Find Academic Supervisors

Sukyoung RyuView profile

Professor

• Programming languages
• Program analysis
• Static analysis
• +5 more

Sukyoung Ryu is a Professor at Korea Advanced Institute of Science and Technology (KAIST) specializing in programming languages and program analysis. With over 14 years of continuous research contributions since 2011, Dr. Ryu has established herself as a leading researcher in the programming languages community, serving in numerous leadership roles across major conferences including as Steering Committee Chair for APLAS 2023-2024 and New Faculty Symposium Co-Chair at ASE 2025. Dr. Ryu's research focuses on bridging theoretical foundations with practical applications through: Static analysis of JavaScript and other programming languages WebAssembly specification and tooling development Programming language mechanization and formal semantics C-to-Rust translation for memory safety Binary lifting and program verification Her recent publications reveal a strong trajectory toward formal specification of programming languages, particularly WebAssembly, with tools like SpecTec, WEST, and Wasm-R3. These works combine deep theoretical insights with practical implementations that improve language conformance, testing frameworks, and developer tooling. Her research consistently addresses real-world challenges in programming language implementation while maintaining rigorous formal foundations. Dr. Ryu actively contributes to community building and mentoring through: Co-chairing the New Faculty Symposium at ASE 2025 Organizing RTFM (Red-Hot Topics in Faculty Mentoring) panels Participating in PLMW (Programming Languages Mentoring Workshop) Serving on doctoral symposium committees

Mattia FazziniView profile

Assistant Professor

• Software Engineering
• Software Testing
• Software Maintenance
• +3 more

Mattia Fazzini is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota's College of Science and Engineering. His research focuses on improving software quality through innovative techniques in software testing, maintenance, and security. His work primarily targets mobile applications, particularly Android platform challenges. Dr. Fazzini received his Ph.D. in Computer Science from the Georgia Institute of Technology before joining the University of Minnesota. His educational background provided the foundation for his research in software engineering with emphasis on practical solutions for real-world software quality problems. His research interests center around software engineering with specific focus on software testing methodologies, maintenance techniques, and security considerations. Dr. Fazzini's work addresses critical challenges in mobile application development, including test oracle generation, API compatibility issues, bug reproduction, and energy efficiency concerns in Android applications. His research has evolved to incorporate AI techniques, as evidenced by recent work leveraging LLMs for DevOps automation. His publication record demonstrates consistent contributions to top-tier software engineering venues, with a clear trajectory toward increasingly sophisticated approaches to software quality assurance. His work shows strong emphasis on practical tool development alongside theoretical contributions, with numerous tool papers and competitions organized around his research themes. IEEE TCSE Distinguished Paper Award for 'Automatically Removing Unnecessary Stubbings from Test Suites' ACM Distinguished Paper Award for 'Characterizing Human Aspects in Reviews of COVID-19 Apps' Dr. Fazzini actively mentors students, with multiple undergraduate and graduate researchers contributing to his publications. He has served in significant organizational roles for major conferences including ASE, ICSE, ISSTA, and MOBILESoft, demonstrating leadership in the software engineering community. His teaching portfolio includes both undergraduate and graduate courses in software engineering and program design.

Sonia HaiducView profile

Associate Professor

• Software Engineering
• Software Maintenance and Evolution
• Program Comprehension
• +6 more

Sonia Haiduc is an Associate Professor in the Department of Computer Science at Florida State University, where she leads the SERENE (Software Engineering: Evolution and Maintenance) research lab. Her academic career spans over a decade of contributions to software engineering research, with particular expertise in software maintenance, evolution, and program comprehension. Education: Ph.D. in Computer Science, Wayne State University (2013) M.Sc. in Computer Science, Wayne State University (2009) B.Sc. in Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania (2006) Her research focuses on innovative approaches to assist software developers in understanding and maintaining complex software systems. Key areas include software evolution, program comprehension, concept location, source code search, empirical software engineering, and the application of information retrieval and natural language processing techniques to software engineering problems. Her work bridges theoretical foundations with practical applications to improve developer productivity and software quality. Recent publications demonstrate a growing interest in AI-assisted software development, particularly the application of large language models to program repair, commit message assessment, and code search. Her research shows a consistent trajectory from traditional software engineering topics toward integrating modern AI techniques to solve longstanding challenges in software maintenance and evolution. Scientific Awards: ACM SIGSOFT CAPS Travel award (2012, 2010) Google Anita Borg Scholarship (2011) Wayne State University Travel Awards (multiple years) Outstanding Graduate Research Assistant in Computer Science (2010) Dr. Haiduc actively mentors doctoral students, currently advising three Ph.D. candidates while serving on committees for additional graduate students. She has secured research funding including an NSF SHF grant for 'Text Retrieval in Software Engineering 2.0' and an FSU CRC grant for 'Query-Specific Source Code Search Engine Configuration.' She founded and leads the SERENE lab at Florida State University, which focuses on novel approaches to software maintenance, evolution, program comprehension, mining software repositories, and applications of natural language processing in software engineering.

• Static Program Analysis
• Formal Methods
• Quantum Computing
• +3 more

Valentin Perrelle is a researcher at the French Alternative Energies and Atomic Energy Commission (CEA), specifically within the Laboratory for Integration of Systems and Technologies (LIST) in France. He actively contributes to academic conferences in software engineering and programming languages, with a focus on static program analysis, formal methods, and quantum computing. His research interests center on static program analysis , particularly abstract interpretation and loop unrolling heuristics enhanced by machine learning, and formal methods for software verification. Additionally, he has made notable contributions to quantum computing , including the formalization of quantum programming languages and the verification of quantum circuits. His recent publications reveal a dual focus: advancing classical program analysis through machine learning techniques and developing formal foundations for quantum computing. In static analysis, he investigates how end-to-end learning can optimize loop unrolling heuristics. In quantum computing, his work addresses the challenges of measurement and classical control in quantum circuits, as well as the usability of formal methods for verification. Valentin Perrelle is affiliated with CEA LIST, a prominent research laboratory in France that specializes in digital systems and technologies for industrial applications. The laboratory's research spans embedded systems, cybersecurity, and advanced computing, providing a collaborative environment for his work in software analysis and quantum verification.

Yun PengView profile

Assistant Professor

• Intelligent Code Analysis
• Automatic Program Repair
• Software Ecosystem
• +3 more

Dr. Yun Peng is an Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, specializing in software engineering with a focus on intelligent code analysis, automatic program repair, and software ecosystems. With an active research profile, Dr. Peng serves on program committees for major conferences including ASE and ESEC/FSE. Dr. Peng's educational background includes doctoral training focused on programming languages and software engineering, though specific institutions aren't detailed in the available information. Their research primarily addresses challenges in type inference, code analysis, and the application of large language models to software engineering tasks. Research interests center on intelligent code analysis techniques, particularly in type inference systems for dynamic languages like Python. Dr. Peng has pioneered hybrid approaches combining static analysis with deep learning, as demonstrated in their influential ICSE 2022 paper on HiTyper. Recent work has shifted toward leveraging large language models for code review, program repair, and API recommendation, reflecting the field's evolution. Current projects examine LLM applications in secure code review, code efficiency optimization, and vulnerability detection in smart contracts. Dr. Peng's publication record shows a clear progression from foundational type inference research toward cutting-edge LLM applications in software engineering. The work spans multiple dimensions of code quality including security, performance, and maintainability, with tools like HiTyper, TypeGen, and APIBench making significant contributions to the research community. As a program committee member for top-tier conferences, Dr. Peng actively contributes to the software engineering research community. Their work has been recognized through publication in premier venues including ICSE, ASE, and ESEC/FSE, demonstrating consistent high-impact contributions to the field. Dr. Peng maintains an active research group focused on advancing the state of the art in intelligent code analysis, with current projects exploring the intersection of large language models and traditional program analysis techniques. The research has practical applications in developer productivity tools, security analysis systems, and software maintenance automation.

Norbert SiegmundView profile

Professor

• Software Engineering
• Machine Learning Systems
• Performance Analysis
• +3 more

Norbert Siegmund is Professor and Chair of Software Systems at Leipzig University's Institute of Computer Science in Germany. His research bridges software engineering and artificial intelligence, focusing on configurable software systems, performance analysis, and energy consumption optimization. He has established himself as a leading researcher in the field of configurable systems through extensive publications in top-tier software engineering venues. His research interests span multiple interconnected domains: Configurable, complex systems that evolve over time Non-functional properties, particularly performance and energy consumption Sampling, learning, and optimization methods from AI applied to software engineering Contemporary empirical methods in software engineering MLOps and DevOps practices Social aspects of AI-production teams His publication record reveals a consistent focus on creating accurate performance models for configurable software systems, with increasing attention to energy consumption aspects in recent years. The research trajectory shows evolution from basic performance modeling to incorporating machine learning techniques, transfer learning across environments, and addressing energy efficiency concerns. His scientific achievements have been recognized through several prestigious awards: ACM SIGSOFT Distinguished Paper Award at ICSE 2015 SPLC Best Paper Award in 2012 Multiple keynote invitations including at QAVS 2021 Open-Source Award from the state of Thuringia (2020) Best PhD award from University of Magdeburg (2013) Prof. Siegmund has secured significant research funding including DFG projects like 'Green Configuration' (€590,000) and 'Pervolution' (€290,000), as well as BMBF projects related to AI and software engineering. He has mentored numerous students through research projects and has served on program committees for major conferences including ICSE, ASE, ESEC/FSE, and SPLC. His teaching portfolio includes courses on Software Engineering for AI-Enabled Systems, Configurable Software Systems, and Automated Software Engineering.

Rrezarta KrasniqiView profile

Assistant Professor

• Software Engineering
• Requirement Engineering
• Software Quality
• +5 more

Rrezarta Krasniqi is an Assistant Professor of Software Engineering in the Department of Software and Information Systems at the University of North Carolina at Charlotte. Her work focuses on improving software quality through innovative approaches to bug detection and system-wide quality issue analysis. Her educational background includes: B.S. in Mathematics and Computer Science from the University of Prishtina M.S. in Computer Science from Midwestern State University M.S. in Computer Science and Engineering from the University of Notre Dame Ph.D. in Computer Science and Engineering from the University of North Texas Dr. Krasniqi's research centers on quality-related bug detection problems, with particular emphasis on security, usability, and reliability issues that emerge from long-term software maintenance. She develops tools and techniques to enhance understanding of complex system-wide quality issues using code analysis, program comprehension, and AI-driven approaches. Her work combines technical depth with empirical research methodologies to identify root causes of software quality problems and develop more reliable software systems. Her recent publications demonstrate a consistent research trajectory focused on software quality concerns, with increasing integration of NLP and machine learning techniques. Her work spans from foundational bug detection methods to more recent applications of large language models in code translation benchmarking. Dr. Krasniqi actively contributes to the software engineering research community through service on numerous program committees including ASE, ICSE, ICSME, and EASE conferences. In 2025, she served as Tool and Demo Track co-chair for SANER and presented her work on code translation with LLMs at ASE. Before joining UNC Charlotte, she taught computer science courses at various institutions and worked for over three years as a senior Java developer in industry, contributing to web-based application development and maintenance.

Jinfeng WenView profile

Assistant Professor

• Serverless Computing
• Cloud Computing
• Performance Optimization
• +2 more

Jinfeng Wen is an Assistant Professor at Beijing University of Posts and Telecommunications (BUPT), working with Prof. Shangguang Wang. He received his Ph.D. from Peking University (PKU) in July 2024 under the supervision of Prof. Xuanzhe Liu. He also collaborates with Dr. Zhenpeng Chen and Prof. Federica Sarro at University College London (UCL). His educational background includes: Ph.D. in Computer Science from Peking University (2024) Jinfeng Wen's research focuses on performance optimization of serverless computing, software engineering, cloud computing, and services computing. His work addresses critical challenges in serverless platforms, including performance variance, misconfiguration detection, cold start latency, and application update frameworks for satellite computing. His research has significant implications for improving the efficiency and reliability of modern cloud-based applications. His recent publications demonstrate a strong trend toward addressing practical challenges in serverless computing through innovative frameworks and empirical studies. His work spans both theoretical foundations and practical implementations, with a focus on real-world applications and performance optimization across multiple cloud platforms. His notable scientific awards include: CCF TCSC Outstanding Doctoral Dissertation Award (2025) Outstanding Reviewers, WWW 2024 Ubiquant Scholarship, Peking University (2023) Multiple Merit Student awards from Peking University Jinfeng Wen actively contributes to the academic community as a Program Committee member for prestigious conferences including ASE, ICSE, and WWW. He also serves as a reviewer for top-tier journals such as ACM Transactions on Software Engineering and Methodology (TOSEM) and IEEE Transactions on Services Computing (TSC). His research is supported by the General Program of the China Postdoctoral Science Foundation (2025). He previously served as a Teaching Assistant for 'Introduction to Computing (A)' and 'Scientific Writing' at Peking University.

Kexin PeiView profile

Assistant Professor

• Security
• Software Engineering
• Machine Learning

Kexin Pei is a Neubauer Family Assistant Professor at the Department of Computer Science, University of Chicago. She received her PhD from the Department of Computer Science at Columbia University. Her academic career spans research and teaching in the fields of Security, Software Engineering, and Machine Learning. Her educational background includes: PhD in Computer Science from Columbia University Dr. Pei's research interests focus on developing data-driven program analysis to improve the security and reliability of both traditional and AI-based software systems. She is particularly interested in creating machine learning models that can reason about program structure and behavior to precisely and efficiently analyze, detect, and fix software bugs and vulnerabilities. Her work bridges the gap between theoretical computer science and practical security applications, with significant contributions to binary analysis, program understanding, and AI security. Her research output shows a strong trend toward integrating machine learning with traditional program analysis techniques. Many of her recent publications explore how neural networks and language models can enhance software security and reliability, particularly in binary analysis, vulnerability detection, and code understanding. Her work often involves developing novel frameworks that combine execution traces, code structure, and semantic understanding to create more robust analysis tools. Dr. Pei has received several prestigious awards for her research: Best Paper Award at MASEC@NeurIPS 2023 Best Paper Award Runner-Up in CSAW 2018 Top-10 Finalist of Applied Research Competition ACM SigMobile Research Highlight MLSec @NIPS'17 CACM research highlight Distinguished Artifact Award at FSE 2016 Dr. Pei actively mentors students and has advised several PhD and Master's students. Her group includes PhD students Jie Zhu, Weichen Li, and Jun Yang (with Shan Lu), as well as MS students Sam Huang and Yiming Cheng (with Junchen Jiang). She has also worked with numerous student collaborators and visiting students from institutions including MIT, Georgia Tech, and UChicago. Her research has been supported by grants from various sources, including collaborations with Google DeepMind and Microsoft Research where she completed internships. Dr. Pei is involved in several research labs and teams, including collaborations with the CUMLSec group (as seen in repositories like trex and XDA on GitHub). Her work often involves interdisciplinary teams combining expertise in security, machine learning, and software engineering to tackle complex problems in program analysis.

Haiming ChenView profile

Researcher

• Web data and programming techniques
• computation models
• formal languages and automata theory
• +4 more

Haiming Chen is a researcher at the Institute of Software, Chinese Academy of Sciences, specializing in programming languages, formal methods, and software security. His work bridges theoretical computer science with practical security applications. His research interests focus on web data and programming techniques, computation models, formal languages and automata theory, program analysis, and programming languages. Chen's work demonstrates a strong emphasis on practical applications of formal methods, particularly in the domain of regular expressions and vulnerability analysis. Chen's publication record shows a consistent focus on regular expression technologies and software security. His recent work on vulnerability-affected versions identification represents a shift toward broader software security concerns, while maintaining his technical expertise in formal language theory. His research demonstrates a progression from theoretical aspects of regular expressions toward practical security applications. As an active contributor to open source projects like Ciphey (a decryption/decoding tool), Chen bridges academic research with practical tool development for the security community.

Dominik WermkeView profile

Assistant Professor

• Human-Centered Security
• Usable Security
• Software Supply Chain Security
• +3 more

Dominik Wermke is an Assistant Professor in the Department of Computer Science at North Carolina State University. He is affiliated with the Secure Computing Institute (SCI), the Wolfpack Security and Privacy Research (WSPR) Lab, and the Secure Software Supply Chain Center (S3C2). Dr. Wermke's research focuses on computer security with an emphasis on human-centered security and usable security. His work investigates how security mechanisms interact with the needs, practices, and limitations of both software professionals and end users. His primary research areas include: Human-Centered Security and Privacy: Examining how developers and security professionals understand and respond to security concerns in complex environments Software Supply Chain Security: Studying how software is packaged, built, and distributed with focus on reproducibility, dependency management, and vulnerability metadata Open Source Security and Trust: Analyzing practices in distributed development environments, including contribution workflows and vulnerability handling His research employs mixed-methods approaches including interviews, large-scale software ecosystem analyses, user experiments, and surveys to identify behavioral patterns and systemic risks in security practices. Dr. Wermke's publications demonstrate consistent focus on understanding security challenges at the intersection of technical systems and human factors, particularly in software development processes and supply chain contexts. He actively mentors students at NC State, encouraging prospective PhD candidates to explore research opportunities through internships and coursework. Students interested in working with him should contact him after applying to the graduate program. As a member of multiple research centers, Dr. Wermke collaborates across the Secure Computing Institute, Wolfpack Security and Privacy Research Lab, and Secure Software Supply Chain Center to advance security research with practical applications for software professionals.

Cristian CadarView profile

Professor

• Software Engineering
• Testing and Verification
• Computer Systems
• +3 more

Cristian Cadar is Professor of Software Reliability in the Department of Computing at Imperial College London's Faculty of Engineering, where he leads the Software Reliability Group. His research focuses on developing automatic techniques to enhance software reliability and security through innovative program analysis and testing methods. His primary research interests span Software Engineering , Testing and Verification , Computer Systems , and Security , with particular expertise in symbolic execution, compiler fuzzing, and memory safety. Cadar's work bridges theoretical foundations with practical implementations that address real-world software defects. Analysis of his recent publications reveals a consistent focus on improving software testing methodologies, particularly in symbolic execution scalability, compiler verification, and network application security. His research demonstrates a progression from foundational program analysis techniques toward increasingly practical implementations that address industry needs in software reliability. Notable scientific awards include: EuroSys Jochen Liedtke Award HVC Award BCS Roger Needham Award ACM SIGOPS Hall of Fame Award ACM CCS Test of Time Award Cadar has secured significant research funding through an ERC Consolidator Grant and an EPSRC Early-Career Fellowship. He actively mentors students and leads research initiatives through the Software Reliability Group. His work on the KLEE symbolic execution system has established an important open-source foundation adopted by both academic and industrial research groups. He leads the Software Reliability Group at Imperial College London, which focuses on developing practical tools and techniques for improving software reliability. The group's work combines theoretical program analysis with practical implementations that address real-world software defects across various domains including security, compiler correctness, and memory safety.

Julia RubinView profile

Associate Professor

• Software Quality
• Software Security
• AI Reliability
• +7 more

Julia Rubin is an Associate Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Canada, holding the prestigious Canada Research Chair in Trustworthy Software. She leads the UBC Research Excellence Cluster on Trustworthy ML and serves as an Associate Faculty Member in the Department of Computer Science. Her academic journey includes a PhD from the University of Toronto and postdoctoral research at MIT, complemented by nearly a decade of industry experience at IBM Research where she worked as a research staff member and group manager. PhD in Computer Science, University of Toronto Postdoctoral Research, MIT Department of Electrical Engineering and Computer Science Research Staff Member and Group Manager, IBM Research (10 years) Professor Rubin's research centers on quality, security, and reliability of software and AI systems. Her work develops automated solutions for analyzing, auditing, and improving these systems, with particular focus on reliable approaches for malware detection, code management techniques combining generative AI with program analysis, and technology for regulatory compliance of AI systems. Her research spans mobile security, smart contract vulnerabilities, program analysis techniques, and trustworthy machine learning. Analysis of her 15 most recent publications reveals strong emphasis on software security (particularly in mobile and blockchain applications), program analysis techniques (especially slicing methods), and microservice architecture. Her work consistently bridges theoretical foundations with practical applications, often involving empirical studies and tool development. The publications demonstrate evolution from traditional software analysis toward AI-integrated approaches while maintaining rigorous methodology. 2023 Alexander von Humboldt Research Fellowship for Experienced Researchers 2023 Killam Faculty Research Fellowship 2022 CS-Can | Info-Can Outstanding Early Career Computer Science Researcher Award Multiple Distinguished/Best Paper Awards at major conferences Canada Research Chair, Tier II IBM CAS Project of the Year Award Professor Rubin actively mentors students and contributes to the academic community through program committee roles, including serving as Program Co-Chair for ASE 2022. She has secured significant research funding through fellowships and industry partnerships, including IBM collaborations. Her research group (ReSeSS Research Lab) focuses on developing practical solutions for software trustworthiness challenges. She leads the ReSeSS Research Lab at UBC, which focuses on developing automated solutions for analyzing, auditing, and improving software and AI systems. The lab's work spans multiple projects in trustworthy software, with particular emphasis on reliable and explainable approaches for security analysis, code management, and regulatory compliance of AI systems.

• Political Science
• Development Economics
• Political Violence
• +4 more

Macartan Humphreys is the director of the Institutions and Political Inequality group at the WZB Berlin Social Science Center and holds the position of honorary professor of social sciences at both Trinity College Dublin and Humboldt University Berlin. His academic work spans political science, development economics, and methodological innovation, with a particular focus on causal inference, field experiments, and mixed-methods research. Humphreys' research interests encompass political violence, development economics, causal inference methodologies, and the political economy of natural resources. His work often examines how information technologies affect political engagement, the relationship between traditional and state authorities, and the impact of development assistance on collective action capacity. A significant portion of his recent work has focused on methodological innovations, particularly in integrating qualitative and quantitative approaches through causal modeling and Bayesian updating. Humphreys' publication record demonstrates a clear evolution from early work on civil wars and resource conflicts toward increasingly sophisticated methodological contributions. His most recent publications center on research design frameworks (MIDA), causal modeling for mixed-methods research, and applications of these approaches to pressing policy questions like vaccine acceptance during the pandemic. His work consistently bridges theoretical innovation with practical applications across diverse contexts, particularly in developing countries. Humphreys has authored or co-authored numerous influential publications in top journals including American Political Science Review, Journal of Conflict Resolution, and Science. His methodological contributions, particularly the DeclareDesign framework and the Integrated Inferences approach, have gained significant traction in political methodology circles. He has also contributed to important policy-relevant research on topics including democratic practices, gender quotas, and the political economy of development assistance. As director of the Institutions and Political Inequality group at WZB Berlin, Humphreys leads research examining how institutional arrangements affect political inequality. His work often involves large-scale field experiments conducted across multiple countries, demonstrating a commitment to rigorous empirical testing of theoretical propositions in real-world settings. His research has been supported by major funding agencies and has influenced both academic debates and policy discussions in international development contexts.

• Quantum Computing
• Quantum Algorithms
• Quantum Complexity Theory
• +3 more

Shelby Kimmel is an Assistant Professor of Computer Science at Middlebury College, where she works with undergraduate students in both classroom instruction and research projects. Previously, she was a Hartree Postdoctoral Fellow at the University of Maryland in QuICS (Joint Center for Quantum Information and Computer Science). She earned her PhD in physics from MIT and completed her undergraduate studies in astrophysics at Williams College. Dr. Kimmel specializes in quantum computing research, focusing on the design of quantum algorithms and proving their superiority over classical alternatives. Her work includes creating efficient methods for characterizing errors in experimental quantum computers, as well as exploring quantum complexity theory and quantum information theory. She has made significant contributions to the field of quantum query complexity, developing algorithms that solve problems with fewer queries than classical approaches. Her research spans multiple areas within quantum computing, with a particular emphasis on graph problems, connectivity algorithms, and process tomography. Analysis of her publication record shows a consistent focus on developing practical quantum algorithms with provable advantages over classical methods, while also addressing the experimental challenges of implementing these algorithms on real quantum hardware. Her work bridges theoretical computer science and experimental quantum physics. Dr. Kimmel actively mentors students and has advised numerous undergraduate researchers who have gone on to pursue PhDs at prestigious institutions. She also serves as an advisor to Middlebury College Women in Computer Science and has mentored women in physics through programs at University of Maryland and MIT. She maintains an active speaking schedule, presenting her research at major quantum computing conferences and institutions worldwide, from the Simons Institute to IBM research facilities. Her talks cover diverse aspects of quantum algorithms, including path detection, quantum connectivity, and error characterization techniques. Beyond her academic work, Dr. Kimmel enjoys playing and teaching traditional Korean drumming, demonstrating her commitment to cultural engagement alongside her scientific pursuits.