Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints
Emily J. King is a tenured Associate Professor in the Department of Mathematics at Colorado State University (CSU), College of Natural Sciences. She previously held a faculty position at the University of Bremen and has been actively contributing to the mathematical community through research, mentorship, and academic leadership. Her primary research interests include Frame Theory , Harmonic Analysis , Algebraic and Geometric Combinatorics , and Data Science , with applications in signal and image processing, Earth science, and artificial intelligence. She integrates deep mathematical theory with practical data analysis challenges. Her recent scholarly output reflects a strong focus on equiangular tight frames, combinatorial structures in frames, mathematical models for attention mechanisms, and applications to satellite imagery and cloud processes. Her work often bridges pure and applied mathematics, with a growing emphasis on interpretable AI and data science foundations. Dr. King has supervised several doctoral and master’s students, including Lander ver Hoef, Sören Schulze, Harley Meade, and Kristina Moen. She is a co-PI on an NSF grant focused on cloud processes and has been recognized for mentoring excellence, as evidenced by her student Emma Slack receiving the inaugural Outstanding Undergraduate in Mathematics award. NSF Grant Co-PI (2024) Outstanding Undergraduate in Mathematics award (mentored student, 2023) She is a founding co-organizer of the international Codes and Expansions (CodEx) Seminar and has organized sessions at major conferences such as SIAM AG and the Joint Mathematics Meetings. She frequently delivers invited talks at universities and research institutes worldwide, including upcoming presentations at the Air Force Institute of Technology, SIAM AG25, and TU Clausthal. Dr. King’s academic lineage includes John Benedetto as her mathematical advisor and Chandler Davis as her mathematical grandfather. She is actively involved in interdisciplinary research, particularly in marine data science, having co-spoken for the Helmholtz School for Marine Data Science (MarDATA).
Professor Wang Li-Lian is a faculty member in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore. He holds the rank of Professor of Applied Mathematics and has been affiliated with NTU since 2006, progressing through roles including Assistant and Associate Professor before his current position. His research focuses on spectral methods, computational acoustics/electromagnetics, and PDE-based image processing. He has supervised multiple PhD and Master's students, including Zhang Jing, Gu Ying, Yang Zhiguo, and others. Education and career highlights include a Postdoctoral Research Associate at Purdue University (2002-2003), followed by a Visiting Assistant Professorship (2003-2005). His academic work spans spectral element methods, fractional differential equations, and high-order numerical techniques for wave scattering problems. Notable contributions include advancements in spectral-Galerkin methods for nonlocal operators and exact nonreflecting boundary conditions for Maxwell's equations. Research interests emphasize high-accuracy numerical schemes, with applications to fractional PDEs, metamaterial simulations, and image processing. His publications (over 100+ articles) reflect expertise in spectral methods, computational physics, and mathematical modeling. Teaching responsibilities include courses on partial differential equations, numerical analysis, and scientific computing.
Dr. Dierck Hillmann is an Associate Professor at the Faculty of Science, Department of Biophotonics and Medical Imaging, Vrije Universiteit Amsterdam. He holds a PhD in Holoscopy from Luebeck University (2013). His research focuses on advanced optical imaging techniques, particularly Optical Coherence Tomography (OCT), with applications in retinal imaging, functional signal analysis, and computational imaging. He is affiliated with the LaserLaB - Biophotonics and Microscopy research group. Key research areas include improving OCT resolution through holographic methods, functional imaging of retinal neurons and photoreceptors, and developing computational adaptive optics to enhance imaging quality. His work addresses challenges like speckle reduction, aberration correction, and real-time data processing in biomedical imaging. Dr. Hillmann’s contributions span over 37 publications, including innovations in full-field OCT, optoretinography, and phase-sensitive measurements. He teaches courses such as Computational Optical Imaging and Light-Tissue Interaction. His current project explores imaging individual retinal cells and their functions using advanced techniques. No scientific awards are explicitly listed, but his extensive publication record reflects significant academic impact. Students advised are not specified in the provided materials.
Stevan Pilipović is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad. His research spans generalized functions, integral transforms, pseudodifferential operators, and fractional calculus. He holds a PhD in Mathematics from the University of Novi Sad (1979) and has led key academic roles, including editorships in journals like Integral Transforms and Special Functions and Pseudo-Differential Operators and Applications . Education: BSc in Mathematics, University of Novi Sad (1973) MSc in Mathematics, University of Belgrade (1977) PhD in Mathematics, University of Novi Sad (1979) Professional Activities: Leader of the Generalized Product and Integral Transformations seminar Editorial roles in multiple international journals Supervisor of numerous research projects in functional analysis and mathematical physics Research Interests: Pilipović’s work emphasizes microlocal analysis, fractional calculus in viscoelasticity, and stochastic PDEs. Key contributions include monographs on generalized functions and Fourier analysis, with over 400 publications. His research bridges pure mathematics (e.g., ultradistribution theory) and applications in mechanics and signal processing. Grants and Collaborations: Active in international collaborations, including projects on fractional Zener models and stochastic dynamics. His work frequently intersects with engineering and physics, addressing real-world problems in material science and wave propagation.
Kevin K. Lehmann is the William R. Kenan, Jr., Professor of Chemistry at the University of Virginia, within the Department of Chemistry in the College of Arts & Sciences. He is a leading researcher in molecular spectroscopy, with a focus on ultrasensitive detection methods such as cavity ring-down spectroscopy (CRDS) and double-resonance techniques. His educational background includes a B.S. from Cook College, Rutgers University (1977), a Ph.D. from Harvard University (1983), and a Junior Fellowship at the Harvard Society of Fellows. Lehmann's research is centered on advancing trace gas sensing using optical methods, particularly CRDS with high-reflectivity cavities and telecom-grade lasers. His group has pioneered Doppler-free two-photon CRDS and sub-Doppler double-resonance spectroscopy using frequency combs, enabling high-precision measurement of molecular transitions in gases like methane and nitrous oxide. These methods have applications in atmospheric science, planetary exploration (e.g., Mars missions), and combustion diagnostics. He also investigates meta-science questions around the reproducibility of spectroscopic data. The recent publications highlight a strong trend in high-resolution, quantum-limited spectroscopic techniques applied to small polyatomic molecules. There is a clear focus on enhancing selectivity and sensitivity through nonlinear optical effects, cavity enhancement, and advanced detection schemes. Applications span environmental monitoring, astrochemistry, and fundamental molecular physics. Fellow of the Optical Society, 2011 W.R. Kenan Professor of Chemistry, 2009 Earle K. Plyler Award in Molecular Spectroscopy, 2003 Thomas A. Edison Patent Award, 2002 Fellow of the American Physical Society, 1995 Lehmann has advised numerous graduate students and postdoctoral researchers, and his lab has been supported by grants from agencies involved in space exploration, environmental science, and fundamental physics. His work has led to commercial instrumentation through Tiger Optics, Inc. He maintains strong international collaborations, particularly with researchers in Sweden on methane spectroscopy. While specific grant details are not listed, the scope and impact of his research suggest sustained funding from NSF, NASA, and DOE. His laboratory focuses on optical cavity-based sensors and high-resolution spectroscopy setups, integrating frequency combs, narrow-linewidth lasers, and cryogenic pre-concentration systems for trace analysis. The team combines experimental innovation with theoretical modeling to interpret complex spectra and improve measurement fidelity.
Andrey Anikin is an Associate Professor and Researcher in Cognitive Science at the Department of Philosophy, Lund University. His work focuses on vocal communication and emotions, exploring how the voice conveys information beyond linguistic codes through nonverbal vocalizations and acoustic phenomena. Research Interests Dr. Anikin investigates how vocal qualities like roughness, laughter, and screams convey emotional and social information. His research takes a cognitive and biological approach, examining sensory biases and auditory attention in vocal communication. His work aims to illuminate the evolutionary origins and universal features of vocal communication across human cultures and animal species. Key areas include emotion perception, nonverbal vocalizations, acoustic analysis, and vocal communication systems. Research Output Trends Dr. Anikin's recent publications (2025) demonstrate a strong focus on acoustic properties of vocalizations across species. His work bridges biology, psychology, and acoustics with methodological innovations in analyzing nonlinear phenomena, voice roughness, and formant structures. His research shows increasing interdisciplinary collaboration, particularly with zoologists and signal processing experts, while maintaining a core focus on the cognitive mechanisms underlying vocal communication. Research Projects What makes baby cries impossible to ignore? (2024-2026): Funded by the Swedish Research Council, this active project investigates the sensory mechanisms behind infant cry perception. Sensory biases in nonverbal communication (2021-2023): A completed project funded by the Swedish Research Council that examined how sensory systems shape nonverbal communication. Research Environment Dr. Anikin is affiliated with the Cognitive Zoology Group and the Lund University Cognitive Science (LUCS) program. He contributes to the LU Profile Area: Natural and Artificial Cognition. His work connects with the UN Sustainable Development Goals through interdisciplinary research on communication and cognition, with implications for understanding human well-being and social interaction.
Daniel Hlubinka is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University. He has been an academic staff member since 1999 and was promoted to associate professor in 2007. His teaching includes courses like Statistics for Financial Mathematicians 2 (NMFM332) and Proseminar in Probability and Mathematical Statistics (NMSA262). He supervises bachelor's, diploma, and doctoral theses, with over 30 bachelor's, 20 diploma, and 5 doctoral theses supervised to date, including former students now working as associate professors. Education: Mathematical Physics (1989-1994) at Charles University; Erasmus stay at Limburgs Universitaire Center (1994-1995); Doctorate (1995-1999) under Professor Josef Štěpán. Research Interests: Statistics, theoretical foundations, multivariate functional processes, nonparametric asymptotics, data depth, optimal transport, and mathematics of chance. Academic Affiliations: Member of the Union of Czech Mathematicians and Physicists, Czech Mathematical Society, Czech Statistical Society, European Mathematical Society, Bernoulli Society, and Institute of Mathematical Statistics. Hlubinka's recent research focuses on functional data analysis, multivariate quantiles, and nonparametric testing. His work applies optimal transport theory, empirical characteristic functionals, and permutation tests to functional statistical problems. He contributes to methodological advancements in depth-based classification, time reversibility testing, and regression models.
Thomas G. Anderson is an Assistant Professor in the Department of Computational Applied Mathematics and Operations Research at Rice University. His research focuses on numerical analysis, spectral methods, and scientific computing, with applications in wave propagation and fluid dynamics. He joined Rice in 2023, following postdoctoral work at the University of Michigan and a PhD in applied and computational mathematics from Caltech. Education: PhD in Applied and Computational Mathematics, Caltech (Department of Computing+Mathematical Sciences) MS in Applied Mathematics, New Jersey Institute of Technology Bachelor’s in Applied Mathematics, New Jersey Institute of Technology Research Interests: Development of high-order numerical methods for partial differential equations Fast algorithms for singular integral operators in potential theory Parallelizable algorithms for long-time wave propagation simulations Volumetric discretization techniques for complex geometries Articles Trends: His recent work emphasizes efficient computational methods for wave and fluid dynamics, including hybrid frequency-time analysis, fast evaluation of volume potentials, and stabilization of viscous liquid layers. He explores both theoretical foundations and practical algorithm design. Grants and Advising: No specific grants or advisees listed in the provided text. Labs/Teams: Active collaboration with computational mathematics groups at Rice and prior institutions, including work at Lawrence Livermore National Laboratory.
A. Stewart Fotheringham is a Regents' Professor of Computational Spatial Science and Director of the Spatial Analysis Research Center (SPARC) at Arizona State University’s School of Geographical Sciences and Urban Planning. He is also a Distinguished Sustainability Scientist at the Julie Ann Wrigley Global Institute of Sustainability. His research focuses on spatial data analysis using statistical, mathematical, and computational methods, with expertise in spatial interaction modeling and local statistical analysis, particularly multiscale geographically weighted regression (MGWR). He has conducted substantive research in health geography, crime patterns, retailing, migration, and transportation. Affiliations: ASU’s Institute for Social Science Research, National Academy of Sciences, Academia Europaea Education: Ph.D., M.A. (McMaster University), B.Sc. (Aberdeen University) Awards: Science Foundation Ireland Research Professorship (2004), over $15 million in funding His research interests include spatial statistics, geographic information science, and the development of methodologies to analyze spatial heterogeneity. He has authored 12 books and nearly 200 publications, emphasizing reproducibility in geospatial research and advancing computational tools like MGWR. Recent work examines spatial voting dynamics, housing valuation, and health disparities using local modeling techniques. He advises on grants, serves on the Transportation Research Board’s Executive Committee, and contributes to global sustainability initiatives.
Dragoljub J. Kecskic is a Full Professor at the Department of Mathematical Analysis, Faculty of Mathematics, University of Belgrade . He has held this position since 2019, following progressive academic appointments from trainee assistant (1995–1999) to associate professor (2013). His research focuses on Operator Theory on Hilbert Spaces , C*-algebras , and related subfields. Education: BSc in Theoretical Mathematics and Applications (1995, University of Belgrade) MSc in Elementary Mappings on Operator Ideals (1998) PhD in Elementary Operator Kernels and Images (2003) His research interests span operator theory, functional analysis, fixed point theory, and integral transforms. Key areas include orthogonality in operator spaces, elementary operator structures, and applications of fixed point theorems in mathematical analysis. The 15 most recent articles highlight a sustained focus on operator algebras (2023: C*-algebra orthographs; 2018: compact operators), fixed point theory (2012: symmetric spaces; 2010: quasi-contraction counterexamples), and functional inequalities (2019: noncompactness measures; 2005: Gateaux derivatives). Collaborations with Z. Lazović, I. Aranđelović, and S. Stefanović are prominent. No scientific awards or grants are explicitly mentioned in the provided texts.
Dr. Bastian Pfau serves as Department Head of the “Imaging and Coherent X-rays” (B2) division and Project Coordinator for “Transient Structures and Imaging with X-rays” at the Max Born Institute in Berlin, where he has conducted postdoctoral research since 2016. His work pioneers nanoscale magnetic imaging using coherent X-ray techniques, with significant contributions to ultrafast magnetization dynamics and topological spin structures. His academic foundation includes a Dr. rer. nat. (PhD) in Physics from Technical University Berlin (2013) with thesis “Imaging magnetic nanostructures using soft x-ray Fourier transform holography,” and a Diplom (MSc) in Physics from Technical University Dresden (2006) focused on “Combining photon correlation spectroscopy and fluctuation analysis for investigating diffusion dynamics.” Additional research experience spans Lund University (2014-2015), Technical University Berlin (2010-2013), and Helmholtz Center Berlin (2006-2010). Dr. Pfau’s research centers on developing and applying X-ray holography and coherent diffraction imaging to visualize magnetic nanostructures at nanometer-femtosecond scales. His group specializes in ultrafast magnetization dynamics , skyrmion imaging , and element-specific magnetic probing using soft X-rays. Key innovations include achieving 5 nm resolution magnetic imaging and demonstrating all-optical helicity-independent switching via plasmonic nanostructures, with applications in next-generation spintronic devices and magnetic storage technologies. Analysis of his 15 most recent publications reveals dominant themes in nanoscale magnetic imaging (particularly skyrmions and topological textures), ultrafast opto-magnetic effects using extreme ultraviolet radiation, and advanced X-ray methodologies for capturing transient magnetic states. His work consistently bridges fundamental physics with practical instrumentation development, as evidenced by contributions to laser-driven plasma sources and tabletop X-ray setups. As Department Head of B2, Dr. Pfau leads a multidisciplinary team operating cutting-edge X-ray microscopy facilities at MBI. The group maintains strong collaborations with international synchrotron facilities (including BESSY II) and free-electron laser centers, focusing on developing MHz-repetition-rate pump-probe capabilities and high-resolution magnetic imaging techniques. Current projects emphasize real-time visualization of light-induced phase transitions and magnetic switching phenomena in functional materials.
Dr. Tharindu P. De Alwis is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is actively engaged in teaching and research, with a focus on high-dimensional data analysis and machine learning applications. Ph.D. in Mathematics (Statistics), Southern Illinois University Carbondale M.S. in Mathematics, Southern Illinois University Carbondale B.Sc. in Statistics and Operations Research, University of Peradeniya, Sri Lanka His research centers on dimension reduction techniques, particularly Sufficient Dimension Reduction (SDR), envelope methods, and their applications in multivariate time series and spatial-temporal data. He integrates deep learning and neural networks into statistical modeling, with recent work on stacking-based deep neural networks and Fourier-based SDR methods. His work bridges statistical theory with practical machine learning applications in complex datasets. His recent publications (2021–2024) demonstrate a strong focus on developing innovative statistical and machine learning methods for time series and high-dimensional regression. Key themes include nonlinear modeling, dimension reduction, R package development, and AI-augmented reliability analysis. These works reflect interdisciplinary applications in engineering, data science, and systems safety. Dr. De Alwis has presented his research at national academic conferences in the USA and has publications in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety , as well as preprints on arXiv and software on CRAN. He teaches a variety of courses including Precalculus with Trigonometry, Linear Algebra, Applied Statistics, and Data Science. While no formal advisees or grants are mentioned, his active publication record suggests ongoing research mentorship and scholarly engagement. He previously served as a post-doctoral scholar at Worcester Polytechnic Institute before joining UWF.
Mahmoud Karimi is a Senior Lecturer at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), leading the Vibroacoustics Research Group within the Centre for Audio, Acoustics and Vibration. He holds a PhD in Mechanical Engineering from UNSW with specialization in vibration and acoustics, and has conducted visiting research at University of Cambridge, Technical University of Munich, and INSA Lyon. His research focuses on computational hydroacoustics, vibroacoustics, and uncertainty quantification in noise/vibration problems. Academic Leadership : Editor-in-Chief of Acoustics Australia since 2025 Research Income : Attracted $6M in competitive grants ($2M as Chief Investigator) since 2017 Technical Expertise : Specializes in acoustic black hole structures, flow-induced vibration modeling, and leak detection in buried pipelines Scientific Awards : Recipient of ARC DECRA Fellowship (DE190101412) 2019-2022 Research Trends : His 91+ publications demonstrate expertise in hybrid acoustic modeling techniques, sustainable hempcrete development, and vibration energy harvesting solutions with applications in mining, rail systems, and water infrastructure. International Collaborations: University of Cambridge (UK), Technical University of Munich (Germany), INSA Lyon (France) Teaching Portfolio: Advanced numerical methods, dynamics & control, and computational modeling at UTS
Bin Han is a Professor of Mathematics at the Department of Mathematical and Statistical Sciences , University of Alberta, Canada. He holds a PhD (1998), MSc (1994), and BSc (1991) in Mathematics from the University of Alberta, Chinese Academy of Sciences, and Fudan University, respectively. Research Interests: Computational Mathematics: High-order finite difference methods, numerical solutions of PDEs (Helmholtz, elliptic interface, Burgers' equations), and Fourier/wavelet-based algorithms. Applied Harmonic Analysis: Framelets/wavelets with applications in image processing, data sciences, and deep learning, focusing on directional and quasi-tight properties. Wavelet Theory: Construction of wavelets on bounded intervals for boundary value problems, Gibbs phenomenon analysis, and stability of refinable functions. Computer Aided Geometric Design (CAGD): Subdivision schemes, spline approximation, and isogemetric analysis. Article Trends: His recent work (2021-2022) emphasizes high-order finite difference methods for Helmholtz and interface problems, directional tensor product complex tight framelets for image processing, and quasi-tight framelets with balancing orders for robustness and sparsity. Scientific Awards: NSERC Postdoctoral Fellowship (1999-2000) Advising & Grants: He has supervised PhD students Qiwei Feng, Michelle Michelle, Ran Lu, and Chenzhe Diao. His research is supported by NSERC, Westgrid, Compute Canada, and MITACS.
