جست‌وجوی استاد و پژوهشگر

Reza Zadehمشاهده پروفایل

استاد مدعو

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +۷ مورد دیگر

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.

Martin Skovgaard Andersenمشاهده پروفایل

دانشیار

• Optimization
• Signal Processing
• Machine Learning
• +۱۲ مورد دیگر

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

Sebastian Angelمشاهده پروفایل

دانشیار

• Systems
• Security
• Privacy
• +۴ مورد دیگر

Sebastian Angel is an Associate Professor and Chair of Undergraduate Curriculum in the Department of Computer and Information Science at the University of Pennsylvania . He leads research in systems, security, privacy, and networking, with a focus on privacy-preserving systems, accountability in online services, consistency in distributed systems, and next-generation operating systems. Research Areas: Systems, Security, Privacy, Networking, Distributed Systems, Operating Systems Labs & Teams: Distributed Systems Laboratory, Security and Privacy Laboratory, Warren Center for Network and Data Sciences Education: Ph.D. in Computer Science from the University of Texas at Austin (2018), ACM SIGOPS Dennis M. Ritchie Dissertation Award, Bert Kay Best Dissertation Award Recent Publications (2025–2022) span serverless computing, zero-knowledge proofs, distributed transactions, privacy-preserving ad tech, and verifiable execution. His 2025 work includes serverless workflow optimization and structural logic verification, while 2024 focuses on caching frameworks and stateful serverless. Earlier 2023–2022 projects include secure federated learning, confidential cloud services, and private information retrieval. Scientific Awards: NSF CAREER Award (2021) JPMorgan Faculty Award (2021) ACM SIGOPS Dennis M. Ritchie Dissertation Award (2018) Bert Kay Best Dissertation Award (2018) ACM SIGMOD Research Highlights Award (2024) VLDB 2024 Best Paper Award Nominee Advising: Current PhD Students: Elizabeth Margolin, Jess Woods, Yuxuan Zhang Current Masters Students: Felix Adena, Sydnie Shea Cohen Alumni: Eleftherios Ioannidis (2025), Yiping Ma (2025), Haoran Zhang (2024), Ke Zhong (2024), Selin Butun (2025), Martin Sander (2025), Seungmin Han (2024), Andrew Beams (2022), Yifeng Mao (2021), Varad Deshpande (2020) Current Courses: CIS 4510/5510: Computer and Network Security (Fall 2025). He also organizes conferences like S&P, OSDI, SOSP, PETS, and EuroSys.

Pedro Orvalhoمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Automated Reasoning
• Formal Methods
• +۴ مورد دیگر

Pedro Orvalho is a Research Associate in the Department of Computer Science at the University of Oxford, working with Professor Marta Kwiatkowska on the FUN2MODEL ERC project. His research bridges theoretical computer science with practical applications in software engineering and programming education. His educational background includes: PhD in Computer Science and Engineering (2025) from Instituto Superior Técnico, Universidade de Lisboa MSc in Information Systems and Computer Engineering (2019) from Instituto Superior Técnico BSc in Information Systems and Computer Engineering (2017) from Instituto Superior Técnico Orvalho's research spans Artificial Intelligence, Automated Reasoning, Formal Methods, and Program Repair, with significant contributions to programming education tools. His work integrates formal methods with machine learning techniques to develop novel approaches for program verification and repair, particularly focused on introductory programming assignments. His scientific achievements have been recognized with prestigious awards: Vencer o Adamastor (VoA) - 3rd Edition (2025) ELISE Mobility Grant (2024) COST Travel Grant (2022) Excellence in Teaching IST Awards (2021 and 2024) ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2021) FCT PhD Scholarship (2020-2024) With five years of teaching experience at Instituto Superior Técnico, Orvalho has developed educational tools like GitSEED and MENTOR that bridge his research with practical classroom applications. His research has been supported by multiple grants including the ERC FUN2MODEL project and FCT PhD Scholarship, demonstrating both academic and practical impact. He maintains active collaborations with researchers from Czech Technical University in Prague, Carnegie Mellon University, and industry partners like OutSystems, contributing to an international research network focused on software reliability and educational technology.

Yu [Kevin] Caoمشاهده پروفایل

استاد

• Microelectronics
• Computer Engineering
• Semiconductor Technology
• +۵ مورد دیگر

Yu [Kevin] Cao is the Louis John Schnell Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on microelectronics co-design for energy-efficient computing, spanning integrated circuit design, semiconductor physics, and machine learning methodologies. He leads the Microelectronics Co-design Research Group and actively collaborates with institutions like Georgia Institute of Technology, Sandia National Laboratories, and Notre Dame. His research interests include AI hardware acceleration , in-memory computing , cryogenic CMOS design , and 3D integration of heterogeneous chiplets . Current initiatives explore reconfigurable on-package systems for AI, spiking neural networks on neuromorphic hardware, and low-temperature logic technologies. Recent publications and projects highlight advancements in AI accelerators , RRAM-based compute-in-memory , graph convolutional networks , and 3D integration . His group develops tools like MN-SIM 2.0 for memristor modeling and investigates novel materials for neuromorphic systems. Grants include collaborative NSF funding for chiplet-based AI systems, CoCoSys center funding from SRC, and DOE/Sandia projects on neuromorphic hardware. Future work emphasizes scalable co-design frameworks for intelligent systems and heterogeneous integration challenges.

Katja Hoseمشاهده پروفایل

استاد

• Query Processing
• Semantic Web
• Linked Data
• +۷ مورد دیگر

Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.

• Database Theory
• Logic Programming
• Knowledge Graphs
• +۷ مورد دیگر

Georg Gottlob is a Professor at the University of Oxford's Department of Computer Science, with additional affiliation at TU Vienna's Faculty of Informatics. He has maintained an exceptionally productive research career spanning over four decades, with 494 publications documented in the DBLP database from 1983 to the present. His research interests focus on Database Theory , Logic Programming , and Knowledge Graphs , with particular expertise in hypertree decompositions, Datalog systems, and existential rules. His work bridges theoretical foundations with practical applications, as evidenced by his development of the Vadalog system for knowledge graph reasoning. Gottlob's recent publications (2023-2025) demonstrate continued innovation in query optimization, rule-based reasoning, and the integration of large language models with database systems. His work shows a consistent trend toward making theoretical advances in database theory practically applicable, particularly in the context of knowledge graphs and semantic web technologies. Scientific Awards: 2020 ACM PODS Alberto O. Mendelzon Test-of-Time Award for influential contributions to database theory Gottlob maintains extensive research collaborations with scholars including Reinhard Pichler, Andreas Pieris, and Matthias Lanzinger. His work has significant practical impact through systems like Vadalog, which combines machine learning with logical reasoning for knowledge graph applications. He has supervised numerous PhD students (though specific names aren't listed in the DBLP record) and has been instrumental in advancing the field of database theory from theoretical foundations to real-world applications. His research group focuses on the intersection of database theory, knowledge representation, and artificial intelligence, with particular emphasis on developing efficient algorithms for complex query processing and reasoning tasks over large knowledge graphs.

Romain Raveauxمشاهده پروفایل

دانشیار

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +۵ مورد دیگر

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

Ming Yanمشاهده پروفایل

دانشیار

• Optimization Methods
• Sparse Recovery
• Inverse Problems
• +۷ مورد دیگر

Ming Yan is an Associate Professor and Assistant Dean at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), where he has been serving since 2022. Prior to this position, he was an Associate Professor at Michigan State University from 2021 to 2023 (on leave from 2022 to 2023) and an Assistant Professor from 2015 to 2021. His academic journey includes postdoctoral positions at UCLA and Rice University. His educational background includes: Ph.D. in Mathematics from University of California, Los Angeles (UCLA), 2012 M.S. in Mathematics from University of Science and Technology of China (USTC), 2008 B.S. in Mathematics from University of Science and Technology of China (USTC), 2005 Ming Yan's research focuses on optimization methods , particularly for sparse recovery and inverse problems . His work extends to federated and machine learning algorithms , parallel and distributed algorithms for large-scale data , and variational techniques in image processing . His approach often combines theoretical analysis with practical applications, developing algorithms that address computational challenges in modern data science. His recent publications demonstrate a strong focus on decentralized optimization , primal-dual algorithms , and sparse recovery techniques . These works span theoretical developments in convergence analysis and practical applications in machine learning and signal processing. His research has evolved from foundational work in image processing to more recent contributions in distributed learning and federated systems, reflecting the changing landscape of data science. Ming Yan is actively involved in mentoring students and has indicated that he is recruiting Ph.D. students, postdocs, and visiting students at CUHK-Shenzhen. His teaching portfolio includes courses in calculus, optimization, and computational methods across multiple institutions. His research is supported by various grants, though specific details are not provided in the available information. He has developed several algorithms and software implementations, including the PD3O framework for convex optimization algorithms.

• Cryptography
• Computer Security
• Algorithms
• +۶ مورد دیگر

Rafail Ostrovsky is a Distinguished Professor of Computer Science and Mathematics (by courtesy) at UCLA's Henry Samueli School of Engineering and Applied Science. He holds the Norman E. Friedman Chair in Knowledge Sciences and serves as Director of the Center for Information and Computation Security. With over 350 refereed publications and 15 issued USPTO patents, he is one of the most influential researchers in theoretical computer science and cryptography. Professor Ostrovsky's research spans cryptography, network algorithms, and search and classification of large-scale, high-dimensional data. His work focuses on foundational aspects of secure computation including zero-knowledge proofs, secure multi-party computation, private information retrieval, and privacy-preserving data analysis. His contributions to streaming algorithms, metric embedding, and clustering for high-dimensional data have established important theoretical frameworks with practical applications. He has pioneered techniques for secure computation that maintain privacy while enabling collaborative data analysis. His recent publications demonstrate continued leadership in advancing secure computation protocols, with emphasis on efficiency improvements, practical implementations, and novel applications in blockchain technology and distributed systems. His work consistently addresses fundamental theoretical challenges while maintaining relevance to real-world security problems, bridging the gap between theoretical cryptography and practical security solutions. Selected Honors: 1993 Henry Taub Prize 2017 IEEE Computer Society Edward J. McCluskey Technical Achievement Award 2018 RSA Award for Excellence in Mathematics 2022 W. Wallace McDowell Award (the highest award given by the IEEE Computer Society) Professor Ostrovsky has served in significant leadership roles including chair of the IEEE Technical Committee on Mathematical Foundations of Computing (2015-2018) and chair of the IEEE FOCS 2011 Program Committee. He has served on over 40 international conference program committees and currently serves on the editorial boards of the Journal of ACM and Algorithmica Journal. As a Fellow of the National Academy of Inventors, AAAS, ACM, IEEE, and IACR, and as a foreign member of Academia Europaea, his contributions have been widely recognized across multiple disciplines. He actively teaches advanced courses including Introduction to Cryptography (CS183), Foundations of Cryptography (CS282A/M209A), and Cryptographic Protocols (CS282B/M209B), mentoring the next generation of security researchers while continuing to push the boundaries of secure computation through his research.

Sriram Sankararamanمشاهده پروفایل

استاد

• Machine Learning
• Computational Genetics
• Genomics
• +۷ مورد دیگر

Professor Sriram Sankararaman is a computational scientist with triple appointments at the University of California, Los Angeles in the Computer Science Department , Department of Human Genetics , and Department of Computational Medicine . His interdisciplinary research bridges computer science, statistics, and biomedicine. PhD in Computer Science from UC Berkeley Postdoctoral training at Harvard Medical School Research focuses on statistical and computational methods for understanding genomic datasets, including: Population genetic inference and archaic admixture analysis Genetic architecture of complex traits Machine learning for clinical data integration Privacy-preserving genomic analysis Recent publications address scalable algorithms for biobank-scale data, evolutionary insights from archaic DNA, and interpretable machine learning models. His lab develops tools like SCOPE and RHE-mc for population structure and variance components analysis. Scientific Honors NSF Career Award (2020) Microsoft Investigator Fellow (2019) Northrop Grumman Excellence in Teaching (2019) Okawa Foundation grant (2017) Alfred P. Sloan Fellow (2017) NIH Pathway to Independence Award (2014) Visvesvaraya Medal (2004) As director of the Machine Learning and Genomics Lab , he leads research on evolutionary genomics, medical genetics, and scalable algorithms for integrating multi-modal biomedical datasets.

William Culhaneمشاهده پروفایل

پژوهشگر ارشد

• Big Data Processing
• Cloud Computing
• Distributed Systems
• +۷ مورد دیگر

William Culhane is a Post-doctoral Research Associate working with the Large-Scale Distributed Systems (LSDS) research group at Imperial College London under Dr. Peter Pietzuch. His research focuses on big data processing in cloud computing environments, with expertise spanning distributed systems architecture, network-aware algorithms, and practical implementation of theoretical models. His academic credentials include: PhD in Computer Science from Purdue University (2015), Thesis: "Optimal 'Big Data' Aggregation Systems – From Theory to Practical Application" Masters in Computer Science from Purdue University (2011) BS in Computer Science and Engineering with Music minor from Ohio State University (2008), Cum Laude with Distinction Culhane's research program bridges theoretical computer science with practical systems implementation. His early work established mathematical models for optimal aggregation overlays, demonstrating how specific properties of aggregation functions guide theoretically optimal overlay construction. This evolved into practical systems like LOOM that implement these models for real-world big data processing. His current research on Meta-Dataflows (MDF) enables users to specify solution spaces of data analysis approaches and automatically discover optimal outcomes without code changes. The SquirrelJoin project addresses network skew in distributed joins by dynamically rerouting traffic to underutilized network segments. His publication record shows consistent progression from theoretical foundations (INFOCOM'15, SIGCOMM'14) to applied systems (VLDB 2017, Middleware'14) with practical impact. His professional trajectory includes significant industry exposure through a Google Techstop internship and research at Qatar University, where he explored anonymized database relations for third-party cloud processing. These experiences informed his approach to balancing theoretical purity with practical constraints in distributed systems design. Culhane has developed numerous open-source tools for the research community including LaTeX macros for consistent document formatting, Linux deployment scripts for distributed system management, and JavaScript interactive graphing utilities. These practical contributions demonstrate his commitment to improving research infrastructure beyond his primary publications.

• Probability Theory
• Random Graphs
• Large Deviations
• +۳ مورد دیگر

Rajat Hazra is an Associate Professor at the Mathematical Institute of Leiden University, specializing in Probability Theory. He is affiliated with the NETWORKS consortium, which received a COFUND grant from the European Commission in 2020. Institution: Leiden University Academic Unit: Mathematical Institute Rank: Associate Professor Email: r.s.hazra@math.leidenuniv.nl His research focuses on theoretical probability, particularly in large deviation principles, random graphs, and spectral graph theory. A 2021 publication analyzed maximal eigenvalues of inhomogeneous Erdos-Renyi graphs using variational methods in graphon space. Scientific Awards: COFUND grant from European Commission (2020)

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

Sandra Martinمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Neuroscience
• Aging and Cognition
• Neurolinguistics
• +۳ مورد دیگر

Sandra Martin is a Postdoctoral Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, where she works in the Lise Meitner Research Group Cognition and Plasticity. Her research combines neuroimaging and neuromodulation methods with graph theory, large language models, and behavioral modeling to explore cognitive changes across the adult lifespan, with a particular focus on age-related changes in memory processes and their interaction with executive functions. Education: PhD in Cognitive Neuroscience (2018-2023) from the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig M.Sc. in European Master of Clinical Linguistics (2014) from University of Potsdam, Germany & University of Groningen, The Netherlands B.A. in Linguistics (2012) from Humboldt University Berlin, Germany Research Interests: Dr. Martin's research focuses on cognitive aging , neurolinguistics , and network neuroscience . She investigates how domain-general networks contribute to language prediction and semantic cognition across the adult lifespan. Her work often employs non-invasive brain stimulation techniques like transcranial magnetic stimulation (TMS) to understand the causal roles of specific brain regions in cognitive processes. She is particularly interested in how executive resources shape language predictability effects and how these relationships change with age. She's a strong advocate for open and reproducible research practices including preregistration and registered reports. Her recent publications reveal a strong trend toward understanding the neural mechanisms of semantic cognition and language processing across the lifespan. She combines multiple methodologies including fMRI, TMS, graph theory, and computational modeling to investigate how brain networks reorganize with age and how this affects cognitive performance. Her work has important implications for understanding cognitive aging and developing interventions for language disorders like aphasia. Scientific Awards: Doctoral fellowship from the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) Advising and Grants: Dr. Martin has been involved in several research projects, including a DFG-funded project on 'The role of domain-general networks in natural language predictions' together with Prof. Dr. Gesa Hartwigsen (Leipzig University) and Prof. Dr. Jonas Obleser (University of Lübeck). While there's no explicit mention of students she's formally advising, her role as a postdoc suggests she likely mentors junior researchers. She actively promotes open and reproducible research practices and co-organized a symposium on Good Scientific Practice titled 'Doing Good – Scientific Practice under Review'. Labs and Teams: Dr. Martin is part of the Lise Meitner Research Group Cognition and Plasticity at the Max Planck Institute for Human Cognitive and Brain Sciences. She collaborates with researchers from Leipzig University and University of Lübeck. Her work often involves interdisciplinary teams combining expertise in cognitive neuroscience, linguistics, and clinical neurology, particularly in studying aphasia and language recovery after stroke. Her research group maintains open science practices with code and data available on GitLab and OSF.