Jia-Bin Huang is an Associate Professor in the Department of Computer Science at University of Maryland, College Park , with a secondary appointment at the University of Maryland Institute for Advanced Computer Studies . His work bridges computer vision , computer graphics , and machine learning . His research focuses on 3D scene reconstruction , neural radiance fields , generative models , and multimodal foundation models . He has made significant contributions to video super-resolution , text-driven 3D modeling , and inverse rendering techniques. 15 recent publications (2024-2025) at top venues: CVPR , NeurIPS , SIGGRAPH Asia , 3DV , and ECCV Pioneering work in Urban Scene Inverse Rendering , Generative Video Editing , and 3D Human Digitization He has received multiple awards including the 3M Non-Tenured Faculty Award , ETRA Best Paper , and NSF Grants . His lab trains 12 PhD students and has graduated 18 Masters/PhD students now at institutions like Stanford , Meta , and Google .
Ajay B. Limaye is an Assistant Professor in the Department of Environmental Sciences at the University of Virginia. His research spans terrestrial and planetary landscapes, focusing on fluvial geomorphology, quantitative stratigraphy, and planetary surface processes. He employs remote sensing, geospatial analysis, numerical modeling, and laboratory experiments to study river dynamics, sedimentary deposits, and climate records on Earth, Mars, and Titan. His work integrates NSF and NASA-funded projects, including the development of a Landscape Evolution Laboratory with a 7m×3m experimental basin for controlled landscape modeling. His research explores feedbacks between landslides and ecology in central Virginia, Martian deltaic deposits, and submarine channel systems. He teaches courses in geomorphology, planetary geology, and fundamental geosciences. NSF CAREER Award (2023) : "GLOW: Sequencing rivers with machine learning and bioinformatics" Keck Institute Fellowship (2010) : High-resolution stratigraphy of Mars polar deposits Recent publications analyze braided river dynamics (e.g., Brahmaputra-Jamuna River), meander bend geometry, landslide-vegetation interactions, and planetary hydrology. His experimental work on autogenic fluvial terraces and turbidity maximum zones in estuaries demonstrates interdisciplinary methodological rigor.
Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.
Peter Massopust is a Privatdozent at the Technical University of Munich (TUM), where he is affiliated with the School of Computation, Information and Technology and the Department of Mathematics. His research spans multiple areas of mathematical analysis with a focus on fractal geometry, wavelet theory, and approximation methods. His educational background includes: Habilitation in 2011 from Technical University of Munich Ph.D. in Applied Mathematics from Georgia Institute of Technology (1986) MS in Mathematics from Georgia Institute of Technology (1985) MS in Physics from Georgia Institute of Technology (1981) Dr. Massopust's research interests primarily focus on Wavelets and Frames, Harmonic and Functional Analysis, Fractal Geometry and Fractal Interpolation Theory, and Splines and Approximation Theory. His work bridges theoretical mathematics with practical applications in signal processing, image analysis, and computational methods. His approach often combines classical mathematical techniques with innovative fractal-based methods to solve complex problems in approximation theory and functional analysis. His research has significantly contributed to the development of fractal interpolation functions, complex splines, and wavelet theory, with applications spanning from pure mathematics to engineering problems. His publication record demonstrates a consistent focus on fractal-based mathematical methods, with recent work expanding into quaternionic analysis, complex B-splines, and applications in signal processing. His research shows a clear trajectory from foundational work in fractal geometry to increasingly sophisticated applications in multidimensional signal analysis and computational mathematics. His scientific achievements have been recognized through several prestigious awards: Fulbright Scholarship (1980-1981) GIAN (Global Initiative for Academic Network) Award from the Republic of India (2016, 2017) Dr. Massopust has secured substantial research funding from various national and international sources, including the German Research Foundation (DFG), Bayerische Forschungsallianz, VolkswagenStiftung, and collaborations with Sandia National Laboratories and the National Science Foundation. His research program has consistently focused on advancing mathematical methods for signal and image processing, with particular emphasis on fractal-based approaches and wavelet theory. He has also been instrumental in fostering international collaborations, particularly through the EuroTech network and with institutions in Australia and India. Among his notable contributions is the GHM (Geronimo-Hardin-Massopust) Scaling Vector and DGHM (Donovan-Geronimo-Hardin-Massopust) Multiwavelet, developed at the Georgia Tech Research Institute in 1995. This work has had significant impact in the field of wavelet analysis and its applications.
Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.
Sudhir P. Mudur is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal. He has held leadership roles including Chair of the Department and Visiting Professorships at institutions like the Central University of Florida, Ecole Minerales de Nantes, and Michigan State University. With over four decades of experience, Mudur's work spans fundamental and applied research, academic administration, and interdisciplinary collaboration in computer graphics, visualization, and cultural heritage. Education: Bachelor of Technology (B. Tech. Honours) in Electrical and Electronic Engineering, Indian Institute of Technology (IIT) Bombay, 1970. PhD in Computer Science, Tata Institute of Fundamental Research (TIFR), Mumbai, 1976. Research Interests: Mudur focuses on 3D graphics, virtual/augmented reality, computer vision, and cultural heritage documentation. His work includes realistic rendering techniques, geometric modeling, and applications in CAD/CAM, entertainment, and medical visualization. Recent projects involve 3D reconstruction from GIS data, interactive performance systems (ISSv2), and AR/VR for historical narratives. Key Contributions: Development of the Illimitable Space System (ISSv2) for real-time interactive applications in media arts. Advances in point cloud processing and shape reconstruction from unorganized data. Documentation of cultural heritage through virtual environments and 3D scanning. Research on global illumination and Monte Carlo methods in rendering. Leadership in academic administration, including roles at the National Centre for Software Technology (NCST) in India. Awards and Recognition: Best Paper Award at ICVGIP 2002. Silver Prize at Quebec University Research Forum 2006. Third Best Paper Award in Computers & Graphics (1999). Recognition for contributions to Indian computer graphics and software innovation. Teaching: Mudur has taught extensively, including courses on computer graphics, game development, system software, and multimedia computing at Concordia and institutions worldwide. His teaching spans both undergraduate and graduate levels, emphasizing practical skills and theoretical foundations.
Victor Churchill is an Assistant Professor of Mathematics at Trinity College since 2023. He holds a Ph.D. and A.M. from Dartmouth College, an M.S. from New York University's Courant Institute, and a B.A. from Boston College. His research focuses on computational mathematics, scientific machine learning, and image reconstruction, particularly in Bayesian uncertainty quantification for synthetic aperture radar imaging and learning unknown dynamical systems using neural networks. He has held a postdoctoral position at The Ohio State University under Dr. Dongbin Xiu and previously worked at Dartmouth under Dr. Anne Gelb. Research Highlights: His work includes deep learning of PDEs, ensemble prediction for robust neural network training, and chaotic system learning from partial observations. Recent contributions address coarse time-scale observations and uncertainty quantification in SAR imaging. He was awarded the SIAM Science Policy Fellowship (2023-2024) to engage with federal science policy advocacy. Teaching: He teaches computational science courses at both undergraduate and graduate levels, integrating his research into lectures through case studies and data-driven examples. His pedagogical approach emphasizes applied computational mathematics and real-world problem-solving. Affiliations: Previously affiliated with The Ohio State University as a Visiting Assistant Professor of Scientific Computation. Active in computational math communities, including SIAM policy engagement. Personal Interests: An avid runner with marathon personal bests, he also enjoys bonsai cultivation, architectural design, and animal care. His unconventional hobbies include experimenting with hair color transformations.
Professor Thanh Tran is a faculty member at the School of Mathematics & Statistics, University of New South Wales (UNSW Sydney). His research focuses on computational mathematics, particularly numerical methods for deterministic and stochastic partial differential equations (PDEs), with applications in physical sciences, engineering, and magnetic materials. He has secured multiple competitive research grants, including ARC Discovery Projects and international collaborations. Education: PhD from UNSW Sydney Research Grants: ARC Discovery Projects (2012-2026), UNSW Goldstar Awards (2011, 2015), Go8-Germany Joint Research Scheme (2013) Teaching: Courses include Stochastic Differential Equations (MATH3361/5361), Mathematics for Actuarial Studies (MATH1251), and Applied Analysis (MATH3051) His work on numerical methods spans preconditioning techniques, error estimation, adaptivity, and finite element-boundary element coupling. Recent publications emphasize stochastic PDEs and nonlinear evolution equations, with applications in magnetohydrodynamics and chemotaxis models. Scientific awards highlight his contributions to interdisciplinary mathematics, particularly through collaborative projects on magnetic memory materials and random boundary problems. He actively supervises PhD and Honours students in computational mathematics, focusing on Maxwell-Landau-Lifshitz-Gilbert equations and shape calculus.
Dr. Simão Marques is a Senior Lecturer in the School of Mechanical Engineering Sciences at the University of Surrey. His expertise lies in computational methods for aerodynamic and aeroelastic analysis, with a focus on high-fidelity simulation and multidisciplinary optimization. He holds a PhD and BEng (Hons) in Aeronautical Engineering. Research interests include advanced CFD development, aeroelastic instability prediction, and automated CAD parameterization for optimal design. Key projects involve the ONEheart initiative (funded by the UK ATI) and collaborations with Airbus, DLR, and Queen’s University Belfast. His work emphasizes reduced-order modeling techniques (e.g., POD-DEIM, DEIM) to accelerate aerodynamic and aeroelastic simulations. Recent studies address nonlinear fluid-structure interactions, uncertainty quantification, and efficient sensitivity-driven design frameworks. Notable collaborations include industry partnerships focused on CAD-based optimization and aeroelastic analysis. His research aims to reduce design cycles and enhance aircraft performance through innovative computational methods.
Victoria Fernandez Abrevaya is a post-doctoral researcher at the Max Planck Institute for Intelligent Systems (Perceiving Systems department) in Germany. She holds a PhD from Inria Grenoble (France) under Professors Edmond Boyer and Stefanie Wuhrer, and a MSc in Computer Science from the University of Buenos Aires, Argentina. Her work focuses on 3D reconstruction and understanding of humans from 2D data, with emphasis on facial animation, neural rendering, and generative models. Research interests: 3D computer vision and shape modeling Neural rendering techniques Diffusion models for motion and appearance synthesis Biometric fairness in face analysis Real-time face capture systems Geometry-constrained multi-human rendering Recent work explores occluded face expression reconstruction (OFEr 2025), interactive dynamics modeling (InterDyn 2025), and latent realignment for motion diffusion (Lead 2025). Her SPARK system (2025) enables real-time monocular face capture through self-supervised learning. Prior contributions include FLAME (2023), a popular 3D face model framework, and work on multilinear autoencoders for dynamic facial analysis (2018). She co-developed ImAvatar (2022), an implicit morphable head avatar system from videos.
Prof. Dr. Hans Joachim Oberle is a retired professor in the Department of Mathematics at the University of Hamburg, affiliated with the Applied Mathematics (AM) division. His academic career included significant contributions to optimal control theory, calculus of variations, and numerical analysis. He authored influential textbooks such as 'Mathematik für Ingenieure' and developed numerical methods for solving optimal control problems, notably the BNDSCO software package. His research emphasizes practical applications in aerospace engineering, climate modeling, and robotics, with a focus on trajectory optimization, fuel efficiency, and spline interpolation techniques. Key affiliations: Faculty of Mathematics, Computer Science & Natural Sciences Research focus areas: Optimal control, numerical methods, mathematical modeling Software contributions: BNDSCO for boundary value problems His work bridges theoretical mathematics with engineering challenges, including aeroassisted orbital transfers and CO₂ emission reduction models. Over his career, he supervised numerous student theses on topics ranging from robot arm control to anti-angiogenic cancer therapy optimization. Despite retirement, his educational materials and computational tools remain widely used in academic and industrial settings.
Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.
Edith Tretschk is a Research Scientist at Meta Reality Labs Research in the San Francisco Bay Area. She completed her Ph.D. in Computer Science at Saarland University and Max Planck Institute for Informatics (2018-2023), advised by Christian Theobalt . Her work bridges computer graphics , computer vision , and machine learning , with a focus on 3D reconstruction and quantum computing applications. Education Ph.D. in Computer Science (2018-2023), Max Planck Institute for Informatics & Saarland University M.Sc. in Computer Science (2017-2023), Graduate School of Computer Science, Saarland University B.Sc. in Computer Science (2014-2017), Saarland University Research Focus Her research explores 3D reconstruction of dynamic scenes, neural rendering , and quantum computing for vision tasks. Recent work includes time-consistent scene flow (SceNeRFlow), quantum auto-encoders (3D-QAE), and physics-driven template matching (φ-SfT). Article Trends Her publications span 3D vision , quantum algorithms , and neural scene modeling . Key themes include non-rigid deformation, quantum-hybrid approaches, and physics-based reconstruction. Scientific Recognition Bachelor Award (2017) for top CS graduates Deutschlandstipendium scholarship (2015-2017) NeurIPS Top Reviewer (2022) Additional Contributions She has delivered invited talks at World Labs, Meta, Nvidia, and Epic Games. Active as a reviewer for CVPR, ECCV, ICCV, and NeurIPS, she has also contributed to open-source projects and datasets.
Bruce Piper is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute, with contact information including email piperb@rpi.edu and phone number 518-276-6892. His research interests include: Mathematics Education Computer Aided Geometric Design Approximation Theory Shape Preserving Interpolation Data Analysis Techniques Dr. Piper's work spans both theoretical and applied mathematics. In theoretical domains, he has investigated shape preserving interpolation, specifically focusing on the preservation of monotonic curvature and 3-convexity problems. He has developed novel surface representations for convexity preserving interpolation. His applied research bridges mathematical techniques with antenna engineering, particularly in spherical conformal antenna design using NURBS techniques and electromagnetic modeling of conformal wideband antennas. His publication record reveals a progression from geometric design fundamentals to practical engineering applications. The research shows strong interdisciplinary connections between pure mathematics and electrical engineering, with significant contributions to both fields. His work on cubic spirals and Hermite interpolation has theoretical importance, while his antenna modeling research has practical implications for wireless communications technology. Dr. Piper has contributed to mathematics education through curriculum development for undergraduate math majors, creating courses that teach data visualization, classification, clustering, and ridge regression. He has also implemented mentorship programs where upper-class students guide first-year students in Calculus courses to improve STEM retention rates.
Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.
