جست‌وجوی استاد و پژوهشگر

• Enumerative combinatorics
• permutations
• bijections
• +۷ مورد دیگر

Sergi Elizalde is a Professor of Mathematics at Dartmouth College, affiliated with the Department of Mathematics within the Arts & Sciences school. He holds the position of Faculty Advisor for the Byrne Scholars Program. Education: B.A., Universitat Politècnica de Catalunya Ph.D., Massachusetts Institute of Technology Research Interests: Elizalde specializes in enumerative and algebraic combinatorics, with a focus on permutations, pattern avoidance, lattice paths, bijections, generating functions, and applications to computational biology and dynamical systems. His work bridges combinatorial theory with interdisciplinary fields like cancer modeling through Markov chain models of chromosomal instability. Teaching & Mentoring: Elizalde has taught courses in graph theory, algebraic combinatorics, and calculus. He currently mentors several Ph.D. students and has advised numerous former students who have completed their doctoral degrees under his supervision. Professional Contributions: He maintains a personal research website and collaborates on projects such as the implementation of Markov chain models for studying tumor evolution. His research has been featured in high-impact journals like Nature and Advances in Applied Mathematics .

Sebastian Königمشاهده پروفایل

استادیار

• Theoretical Nuclear Physics
• Effective Field Theory
• Finite-Volume Techniques
• +۴ مورد دیگر

Sebastian König is an Assistant Professor in the Department of Physics at North Carolina State University (NC State), affiliated with the College of Sciences. He joined NC State in 2020 as FRIB-TA bridge faculty. König holds a PhD from Bonn University, Germany (2013), followed by postdoctoral research at The Ohio State University and a Herzberg Fellowship at TU Darmstadt. His research focuses on theoretical low-energy nuclear physics, particularly effective field theory applied to few-nucleon systems, finite-volume techniques for lattice field theory, and computational methods for quantum systems. He also explores applications in computer science for efficient numerical simulations. Recent work emphasizes universal aspects of few-body systems, resonance calculations, and finite-volume effects. His 2024 publications highlight advancements in eigenvector continuation, complex scaling, and rare-isotope physics. König collaborates widely, contributing to facilities like FRIB (Facility for Rare Isotopes Beams). No scientific awards are explicitly listed, but his active research and grant-funded positions reflect recognition in the field. His advising and grants are not detailed here, though his bridge faculty role suggests involvement in interdisciplinary projects. His work is rooted in computational and theoretical tools for understanding nuclear interactions at fundamental scales.

Gábor Bergmannمشاهده پروفایل

پژوهشگر

• Model-Driven Engineering
• Incremental Computing
• Query Evaluation
• +۵ مورد دیگر

Gábor Bergmann is a researcher at Budapest University of Technology and Economics, Hungary, and a member of the MTA-BME Lendület Research Group on Cyber-Physical Systems. He actively contributes to the fields of model-driven engineering and incremental computing, with a focus on efficient query evaluation and program analysis. His research interests include: Model-Driven Engineering Incremental Computing Query Evaluation Program Analysis using Datalog Static and Whole-Program Analysis Cyber-Physical Systems The recent publications of Gábor Bergmann demonstrate a strong trend in incremental computation techniques applied to program and model analysis, particularly leveraging Datalog and lattice-based frameworks. His work focuses on optimizing performance and scalability of analyses through change-driven evaluation, with applications in model synchronization, inter-procedural analysis, and EMF-based systems. He has served on program committees, particularly in the Foundations Track of the MODELS conference series, indicating active engagement in the academic community. While no formal advising or grant information is available from the provided text, his involvement in a Lendület research group suggests participation in funded research projects. He has not received any explicitly mentioned scientific awards in the provided data.

Bipasha Chakrabortyمشاهده پروفایل

مدرس

• Theoretical Particle Physics
• Lattice QCD
• Flavour Physics
• +۳ مورد دیگر

Dr. Bipasha Chakraborty is a Lecturer in Lattice QCD and a Leverhulme Trust Early Career Fellow at the University of Southampton's High Energy Physics group. She specializes in theoretical particle physics, focusing on lattice QCD simulations to study the strong force (QCD) and precision tests of the Standard Model. Her research bridges fundamental physics with quantum computing, notably co-developing quantum algorithms for Quantum Electrodynamics in the UK. She actively supervises PhD students and teaches programming/data analysis labs (PHYS1201). Research Interests: Quantum Chromodynamics (QCD) simulations Flavour physics and precision Standard Model tests Hadron structure and interactions Quantum computation of quantum field theories Notable Achievements: Joseph Fourier Prize (2022) Lead researcher in quantum computing for particle physics

Camilla Hollantiمشاهده پروفایل

استاد

• Algebra
• Discrete Mathematics
• Cryptography
• +۴ مورد دیگر

Camilla Hollanti is a Professor in the Department of Mathematics and Systems Analysis at Aalto University , part of the School of Science . Her research focuses on algebra, number theory, and their applications in coding theory, cryptography, and secure distributed communication systems. She leads the Algebra, Number Theory, and Applications (ANTA) research group. Her work spans theoretical foundations and practical implementations, including lattice-based cryptography, secure distributed matrix multiplication, private information retrieval (PIR), and codes for wireless networks. Hollanti has published extensively in top-tier journals and conferences, with over 130 publications. Her contributions address challenges in data security, reliability, and efficiency in emerging technologies like federated learning and quantum communication. Key research themes include algebraic geometry codes for PIR , well-rounded lattices for wiretap channels , and secure computation in distributed systems . Recent efforts emphasize post-quantum cryptography and robust protocols against adversarial servers. Hollanti holds a Ph.D. in Mathematics and has been recognized with several awards (though specific names are not listed in the provided texts). She collaborates internationally and actively contributes to interdisciplinary projects at Aalto University.

Jayathi Y. Murthyمشاهده پروفایل

استاد

• Nanoscale Heat Transfer
• Computational Fluid Dynamics
• Multiscale Multi-Physics Simulations
• +۱۲ مورد دیگر

Jayathi Y. Murthy is a Professor in the Department of Mechanical and Aerospace Engineering at the UCLA Henry Samueli School of Engineering and Applied Science. She served as the Ronald and Valerie Sugar Dean of the school from January 1, 2016, to July 31, 2022, becoming the first woman to hold this position. Her leadership emphasized transformative growth in areas such as engineering in medicine, artificial intelligence, sustainable urban systems, and advanced materials. Research Interests: Murthy's research spans nanoscale heat transfer , computational fluid dynamics (CFD) , and multiscale multi-physics simulations of micro- and nano-electromechanical systems (MEMS/NEMS). She also investigates uncertainty quantification in simulations, particularly in sub-micron thermal transport. Her work bridges fundamental thermal sciences with industrial applications, including energy systems and microelectronics. The 15 most recent articles reflect a strong focus on thermal modeling at micro- and nanoscales , advanced numerical methods in CFD , and uncertainty-aware simulations . Keywords include nanotechnology, heat transfer, computational engineering, and materials science. Subfields span phonon transport, adaptive mesh refinement, hybrid numerical schemes, and inverse methods, showing a consistent trajectory toward high-fidelity, predictive modeling in thermal systems. Scientific Awards and Honors: National Academy of Engineering (NAE) Member Foreign Fellow, Indian National Academy of Engineering (INAE) Fellow, American Society of Mechanical Engineers (ASME) ASME Heat Transfer Memorial Award (2016) ASME Electronics and Photonics Packaging Division Clock Award Advising and Grants: While no specific students are listed, Murthy has led major research centers, including the $21 million NNSA-supported PRISM Center at UT Austin. She has authored over 330 publications and edited the second edition of the Handbook of Numerical Heat Transfer . Her editorial roles include service on Numerical Heat Transfer and the International Journal of Thermal Sciences . Labs and Research Teams: Murthy led the Center for Prediction of Reliability, Integrity and Survivability of Microsystems (PRISM), a multidisciplinary research hub. Her work involves collaborations across mechanical engineering, materials science, and computational modeling, often integrating industrial and academic partners.

• Computer Graphics
• Visualization
• 3D Reconstruction
• +۳ مورد دیگر

Susanne Weitz is a Researcher at the Chair of Computer Graphics and Visualization, Technical University of Munich, working under Prof. Westermann. Her office is located in room 02.13.052 at Boltzmannstr. 3, 85748 Garching b. München, with contact email susanne.weitz@tum.de and office hours Monday–Thursday 9:00–15:00. Her research focuses on advancing computer graphics through 3D Gaussian Splatting, topology optimization, and neural rendering techniques. Key interests include efficient novel view synthesis for consumer devices, scientific visualization of meteorological data, and geometry processing for cinematic applications. She bridges theoretical innovation with practical implementation, emphasizing real-time performance and accessibility. Analysis of her 2022–2025 publications reveals a strong trajectory in deep learning-enhanced graphics, particularly for 3D reconstruction and visualization. Work spans Gaussian Splatting optimizations, turbulence analysis in fluid dynamics, and lattice structure design, demonstrating interdisciplinary applications from medical visualization to climate science. At TUM, she teaches core courses including Game Physics, Visual Data Analytics, Deep Learning in Computer Graphics, and Machine Learning for 3D Geometry across winter and summer terms. Her curriculum integrates cutting-edge research like neural rendering and topology optimization into practical student projects.

Zhenquan Liمشاهده پروفایل

مدرس ارشد

• Computational Fluid Dynamics
• Adaptive Mesh Refinement
• Fluid Flow Modelling
• +۳ مورد دیگر

Zhenquan (Jan) Li is a Senior Lecturer in Mathematics at Charles Sturt University's Computing, Mathematics and Engineering School, affiliated with the Gulbali Research Institute and Data Mining Research Group (DaMRG). His expertise spans computational fluid dynamics, adaptive mesh refinement methods, and mathematical modeling. He holds a PhD in Modelling Shallow Turbulent Fluid Dynamics from the University of Southern Queensland (1996–1999) and a BSc in Mathematics from Hebei University (1978–1982). Dr. Li's research focuses on developing adaptive mesh refinement techniques for fluid flow simulations, validated through benchmarks like lid-driven cavity flows and square cylinder flows. His work emphasizes accuracy, efficiency, and applications to real-world scenarios, such as precipitation modeling and epidemiological studies. He has held academic positions at the University of the South Pacific and Hebei University, including roles as Associate Professor and Lecturer. He is an active member of professional societies, including SIAM, the Australian Mathematical Society, and ASTFE. His 2024 Excellence Award recognizes contributions to Digital Health and computational research. His teaching includes courses like Data Analysis for Business and Foundation Mathematics. Research collaborations span fluid dynamics, geostatistical analysis, and epidemiological modeling. Grants and supervised projects include the Murray Darling Basin PIT data analysis. His labs and teams are part of interdisciplinary groups at the Gulbali Research Institute.

Sonja Barkhofenمشاهده پروفایل

استاد پژوهشی

• Quantum Optics
• Quantum Information
• Quantum Computing
• +۵ مورد دیگر

PD Dr. Sonja Barkhofen is a Research Professor at the University of Paderborn’s Institute for Photonic Quantum Systems (PhoQS). She holds a Privatdozentin title, reflecting her advanced academic standing. Her primary affiliation is the Institute for Photonic Quantum Systems , where she leads research in quantum photonics and related fields. Her research focuses on topological quantum systems , quantum walks , entanglement generation , and photonic quantum technologies . Her work bridges theoretical concepts like zeta function analysis in hyperbolic systems with experimental realizations in quantum optics. Recent breakthroughs include scalable multiphoton entanglement generation and experimental observation of topological quantum criticality. Teaching activities include the course Quantenelektronik (Quantum Electronics), emphasizing practical applications of quantum principles in photonics. Collaborators such as C. Silberhorn, J. Sperling, and A. Altland frequently appear in her publications, reflecting strong interdisciplinary partnerships. Her publications (2021–2024) span Physical Review Research , Communications in Mathematical Physics , and Physik in unserer Zeit . While no awards are explicitly listed, her prolific output and high-impact journals suggest recognition in her field. Active labs/groups include the Hybrid Quantum Photonic Devices group and involvement in academic governance as an Academic Councillor.

• Combinatorics
• Graph Theory
• Discrete Mathematics
• +۵ مورد دیگر

Piotr Micek is a Professor at the Department of Algorithmics, Faculty of Mathematics and Computer Science, Jagiellonian University. He holds a habilitated doctorate and focuses on combinatorics, graph theory, and discrete mathematics. His research spans graph coloring, probabilistic methods, approximation algorithms, and structural graph theory. He has led multiple grants including 'Order and Geometry' (2023–2027) and 'Structural Theory of Posets' (2020–2023). His work bridges theoretical foundations with algorithmic applications in combinatorial geometry and posets. Education: PhD in Mathematics (2008), Habilitation in Computer Science (2020). Research interests include poset dimension, graph algorithms, and combinatorial geometry. He collaborates internationally and has published over 70 papers in top journals/conferences like Journal of the ACM and Combinatorica . Grants include funding from the National Science Center (NCN) for projects on poset dimensions (2023–2027) and structural theory (2020–2023). Advised PhD student Michał Seweryn. His lab, the Algorithmics Research Group, explores foundational and applied aspects of discrete mathematics.

Antoine Tilloyمشاهده پروفایل

استادیار

• Quantum Mechanics
• Quantum Field Theory
• Tensor Networks
• +۷ مورد دیگر

Antoine Tilloy is an Assistant Professor at the Centre Automatique et Systèmes, Mines ParisTech, part of Paris Sciences et Lettres (PSL). He is also affiliated with the Quantic research team at Inria, reflecting his dual academic and research institute role. His work bridges theoretical physics, quantum information, and nuclear engineering, with a focus on tensor networks and quantum field theory. Research Interests: Quantum Field Theory (non-perturbative methods) Tensor Networks for many-body quantum systems Quantum Foundations and Measurement Theory Semiclassical and Quantum Gravity Nuclear Engineering and Fuel Cycle Physics His recent research has explored the application of tensor networks to solve quantum field theories, the philosophical and technical challenges in defining QFT rigorously, and the physics of nuclear fuel recycling. He advocates for a 'pile of dirt' approach—tackling messy, realistic models over highly structured but artificial ones. Scientific Contributions: Active contributor to non-perturbative QFT methods ERC-funded research and postdoctoral supervision Public engagement through blog posts and science communication Advising and Grants: While specific students are not listed, he leads an ERC-funded project and has advertised postdoctoral positions, indicating active mentorship. His research is supported by competitive European funding, underscoring its significance in the field. Labs and Teams: He is a member of the Quantic team at Inria, a leading research group in quantum information and technologies, and conducts his work at the Centre Automatique et Systèmes at Mines ParisTech.

Jack Panterمشاهده پروفایل

مدرس

• Fluid Dynamics
• Interfacial Phenomena
• Wetting of Structured Surfaces
• +۱۲ مورد دیگر

Dr. Jack Panter is a Lecturer in Fluid Dynamics at the University of East Anglia (UEA), affiliated with the School of Engineering, Mathematics and Physics. He is a member of the Fluids & Structures research group and co-manages the Thermofluids Research Laboratory with Dr. Stefano Landini. His research integrates computational and experimental approaches to study multiphase fluid systems, with applications in sustainability, thermal management, and energy storage. Education: PhD in Physics, Durham University (awarded 2020) MSci in Physics and Chemistry, Durham University (2015, First Class) Dr. Panter's research centers on fundamental interfacial and wetting phenomena, with a strong emphasis on computational modeling. He develops and applies phase-field models, the Binary Image Transition State Search (BITSS) algorithm, and the Lattice Boltzmann method to study fluid equilibria, transitions, and dynamics. His work extends into soft matter, including elastic buckling, bio-elasticity, and colloidal organization. Applications include optimizing super-liquid-repellent surfaces with Procter & Gamble and simulating capillary rise for carbon capture technologies with ExxonMobil. His recent publications focus on thermal management of lithium-ion batteries using phase-change materials, hybrid immersion cooling strategies for electric vehicles, and computational simulations of wetting and capillary phenomena. These works reflect a strong trend toward sustainable engineering solutions, energy efficiency, and advanced computational fluid dynamics. Scientific Awards: No awards explicitly mentioned. Dr. Panter is actively involved in research funding and advising. He leads a Royal Society-funded project on capillary coupling and collaborates on a knowledge exchange project on low-cost thermal energy storage. He advises on computational methods and contributes to interdisciplinary research in thermal systems and fluid dynamics. His lab, the Thermofluids Research Laboratory, is equipped with advanced experimental facilities for thermal characterization, battery testing, 3D printing, and capillary force measurement, enabling both fundamental and applied research. Research Group: Thermofluids Research Laboratory – focused on phase-change phenomena, thermal management of electronics and batteries, and thermal energy storage. The lab supports projects in solid-liquid phase change materials, hybrid composites, microstructure design, and capillary phenomena.

• Coding Theory
• Cryptography
• Information Theory
• +۵ مورد دیگر

Ermes Franch is a Postdoctoral Fellow at the Department of Informatics, University of Bergen, Norway. He is actively engaged in research in coding theory and cryptography, contributing to both theoretical developments and cryptanalytic investigations. Research Interests: His work primarily focuses on coding theory , especially low-rank parity-check codes, and their applications in cryptography . He investigates algebraic constructions of error-correcting codes, decoding algorithms, and the security of post-quantum cryptographic schemes such as Integer-RLWE. His research lies at the intersection of information theory, algebra, and computer security. Publication Trends: His recent publications show a consistent focus on generalizations and algorithmic improvements in rank-metric coding, with increasing contributions to high-impact IEEE journals. The work demonstrates a trajectory toward both theoretical depth and practical relevance in secure communications. Scientific Funding and Projects: Associated with the project Sequences and Their Applications funded by the Research Council of Norway (Reference: 311646). Advising and Grants: While no formal students are listed, his collaborative publications with senior researchers like Chunlei Li and Philippe Gaborit suggest active participation in a research group. His work is supported by national research grants, indicating recognition of his contributions in the field. Labs and Research Teams: He is affiliated with the Department of Informatics at the University of Bergen, contributing to research in sequences, coding, and cryptography, likely as part of a larger research group focused on theoretical informatics and security.

Mingshuai Chenمشاهده پروفایل

استادیار

• Formal Verification
• Programming Theory
• Logical Aspects of Computer Science
• +۵ مورد دیگر

Mingshuai Chen is an Assistant Professor at Zhejiang University, leading the Formal Verification Group. He previously held a postdoctoral position at RWTH Aachen University. Education: Ph.D. in Computer Science from Institute of Software, Chinese Academy of Sciences (2019) B.Sc. in Computer Science from Jilin University (2013) His research focuses on formal verification, synthesis, programming theory, and probabilistic/quantum systems. Notable contributions include Exact Bayesian Inference and Lower Bounds for Probabilistic Programs . Scientific awards include: NSFC Excellent Young Scientists Fund Program (Overseas) Distinguished Paper Award at ATVA 2018 Best Paper Award at FMAC 2019 CAS-President Special Award (2019) 2nd Prize@ChinaSoft'24 He serves on program committees for OOPSLA 2026, TACAS 2026, and multiple other conferences.

Mahdi Jafari Siavoshaniمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Data Science
• +۱۴ مورد دیگر

Mahdi Jafari Siavoshani is an Assistant Professor in the Department of Computer Science and Engineering at Sharif University of Technology, where he leads the Information, Network, and Learning (INL) Lab. He holds a PhD and MSc from the Swiss Federal Institute of Technology (EPFL), and a BSc in Electrical Engineering and Physics from Sharif University of Technology. Prior to his current role, he was a postdoctoral fellow at the Institute of Network Coding, The Chinese University of Hong Kong. His research spans fundamental problems in information processing, transmission, and analysis, with a focus on theoretical limits and practical algorithm design. Key areas include Machine Learning, Optimization, Data Science, Information and Communication Theory, and Computer Networks. His work integrates probabilistic modeling, network coding, and deep learning to address challenges in secure communication, traffic classification, and distributed systems. The recent publications highlight a strong trend in network coding, information theory, and machine learning applications. His work explores coding-theoretic solutions for secrecy, load balancing in cache networks, belief propagation algorithms, and encrypted traffic classification using deep learning. These contributions reflect a blend of theoretical depth and practical relevance in modern communication systems. Assistant Professor, Department of Computer Science and Engineering, Sharif University of Technology PhD and MSc, Swiss Federal Institute of Technology (EPFL) BSc in Electrical Engineering and Physics, Sharif University of Technology Postdoctoral Fellow, Institute of Network Coding, The Chinese University of Hong Kong He advises research in the INL Lab and teaches courses such as Computer Networks, Engineering Probability and Statistics, Network Coding, and Stochastic Processes. While no formal students are listed, his lab environment fosters collaborative research. There is no mention of external grants or funding in the provided texts. The INL Lab serves as the primary research unit, focusing on information theory, network algorithms, and machine learning applications. The lab integrates theoretical research with practical implementations, particularly in networked systems and data analysis.