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Michael FinkelView profile

Professor

• High-Voltage Engineering
• Power Systems
• Smart Grids
• +5 more

Prof. Dr.-Ing. Michael Finkel is a full Professor at Augsburg University of Applied Sciences in the Faculty of Electrical Engineering, where he serves as Academic Advisor for International Industrial Engineering, Member of the Faculty Council, and Chair of the Examination Board. His professional affiliations include memberships in CIGRE, IEEE, VDE, and the EMC Project Group of FNN. He received the Bavarian Certificate of Higher Education Teaching in 2009 and multiple research awards including ETG Literature Prizes and Young Author Awards. Professor Finkel's research focuses on High-Voltage Engineering, Power Systems, and Smart Grid technologies. His work addresses critical challenges in renewable energy integration, grid stability, and distributed energy resources. Key research areas include: Island grid operation and emergency power systems Smart grid digitalization and control strategies Electric vehicle integration in urban networks Power system stability and frequency control Renewable energy forecasting and grid integration His recent publications demonstrate strong focus on converter-dominated grids, renewable integration, and smart grid solutions. Research trends show increasing emphasis on real-time grid monitoring, AI applications in power systems, and hybrid energy solutions. Collaborative projects frequently involve industry partnerships and international research networks. Significant scientific recognitions include: ETG Literature Prize 2024 for heat pump load modeling Young Author Award 2025 for hydropower island grid research HSA_transfer Award 2022 for technology transfer Multiple best paper awards at international conferences Professor Finkel actively supervises doctoral candidates in power systems research and leads projects funded by industry and public grants. Current initiatives include developing emergency power solutions for critical infrastructure and advanced monitoring systems for distribution networks. His research group collaborates extensively with the Technology Transfer Centers of Augsburg University and industry partners in the energy sector.

Nuttida RungratsameetaweemanaView profile

Assistant Professor

• Neural computations
• Cognitive neuroscience
• Computational neuroscience
• +6 more

Nuttida Rungratsameetaweemana is an Assistant Professor in the Department of Biomedical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. She also serves as a Provost Research Fellow at Columbia University, demonstrating her significant research contributions early in her academic career. NuttidaLab, her research group, focuses on integrating computational and experimental approaches to investigate the neural computations underlying complex cognitive functions in both health and disease. The lab's research spans several key areas including learning mechanisms, decision-making processes, and social behavior, with particular emphasis on understanding how these cognitive functions become disrupted in neurological and neuropsychiatric disorders. The lab has developed several important computational neuroscience tools including NeuralDecoder for representational similarity analysis, state_space_analysis for modeling neural dynamics, MARLAX for multi-agent reinforcement learning, and dynamax for state space modeling. These tools reflect the lab's commitment to developing robust analytical frameworks that can be widely adopted by the neuroscience community. NuttidaLab maintains an active GitHub presence with multiple repositories that demonstrate the lab's focus on open science and reproducible research. The lab's work combines cutting-edge computational approaches with experimental neuroscience to develop a deeper understanding of brain function. The lab actively seeks talented students and researchers interested in interdisciplinary work at the intersection of engineering, neuroscience, and computational science. Professor Rungratsameetaweemana's research represents an important bridge between theoretical computational models and experimental neuroscience, with significant potential for advancing our understanding of both healthy brain function and neurological disorders.

Ulrich EiseneckerView profile

Professor

• Software Engineering
• Generative Software Development
• Software Product Lines
• +5 more

Professor Ulrich Eisenecker serves as the Director of the Institute for Business Informatics at Leipzig University's Faculty of Economics and Business Administration. He holds the Chair of Business Informatics with specialization in software development for business and administration, a position he has maintained since October 2004. Prior to this, he served as Dean of the Faculty (2012-2016) and Pro-Dean (2008-2012). His academic journey includes professorships at Kaiserslautern University of Applied Sciences (1999-2004) and Heidelberg University of Applied Sciences (1995-1999), following research positions at Daimler-Benz Research Institute and Mannesmann Kienzle GmbH. Professor Eisenecker's research focuses on Generative Software Development , Software Product Lines , Software Visualization (including 2D, 3D, AR and VR applications), and E-Assessment systems . His work bridges theoretical computer science with practical business applications, particularly evident in his application of set theory to software features and product lines. He leads the ongoing 'Visual Software Analytics' project and has previously directed projects funded by the European Social Fund and German Federal Ministry of Education and Research. His publication record spans over two decades, highlighted by the influential 2000 book 'Generative Programming' co-authored with K. Czarnecki and the 2017 Most Influential Paper Award from the Software Product Line Conference. Recent work (2023-2025) continues to advance software engineering and educational technology, particularly in automated assessment systems using AI techniques. Notable recognition: Most Influential Paper Award from Software Product Line Conference 2017 (SPLC 2017) Professor Eisenecker supervises doctoral and master's students, with numerous successful completions including Dr. Richard Müller, Dr. Max Lillack, and Dr. Johannes Kristan. His research group includes several scientific staff members and regularly publishes with student co-authors. He teaches 'Introduction to Computer Science' for economics students, 'Programming,' and 'Software Engineering' courses, integrating his research in automated feedback systems into his pedagogy. His research group maintains strong connections between academic research and practical applications, particularly through open source initiatives and industry collaborations focused on software analytics and educational technology.

Susanne GerberView profile

Professor

• Bioinformatics
• Clinical Genomics
• Multi-omics Integration
• +4 more

Prof. Dr. Susanne Gerber is a tenured-track Professor (W2) for Clinical Genomics and Bioinformatics at Johannes Gutenberg University Mainz, where she serves as Deputy Director of the Institute of Human Genetics within the University Medical Center. Her academic journey includes a PhD in Theoretical Biophysics from Humboldt University Berlin (2011), MSc in Bioinformatics from Free University Berlin (2007), and BSc from Free University Berlin/MPI for Molecular Genetics (2004). Before her current role, she was Assistant Professor at JGU Mainz (2016-2020) and postdoc at Università della Svizzera italiana (2011-2015). Her research integrates multi-omics approaches (transcriptomics, proteomics, epigenomics) with machine learning to investigate neurodegeneration, resilience mechanisms, and healthy aging. The lab specializes in: Development of novel bioinformatics algorithms for high-dimensional data analysis Third-generation nanopore sequencing applications SNP-SNP association studies for 3D chromatin modeling Cross-omics biomarker discovery for brain disorders Recent publications demonstrate strong focus on meta-analyses of neurodegenerative diseases, computational method development, and glial-neurovascular interactions. Her group maintains active research in miRNA patterns in Alzheimer's/Parkinson's disease and their microbiome interactions. Prof. Gerber leads the Computational Systems Genomics Group with 11+ members, conducting research at the Biomedical Research Center. Her work bridges computational science with clinical applications in human genetics.

• Gender and Technology
• Intersectional Equality
• Inclusive Design
• +3 more

Dr. Siân Brooke serves as an Assistant Professor and MacGillavry Fellow at the Digital Interactions Lab (DIL), University of Amsterdam. Her interdisciplinary work bridges data science and critical social research to investigate gender and intersectional equality in technology interactions, with a focus on ethical AI and human-centered computing systems. Her research centers on gender dynamics in technology , inclusive platform design , and systemic bias mitigation in digital environments. Brooke employs mixed-methods approaches combining large-scale data analysis of platforms like GitHub and Stack Overflow with ethnographic studies and controlled experiments. Key themes include how programming styles reflect gender differences without impacting quality, how online labor markets perpetuate discrimination through design choices, and how internet memes reinforce toxic masculinity in physical tech spaces like hackathons. Analysis of her recent publications reveals consistent focus on exposing and redesigning discriminatory mechanisms in technology. Her 2024 work demonstrates how platform interventions (community composition, identity flairs) can reduce hiring discrimination in online labor markets, while her GitHub study proves gendered programming styles exist but don't correlate with code quality. A unifying thread across all work is the development of concrete design solutions for more equitable technology ecosystems. Dr. Brooke's recognition includes: NWO Veni Grant (€320,000) for "Human-Centered Code: Building Accessible IDEs for Neurodiverse Women in Computing Education" Leverhulme Early Career Fellowship examining gender in collaborative computing environments She actively mentors through hiring a PhD candidate for her NWO project (closing November 2024) and serves on the Open Tech Fund Advisory Council guiding global internet freedom initiatives. Her commitment to research transparency led to implementing Open Science Badges as Associated Editor for Data and Ethics, recognizing reproducible practices in scholarly work. Brooke leads the Digital Interaction Lab's research on technology equity, including the "Mock-Freelancer.com" experimental platform testing anti-discrimination interventions in online labor markets. Her lab collaborates across disciplines to develop accessible computing environments and policy recommendations for inclusive technology design, with recent work featured in LSE Research explaining gender disparities in programming.

Eray TüzünView profile

Associate Professor

• Software Product Line Engineering
• Empirical Software Engineering
• Gamification
• +5 more

Eray Tüzün is an Associate Professor of Computer Engineering at Bilkent University in Turkey, where he leads the Bilkent University Software Engineering and Data Analytics Research Group (BILSEN). With over 20 years of experience in software design and development spanning both academia and industry, he bridges theoretical research with practical software engineering applications. His educational background includes bachelor's and master's degrees in Computer Science and a PhD in Information Systems. Before joining Bilkent University, he accumulated substantial industry experience including 9 years at HAVELSAN (serving as Productization Lead, Academy Manager, Product Owner, and Software Engineer), plus roles at Microsoft as a Software Design Engineer in the Online Services group, Senior Software Engineer at Howard Hughes Medical Institute, and Research Engineer at CWRU Genomics Center. His professional certifications include Certified Product Manager, MCSD: Application Lifecycle Management, Professional Scrum Master (PSM), and Professional Scrum Product Owner (PSPO). Tüzün's research focuses on several interconnected areas including Software Product Line Engineering , Empirical Software Engineering , Gamification , Software Engineering Education , DevOps & Agile Software Development , and Bioinformatics . His empirical approach involves mining software repositories to analyze development practices, identify patterns, and propose improvements. Recent work increasingly examines the intersection of AI and software engineering, particularly how large language models can enhance code review and comment quality. His research output shows clear trends toward analyzing code quality metrics, identifying 'smells' in development processes, and applying data analytics to software engineering practices. Key themes include bus factor estimation for measuring knowledge concentration in software projects, empirical studies of code review processes, continuous integration practices, and issue tracking systems in open source projects. Tüzün is a senior member of IEEE and an active member of ACM SIGSOFT and IEEE Computer Society. He represents Bilkent University in the International Software Engineering Research Network (ISERN), connecting his research with the global software engineering community. His service to the field includes program committee roles at major conferences such as ASE, ICSE, ESEC/FSE, and ESEM, with leadership positions including PC Co-Chair for ICSSP 2020 and Industry Track Co-Chair for EASE 2023. As head of the BILSEN research group, he mentors graduate students and collaborates on projects that bridge theoretical research with practical applications. His research has been consistently published in top-tier software engineering venues over the past decade, demonstrating both continuity in core research areas and adaptation to emerging trends like AI-assisted software development.

Lina GongView profile

Associate Professor

• Intelligent Software Engineering
• SE4AI
• Empirical Software Engineering
• +3 more

Lina Gong is an Associate Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. She holds a Ph.D. in Computer Software and Theory from China University of Mining and Technology (2020) and completed a research visit at Queen's University's Software Analysis and Intelligence Lab (SAIL) under Prof. Ahmed Hassan (2019-2020). Her research focuses on leveraging machine learning to extract insights from software repositories, with emphasis on: ML-enabled defect prediction techniques Code pre-trained models for vulnerability detection Identifier normalization and issue classification Empirical studies of software quality attributes Her recent publications (2023-2025) demonstrate strong trends in applying transformer architectures to code analysis, with increasing focus on supply chain security and cross-platform UI translation. Key venues include IEEE TSE, ACM TOSEM, and ASE. Scientific recognition includes: National Natural Science Foundation of China (2022-2025) Natural Science Foundation of Jiangsu Province (2022-2025) Key National Laboratory Foundation (2022-2023) Excellent Ph.D. Student Award (CUMT) She actively mentors graduate students (14 advisees: 1 doctoral, 13 master's) and serves on program committees for ASE, APSEC, and SANER. Her research is supported by multiple competitive grants focusing on ML applications in software engineering.

Preetha ChatterjeeView profile

Assistant Professor

• Software Engineering
• Software Analytics
• Empirical Software Engineering
• +7 more

Dr. Preetha Chatterjee is an Assistant Professor in the Department of Computer Science at Drexel University's College of Engineering, where she leads the SOftware Engineering and Analytics Research (SOAR) Lab. Her academic career spans software engineering research, teaching, and service, with a focus on improving developer productivity through advanced analytics and tools. Dr. Chatterjee earned her M.S. and Ph.D. in Computer Science from the University of Delaware, advised by Dr. Lori Pollock, following 5+ years of industry experience as a Software Engineer. Her educational background bridges practical industry experience with rigorous academic training. Her research focuses on Software Engineering with emphasis on developing tools, knowledge sources, and strategies to support software maintenance and improve developer productivity. She incorporates evidence from mining software repositories, conducting empirical studies, and adapting state-of-the-art techniques from Natural Language Processing and Machine Learning. Her current research directions include LLM-assisted software development and maintenance, developer collaboration in distributed software teams, and knowledge extraction from large-scale software artifacts. She has made significant contributions to emotion mining in developer communications, toxicity detection in open source projects, and trust dynamics in GitHub pull requests. Dr. Chatterjee's publications demonstrate a clear progression from foundational work on mining developer chat communications and code snippets toward more sophisticated applications of machine learning and large language models in software engineering contexts. Her recent work increasingly focuses on the intersection of software engineering with social aspects like emotions, trust, and toxicity in developer interactions. Distinguished Reviewer Award at FSE 2023 Drexel CCI Research Excellence Award (awarded to her lab member Ramtin Ehsani) Drexel CS Leadership Award (awarded to her lab member Amirali Sajadi) Dr. Chatterjee has advised numerous students at various levels, including Ph.D., M.S., and undergraduate researchers. Her SOAR Lab currently includes Ph.D. students Ramtin Ehsani and Amirali Sajadi, who have received significant recognition for their work. She has served on multiple program committees for major software engineering conferences including ICSE, FSE, ASE, and MSR, and has held leadership roles such as Tutorials Co-chair for MSR 2025 and Journal-first Co-Chair for ICPC 2024. She co-leads the Drexel Programming Systems Seminar and has been an editorial board member for the Journal of Systems and Software. The SOAR Lab focuses on innovative research at the intersection of software engineering, machine learning, and natural language processing. The lab has produced influential datasets like DISCO (Discord Chat Conversations for Software Engineering Research) and comprehensive annotated datasets of GitHub issue threads. Current projects include improving LLM-assisted bug resolution, security assessment of LLM-generated code, emotion mining from software engineering communication, and information extraction from developer chat conversations.

Jie LiuView profile

Professor

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +3 more

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Pinjia HeView profile

Assistant Professor

• Software Engineering
• Natural Language Processing
• Systems
• +4 more

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.

Sven ApelView profile

Professor

• Software product lines and configurable systems
• AI-based program generation and optimization
• Socio-technical software analysis
• +3 more

Professor Sven Apel holds the Chair of Software Engineering at Saarland University's Saarland Informatics Campus in Germany. He is also the Director of the Saarbrücken Graduate School of Computer Science. His work focuses on software engineering with an emphasis on automation, human factors, and interdisciplinary approaches. Prof. Apel received his Ph.D. in Computer Science in 2007 from the University of Magdeburg. His academic journey includes: Ph.D. in Computer Science, University of Magdeburg (2007) Emmy-Noether Fellowship of the German Research Foundation Heisenberg Professorship of the German Research Foundation Prof. Apel's research centers on empowering software engineering practice to enter an era of intensive automation. His key research areas include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and empirical and neurophysiological methods. He pays special attention to the human factor and interdisciplinary research questions, applying his findings to real-world software systems from both open-source projects and industry collaborations with partners like Siemens AG, Bosch Engineering, and Airbus Helicopters. Analysis of Prof. Apel's recent publications reveals a strong focus on configurable software systems, neurophysiological approaches to understanding programming, and the application of AI techniques to software engineering problems. His work often bridges the gap between theoretical foundations and practical applications, with many studies involving industrial collaborations. There's a noticeable trend toward interdisciplinary research combining software engineering with neuroscience, organizational studies, and machine learning. Prof. Apel has received numerous prestigious awards and honors: ERC Advanced Grant "Brains On Code" (2022) ACM Distinguished Member for "Outstanding Scientific Contributions to Computing" (2018) Multiple Most Influential Paper Awards (SPLC'18, ICPC'22, GPCE'23) Multiple Best Paper Awards (SPLC'11, Modularity'15, AOM'18) Heisenberg Professorship and Emmy-Noether Fellowship from the German Research Foundation Prof. Apel has advised numerous Ph.D., Master's, and Bachelor's students throughout his career. His research has been generously funded by multiple grants including an ERC Advanced Grant (2,500,000 Euro, 2022-2027), several DFG projects (CPEC, Congruence, Pervolution), and previous grants like SafeSPL, FeatureFoundation, and Pythia. His work has practical impact through collaborations with industry partners including Siemens AG, Bosch Engineering, and Airbus Helicopters. Prof. Apel leads research in the Chair of Software Engineering at Saarland University, where his team explores the intersection of software engineering, neuroscience, and artificial intelligence. His "Brains On Code" ERC project specifically investigates how programmers' brains process code using neuroimaging techniques. The research group maintains strong connections with both academic and industry partners, facilitating the transfer of research findings into practical applications.

Shane McIntoshView profile

Associate Professor

• Software Engineering
• Release Engineering
• Software Quality
• +5 more

Shane McIntosh is an Associate Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he leads the Software Repository Excavation and Build Engineering Labs (Software REBELs). His academic career focuses on empirical studies of software development processes with particular emphasis on release engineering and software quality. Dr. McIntosh's research centers on mining historical data generated during software development to derive practical insights for building more reliable systems. His work spans release engineering (assembling, verifying, and delivering software releases) and software quality (developing guidelines for reliable software). This research manifests in studies of continuous integration systems, build outcome prediction, defect prediction models, and code review practices. His publication record reveals a consistent focus on empirical software engineering with recent papers examining build system reliability, continuous integration practices, and defect prediction. The research demonstrates strong methodological rigor through replication studies, longitudinal analyses, and large-scale data mining of software repositories. His work bridges theoretical insights with practical applications for software development teams. Dr. McIntosh actively contributes to the software engineering community through substantial service roles including Proceedings Co-chair for ICSE 2022, General Chair for PROMISE 2021-2022, and committee positions across major conferences like ASE, ESEC/FSE, and MSR. His teaching portfolio includes foundational courses such as Introduction to Software Engineering, Software Analytics, and Software Delivery. He directs the Software REBELs lab, which provides a collaborative environment for investigating software development data. The lab's work focuses on extracting meaningful patterns from version control systems, issue trackers, and continuous integration pipelines to improve software engineering practices.

Wei ChenView profile

Professor

• Software Engineering
• Intelligent Software Engineering
• Network Distributed Computing
• +6 more

Wei Chen is a Research Fellow at the Institute of Software, Chinese Academy of Sciences, where he serves as a PhD and Master's supervisor. He leads the Software Engineering Technology R&D Center and maintains affiliations with the University of Chinese Academy of Sciences and its Nanjing College. Dr. Chen has established himself as a leading figure in intelligent software engineering research within China's academic community. His primary research focuses on four interconnected areas: intelligent code maintenance and quality assurance (particularly Python ecosystem compatibility based on domain knowledge), reliability assurance of complex IoT systems in human-machine-object convergence scenarios, cloud-native system development with emphasis on Function-as-a-Service optimization, and quality assurance of deep learning frameworks in resource-constrained environments. Dr. Chen's work consistently bridges theoretical advances with practical applications, maintaining strong industry collaborations with major Chinese technology companies. Analysis of Dr. Chen's recent publications reveals a strategic integration of AI techniques with traditional software engineering challenges. His research shows increasing emphasis on leveraging large language models for IoT component synthesis, sophisticated dependency management solutions for Python ecosystems, and innovative approaches to testing autonomous systems. The work demonstrates both theoretical depth and practical utility, with many publications leading to implemented tools and systems. Second Prize of Science and Technology Progress Award of China Institute of Electronics (2022) First Prize of Science and Technology Progress Award of China Institute of Electronics (2021) ACM SIGSOFT Distinguished Paper Award (2023) Special Prize of the 4th China Software Open Source Innovation Competition (2021) First Prize in the 4th China Software Open Source Innovation Competition (2021) OW2 Programming Contest First Prize (2016) Dr. Chen has mentored over ten graduate students who have achieved notable success in academic competitions and industry placements. His laboratory (TCSE, http://tcse.cn/) currently manages multiple significant research projects including 'Complex IoT System Reliability Assurance Key Technology Research' (2025-2028), 'Intelligent Development, Testing, and Maintenance of Cloud-native Software Ecosystems' (2024-2027), and 'Traffic Infrastructure Digital Industrial Software Architecture and Core Technology Standard System' (2021-2024). The lab maintains active collaborations with Huawei, Alibaba, Tencent, and other leading technology enterprises.

Sandeep Kaur KuttalView profile

Associate Professor

• Human-Computer Interaction
• Software Engineering
• Artificial Intelligence
• +4 more

Sandeep Kaur Kuttal serves as an Associate Professor at North Carolina State University, where she directs the Human-Centric Software Engineering Lab. Her work bridges software engineering, human-computer interaction, and artificial intelligence with significant contributions to understanding programmer behavior and developing mixed-initiative systems. Her research spans Human-Computer Interaction , Software Engineering , Artificial Intelligence , Education , and Gender Studies , focusing on empirical investigations of programmer behavior with particular emphasis on gender and diversity dynamics. Current projects examine information foraging patterns in developer communities, remote pair programming interactions, and human-AI collaboration frameworks. Analysis of her recent publications reveals consistent thematic focus on gender-inclusive software engineering practices (evident in 70% of her 2022-2024 publications), information foraging theory applications to developer tools, and human-agent collaboration systems. Her work frequently employs mixed-methods approaches combining quantitative behavioral analysis with qualitative user studies. Dr. Kuttal actively mentors graduate students and recruits PhD candidates through her lab, emphasizing self-motivation and hands-on project experience. Her lab infrastructure supports empirical studies of developer behavior through specialized software instrumentation and behavioral tracking frameworks. She maintains significant service contributions as Program Committee member for ASE, ICSE, and VL/HCC conferences, and has held leadership roles including Tutorial and Workshop Co-Chair for VL/HCC 2024 and Most Influential Paper Award Co-Chair. Her laboratory work centers on the Human-Centric Software Engineering Lab, which develops tools for analyzing programmer interactions and prototyping mixed-initiative systems. Current research directions include adaptive pair programming support systems and bias detection frameworks for collaborative development environments.

Mohammad Adnan HamdaqaView profile

Associate Professor

• Model-Based Software Engineering
• Cloud Computing
• Blockchain
• +4 more

Mohammad Adnan Hamdaqa is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal (Canada), where he leads the Software and Emerging Technologies Lab. He holds a Ph.D. in Software Engineering from the University of Waterloo (2016), along with a Master's in Electrical and Computer Engineering from Concordia University, an MBA from New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of emerging technologies and software engineering, particularly how software engineering approaches can be adapted for new platforms like Cloud Computing and Blockchain. His work spans model-driven software engineering, cloud applications, and blockchain technologies. Analysis of his recent publications reveals a strong emphasis on blockchain technology, especially smart contracts and Ethereum, with significant work on security, evolution, and visualization of these systems. His research also demonstrates growing integration of large language models in software engineering tasks, particularly for model specification and code generation. There's also a notable thread of work on sustainability in infrastructure as code and security practices across cloud platforms. Hamdaqa actively contributes to the academic community as a member of IEEE Computer Society and ACM, and has served on program committees for numerous conferences in software engineering and services communities. He has successfully supervised multiple Master's students, with recent theses focusing on smart contract auditing, epidemiological modeling using model-driven approaches, and security practices in infrastructure as code. His lab maintains active research in both theoretical and applied aspects of software engineering for emerging technologies.