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Vincent HeuvelineView profile

Professor

• Uncertainty Quantification
• Data Mining
• High-Performance Computing
• +5 more

Prof. Dr. Vincent Heuveline is a distinguished academic and researcher currently serving as a Professor at Heidelberg University. He leads the "Engineering Mathematics and Computing Lab" (EMCL) at the Interdisciplinary Center for Scientific Computing and is also the Group Leader of the "Data Mining and Uncertainty Quantification" (DMQ) research group at HITS gGmbH. Additionally, he serves as the Director of the Computing Center at Heidelberg University, demonstrating his leadership in both academic research and institutional IT infrastructure. Vincent Heuveline was born in 1968 and pursued his education in Mathematics, Physics, and Computer Science at the Universities of Caen (France) and Würzburg (Germany). He earned his PhD in Computer Science in 1997 from the Université de Rennes and completed his habilitation in Mathematics at the University of Heidelberg in 2002. His academic career includes a professorship at Karlsruhe University (KIT) from 2004 until he moved to Heidelberg University in May 2013. Prof. Heuveline's research spans several cutting-edge areas in computational science and data analysis. His primary research interests include Uncertainty Quantification, Data Mining, High-Performance Computing, Hardware-aware Computing, Software Design for Scientific Computing, Optimization and Optimal Control for Large Systems, Cloud Computing, and IT Infrastructure. His work bridges theoretical computer science with practical applications in medical diagnostics, fluid dynamics, and metabolic modeling. An analysis of his recent publications reveals a strong interdisciplinary focus, particularly at the intersection of artificial intelligence, medical diagnostics, and computational science. He has made significant contributions to newborn screening technologies using machine learning, cardiac MRI analysis with AI enhancement, metabolic modeling for infants, and high-performance computing frameworks. His research demonstrates a consistent pattern of applying advanced computational methods to solve complex problems in healthcare and scientific domains. Prof. Heuveline leads two major research initiatives: the Engineering Mathematics and Computing Lab (EMCL) at the Interdisciplinary Center for Scientific Computing and the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. These labs bring together multidisciplinary teams to tackle challenges in scientific computing, uncertainty quantification, and data-driven research. His leadership extends to the institutional level as Director of Heidelberg University's Computing Center, where he oversees critical IT infrastructure supporting academic research.

• Gambling Research
• Consumer Behavior
• Data Science
• +3 more

Steffen Otterbach serves as Academic Senior Councilor (Akademischer Oberrat) and Director of the Gambling Research Center, affiliated with the Field of Household and Consumer Economics and the Institute for Health Care and Public Management. He teaches Consumer Behavior, Multivariate Data Analysis, and Sustainability & Change for the Winter Semester 2025/26. Research Interests: His work centers on gambling behavior in digital environments—including in-game elements and social media advertising—with strong emphasis on consumer decision-making, public health implications, and labor economics. He employs advanced methodologies like machine learning, large language models, and multivariate analysis to investigate behavioral patterns, regulatory impacts, and health outcomes. Publication Trends: Recent publications (2023-2024) demonstrate intense focus on gambling research using computational linguistics for youth perspectives and systematic reviews of advertising strategies. His interdisciplinary approach bridges computer science, psychology, and public policy while extending to cardiovascular health, workplace dynamics, and sustainability through data-driven social science. Scientific Awards: No awards documented in available sources. Advising and Grants: Student supervision and grant details not specified in source materials. Labs and Teams: Leads the Gambling Research Center (Forschungsstelle Glücksspiel) and contributes to the Field of Household and Consumer Economics, driving empirical studies on digital gambling ecosystems and consumer protection frameworks.

WolffgangView profile

Professor

• Artificial Intelligence
• Deep Learning
• Natural Language Processing
• +7 more

Professor Wolffgang is an academic at the University of Münster, affiliated with the Institute of Customs and International Trade Law. He coordinates international conferences on protectionism and tariff reforms, and has made significant contributions to artificial intelligence applications in medical diagnostics and economic modeling. His research spans two primary domains: Medical Informatics: Applying AI/ML to prostate cancer diagnosis and EHR data extraction Economic Simulation: Developing multi-agent systems and modeling frameworks Recent publications demonstrate his work in: Interpretable AI for histology image analysis Automated data extraction from medical records Model-driven web application development Professor Wolffgang actively participates in international academic discourse, most recently moderating panels on customs reform during global economic shifts.

Christian SommerView profile

Researcher

• Physical Geography
• Geoinformatics
• Remote Sensing
• +7 more

Christian Sommer is a physical geographer and researcher at the University of Tübingen , affiliated with the Heidelberg Academy of Sciences and Humanities through the ROCEEH project. His expertise lies in GIS, remote sensing, data mining, and geomorphological field techniques. Education: Dr. rer. nat. (2021), M.Sc. in GIS & Remote Sensing (2016), B.Sc. in Physical Geography (2012). Research: Focuses on Pleistocene landscapes in Africa and Eurasia, particularly Late Pleistocene studies in Eastern South Africa, analyzing human-environment interactions through spatial methodologies. Recent article trends span hunter-gatherer resource selection, gully erosion dynamics, prehistoric cultural network mapping, luminescence dating techniques, and paleoclimate modeling, reflecting interdisciplinary approaches across archaeology, geomorphology, and computational methods. Awards: Sustainability Prize 2024 for thesis supervision. Teaching: Courses on applied GIS, remote sensing, and physical geography field methods at University of Tübingen. Students: Supervised 8+ theses on topics ranging from UAV-based erosion analysis to 3D archaeological modeling.

Mandy GoramView profile

Researcher

• Adaptive Learning Systems
• Learning Analytics
• Context-Based Adaptive Systems
• +5 more

Mandy Goram is a researcher at FernUniversität in Hagen affiliated with the Faculty of Mathematics and Computer Science and Cooperative Systems research group. She holds an M.Sc. in Practical Computer Science and B.Sc. in Business Information Systems from the same institution and has lectured at Informatica Feminale summer school since 2015. Her educational background includes: M.Sc. in Practical Computer Science specializing in Humans and Computers, Recommender Systems, and Intelligent Assistants B.Sc. in Business Information Systems specializing in Human-Computer Interaction and Database Systems Germany Scholarship recipient Goram's research centers on adaptive personalized learning environments (APLE project) supporting self-regulation in distance education. She develops context-based adaptive assistance systems using machine learning, NLP, and information extraction while prioritizing GDPR compliance. Her work bridges AI, multi-agent systems, and human-computer interaction with applications in conversational interfaces and learning analytics. Analysis of her 11 publications (2013-2020) reveals consistent focus on context-aware adaptive systems integrating legal requirements, particularly GDPR. Key trends include explainable AI for educational settings, privacy-preserving collaboration environments, and practical applications in business intelligence and customer experience management across insurance and banking sectors. No scientific awards were documented in the source material. She has supervised three theses: Stefan Walter's 2021 Master's on GDPR-compliant conversation partner identification, Yannik Worthmann's 2020 Bachelor's on context-based learning materials under GDPR, and Arda Treijner's 2019 Bachelor's on user data tracking for meinDorf55+. Goram leads the ongoing APLE project funded by FernUniversität Hagen's Research Focus DDLL with a 36-month duration. As part of the Cooperative Systems research group, she collaborates with Dirk Veiel on context-based adaptive systems emphasizing 'Compliance by Design' principles. Her work integrates technical innovation with regulatory frameworks through multi-agent architectures and deep learning applications.

George G. CabralView profile

Assistant Professor

• machine learning
• online learning
• concept drift
• +8 more

George G. Cabral is an Assistant Professor (Lecturer) in the Department of Computing at the Federal Rural University of Pernambuco (UFRPE) in Recife, Brazil. His academic career spans research and teaching in machine learning, with a particular focus on applications to software engineering and other real-world domains. Ph.D. in Computer Science, Federal University of Pernambuco (2014) M.Sc. in Computer Science, Federal University of Pernambuco (2008) B.Sc. in Computer Engineering, University of Pernambuco (2005) PostDoc, University of Birmingham (2019) Dr. Cabral's research primarily focuses on machine learning algorithms applied to real-world problems, with special emphasis on online learning and concept drift in data streams. His work explores class imbalanced learning techniques, ensembles of learning machines , and novelty detection methods. A significant portion of his research applies these techniques to software defect prediction , where he investigates just-in-time approaches that can identify potential bugs as software is being developed. He also applies similar methodologies to intrusion detection systems and public accounting domains, demonstrating the versatility of his research interests across different application areas. Analysis of Dr. Cabral's publication history reveals a consistent trajectory from foundational work on one-class classification techniques toward increasingly applied research in software engineering contexts. His most recent publications (2022-2023) demonstrate expansion into public sector applications, particularly in data auditing for Brazilian municipal governments, while maintaining his core focus on concept drift challenges in software defect prediction. The publications consistently address methodological challenges like verification latency and class imbalance evolution in streaming data environments. Dr. Cabral actively contributes to the academic community as a committee member for the Artifact Evaluation track at major software engineering conferences including ASE (Automated Software Engineering) since 2021 and ICSE (International Conference on Software Engineering). He serves as a reviewer for journals such as Neurocomputing and Applied Soft Computing, and has taught undergraduate courses including Artificial Intelligence, Neural Networks, Computing Theory, and Programming at UFRPE.

Carlos CetinaView profile

Professor

• Software Product Lines
• Model Driven Engineering
• Search-based Software Engineering
• +3 more

Carlos Cetina is a Full Professor at Universitat Politècnica de València in Spain and Head of the SVIT Research Group. He serves as Director of the Master in Advanced Software Engineering for Mobile Devices and holds an Honorary Associate Professor position at University College London. His work bridges academic research with industrial software engineering practices, focusing on accelerating software development and maintenance through innovative engineering approaches. Dr. Cetina's research centers on Software Product Lines, Model-Driven Engineering, and Search-Based Software Engineering, with a growing emphasis on applying these techniques to game software development. His work investigates how variability-intensive systems can be better engineered and maintained, particularly through evolutionary algorithms and model-based approaches. He has pioneered methods for bug localization in software models of video games and explored the application of phylogenetics to software engineering problems. His recent publication trajectory reveals a strong focus on empirical studies in game software engineering, with particular attention to procedural content generation, energy consumption analysis in game engines, and comparative evaluations of model-driven versus code-driven development approaches. He has established himself as a leading researcher in applying search-based techniques to solve complex problems in software model analysis and manipulation. Award winner for Accelerating Video Game Development Award Finalist for Feature Location in software models As Head of the SVIT Research Group, Dr. Cetina leads a team dedicated to advancing software engineering practices through rigorous research that directly addresses industry challenges. His work on variability-intensive systems has significant implications for software product line engineering and has been widely adopted in both academic and industrial contexts. His research group actively collaborates with industry partners to ensure practical relevance of their findings.

Deepak D'SouzaView profile

Professor

• Program verification
• Static analysis
• Model-checking
• +4 more

Deepak D'Souza is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science (IISc) Bangalore. He has been actively involved in research and teaching in theoretical computer science, particularly focusing on program verification, static analysis, and formal methods. His work bridges theoretical foundations with practical applications in software engineering and verification. His research interests span program verification, static analysis, model-checking, and models and logics for real-time systems. D'Souza has made significant contributions to automata theory, particularly in its applications to verification problems. He co-edited the book 'Modern Applications of Automata Theory' published by World Scientific, which focuses on verification and model checking technologies. His recent work shows a strong emphasis on logical LTL games, controller synthesis, and verification of embedded systems. Analysis of his publications reveals a consistent focus on verification techniques across different domains - from theoretical foundations in automata theory to practical applications in embedded systems like FreeRTOS. His work demonstrates a progression from foundational theoretical work to increasingly applied verification problems, while maintaining strong theoretical rigor. Recent publications show growing interest in synthesis techniques and applications of formal methods to real-world systems. D'Souza has mentored numerous PhD, MS, and ME students, with a current focus on verification problems. His research has been supported by significant grants from Siemens, DRDO, Robert Bosch Center for CPS, UKIERI, and Infosys, demonstrating both academic and industrial relevance of his work. He is actively engaged in the academic community, serving as General Chair for ATVA 2025, Program Committee member for numerous conferences, and previously as President of the Indian Association for Research in Computing (IARCS) from 2018-2023. He also chairs the Steering Committee for FSTTCS (2025-2027). D'Souza teaches advanced courses including Automata Theory and Computability, Formal Methods in Software Engineering, and Program Synthesis meets Machine Learning, demonstrating his commitment to educating the next generation of researchers in formal methods and verification.

Yan LeiView profile

Professor

• Fault Localization
• Program Repair
• Software Testing
• +3 more

Yan Lei is a Professor at the School of Big Data & Software Engineering, Chongqing University, China, specializing in software quality improvement through advanced fault localization, program repair, and testing methodologies. His research bridges software engineering with data science to address challenges in deep learning systems and hardware description language (HDL) programs, evidenced by extensive publications in CCF-A venues including ASE, FSE, and TSE. Dr. Lei obtained his Ph.D. under Prof. Xiaoguang Mao at Chongqing University and conducted research at UC Davis with Prof. Zhendong Su. His educational background informs his interdisciplinary approach to software engineering challenges. His research focuses on three interconnected pillars: Fault Localization and Program Repair: Developing deep learning and metamorphic techniques for precise bug identification and automated repair Data Science for SE: Applying representation learning and contrastive methods to software testing challenges Testing Deep Learning Systems: Addressing API misuses and error-handling bugs in neural network applications Analysis of his 2023-2024 publications reveals a strategic shift toward multi-fault scenarios and cross-framework solutions, with increasing emphasis on hardware-aware testing and compilation error repair. His work consistently integrates generative models and semantic learning to overcome data imbalance issues. His scientific recognition includes: ACM SIGSOFT Distinguished Paper Award for coincidental correctness detection research at ASE 2024 IEEE TCSE Distinguished Paper Award for flaky test prediction at SANER 2024 Dr. Lei directs significant research initiatives including a National Natural Science Foundation project (2023-2026) on multi-fault program repair and previously led a foundational fault localization study (2017-2019). His teaching portfolio spans undergraduate software testing and graduate courses for international students, reflecting commitment to pedagogy. Current projects like Data Fusion for Smart Megalopolis demonstrate applied research impact in Chongqing's technological development.

Martin SchlüterView profile

Professor

• Geodesy
• Photogrammetry
• Structural Health Monitoring
• +5 more

Martin Schlüter is a Professor at the Department of Geodesy and Geoinformatics within the School of Engineering at Mainz University of Applied Sciences. His research focuses on the integration of photogrammetry, geodetic engineering, and machine learning for structural monitoring applications. Key institutional affiliations include leadership in the i3mainz research center and collaboration with the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF). His research interests span Geodesy , Photogrammetry , and Structural Health Monitoring , with recent emphasis on machine learning applications for optical measurement systems. Schlüter pioneers modular digital camera tachymeters (MoDiTa) for bridge vibration analysis, railway safety, and BIM integration. His work bridges theoretical geodetic principles with practical engineering solutions for infrastructure monitoring, emphasizing real-time data processing and sensor fusion. Schlüter leads significant research projects including BAM (Big-Data Analytics in Environmental Monitoring) and VCSA3D (Virtual Crime Scene Analysis) , securing institutional funding through Mainz University's Best Practice Research Program. His laboratory specializes in optical measurement systems, developing calibration protocols for X-ray micro-CT and laser tracking applications. Current work focuses on AI-driven structural monitoring for transportation infrastructure and heritage preservation. Professional contributions include active participation in the DGPF and editorial roles in German surveying journals. His research demonstrates consistent innovation in merging traditional geodetic methods with emerging computational techniques for precision engineering applications.

• Galactic Astronomy
• Stellar Populations
• Chemical Abundances
• +3 more

Puragra Guhathakurta is a Professor of Astronomy and Astrophysics at the University of California Santa Cruz, where he has held a continuous faculty position since November 1994. His research focuses on galactic structure and evolution through detailed studies of stellar populations in the Milky Way, Andromeda (M31), and Triangulum (M33) galaxies using multi-wavelength surveys. His educational background includes: PhD and MS in Astrophysical Sciences, Princeton University (1985-1989) Physics, University of Calcutta, Science College, Rajabazar (1984-1985) BSc in Physics, Saint Xavier's College, Kolkata (1980-1983) La Martiniere for Boys, Kolkata (1970-1980) GuhaThakurta's primary research interests encompass: Galactic and Extragalactic Astronomy Stellar Populations and Chemical Abundances Galactic Structure and Kinematics Chemical Evolution of Galaxies Stellar Halos and Tidal Streams Dwarf Galaxy Dynamics Analysis of his 15 most recent publications (2023-2025) reveals a consistent focus on chemical abundances and kinematics in nearby galaxies. His work integrates Hubble Space Telescope imaging, ground-based spectroscopy, and Gaia astrometry to investigate weak CN absorption features in evolved stars, stellar halo structures, and the evolutionary histories of dwarf galaxies. A significant theme involves using resolved stellar populations to constrain galaxy formation models in the local universe. His major professional recognitions include: Hubble Fellowship (Princeton University, 1992-1994) Three-year Membership at the Institute for Advanced Study (1989-1992) While specific details of his advising and grant history are not documented in the source material, his extensive publication record demonstrates sustained research leadership. He is a principal investigator or key collaborator in major observational programs including SPLASH, PHATTER, HALO7D, and TREX, which have produced foundational datasets for studies of M31 and M33.

Martin HeppView profile

Professor

• Web Science
• Internet of Things
• Big Data and Data Science
• +5 more

Martin Hepp is Professor of Web Science and Digitalization at the University of the German Federal Armed Forces Munich, where he leads the E-Business and Web Science Research Group. His academic career spans multiple prestigious institutions including the University of Innsbruck, Florida Gulf Coast University, and IBM Research. He holds a PhD in Business Information Systems from the University of Würzburg where he graduated Summa Cum Laude. His research focuses on shared data structures at Web scale, with particular expertise in Semantic Web technologies, ontology engineering, and data quality management. He is best known for developing the GoodRelations vocabulary for e-commerce, which since November 2012 has become the e-commerce core of schema.org, the official data markup standard adopted by major search engines including Google, Yahoo, Bing, and Yandex. His work is widely implemented by companies ranging from Google and Yahoo to BestBuy, Kmart, Volkswagen, and Renault, as well as tens of thousands of smaller businesses. His publication record shows a consistent focus on practical applications of Semantic Web technologies in business contexts, with recent work emphasizing Linked Data applications, product classification systems, and e-commerce standards. His research demonstrates strong industry impact, particularly through the adoption of GoodRelations as a foundational standard for web commerce data. Linked Data-a-thon Award (4th prize) at ISWC 2009 ACM Senior Member appointment (2009) FIT-IT outstanding research proposal award (2nd place, 2007) Dissertation Award from Alcatel SEL Foundation (2004) Dissertation Award from Unterfränkische Gedenkjahrstiftung (2004) Professor Hepp has served on numerous editorial boards including the International Journal on Semantic Web and Information Systems, and has been a program committee member for over sixty conferences including ECIS, EKAW, ESWC, ISWC, and WWW. He has organized more than fifteen workshops and conference tracks on conceptual modeling and Semantic Web topics. His work bridges academic research with practical business applications, particularly in the areas of e-commerce standards and semantic data interoperability.

Stefano ZacchiroliView profile

Professor

• Software Engineering
• Open Source Software
• Free Software
• +5 more

Stefano Zacchiroli is a Full Professor of Computer Science at Télécom Paris, part of the Polytechnic Institute of Paris. He is affiliated with the Laboratory of Information Processing and Communication (LTCI) where he conducts research on digital commons, open source software engineering, computer security, and the software supply chain. As a member of the ACES team, he focuses on critical aspects of software preservation and development. His research spans several interconnected domains in software engineering. His work on digital commons explores how to preserve and make accessible the collective knowledge of software development. His research on open source software engineering investigates the processes, tools, and social dynamics that make open collaboration successful. In computer security, he examines vulnerabilities in the software supply chain, while his work on formal methods applies mathematical rigor to software development challenges. His leadership in Software Heritage demonstrates his commitment to preserving software as a crucial part of our digital heritage. His recent publications reveal a strong focus on software preservation, repository analysis, and security in the software supply chain. There's a clear trend toward understanding how software evolves over time, how to verify its integrity, and how to ensure reproducible builds. His work bridges theoretical computer science with practical software engineering challenges, particularly in the context of open source ecosystems. His scientific recognition includes: 2015 O'Reilly Open Source Award 2022 Google Award for Inclusion Research As an academic advisor, Zacchiroli has likely mentored numerous students through his position at Télécom Paris. His involvement in numerous conference program committees demonstrates his active role in the academic community. His research has been supported by institutional affiliations and likely various grants related to software preservation and open source research. Zacchiroli leads or contributes to several important initiatives, most notably Software Heritage, which serves as the largest public archive of software source code. His work with the Debian project and the Open Source Initiative demonstrates his commitment to practical applications of his research. The ACES team at LTCI provides the academic home for much of his current research, focusing on critical aspects of software engineering and preservation.

Junjie ChenView profile

Professor

• Software Testing
• Deep Learning
• Automated Debugging
• +4 more

Junjie Chen is a Professor at the College of Intelligence and Computing, Tianjin University, where he leads the Software Engineering Team. He has been a Professor since January 2024, after serving as an Associate Professor from July 2019 to January 2024. Prior to his position at Tianjin University, he completed his PhD in Computer Science at Peking University under the supervision of Prof. Bing Xie, Prof. Lu Zhang, Prof. Dan Hao, and Prof. Yingfei Xiong. During his PhD studies, he was also a visiting PhD student at The University of Texas at Dallas under Prof. Lingming Zhang. His educational background includes a Bachelor's degree in Software Engineering from Beihang University (2010-2014). Professor Chen's research focuses on four main areas: Software Fuzzing : Focusing on fundamental software testing for compilers, operating systems, chip design systems, and AI infrastructure. Intelligent Software Engineering : Applying LLMs and deep learning to solve software engineering challenges like code generation, test generation, and code review. Trusted AI : Improving AI security, fairness, robustness, and performance through adversarial attacks, data optimization, and software engineering methodologies. Software Maintenance : Researching bug localization and fixing, as well as AIOps for anomaly detection and diagnosis. His recent publications (2024-2025) demonstrate a strong focus on the intersection of software testing, compiler infrastructure, and large language models. His work spans from practical compiler testing techniques to advanced applications of AI in software engineering. His research shows a clear trajectory toward addressing the challenges of modern software systems, particularly those involving AI and deep learning components. Professor Chen has received numerous prestigious awards including: National Key R&D Program Young Scientist (2024) Huawei Spark Award (2024) National Excellent Young Scientists Fund recipient (2023) China Institute of Electronics Natural Science First Prize (2023) CAST Young Elite Scientists Sponsorship Program (2022) Multiple ACM SIGSOFT Distinguished Paper Awards He is actively involved in academic service, serving on editorial boards for JCST and ASEJ, and as a program committee member for major conferences including ICSE, ASE, FSE, and ISSTA. He has also co-organized workshops and seminars, including the Compiler Technology Seminar under the CCF System Software Committee. Professor Chen leads a research team at Tianjin University that is actively recruiting PhD and Master's students with strong programming skills and interests in Software Engineering, LLMs, Security, and Program Analysis.

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.