretical computer science communities through institutes like Simons. Current research shows increasing focus on quantum-classical connections—applying quantum information tools (e.g., classical shadows) to classical learning problems—while maintaining rigorous theoretical standards evidenced by consistent publications in top theory venues. Future directions likely involve deeper quantum complexity applications and expanding the quantum-RL interface.

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