Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.
Yan Guo is the L. Herbert Ballou University Professor of Applied Mathematics at Brown University. He holds a B.S. from Peking University (1987) and a Ph.D. in Mathematics from Brown University (1993). His research focuses on partial differential equations (PDEs) in kinetic theory, plasma physics, and stellar dynamics, emphasizing nonlinear stability and instability phenomena. He has held roles including Courant Instructor, Manning Assistant Professor, and has been continuously affiliated with Brown since 1995. Education: B.S., Peking University (1987); Ph.D., Brown University (1993). Research interests include rigorous mathematical analysis of PDEs arising in kinetic theory (Boltzmann/Vlasov equations), fluid dynamics (Euler/Navier-Stokes), and stability of galactic models. Notable contributions include nonlinear stability of Maxwellian states, instability of plasma equilibria, and mathematical frameworks for galaxy dynamics. His publications address foundational problems in kinetic theory and fluid dynamics, with recent work on the Euler-Maxwell system and Boltzmann equation regularity. Awards include the Sloan Research Fellowship (1998-2003) and NSF Postdoctoral Fellowship (1995-1998). Guo serves as Managing Editor of the Journal of Partial Differential Equations and Associate Editor for multiple journals, including SIAM Journal of Mathematical Analysis. He has organized conferences on nonlinear wave equations and PDEs since 1998.
Johannes Skaar is a Professor at the Department of Physics, University of Oslo (UiO). He holds a 100% position there since 2017, previously at NTNU. His research focuses on quantum field theory, quantum optics, electromagnetics, metamaterials, photonics, and quantum information. He teaches advanced courses such as FYS4170 Relativistic Quantum Field Theory and FYS1005 Classical Mechanics. His work spans theoretical physics with notable contributions to single-photon states, metamaterial properties, and quantum cryptography security. Skaar’s research integrates foundational physics with applied technologies like metamaterials and quantum communication systems. His studies on Fresnel equations and magnetic permeability have advanced electromagnetic theory. He frequently publishes in top journals like Physical Review A and Physical Review Letters . Research groups: Theoretical Physics at UiO.
Prof. Esra Şengelen Sevim is an active faculty member at Istanbul Bilgi University, affiliated with the Department of Mathematics within the Faculty of Social Sciences and Humanities. She currently holds the academic rank of Professor and has been instrumental in shaping academic programs, serving as Head of the Department of Mathematics, Vice Dean of the Faculty, Program Coordinator of Financial Mathematics, and Director of the Graduate Program in Mathematics. PhD in Mathematics, Istanbul Technical University, 2010 MSc in Mathematics, Istanbul Technical University, 1996 Her research interests span Mathematics, Financial Mathematics, Applied and Pure Mathematics, with a strong emphasis on interdisciplinary applications in finance and modeling. Her academic trajectory reflects deep engagement with both theoretical and applied aspects of mathematical sciences. Although no specific publications are listed in the provided text, her sustained involvement in research is evident through international fellowships and visiting positions at institutions such as Indiana University-Purdue University and Fudan University. Scientific Awards and Recognitions: Visiting Scholar, Indiana University-Purdue University, Indianapolis, IN, USA (2015) Visiting Scholar, Indiana University-Purdue University, Indianapolis, IN, USA (2014) Research Fellowship, Fudan University, Shanghai, China (2013) Visiting Scholar, Indiana University-Purdue University, Indianapolis, IN, USA (2012) Postdoctoral Research Fellowship, Indiana University-Purdue University, Indianapolis, IN, USA (2010-2011) Prof. Sevim has played a significant role in academic leadership and mentorship through her coordination of graduate programs, Erasmus exchange initiatives, and departmental administration. While formal advisees are not listed, her directorship of the Graduate Program in Mathematics suggests active supervision of Master’s and possibly PhD students. She has not received externally funded grants in the provided text, but her international fellowships indicate competitive research support. She is involved in institutional and academic development through her roles as Erasmus Coordinator and program director, contributing to internationalization and curriculum design in mathematical sciences.
Jiayun (Peter) Wang is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). His research focuses on advancing AI-driven solutions in medical imaging, computational imaging, and computer vision. Current projects emphasize applying deep learning to diagnose ocular conditions like dry eye syndrome and improving 3D reconstruction techniques. Collaborations with institutions such as UC Berkeley, Microsoft, and NVIDIA highlight his interdisciplinary approach to solving real-world medical and imaging challenges. Research Interests: Medical AI and Healthcare Analytics Deep Learning Applications in Ophthalmology 3D Reconstruction and Scene Understanding Physics-Informed Neural Networks Compressed Sensing MRI Key Contributions: Developed machine learning models predicting dry eye-related outcomes using meibography images Pioneered physics-aware neural operators for ultrasound lung aeration mapping Advanced open-vocabulary 3D object detection systems Labs/Teams: Collaborates with Caltech's AI4Health initiative and NVIDIA's research group, contributing to medical imaging advancements through interdisciplinary teams.
Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.
Dina Khaled Sayed Abdelhadi is a Doctoral Assistant at the Communication Theory Laboratory (LTHC) within the School of Computer and Communication Sciences (IC) at École Polytechnique Fédérale de Lausanne (EPFL). She is also a PhD student in the Doctoral Program in Computer and Communication Sciences (EDIC), hosted under the EDOC school at EPFL. Her work is centered in the Institute of Computer and Communication Sciences (IINFCOM), focusing on theoretical and applied aspects of communication systems. Her research interests are rooted in Information Theory , Communication Theory , and Signal Processing , with potential applications in wireless networks and data transmission. Given her affiliation with LTHC, her work likely involves advanced mathematical modeling, probabilistic methods, and algorithm design for communication systems. No scientific awards or publications were mentioned in the provided text. However, her position as a Doctoral Assistant suggests active involvement in research projects funded by EPFL or external agencies, possibly including collaborations within the laboratory or international consortia. She is advised by faculty within the LTHC group, though no advisor is explicitly named. There is no mention of students supervised by her. She is part of a structured doctoral program, indicating rigorous academic training and research progression. The Communication Theory Laboratory (LTHC) at EPFL is known for cutting-edge research in information theory, network coding, and communication algorithms. As a member of this lab, Dina contributes to ongoing projects that bridge theoretical advances with practical communication technologies.
Madison Lore is an incoming Assistant Professor in the Department of City and Regional Planning at Cornell University's College of Architecture, Art, and Planning, beginning her tenure in January 2026. Her interdisciplinary research integrates urban planning, data science, and sustainability, focusing on how large-scale data and information environments shape public behaviors and perceptions around sustainable transitions in housing, transportation, and energy systems. She holds a Ph.D. from the School of Community and Regional Planning at the University of British Columbia, a Master's in Applied Mathematics, and a dual Bachelor's in Mathematics and Physics from Rensselaer Polytechnic Institute. Her academic journey reflects a strong technical foundation applied to pressing urban challenges. Madison’s research interests span urban data science, machine learning, infrastructure and land use planning, social policy, and sustainable transportation. She investigates how algorithmic and data-driven methods can be used responsibly to uncover social norms, institutional influences, and individual support for sustainable policies, particularly in contexts of information overload. Her recent publications demonstrate a strong trajectory in applying hybrid deep learning and natural language processing to urban text data, evaluating equity in public mobility, and modeling transportation preferences through digital footprints. These works reflect a consistent theme: leveraging data analytics to promote equitable and sustainable urban futures. Vanier Canada Graduate Scholarship (2023–2026) Bombardier Sustainable Transportation Fellowship (2022) The Bill and Nancy Siegmann Applied Mathematical Modeling Prize (2018) Leonhard Euler Award for Excellence in Mathematical Modeling (2016) Climate Social Science Network Grant on Big Oil’s Climate Disinformation (2024) Madison has presented her work at major conferences including the Association of Collegiate Schools of Planning, the International Conference on Travel Behavior Research, and the American Planning Association National Conference. While no formal advisees are listed, her role as an incoming assistant professor suggests future mentorship of graduate students in urban planning and data analytics. She is affiliated with the PLACE Lab and brings expertise from prior work in nuclear physics and applied mathematics into her current urban sustainability research.
Mengdi Wang is a Professor at Princeton University with primary appointments in the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, and courtesy appointments in the Department of Computer Science and Omenn-Darling Bioengineering Institute. She co-directs Princeton AI for Accelerated Invention and is affiliated with the Princeton ML Theory Group and Princeton Language+Intelligence Initiative, with prior visiting roles at DeepMind, IAS, and Simons Institute. Her educational background includes a PhD in Electrical Engineering and Computer Science (with Mathematics minor) from MIT (2013), advised by Dimitri P. Bertsekas at LIDS, and undergraduate studies in Automation at Tsinghua University: PhD: MIT, Electrical Engineering and Computer Science (2013) Bachelor: Tsinghua University, Automation Her research establishes theoretical foundations for machine learning with emphasis on reinforcement learning algorithms, generative AI, and large language models. She investigates data-driven stochastic optimization, statistical limits of reinforcement learning, representation learning, and diffusion models, developing provably robust algorithms for complex systems. Her work bridges theoretical guarantees with real-world applications in healthcare, biotech drug discovery, fintech, and scientific acceleration, focusing on how AI can transform discovery processes across disciplines. Her scientific contributions are recognized by prestigious awards: Young Researcher Prize in Continuous Optimization (Mathematical Optimization Society, 2016) Princeton SEAS Innovation Award (2016) NSF Career Award (2017) Google Faculty Award (2017) MIT Tech Review 35-Under-35 (China region, 2018) WAIC YunFan Award (2022) Donald Eckman Award (American Automatic Control Council, 2024) Professor Wang actively mentors students and recruits undergraduate interns, visitors, and postdocs for her research group. Her work is supported by major grants from NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, and GenMab. She serves as Program Chair for ICLR 2023 and Senior Area Chair for NeurIPS, ICML, and COLT, while editing for Harvard Data Science Review and Operations Research. She leads Princeton AI for Accelerated Invention, which develops AI-driven solutions for scientific discovery, collaborating closely with Princeton's ML Theory Group and Language+Intelligence Initiative to advance algorithmic innovation and interdisciplinary applications.
Dr. Anna Baldycheva is a Senior Lecturer in Electronic Engineering at the University of Exeter, within the College of Engineering, Mathematics and Physical Sciences. She leads the interdisciplinary STEMM Laboratory, focusing on applied R&D in smart materials, photonics, AI, and IoT. With prior research experience at MIT, Trinity College Dublin, and Tyndall National Institute, she has established herself as an internationally recognized innovator and entrepreneur in emerging technologies. PhD in Electronic and Electrical Engineering, Trinity College Dublin (2008–2012) BSc (Hons) in Physics, St. Petersburg State University (2003–2008) Postgraduate Certificate in Academic Practice, University of Exeter (2016–2017) Postgraduate Certificate in Technology Management, Smurfit Business School (2009–2010) Her research spans Nano-Engineering, Opto-Electronics, Photonics, AI, and IoT , with a strong emphasis on real-world applications. She pioneers work in fluid opto-electronics , graphene nanocoatings , and AI-driven emotion recognition and early cancer detection . Her lab develops smart composite materials for flexible electronics, e-textiles, and structural applications, integrating machine learning into healthcare, education, and communications systems. The recent publications highlight a strong trend toward applied interdisciplinary innovation , combining materials science with AI and photonics for healthcare diagnostics, energy-efficient computing, and educational technology. Her work frequently bridges fundamental physics with commercialization potential, as seen in spin-out technologies like GSurf and the Electronic-Nose for lung cancer detection. Fellow, Royal Microscopical Society (RMS) Fellow, Higher Education Academy (FHEA) Expert, Future and Emerging Technologies, European Commission Featured in Forbes and Forbes Tech Council Editor-in-Chief, InSTEMM Journal Associate Editor, Nature Scientific Reports and Discover Nano Trustee, Royal Microscopical Society Founder, STEMM Global Scientific Society Founder, It’s Her! Women in STEMM Initiative Dr. Baldycheva actively supervises PhD students and has secured industrial collaborations with organizations such as Qinetiq and Lumentum. She leads multiple outreach initiatives, including STEMM Junior for underprivileged children, and serves on the committee for the Jocelyn Bell Brunel PhD Scholarship. She has raised significant research funding through national and international grants, though specific grant names are not listed. She leads the STEMM Laboratory , a multidisciplinary research group with divisions in Smart Composite Materials, Machine Learning & AI, and Opto-Electronics & Photonics. The lab emphasizes industry collaboration and technology transfer, having produced a university spin-out (GSurf) and multiple media-highlighted innovations.
Sanjay Srinivasan is a Professor of Petroleum and Natural Gas Engineering and the John and Willie Leone Family Chair in the Department of Energy and Mineral Engineering at Penn State University. He serves as Director of the EMS Energy Institute and leads the Penn State Initiative for Geostatistics and GeoModeling Applications. His research focuses on petroleum reservoir characterization, CO2 sequestration, and integration of seismic data in reservoir models through advanced geostatistical and machine learning methods. Ph.D., Petroleum Engineering, Stanford University M.S., Petroleum Engineering, University of Southern California B. Tech, Petroleum Engineering, Indian School of Mines Srinivasan’s work addresses reservoir recovery processes, unconventional reservoirs, and subsurface energy security. His methodologies include probabilistic modeling, data assimilation, and AI-driven workflows for fracture network mapping and porous media generation. Key applications span Gulf of Mexico deepwater plays and geological carbon storage. Recent publications highlight trends in: Reinforcement learning for geostatistical workflows and well optimization Physics-informed GANs for 3D porous media modeling Probabilistic integration of geomechanical and geostatistical inferences Machine learning approaches for seismic fracture identification CO2 sequestration in heterogeneous reservoirs Scientific awards include Distinguished Member (SPE, 2022), SPE Faculty Pipeline Award (2012), Cox Visiting Fellowship (Stanford, 2010), and SPE Southwest Region Reservoir Description Award (2009).
Nathaniel Nucci is an Associate Professor at Rowan University's College of Science & Mathematics, jointly appointed in the Department of Biological & Biomedical Sciences and Physics & Astronomy. His research bridges biophysics, structural biology, and nanotechnology to understand protein behavior in confined environments. Education Ph.D., Biochemistry and Molecular Biophysics, University of Pennsylvania M.S., Biochemistry and Molecular Biology, University of New Hampshire B.S., Biochemistry and Molecular Biology, University of New Hampshire Research Interests Dr. Nucci's lab focuses on: Protein biophysics in crowded/confining environments Reverse micelle technology for biomolecular studies Hydration dynamics of proteins (NMR-based methods) Structural biology of disease-related proteins (PHDs, p53) Drug delivery systems for protein therapeutics Nanoparticle synthesis with protein conjugation Research Trends His recent publications demonstrate expertise in using reverse micelles to study: Protein structural stability under confinement Hydration dynamics of therapeutic proteins Microenvironmental effects on phase-separating proteins Conformational changes in GPCRs Interfacial interactions in biomolecular systems Scientific Awards Gary J. Hunter Excellence in Mentoring Award (2024) College of Science and Mathematics Excellence in Academic Student Support (2022) Teaching Philosophy Emphasizes applied and experiential learning, integrating recent scientific discoveries into classroom practice and promoting hands-on scientific investigation.
Prof. Dr. Jörg Budde is a faculty member at the University of Bonn , affiliated with the Department of Economics . His academic rank is Professor , and he is actively engaged in research related to managerial accounting, performance measurement, and incentive contracts. Institute: Institute for Applied Microeconomics Email: joerg.budde@uni-bonn.de Contact: +49 228 73-9247 Budde’s research focuses on incentive design and performance evaluation in agency models, particularly under conditions of limited liability and distorted metrics. His work explores topics such as bonus pools , rank-order tournaments , and contractual frameworks that balance risk and incentive alignment. His publications span journals like Journal of Economics , Management Accounting Research , and Journal of Mathematical Economics , with a thematic emphasis on agency theory , information systems , and organizational behavior .
David E. Joyce is a Professor in the Department of Mathematics and Computer Science at Clark University. His work spans pure and applied mathematics, with a focus on geometry, algebra, and chaos theory. He has developed extensive online educational resources, including interactive explorations of Euclid's Elements, trigonometry, complex numbers, and fractal generation tools like Millefiori! and Newton Basins. Research Highlights : Symmetry in mathematics, knot theory, hyperbolic plane tiling, and computational visualization. Education Projects : Created digital courses on trigonometry, complex numbers, and Hilbert's 23 problems. Technical Contributions : Maintains the Huxley File (T.H. Huxley works), explores phylogeny reconstruction, and developed Java-based interactive geometry applications.
Matthew J. Graham is a Research Professor of Astronomy at the California Institute of Technology (Caltech), serving as the Project Scientist for the Zwicky Transient Facility (ZTF). His work bridges astronomy, machine learning, and data science, focusing on time-domain sky surveys that produce hundreds of thousands of public transient alerts per night. Previously, he has worked on the Catalina Real-time Transient Survey (CRTS), NOAO DataLab, Virtual Observatory, and Palomar-Quest Digital Sky Survey. Dr. Graham's primary research interests involve applying machine learning and advanced statistical methodologies to astrophysical problems, particularly the variability of quasars and other stochastic time series. His work addresses the unprecedented data volumes generated by 21st-century astronomy while expanding our ability to work with complex information systems beyond simple correlations. His current projects include real-time low latency inferencing via the NSF-funded A3D3 Institute, reinforcement learning for optimizing astrophysical follow-up campaigns, neural differential models for supermassive black hole variability, and functional analysis of multivariate time series. Analysis of Graham's recent publications reveals a strong focus on time-domain astronomy, particularly leveraging the capabilities of the Zwicky Transient Facility. His work spans multiple areas including gravitational wave counterpart identification, active galactic nuclei variability, supernova characterization, and machine learning applications for transient detection. A notable trend is the integration of artificial intelligence techniques to handle the massive data streams from modern sky surveys, enabling real-time analysis and decision-making that would be impossible with traditional methods. Dr. Graham has been instrumental in developing infrastructure for time-domain astronomy, including the alert distribution system for ZTF and data processing pipelines for handling massive transient datasets. His work on the Catalina Real-time Transient Survey established important methodologies for identifying variable and transient sources that continue to influence the field. As Project Scientist for ZTF, Graham leads a major international collaboration involving Caltech, IPAC, and numerous partner institutions worldwide. The facility represents a significant advancement in time-domain astronomy, providing unprecedented coverage of the dynamic sky and enabling discoveries across multiple areas of astrophysics.
