Orli Herscovici is an Assistant Professor in the Department of Mathematics and Computer Science at St. John’s College of Liberal Arts and Sciences, St. John’s University. Her research focuses on advanced mathematical analysis, combinatorics, and special functions, with notable contributions to fractional calculus, polynomial theory, and combinatorial identities. She has published extensively on topics including Gaussian principal frequencies, deformed fractional transforms, and degenerate polynomials. Her work bridges pure mathematics and applications in probability theory and nonlinear systems. Dr. Herscovici’s academic affiliations include the Mathematics and Computer Science department, where she contributes to teaching and research initiatives. Her publications reflect a deep engagement with interdisciplinary areas such as spectral geometry and umbral calculus. While no specific scientific awards or grants are highlighted in the provided text, her scholarly output demonstrates sustained academic engagement in theoretical and applied mathematics.
Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.
Mohammed Nabil EL KORSO is a Professor at CentraleSupélec, part of the University of Paris-Saclay. He is affiliated with the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, detection/estimation theory, and robust signal processing, with applications in radioastronomy, radar systems, and source localization. He has contributed extensively to methodologies like Kalman filtering, covariance estimation, and array processing. His work emphasizes robust techniques for handling non-Gaussian noise and interference, particularly in radio interferometry and SAR imaging. Key contributions include algorithms for array calibration, RFI mitigation, and subspace estimation. His research also addresses challenges in distributed and adaptive signal processing, with applications to vital signs monitoring and robotic systems. He has published over 50 journal articles and conference papers, focusing on performance bounds (Cramér-Rao, Weiss-Weinstein), Bayesian methods, and practical implementations for large-scale systems. His recent work includes advancements in low-cost interferometric imaging and phase estimation for SAR time series. EL KORSO collaborates with international projects like the Square Kilometre Array (SKA), contributing to technological developments in radio astronomy. He supervises research in signal processing labs and actively participates in academic conferences such as EUSIPCO and ICASSP.
Dr. Pradeepa Yahampath is a Professor in Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering, specializing in signal processing and multimedia communications. Research interests include: Next-generation video coding techniques Reliable multimedia transmission in wireless systems Joint source-channel coding methods Adaptive transform coding Recent publications develop novel coding schemes for correlated sources, fading channels, and OFDM systems. Theoretical frameworks address bandwidth constraints and channel uncertainty challenges. Research contributes to efficient data representation and robust transmission for multimedia applications in bandwidth-limited environments.
Maxim L. Yattselev is a Professor in the Department of Mathematical Sciences at Indiana University–Purdue University Indianapolis (IUPUI) . He has held prior positions as a Visiting Assistant Professor at the University of Oregon and a Visiting Scholar at Vanderbilt University. Education: Ph.D. and M.S. in Mathematics from Vanderbilt University (2007, 2004), M.S. and B.S. from Dnepropetrovsk National University (2001, 2000). Research Interests focus on meromorphic approximation, orthogonal polynomials, integrable systems, random polynomials, and spectral theory. He explores connections between these areas and applications to Painlevé equations, random matrix theory, and convergence of rational approximants. Publications highlight advancements in strong asymptotics for Angelesco systems, spectral theory of Jacobi matrices on trees, universality in random polynomial roots, and convergence properties of Padé and Hermite–Padé approximants. His work often employs Riemann–Hilbert techniques and potential theory. Advising: Mentored Ph.D. students Hanan Aljubran and Ahmad Barhoumi on random polynomials and multiple orthogonal polynomials. Teaching includes advanced courses in complex analysis, real analysis, orthogonal polynomials, and differential equations at IUPUI, University of Oregon, and Vanderbilt University.
Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.
Nicolas Cerf is a Full Professor at the Ecole Polytechnique de Bruxelles, Université Libre de Bruxelles (ULB), where he heads the Centre for Quantum Information and Communication (QuIC). He has been a faculty member at ULB since 1998, initially as an associate professor and promoted to full professor in 2009. Cerf maintains visiting appointments at Caltech, MIT, and the University of Arizona, demonstrating his international standing in the quantum information community. His educational background includes a M.Eng. in Electronics and Telecommunication (1987), M.Sc. in Physics (1988), and Ph.D. in Physics (1993), all from ULB. After his PhD, he was awarded a Marie Curie fellowship and worked at the University of Paris XI, followed by research faculty positions at Caltech before returning to ULB. Nicolas Cerf's research focuses on quantum information science, with significant contributions including the discovery of the role of negative (conditional) entropies in quantum information theory, development of continuous-variable quantum cloning and cryptographic protocols, invention of the adiabatic quantum search algorithm, and establishing the fundamental quantum limit on information transmission via Gaussian bosonic channels. His work spans quantum information theory, quantum cryptography, quantum computation, quantum optics, and quantum foundations. His recent publications (2023-2025) demonstrate continued innovation in quantum information processing, particularly in boson sampling validation, Wigner entropy theory, majorization applications, and quantum channel capacities. These works show a consistent focus on both theoretical foundations and practical applications of quantum information principles. Marie Curie Excellence Award (2006) Caltech President's Fund award (1997) Alcatel-Bell scientific prize (1999) Prize of the Wernaers fund awarded by the Belgian National Fund for Scientific Research (FNRS) (2000) Elected member of the Royal Academies for Science and the Arts of Belgium (2009) COVAQIAL project nominee for 2007 Descartes Prize Nicolas Cerf has supervised numerous PhD students including Sofyan Iblisdir, Jérémie Roland, Gilles Van Assche, and many others. He has hosted many postdocs and senior scientists. His research has been supported by numerous European projects across multiple Framework Programs, including EQUIP, CHIC, RESQ, SECOQC, COVAQIAL, QAP, COMPAS, HIPERCOM, QALGO, QUCHIP, ShoQC, and AppQInfo. As head of QuIC, Cerf leads a research team exploring cutting-edge topics in quantum information. The group maintains strong international collaborations and has been instrumental in establishing Belgium as a significant player in quantum information research. The team's work bridges theoretical developments with potential applications in quantum communication, quantum computing, and quantum cryptography.
Fumio Okura is a professor at Osaka University , specializing in Computer Vision and 3D Reconstruction . His work bridges Photometric Stereo , Neural Rendering , and Medical Imaging , with a focus on cognitive decline prediction and plant modeling . He collaborates extensively with researchers like Hiroaki Santo and Yasuyuki Matsushita . Education: Ph.D. in Computer Science (Osaka University) Research Interests span Computer Vision , Photometric Stereo , 3D Reconstruction , and Biomedical Applications . His recent work includes HoGS for object reconstruction and TreeFormer for botanical structure estimation. Publications trend toward neural rendering , reflectance modeling , and augmented reality . Notable contributions include PPGCN for cognitive detection and MVCPS-NeuS for multi-view photometric stereo. Labs & Collaborations include the Osaka University Computer Vision Lab , working with teams on photometric analysis and medical imaging .
Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.
Professor Anne L'Huillier is a renowned French/Swedish physicist at Lund University, specializing in atomic physics and attosecond science. She holds the rank of Professor in the Department of Atomic Physics within the Faculty of Engineering (LTH). Her research focuses on ultrafast phenomena, particularly high-order harmonic generation and attosecond light pulses for studying electron dynamics. She leads projects like QU-ATTO and New Trends in Attosecond Science, funded by the EU and Swedish Research Councils. Education: Earned her PhD in 1986 from Université Pierre et Marie Curie (Paris) and CEA. Postdoctoral roles at Chalmers Institute of Technology (1986) and University of Southern California (1988). Became Associate Professor at Lund University in 1995 and Full Professor in 1997. Research Interests: Experimental and theoretical studies of attosecond pulses, laser-atom interactions, and ultrafast electron dynamics. Her work enables insights into quantum processes at the atomic scale. Collaborations span global institutions, advancing applications in condensed matter physics and quantum information science. Key Contributions: Pioneered methods to generate attosecond pulses, recognized by the 2023 Nobel Prize in Physics. Her team's innovations include optimizing attosecond source development and applying these pulses to study electron motion in matter. Awards: Nobel Prize in Physics (2023), Wolf Prize in Physics (2022), and multiple grants including Horizon Europe and Wallenberg Foundation funding. Active in conferences and mentoring, with 34 supervised works and 352 research outputs. Labs/Teams: Principal Investigator at NanoLund, a nanoscience center, and member of strategic initiatives like Light & Materials and Photon Science and Technology profile areas.
Matthew Hale is an Associate Professor in the Department of Electrical and Computer Engineering at Georgia Tech. He holds affiliations with the CORE Lab and focuses on control systems, optimization, robotics, and privacy-preserving algorithms. His work emphasizes multi-agent systems and practical applications in autonomous systems. Education: BSE in Electrical and Computer Engineering from the University of Pennsylvania (2012), PhD in Electrical and Computer Engineering from Georgia Tech (2017). Research Interests: His research bridges theoretical control systems with applied robotics, emphasizing distributed optimization, privacy in networked systems, and deception-resistant decision frameworks. Key areas include multi-agent coordination, privacy-aware control, and safe autonomous operations. Publications Trends: Recent work focuses on differential privacy in optimization, hybrid systems models for control, and scalable algorithms for multi-agent systems. Notable topics include privacy-preserving epidemic modeling and autonomous satellite control. Awards: AFOSR YIP (2023), ONR YIP (2022), AFRL Summer Faculty Fellowship (2020), NSF CAREER Award (2019). Advising & Grants: Leads research teams in multi-agent robotics and privacy engineering. Active in securing grants for foundational control theory and applied robotics projects. Labs/Teams: Core contributor to Georgia Tech’s CORE Lab , advancing interdisciplinary work in control systems and robotics.
Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.
Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
Julien Poisat is a Lecturer (equivalent to Assistant Professor) at CEREMADE, Paris-Dauphine University, where he has been affiliated since 2014. Previously, he was a postdoctoral researcher at Leiden University (2012-2014) and completed his Ph.D. at Lyon 1 University (2008-2012). His research focuses on probability theory and statistical mechanics, with specific interests in disordered systems, polymers, random walks, and large deviations. He investigates phenomena such as localization, pinning, and phase transitions in various models including copolymers, charged polymers, and random environments. His recent publications primarily explore rigorous analyses of stochastic systems, with recurring themes including large deviations for random walks, critical behavior of polymer models, and asymptotic properties of disordered systems. Research often involves mathematical techniques from renewal theory, potential theory, and weak convergence methods. He leads the ANR LOCAL grant (2022-2027) focused on localization phenomena in polymers and random walks. Currently advises two doctoral students: Nicolas Bouchot (2021-2024) and Elric Angot (2022-2025). Active in academic community, recently co-organized the 2023 Workshop on Random Walks, Localization and Reinforcement in Paris.
