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Thomas TradlerView profile

Professor

• Algebraic Topology

Thomas Tradler is a Professor of Mathematics at New York City College of Technology, a constituent college of the City University of New York. He received his Ph.D. in Mathematics from The Graduate Center, CUNY. His research focuses on Algebraic Topology, with applications spanning operads, K-theory, homotopy theory, and mathematical physics. Education: Ph.D. in Mathematics, The Graduate Center, City University of New York His work explores advanced mathematical structures including operads, K-theory extensions, factorization algebras, and connections between topology and quantum field theory. Research employs techniques from homological algebra, differential geometry, and combinatorial methods to investigate algebraic and topological invariants. Recent publications (15 most recent from 2006-2018) primarily appear in specialized mathematics journals. The articles demonstrate consistent focus on algebraic topology, K-theory, and mathematical physics, with evolving applications to quantum backgrounds, factorization algebras, and geometric structures. Later works show increased emphasis on differential extensions of K-theory and interactions with physics.

Ondřej KalendaView profile

Professor

• Functional Analysis
• Topology
• Banach Space Theory
• +3 more

Prof. Ondřej Kalenda is a faculty member at the Department of Mathematical Analysis, Faculty of Mathematics and Physics, Charles University, Prague. His research focuses on functional analysis, topology, and Banach space theory, with significant contributions to Valdivia compacta, JB*-triples, and weak non-compactness measures. Education : DSc (Doctoral Thesis, 2011), Habilitation (2004), PhD (1997), all from Charles University. Research Interests : Functional analysis, topological properties of Banach spaces, operator algebras, measure theory, and descriptive topology. Publications : Over 40 research articles on topics like Valdivia compact spaces, projectional skeletons, JB*-triples, and quantitative versions of classical theorems (Schur, Dunford-Pettis). Academic Roles : Guarantor of the Mathematical Analysis master’s program since 2020/21; participant in seminars on Real and Abstract Analysis and Winter Schools. Contact : Email: Ondrej.Kalenda@mff.cuni.cz , kalenda@karlin.mff.cuni.cz Office: K 254 (3025), Karlín, Sokolovská 83, Prague 8

Mattia ZorziView profile

Associate Professor

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +8 more

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

• Data Science
• Machine Learning
• Tensor Factorization
• +5 more

Dr Thiru Balasubramaniam is a Research Fellow at Queensland University of Technology (QUT), working within the Faculty of Science, School of Computer Science. His expertise lies at the intersection of data science, machine learning, and real-world applications, with a specific focus on tensor factorization methods for managing multifaceted data from IoT and Web 3.0 applications. His educational background includes a PhD from Queensland University of Technology and a Bachelor of Engineering from Anna University. Prior to his doctoral studies, he worked as a Research Assistant at the Singapore University of Technology and Design - Massachusetts Institute of Technology (SUTD-MIT) International Design Centre, where he analyzed mobility data to personalize city environments for elderly citizens in Singapore. Dr Balasubramaniam's research interests span multiple areas of data science: Tensor and Matrix Factorization methods Pattern Mining and Text Mining applications Recommender Systems development IoT data processing Web 3.0 applications Real-time analytics for multifaceted data His publication record demonstrates consistent contributions to high-impact venues including IEEE TKDE, ACM TKDD, WWW, WISE, AusDM, and PRICAI. The trend in his recent work shows increasing application of tensor factorization techniques to diverse real-world problems including environmental monitoring, pandemic modeling, social media analysis, and smart grid technology. His research often involves interdisciplinary collaborations, particularly with Professor Richi Nayak at QUT. Scientific recognition includes: QUT-CDS first byte research funding worth 30,000 AUD Dr Balasubramaniam has been actively involved in teaching data analytics subjects at QUT since 2017, including Data Exploration and Mining, Data and Web Analytics, Data Mining Technology and Applications, and Web Computing. His teaching spans both undergraduate and postgraduate levels. His research has been supported through various collaborative grants and institutional funding mechanisms at QUT.

Mareike DresslerView profile

Senior Lecturer

• Real Algebraic Geometry
• Computational Algebraic Geometry
• Polynomial Optimization
• +6 more

Mareike Dressler is a Senior Lecturer (A/Professor, tenured) and ARC Discovery Early Career Research Fellow at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). She joined UNSW in February 2022 as a Lecturer (Assistant Professor, tenure-track) and was promoted to Senior Lecturer with tenure in July 2024. Her educational background includes: PhD in Mathematics from Goethe-Universität Frankfurt/Main (2018), supervised by Thorsten Theobald M.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2013) B.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2010) Mareike's research focuses on real and computational algebraic geometry and polynomial and convex optimization. She also works on problems intersecting with convex geometry, matrix and tensor computation, applied algebraic geometry, algebraic and geometric combinatorics, and real analysis. Her work particularly involves nonnegativity of polynomials, optimization methods, and ranks of matrices and tensors, with a special interest in sums of nonnegative circuits (SONCs) for sparse polynomials. Her research has strong applications in data science and machine learning. Mareike has received several prestigious awards including the ARC Discovery Project 2025 grant for "Quantifying Uncertainty of Risk-Aware Optimization for Safe Decision-Making," the J G Russell Award from the Australian Academy of Science, and the Early Career Impact Award from UNSW Science. She actively supervises research students, including PhD student Hongzhi Liao, Master's student Qi Wang, and recently supervised Moritz Schick to completion of his PhD in 2025. Mareike has secured significant research funding, including an ARC Discovery Early Career Researcher Award (DECRA) for 2024-2026. Mareike is an active member of the academic community, regularly organizing workshops and conferences such as "Optimization Days" at UNSW and minisymposia at major conferences like SIAM Conference on Applied Algebraic Geometry.

• Nuclear Physics
• Particle Physics
• Quark-Gluon Dynamics
• +2 more

Gail Dodge is Dean of the College of Sciences and Professor of Physics at Old Dominion University (ODU), where she has served since 1995. An experimental nuclear physicist, she investigates proton/neutron structure using high-energy electron beams at Jefferson Lab, focusing on quark-gluon dynamics. She previously chaired ODU's Physics Department (6 years), managed NSF's $17M nuclear physics program, and currently chairs the Nuclear Science Advisory Committee advising DOE/NSF on funding priorities. Education: Ph.D. in Physics, Stanford University (1993) M.S. in Physics, Stanford University (1988) B.A. in Physics, Princeton University (1986) Her research explores nucleon substructure, spin asymmetries, and exotic hadronic states, with grants exceeding $7M from NSF/DOE. Recent projects include CLAS detector experiments measuring deuteron spin structure and color transparency in ρ⁰ electroproduction. Her work advances understanding of quark-hadron transitions and nuclear correlations. Publications emphasize precision measurements of spin-dependent cross sections, nucleon resonances, and electroproduction processes. Dominant themes include spin structure functions (e.g., g₁ ), generalized parton distributions, and validation of QCD predictions via polarization observables and exclusive reactions. Awards: Outstanding Faculty Award, State Council of Higher Education for Virginia (2015) Gene W. Hirschfeld Faculty Excellence Award (2012) College of Sciences Faculty Excellence Award (2003) Luise Meyer-Schutzmeister Memorial Award (1991) She advises major initiatives like the CLAS spectrometer experiments and directs the From Quarks to Nuclei NSF grant ($2.3M). At Jefferson Lab, she leads teams studying neutron structure via spectator tagging and develops radial TPCs for BONuS experiments.

Eric MillerView profile

Professor

• Signal and Image Processing
• Tomographic Image Formation
• Inverse Problems
• +4 more

Eric Miller is a Professor of Electrical and Computer Engineering at Tufts University's School of Engineering. He also holds adjunct professorships in Computer Science, Biomedical Engineering, and Mathematics. His academic roles include serving as Chair of the Electrical and Computer Engineering department and leading the Lab for Imaging Science Research (LaISR). Miller earned his SB, SM, and PhD in Electrical Engineering from MIT (1990–1994). His research focuses on signal and image processing, particularly inverse problems, tomographic imaging, and applications in medical imaging, environmental monitoring, and security screening. He has pioneered methods like the parametric level-sets (PaLEnTIR) for reconstruction and shape-based inversion algorithms. His work integrates physics-based modeling with computational techniques, addressing challenges in subsurface sensing, biomedical diagnostics, and materials science. Miller is a Fellow of IEEE and a member of honor societies like Tau Beta Pi and Phi Beta Kappa. Over 489 publications reflect his contributions to imaging science, with recent advancements in AI-driven pedestrian behavior analysis and X-ray anomaly detection.

Marc CASTELLAView profile

Lecturer

• Signal Processing
• Blind Source Separation
• Neural Networks
• +3 more

Prof. Marc CASTELLA is a Lecturer at Telecom SudParis, part of the SOP (Signal and Optimization Processing) unit. His research focuses on signal processing, particularly in blind source separation, nonlinear reconstruction, and optimization techniques. He has extensively contributed to areas such as sparse signal recovery, neural networks, and tensor decomposition. His work often addresses challenges in noisy environments and nonlinear systems. Recent publications include advancements in clipped signal recovery, motor torque estimation, and global optimization methods. Though no specific awards are listed, his prolific publication record reflects his impactful contributions to signal processing and applied mathematics. He collaborates with researchers like Jean-Christophe Pesquet and Arthur Marmin on projects involving polynomial systems and neural network models. His work is applied in diverse fields from industrial electronics to data analysis.

Vladimir BraunView profile

Professor

• Quantum Chromodynamics
• Nonperturbative Phenomena
• Hadron Structure
• +2 more

Prof. Dr. Vladimir Braun leads the Theoretical Physics chair at the University of Regensburg's Faculty of Physics. His research group investigates nonperturbative aspects of quantum chromodynamics and hadronic structure. Key research areas include: QCD factorization for hard exclusive processes Conformal symmetry applications Light-cone distribution amplitudes Lattice QCD computations Recent publications focus on higher-order corrections to deeply virtual Compton scattering, precision determinations of meson/baryon structure, and renormalization challenges in lattice simulations.

Liavas AthanasiosView profile

Professor

• Signal Processing
• Communications
• Information Theory
• +4 more

Athanasios Liavas is a Professor at the Technical University of Crete (TUC), School of Electrical and Computer Engineering (ECE), where he has served as Department Chair (2009-2011) and Vice Chair (2011-2013). He holds a Diploma (1989) and PhD (1993) in Computer Engineering and Informatics from the University of Patras. His career includes postdoctoral research at the Institut National des Télécommunications (1996-1998) as a Marie Curie Fellow, and academic roles at the University of Ioannina and the University of the Aegean before joining TUC in 2004 as Associate Professor. He has been a Professor since 2009. His research focuses on Signal Processing for Communications , Information Theory , and Telecommunications , with recent emphasis on tensor decomposition techniques for biomedical signal analysis and machine learning applications. He leads the Telecommunications Laboratory and teaches courses such as Digital Communication Systems II and Wireless Communication Systems. He served as an Associate Editor for the IEEE Transactions on Signal Processing (2005-2009) and was a member of the IEEE SP COM Technical Committee (2006-2011). His recent work includes advancements in nonnegative tensor completion, parallel algorithms for large-scale tensor factorization, and generalized canonical correlation analysis for multi-subject fMRI data. These contributions address challenges in high-dimensional data reconstruction and brain imaging signal processing, leveraging stochastic optimization and distributed computing frameworks. Liavas has authored over 80 peer-reviewed articles, with key contributions in IEEE journals and conferences. His research spans theoretical signal processing, algorithm design, and practical implementations for telecommunications and biomedical engineering.

Monique LaurentView profile

Professor

• Discrete Optimization
• Semidefinite Programming
• Polynomial Optimization
• +4 more

Monique Laurent is a senior researcher at CWI Amsterdam in the Networks and Optimization Group and holds a part-time appointment as a full professor at the Department of Econometrics and Operations Research of Tilburg University. Her academic career spans institutions in France (CNRS, CNET), Germany (Humboldt Fellow at the University of Bonn), and the USA (visiting scientist at Yale). She is a SIAM Fellow (2017) and recipient of the Khachiyan Prize (2023) . PhD in Mathematics, University of Paris Diderot (1986) Research interests: discrete optimization, semidefinite programming, polynomial optimization, quantum information, and algebraic methods. Her work focuses on bridging continuous and discrete optimization, with applications to quantum information. She has organized major workshops such as the Learning Enhanced Optimization research semester at CWI and contributed to the TENORS EU project (2024-2027). Recent publications highlight her contributions to semidefinite programming hierarchies, polynomial optimization, and their applications to graph theory and quantum computing. Key article trends include: Analysis of sum-of-squares and moment matrix hierarchies for polynomial optimization Applications to quantum information, graph stability, and matrix factorization Convergence rates and error bounds for semidefinite relaxations Scientific awards: Khachiyan Prize (2023) SIAM Fellow (2017) Humboldt Fellowship (1991-1992) She has supervised numerous academic initiatives and students, including a master project on polynomial equation solutions at CWI. Her editorial roles include associate editorships for journals like SIAM Journal on Optimization and Numerical Algebra, Control and Optimization .

• Statistical Learning Theory
• Recommender Systems
• Matrix Completion
• +5 more

Antoine LEDENT is a tenure-track Assistant Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), holding a PhD in Mathematics from the University of Luxembourg (2017) and undergraduate/graduate degrees from Clare College, University of Cambridge. His academic trajectory includes a postdoctoral position at TU Kaiserslautern under Prof. Marius Kloft, establishing his foundation in theoretical computer science. His educational background spans rigorous mathematical training at Cambridge followed by doctoral research on stochastic differential equations at Luxembourg, directly informing his current theoretical work. This path transitioned into computer science through postdoctoral research in algorithmic learning theory. Professor LEDENT specializes in Statistical Learning Theory and Recommender Systems, with groundbreaking contributions to generalization bounds for deep neural networks, contrastive learning frameworks, and matrix completion theory. His research uniquely bridges abstract mathematical principles with practical AI applications, particularly in high-dimensional data analysis and robust recommendation engines, emphasizing provable guarantees over empirical results. Analysis of his 15 most recent publications reveals a dominant focus on theoretical machine learning, with 70% concerning generalization analysis in deep learning and matrix completion. His work consistently targets top-tier venues (ICML, NeurIPS, AAAI), demonstrating expertise in transforming mathematical insights into scalable AI solutions, especially for non-IID data and uncertainty-aware systems. Key recognitions include: Outstanding Meta Reviewer (ICML 2025, top 2%) Area Chair appointments for NeurIPS/ICML 2025 Action Editor for TMLR (2024) Certificate of Excellence in Reviewing (KDD 2023) Top Reviewer distinctions at NeurIPS/ICML/ICLR (2022-2024) He actively mentors PhD candidates including AISG Scholarship recipient NONG Minh Hieu and SENARATH ARACHCHIGE Dilan Dinushka, offering fully funded 4-year positions with pathways to Presidential Scholarships. His recruitment targets mathematically strong candidates for projects in Statistical Learning Theory, Matrix Completion, or Recommender Systems, with stipends exceeding 6000 SGD/month for exceptional performers. Based in Singapore's AI hub at SMU—ranked #41 globally in AI (CSRankings)—he operates within SCIS's dynamic ecosystem that connects academia with regional AI startups and industry partners, leveraging Singapore's strategic position at the Asia-Pacific research nexus.

• Signal Processing
• Audio Signal Processing
• Machine Learning
• +7 more

Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.

César A. UribeView profile

Assistant Professor

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +5 more

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

Ajim UddinView profile

Assistant Professor

• FinTech
• Machine Learning in Finance
• Financial Networks
• +5 more

Ajim Uddin is an Assistant Professor at the MT School of Management, New Jersey Institute of Technology (NJIT). His research focuses on FinTech, machine learning applications in finance, and financial network analysis. He holds a PhD in Business Data Science from NJIT (2022), an MBA and BBA in Finance & Banking from Shahjalal University of Science & Technology (SUST), and an MS in Economics & Finance from Southern Illinois University Edwardsville. Education Highlights: Ph.D., Business Data Science, NJIT (2022) M.S., Economics & Finance, SIUE (2017) M.B.A. & B.B.A., Finance & Banking, SUST (2016/2013) Dr. Uddin's research explores dynamic graph learning frameworks for asset pricing, signed networks in equity models, and institutional investor behavior. His work bridges financial theory with advanced computational methods, addressing topics like mutual fund performance prediction, NPL forecasting, and social media influence on stock markets. His methodologies often integrate graph neural networks, tensor completion algorithms, and attention mechanisms. His publications span journals like Quantitative Finance and Finance Research Letters . Recent trends show a focus on applying machine learning to traditional finance problems, with emphasis on network-based models and algorithmic solutions to market dynamics. Awards & Recognition: Best Contribution to Theory Award (2023, NEDSI Conference) Overall Best Conference Paper Award (2023, NEDSI Conference) While no formal advising relationships or grants are listed, his research actively engages with industry-relevant problems such as institutional investor decision-making and financial crisis prediction. His professional website at web.njit.edu/~au76 provides further research details.