جست‌وجوی استاد و پژوهشگر

• Applied Mathematics
• Numerical Analysis
• Singular Integral Equations
• +۲ مورد دیگر

Dr. Dorota Pylak is an Assistant Professor at the Department of Mathematical Modeling, Institute of Mathematics, Informatics and Landscape Architecture, within the Faculty of Natural and Technical Sciences at John Paul II Catholic University of Lublin, Poland. Her office is located at Konstantynów 1H, 20-708 Lublin. She can be contacted via email at dorota.pylak@kul.pl and her ORCID identifier is 0000-0003-3220-3841. Her research focuses on Applied Mathematics , specializing in: Analytical and numerical solutions of singular integral equations Cauchy kernel transformations and approximation methods Application of orthogonal polynomials (particularly Jacobi polynomials) Finite-difference schemes for partial differential equations Functional analysis in bounded and unbounded domains Her publications demonstrate a consistent focus on developing novel computational methods for singular integral equations, with recent work expanding into quasilinear parabolic equations. Key trends include spectral methods using orthogonal polynomials, analytical solutions for Cauchy-type equations, and stability analysis of numerical schemes. Dr. Pylak actively contributes to academic peer review, serving as a referee for journals including Computational Methods in Applied Mathematics , Mathematical Reviews , and International Journal of Computer Mathematics . She has presented research at international conferences such as CASTR'2017 and the Modern Computational Methods symposium in Będlewo.

Aleksandar Karteljمشاهده پروفایل

دانشیار

• Optimization
• Data mining
• Computational biology
• +۴ مورد دیگر

Aleksandar Kartelj is an Associate professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been working at the Faculty of Mathematics since 2008, starting as a Teaching assistant, then becoming Assistant professor (2015-2023), and currently Associate professor (since October 2023). He also serves as a Guest professor at the Faculty of Natural Science and Mathematics, University of Banjaluka since 2018. Education: B.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2005-2008, GPA 9.94/10.00) M.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2008-2010, GPA 9.92/10.00) PhD in Computer Science from Faculty of Mathematics, University of Belgrade (2010-2014, GPA 10.00/10.00) M.Sc. in Quantitative Finance from Faculty of Economics, University of Belgrade (2010-present) Aleksandar Kartelj's research primarily focuses on optimization and data mining. He is a member of the "Modelling and optimization group" at the Faculty of Mathematics. His work involves designing exact and non-exact optimization techniques such as integer (linear) programming models, branch & bound, beam search, variable neighborhood search, and genetic algorithms. These techniques are applied to solve NP-hard problems in computational biology, transportation, logistics, social networks, and for improving machine learning algorithms. His recent publications (2021-2025) span diverse areas including symbolic regression, constrained longest common subsequence problems, graph protection algorithms, Roman domination in graphs, and applications in computational biology and epidemiology. His work frequently appears in high-impact journals such as Journal of Big Data, Expert Systems with Applications, and Knowledge-Based Systems, with many publications classified as M21a (Q1). Scientific Awards: Best paper award at the 2023 Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) for "Integrating Top-level Constraints into a Symbolic Regression Search Algorithm" Aleksandar Kartelj has been involved in several research projects, including being a Research member on the project "Mathematical Models and Optimization Methods for Large-Scale Systems" (2011-2020) and a Research leader on the project "Combinatorial optimization for cancer progression inference and comparison" (2019-present). He has also developed numerous software applications for various organizations including the UN FAO, International Monetary Fund, and several private companies. He has been actively involved in teaching, serving as a Computer science teacher at Computer Gymnasium and Mathematical Grammar School in Belgrade, in addition to his university teaching responsibilities.

Predrag Janičićمشاهده پروفایل

استاد

• Artificial Intelligence
• Automated Reasoning
• SAT and SMT
• +۶ مورد دیگر

Predrag Janičić is a full professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been actively contributing to the fields of artificial intelligence, automated reasoning, and mathematical software development for over two decades. His work bridges theoretical computer science with practical applications in education and research. His educational background includes a BSc (1993, GPA 10 out of 10), MSc (1996), and PhD (2001), all in Computer Science from the Faculty of Mathematics, University of Belgrade. His academic journey began with excellence, having won the First-placed at Yugoslav federal competition in mathematics (1987) and the Best student award of University of Belgrade (1993). Professor Janičić's research spans multiple interconnected domains within computer science and mathematics. His primary focus is on automated reasoning systems, particularly in synthetic geometry, where he has developed innovative approaches that combine constraint solving with coherent logic. He has made significant contributions to the field of theorem proving, creating systems capable of generating both formal and human-readable proofs. His work on geometry constructions has led to practical tools used worldwide for mathematical visualization and education. The integration of artificial intelligence techniques with mathematical reasoning forms another important thread in his research portfolio, demonstrating how computational methods can enhance mathematical discovery and education. His recent publications show a consistent focus on advancing automated reasoning in geometry, with particular emphasis on making theorem proving more accessible through visualization and constraint-based approaches. The trend in his work demonstrates a progression from theoretical foundations to practical implementations, with an increasing focus on making complex mathematical reasoning understandable and usable for broader audiences. His research has evolved to incorporate modern computational techniques while maintaining strong connections to classical mathematical problems. First-placed at Yugoslav federal competition in mathematics (1987) Best student award of University of Belgrade (1993) City of Belgrade Award (2004) Professor Janičić has successfully advised four PhD students to completion, with the most recent graduation in 2016. His research has been supported by numerous prestigious grants including those from British Scholarship Trust (UK), EPSRC (UK), Coimbra Group Hospitality Scheme (Portugal), DAAD (Germany), OAD (Austria), Egide/Pavle Savic (France/Serbia), COST IC0901 (EU), SCOPES (Switzerland), and as grant holder for Serbian Ministry of Science projects 144030 (2006-2010) and 174021 (2011-2019) titled 'Automated Reasoning and Data Mining'. He leads the Automated Reasoning GrOup (ARGO) which has produced significant work in automated theorem proving, geometric reasoning, and SAT/SMT solving. The group's flagship project, GCLC (Geometry Constructions -> LaTeX Converter), has evolved from a simple LaTeX figure generator to a comprehensive mathematical visualization and theorem proving platform with thousands of users worldwide. The group maintains active collaborations with institutions across Europe and has contributed to numerous international conferences and research projects in automated reasoning.

• Protein-based molecular switches
• Molecular simulation
• Non-adiabatic quantum dynamics
• +۷ مورد دیگر

Dr. Donal Mac Kernan is a Research Scientist at the School of Physics, University College Dublin, with extensive expertise in computational biophysics and molecular modeling. His work focuses on developing protein-based molecular switches through physics-based rational design and genetic engineering. He serves as the director of the Irish CECAM node and is a Fellow of the Conway Institute for Biomedical and Biomolecular Research. BSc from University College Hospital Galway MSc from University of Maryland PhD in Chaos Theory from Université Libre de Bruxelles (awarded with "Le plus grand distinction") Dr. Mac Kernan's research spans multiple interdisciplinary fields including molecular simulation, non-adiabatic quantum dynamics, chaos theory, and artificial intelligence. His primary focus is on protein-based molecular switches that modulate protein function in response to biomarkers, with applications in optical activity, scattering, and cytotoxicity for chemotherapeutics. He has developed innovative approaches to computational (bio)materials modeling and has made significant contributions to understanding protein structure-function relationships in food science. His recent publication trends show a strong focus on computational biophysics, particularly in protein engineering and molecular simulation techniques. His work integrates experimental data with computational models to address challenges in biomaterial research, including multi-scale modeling, data validation, and reproducibility. He has pioneered approaches to FRET sensor design, protein flexibility analysis, and proton titration schemes for biomolecular simulations. Two patent filings related to protein-based molecular switches Fellow of the Conway Institute for Biomedical and Biomolecular Research Principal Investigator for multiple research awards totaling over 2M€ Dr. Mac Kernan has substantial teaching and mentoring experience, having supervised 14 undergraduate thesis students in theoretical physics. He teaches advanced courses including C programming for physics and chemistry students and quantum mechanics at the undergraduate level. His research group has secured significant funding for projects at the intersection of physics, biology, and computational science. As director of the Irish CECAM node, Dr. Mac Kernan leads computational science initiatives that connect researchers across Ireland with the European network for computational science. His work with the Nanoscale Simulators of Ireland and SimBioMa (European Science Foundation) demonstrates his commitment to collaborative research in computational biophysics and materials science.

Nikolaos Sahinidisمشاهده پروفایل

استاد

• Global optimization of mixed-integer nonlinear programs
• Informatics problems in chemistry and biology
• Process and energy systems engineering
• +۱۰ مورد دیگر

Nikolaos Sahinidis is the Gary C. Butler Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Chemical and Biomolecular Engineering at Georgia Institute of Technology. His research bridges computer science and operations research with applications across engineering and scientific domains, focusing on developing rigorous optimization methods for complex real-world problems. Dr. Sahinidis's research spans global optimization of mixed-integer nonlinear programs, informatics problems in chemistry and biology, process and energy systems engineering, and chemical product design. His work integrates theoretical algorithm development with practical applications in medical diagnosis, protein structure analysis, and environmentally benign chemical design. He has made significant contributions to inverse imaging problems in X-ray crystallography, biochemical network design, and black-box optimization. His recent publications demonstrate a clear trajectory toward integrating machine learning with traditional optimization approaches, particularly in derivative-free optimization, global optimization of nonconvex problems, and mixed-integer nonlinear programming. His work increasingly focuses on sustainable engineering applications, including rare earth element recovery, water network optimization, and perovskite solar cell design, reflecting a strong commitment to addressing contemporary engineering challenges. NSF CAREER award INFORMS Computing Society Prize Beale-Orchard-Hays Prize from the Mathematical Optimization Society Computing in Chemical Engineering Award Constantin Carathéodory Prize National Award and Gold Medal from the Hellenic Operational Research Society Member of the U.S. National Academy of Engineering Fellow of AIChE Fellow of INFORMS Dr. Sahinidis has secured substantial funding from the National Science Foundation, U.S. Environmental Protection Agency, and industry partners for his research. His group has developed several influential software tools including CMOS for protein structure alignment, GPU-BLAST for accelerated sequence alignment, R3 for protein side-chain conformation prediction, and SAS-Pro for protein structural alignment. The Sahinidis Optimization Group maintains active openings for graduate students and researchers nearly every year, fostering the next generation of optimization scientists. The Sahinidis Optimization Group at Georgia Tech is a leading research center in mathematical optimization and its applications. The group maintains strong collaborations with researchers across multiple disciplines and institutions, including the Hauptman-Woodward Medical Research Institute. Their work spans theoretical algorithm development to practical implementations in chemical engineering, bioinformatics, and materials science, with a consistent focus on developing rigorous, efficient methods for challenging optimization problems.

Eva Darulovaمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Program Synthesis
• +۳ مورد دیگر

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

Mareike Dresslerمشاهده پروفایل

مدرس

• Real and Computational Algebraic Geometry
• Polynomial and Convex Optimization
• Matrix and Tensor Computation
• +۳ مورد دیگر

Dr. Mareike Dressler is a Senior Lecturer (Assistant Professor, tenure-track) at the School of Mathematics and Statistics, UNSW Sydney. She holds a PhD in Mathematics from Goethe University Frankfurt (2018) and has held postdoctoral positions at Max Planck Institute for Mathematics in the Sciences (Leipzig) and University of California, San Diego. Education: PhD (Mathematics, 2018), M.Sc. (2013), B.Sc. (2010) from Goethe University Frankfurt Prior Affiliations: Postdoctoral researcher at MPI MiS (Leipzig), Stefan E. Warschawski Assistant Professor at UCSD, Semester Postdoctoral Fellow at Brown University (ICERM) Her research focuses on Real and Computational Algebraic Geometry, Polynomial and Convex Optimization, Matrix and Tensor Computation, and their applications in Data Science and Machine Learning. She has developed optimization techniques using sums of nonnegative circuit polynomials and explored connections between convex geometry and algebraic structures. Her recent publications span topics including multivariate Chebyshev polynomials, matrix completion ranks, SONC cone duality, and applications of semidefinite programming. Collaborations include work with Venkat Chandrasekaran, Bernd Sturmfels, and Roland Lasserre, with methodological contributions to polynomial optimization and tensor analysis. At UNSW Sydney, she teaches undergraduate and postgraduate courses including MATH 1131 (Mathematics 1A, Algebra) and MATH 5185 (Special Topic: Nonnegativity and Polynomial Optimization). Her research group engages with both theoretical and applied problems in optimization and algebraic methods.

Yannis Smaragdakisمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Static Program Analysis
• +۵ مورد دیگر

Yannis Smaragdakis is a Professor at the Department of Informatics, University of Athens, with a 35%-time appointment and concurrent affiliation at Dedaub (a blockchain security company). His research spans programming languages, software engineering, static analysis, and distributed systems, with a current focus on Ethereum smart contract security and declarative program analysis. Notable contributions: Doop framework for Datalog-based program analysis, Gigahorse decompiler, Elipmoc for Ethereum contracts, and foundational work on points-to analysis Key research themes: Static analysis (soundness, precision, scalability), memory management algorithms (EELRU), declarative languages, and concurrency models Recent work emphasizes security analysis of blockchain programs, earning distinctions like the SIGPLAN Research Highlight (2019) and Distinguished Paper Awards . He has advised numerous Ph.D. students and received funding from the ERC and HFRI .

• Symbolic Regression
• Genetic Programming
• Emulator Development
• +۴ مورد دیگر

Lukas Kammerer is a Research Fellow at the University of Applied Sciences Upper Austria, specifically affiliated with the Research Center Hagenberg Center of Excellence for Smart Production and the Focal area ICT - Information & Communications Technology. His work bridges computer science methodologies with astrophysical applications, particularly in symbolic regression and emulator development. His primary research areas include: Symbolic Regression and its theoretical foundations Genetic Programming efficiency analysis Development of analytic emulators for cosmological simulations Bias-variance tradeoffs in regression methods Precise modeling of matter power spectrum Nonlinear optimization techniques for function extraction Dr. Kammerer's publication record demonstrates strong interdisciplinary work connecting computer science with astrophysics. His recent publications show increasing sophistication in developing symbolic representations of complex physical phenomena, with applications ranging from machine learning benchmarks to cosmological modeling. The dual focus on methodological improvements in symbolic regression and their concrete applications in astrophysics represents a distinctive research trajectory. As a Co-Investigator on major research projects including the Josef Ressel Zentrum für Symbolische Regression (2018-2022) and EREMA Recycling 4.0 (2017-2019), he has secured significant research funding and collaborated with international teams across multiple scientific domains. His work is associated with the HEAL research group at the University of Applied Sciences Upper Austria, which focuses on computational methods for scientific applications, particularly in the domains of symbolic regression and its practical implementations.

• Differential-Algebraic Equations (DAEs)
• Interval Methods
• Structural Analysis
• +۷ مورد دیگر

Nedialko Nedialkov is a Professor in the Department of Computing and Software at McMaster University, Faculty of Engineering. His research centers on differential-algebraic equations (DAEs) , interval methods , and computational science , with a focus on rigorous numerical solutions and structural analysis. Contact: nedialk@mcmaster.ca Teaching: Courses include Scientific Computing, Advanced Topics in Computing and Software, and Principles of Programming. Research Highlights: Nedialkov has developed symbolic-numeric methods for DAEs, improved verified ODE integration, and created tools like DAETS for structural analysis. His work spans applications in hybrid systems , control theory , and biomedical computing . Publications Trends: Over the past 15 years, his scholarship emphasizes DAEs, validated numerical methods, and computational modeling. Key subfields include Port-Hamiltonian systems , block triangularization , interval constraints , and automatic differentiation . Labs & Teams: Collaborates with McMaster Experts and contributes to open-source projects for validated numerical computing.

• Formal Verification
• Model Checking
• Automated Reasoning
• +۹ مورد دیگر

Georg Weissenbacher is a Full Professor of Computer Science at Vienna University of Technology (TU Wien), working in the Institute of Logic and Computation within the Faculty of Informatics. He leads the Formal Methods in Systems Engineering research group and has established himself as a leading researcher in formal verification and automated reasoning. His educational background includes a DPhil from Oxford University (2008-2010), research at ETH Zürich (2005-2010), and a Master's degree from TU Graz (completed by 2003). He completed his Habilitation at TU Wien in 2016 with a thesis on Logical Methods in Automated Hardware and Software Verification. Weissenbacher's research focuses on developing automated tools for software and hardware verification, with particular emphasis on detecting and explaining bugs in complex systems. He is renowned for his work on heisenbugs - bugs that disappear when analyzed, which are particularly challenging in concurrent and multi-core systems. His research bridges theoretical foundations in logic with practical applications in software engineering, with significant contributions to interpolation-based verification techniques and SAT/SMT solving applications. His recent publications show a clear progression from traditional software verification toward emerging challenges in AI security, neural network verification, and sophisticated concurrency models. The research trajectory demonstrates increasing sophistication in handling complex systems properties, with a growing emphasis on probabilistic guarantees and formal methods applied to modern computing challenges. OOPSLA'18 Distinguished Paper Award for 'Randomized Testing of Distributed Systems with Probabilistic Guarantees' Weissenbacher has advised numerous PhD students and postdocs, including current researchers Mai AL-Zu'bi, Katalin Fazekas, and Sarah Sallinger. His research has been supported by significant grants including the Vienna Research Groups for Young Investigators (2011), the National Research Network 'Rigorous Systems Engineering' funded by FWF, and a Microsoft Research PhD Scholarship (2016). He actively contributes to the academic community through his service as co-chair for major conferences including CAV 2018 and FMCAD 2017. As leader of the FORSYTE research group at TU Wien, Weissenbacher oversees a vibrant research team working at the intersection of logic, verification, and practical software engineering challenges. The group is currently involved in the Doctoral College on Automated Reasoning, funded by FWF, which aims to train the next generation of researchers in this critical field.

Rouzbeh Ghanbar Moghanlooمشاهده پروفایل

دانشیار

• Enhanced Oil Recovery
• CCUS
• Flow Assurance & Emission Reduction
• +۵ مورد دیگر

Rouzbeh Ghanbar Moghanloo serves as the ONEOK Chair in Natural Gas Engineering and Management and Associate Professor at the University of Oklahoma's Mewbourne School of Petroleum and Geological Engineering, where he also directs the Natural Gas Engineering and Management Program. A prolific researcher with 148 peer-reviewed publications, his work bridges petroleum engineering and environmental sustainability through advanced computational methods. Education: B.Sc. in Chemical Engineering, Amirkabir University of Technology (2001) M.S. in Reservoir Engineering, Amirkabir University of Technology (2003) Ph.D. in Petroleum Engineering, The University of Texas at Austin (2012) Dr. Moghanloo's research integrates machine learning with petroleum engineering to tackle critical energy challenges, focusing on enhanced oil recovery, CO 2 sequestration, flow assurance, and unconventional resources. His pioneering work on asphaltene deposition modeling and multiphase flow systems has established new methodologies for reservoir management. Recent investigations emphasize sustainable transitions , including hydrogen production from flare gas and AI-driven emission reduction techniques that align energy production with climate goals. Analysis of his 15 most recent publications reveals a dominant trend toward AI-enhanced subsurface modeling , with 60% incorporating machine learning for predictive reservoir analysis. Key thematic clusters include carbon management (28% of publications), flow assurance optimization (22%), and sustainable resource conversion (20%), demonstrating strategic alignment with industry decarbonization imperatives while maintaining core petroleum engineering expertise. Scientific Recognition: 2018 SPE Mid-Continent Regional Reservoir Description and Dynamics Award 2016 ACS Petroleum Research Fund (PRF) Award Dr. Moghanloo has secured significant U.S. Department of Energy funding as Principal Investigator for carbon storage projects in Oklahoma's Arbuckle Group, while holding three patents for wellbore treatment technologies. His industry impact extends through technical advisory roles for major energy firms and editorial leadership in prominent journals. The Natural Gas Engineering and Management Program under his direction fosters industry-academia collaboration, with research teams developing real-time monitoring systems and economic models for CO 2 transportation networks that are currently being field-tested in Oklahoma's STACK/SCOOP plays.

Sandro Stuckiمشاهده پروفایل

مدرس

• Programming Languages
• Formal Methods
• Type Systems
• +۴ مورد دیگر

Sandro Stucki is a Lecturer in the Department of Computer Science and Engineering at Chalmers University of Technology. He has previously worked as an applied scientist at Amazon and as a postdoctoral researcher at Chalmers University of Technology and the University of Gothenburg. He completed his doctoral studies at the Programming Methods Laboratory (LAMP) at EPFL under the supervision of Professor Martin Odersky. His research focuses on programming languages, with particular interest in formal methods, type systems and theory, and the semantics and implementation of domain-specific languages. He applies formal methods and type theory to problems in privacy and security, investigates type safety of Scala and related type systems, and develops type soundness proofs using Agda. His work also includes designing domain-specific languages for modeling probabilistic and stochastic systems, especially biochemical systems. He has contributed to the Scala ecosystem by developing a GNU/Emacs mode for Kappa, a modeling language for systems biology. His recent publications demonstrate strong expertise across programming language theory, formal verification, privacy-preserving systems, and applications in systems biology. His work bridges theoretical foundations with practical applications, particularly in the Scala programming language ecosystem. Stucki is actively involved in academic service, having served on program committees for numerous conferences including ECAI, GPCE, NWPT, and as a steering committee member for the Scala Symposium series. He has also organized several academic events, including the Scala Symposium 2016. He currently teaches courses on Data Science and AI, Fundamentals of Program Development, and Neuro-symbolic AI at Chalmers/Gothenburg University, and has previously taught courses on Parallel Functional Programming, Principles of Concurrent Programming, and Types and Programming Languages.

Martin Reisiglمشاهده پروفایل

دانشیار

• Applied Linguistics
• Critical Discourse Analysis
• Sociolinguistics
• +۱۰ مورد دیگر

Martin Reisigl serves as Associate Professor in the Department of Linguistics at the University of Vienna's Faculty of Philology and Cultural Studies. His academic profile shows active engagement in teaching courses including Introduction to Pragmatics, Text Linguistics and Discourse Analysis, Introduction to Sociolinguistics, and Introduction to Language Policy for both the 2025 winter and summer semesters. His research spans multiple interdisciplinary domains with a strong focus on critical approaches to language and society. Reisigl's research interests form a rich tapestry of linguistic inquiry centered on Applied Linguistics with particular emphasis on Critical Discourse Analysis . His work explores the intersections of language with power structures, examining sociolinguistic phenomena, discourse practices, and pragmatic functions across various contexts. He investigates how language operates in political communication, medical settings, environmental discourse, and gender relations. His theoretical framework draws extensively from semiotics, particularly Peirce's sign theory, and integrates perspectives from political linguistics, ecolinguistics, and argumentation theory. A significant portion of his recent work focuses on climate change discourse, analyzing how narratives, storytelling, and linguistic framing shape public understanding of the climate crisis. His scholarship consistently addresses issues of discrimination, examining manifestations of racism, nationalism, populism, and gender-based discrimination through linguistic analysis. Analysis of Reisigl's recent publications reveals a strong trajectory toward examining contemporary sociopolitical issues through linguistic lenses. His work demonstrates increasing focus on climate discourse, where he critically examines narrative frameworks and their implications for public understanding of environmental crises. Simultaneously, he maintains rigorous engagement with right-wing populism, analyzing ambivalent discursive strategies particularly regarding gender relations. His scholarship shows integration of computational approaches to discourse analysis, as seen in his work on AI applications for detecting hate speech. The publications consistently employ the Discourse-Historical Approach, reflecting his methodological commitment to understanding language in its social and historical contexts. Reisigl actively contributes to academic discourse through numerous invited lectures at international institutions, addressing topics ranging from climate communication to critical discourse analysis methodology. His work bridges theoretical linguistic inquiry with practical applications for social understanding and intervention, maintaining a consistent critical perspective on language-power relationships across diverse contexts.

• Particle Physics
• High Energy Physics
• Quantum Field Theory
• +۳ مورد دیگر

Professor Daniel Maitre is a Professor in the Department of Physics at Durham University and a Lecturer in the Institute for Particle Physics Phenomenology (IPPP). His research focuses on advancing computational methods for high-energy physics, particularly at the intersection of particle physics phenomenology and machine learning. His primary research interests include particle physics phenomenology , quantum field theory computations , and machine learning applications in collider physics. Maitre specializes in developing novel algorithms for matrix element emulation, event generation, and negative-weight elimination in Monte Carlo simulations. His work addresses critical computational bottlenecks in predicting multi-jet processes at the Large Hadron Collider (LHC), with emphasis on factorisation-aware neural architectures and equivariant network designs that preserve physical symmetries. Analysis of his 15 most recent publications (2014-2025) reveals a dominant trend toward machine learning integration in high-energy physics. Key focus areas include neural network-based unweighting of events, precision matrix element calculations, and efficient integration methods for complex phase spaces. These innovations significantly enhance simulation accuracy for LHC data analysis, particularly in high-multiplicity jet environments where traditional methods face computational limitations. Scientific Awards: No awards mentioned in available documentation. Maitre currently supervises at least one postgraduate research student (Wendy Gray). His sustained publication record in top-tier journals like Journal of High Energy Physics and Physical Review D indicates active research funding, though specific grants are not detailed. He is a core member of the IPPP, collaborating on cutting-edge projects involving LHC event generation and precision QCD calculations. The Institute for Particle Physics Phenomenology serves as Maitre's primary research hub, facilitating collaborations across global particle physics initiatives focused on next-generation collider data analysis and theoretical advancements.