Automated Question Generation Professors and Supervisors

Jie Liuمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

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.

پروفایل رسمی دانشگاه

Sven Apelمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Software product lines and configurable systems
AI-based program generation and optimization
Socio-technical software analysis
+۳ مورد دیگر

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.

پروفایل رسمی دانشگاه

Masud Rahmanمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Software Engineering
Artificial Intelligence for Software Engineering (AI4SE)
Software Maintenance and Evolution
+۱۱ مورد دیگر

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

پروفایل رسمی دانشگاه

Sallam Abualhaijaمشاهده پروفایل

پژوهشگر

Max Planck Institute for Security and PrivacyGermany

Dr. Sallam Abualhaija serves as a Researcher at the Interdisciplinary Centre for Security, Reliability, and Trust (SnT) at the University of Luxembourg, where she applies cutting-edge AI technologies to software engineering challenges with emphasis on requirements engineering and regulatory compliance. Her work bridges academic research and industry applications through extensive collaboration with corporate partners. She earned her PhD in Computer Science from Hamburg University of Technology (Germany) in 2016, establishing her expertise in computational approaches to software requirements. Her research centers on leveraging natural language processing and large language models for regulatory compliance, particularly GDPR implementation in software systems. Key focus areas include automated compliance checking of privacy policies, data processing agreements, and financial regulations through techniques like question-answering systems and ambiguity resolution. This work directly addresses the critical need for trustworthy AI systems that adhere to legal frameworks while improving software development efficiency. Analysis of her recent publications reveals a strong trend toward practical tool development (e.g., CompAi) and multi-solution studies for GDPR compliance across mobile applications, financial services, and general software systems. Her research consistently integrates industry collaboration, with growing emphasis on LLM-driven approaches since 2023. Dr. Abualhaija actively contributes to the academic community through program committee roles at ASE, ICSE, and RE conferences, and as co-organizer of workshops including MO2RE (Multi-Disciplinary Requirements Engineering) and FinanSE. She has chaired tutorials on replication in NLP for requirements engineering and AI applications in regulatory compliance, demonstrating commitment to knowledge transfer and community building. As a core member of SnT, she contributes to Luxembourg's leading research hub for security, reliability, and trust in ICT systems. Her work aligns with SnT's mission through projects that develop verifiable, privacy-preserving technologies with real-world regulatory impact, particularly in European data protection contexts.

پروفایل رسمی دانشگاه

Alexander Serebrenikمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Alexander Serebrenik is a full professor of social software engineering at the Eindhoven University of Technology in the Netherlands, working within the Department of Mathematics and Computer Science. His academic profile spans decades of interdisciplinary research bridging computer science and social sciences. Professor Serebrenik's research focuses on facilitating software evolution through understanding social aspects of development. His work integrates computer science methods (socio-technical coordination theory, natural language processing, machine learning) with organizational psychology principles. A consistent theme across his publications is empiricism - addressing software engineering challenges through observation and experimentation while balancing social and technical perspectives. His recent work increasingly emphasizes diversity, equity, and inclusion in software engineering, culminating in his 2024 book "Equity, Diversity, and Inclusion in Software Engineering: Best Practices and Insights" (APress). His publication record reveals evolving interests from socio-technical coordination to human factors, community dynamics in open source, and now DEI-focused research. Distinguished Paper Award at ICSE 2023 Distinguished Paper Award at MSR 2023 Distinguished Reviewer Award at FSE 2020 Senior member of IEEE Member of ACM As an academic leader, Professor Serebrenik has mentored PhD students including Tukaram Muske, and actively participates in doctoral symposia. His service includes roles as Diversity and Inclusion Co-Chair at multiple conferences and extensive program committee participation across the software engineering conference landscape. His research group at TU/e maintains an active program studying the social dimensions of software engineering through empirical investigations, contributing significantly to understanding how human factors influence development processes and outcomes.

پروفایل رسمی دانشگاه

Andres Karjusمشاهده پروفایل

دانشگاهی

EBS International Business SchoolEstonia

Andres Karjus is a Senior Researcher at Estonian Business School and Lecturer in Digital Humanities and Artificial Intelligence at Tallinn University, where he is also a member of the CUDAN Cultural Data Analytics lab. His multidisciplinary work bridges linguistics, artificial intelligence, and cultural studies, employing methods ranging from machine learning to cognitive experiments. Estonian Business School: Senior Researcher Tallinn University: Lecturer in Digital Humanities and AI CUDAN Lab: Member of Cultural Data Analytics team University of Edinburgh: PhD from Centre for Language Evolution His research focuses on language and culture dynamics, particularly evolutionary processes driving change over time. Karjus specializes in applying computational methods to humanities and social science questions, with recent projects spanning art history, creative industries, and media analysis. He is particularly known for his work on linguistic evolution in polarized social media environments and quantitative analysis of global film festival circuits. His publications demonstrate a strong trend toward interdisciplinary research combining AI techniques with traditional humanities approaches. Recent work has focused on applying large language models to mixed methods research, analyzing film festival diversity through latent space modeling, and studying linguistic divergence in polarized online communities. These studies consistently employ innovative computational approaches to address complex cultural phenomena. Nationally Recognized Science Communicator (2024) - Estonian Science Foundation Karjus actively engages in knowledge transfer through consulting and workshops on R, statistics, data visualization, and generative AI. He has developed training materials for academic audiences across disciplines and frequently collaborates with public and private sector organizations on cross-sector initiatives. His educational background in both linguistics (University of Edinburgh, University of Tartu) and artificial intelligence (KU Leuven) uniquely positions him to bridge technical and humanities domains. He leads the CUDAN lab's research on cultural data analytics and has developed interactive dashboards for visualizing complex cultural datasets, including the film festival circuit analysis platform. His current projects focus on machine-assisted quantitizing designs for humanities research and exploring socioeconomic factors in cultural representation.

پروفایل رسمی دانشگاه

Dora Demszkyمشاهده پروفایل

استادیار

Stanford UniversityUnited States

Dora Demszky is an Assistant Professor in Education Data Science at Stanford University's Graduate School of Education, with a courtesy appointment in Computer Science. She leads the EduNLP Lab, where she develops natural language processing tools to support equitable, student-centered instruction through analyzing educational discourse including student-teacher interactions, student group work, and textbooks. PhD in Linguistics from Stanford University (advised by Dan Jurafsky) BA summa cum laude in Linguistics with a minor in Computer Science from Princeton University Co-founder of Tarisznya Alapítvány (Knapsack Foundation), a nonprofit supporting underprivileged children in Hungary Dr. Demszky's research combines natural language processing, linguistics, and practitioner input to develop interpretable and scalable education measures. Her work focuses on creating tools that analyze classroom discourse to identify features of high-quality instruction and provide actionable feedback to educators. She has particular expertise in developing AI-powered systems that help teachers improve questioning quality, analyze textbook representation, measure dialect features, and scaffold curricula to meet diverse student needs. Her approach emphasizes the importance of human connections in teaching and learning, using technology to enhance rather than replace these critical interactions. Analysis of Dr. Demszky's recent publications reveals a strong trend toward practical applications of NLP in real educational settings, with increasing emphasis on randomized controlled trials to validate effectiveness. Her work spans multiple educational contexts from K-12 classrooms to higher education, with growing attention to equity considerations in AI-powered educational tools. Recent publications show expansion into mathematical education, speaker diarization for noisy classrooms, and open-source tools for the broader research community. MathemaTikZ dataset received the inaugural best dataset prize at Learning at Scale NCTE classroom transcript dataset received the best IEDMS Publicly Available Educational Dataset Prize Selected as a Leading Woman in AI at the ASU GSV AIR Show Dr. Demszky has secured significant research funding including grants from the Gates Foundation, NSF RAPID program, and Stanford HAI. Her work with teachers extends beyond research through initiatives like the Practitioner Voices Summit, which brought together 60 teachers from 22 states to inform AI research related to classrooms. She actively collaborates with educators to ensure her tools address real classroom needs while maintaining a focus on socially responsible edtech development. At Stanford, Dr. Demszky leads the EduNLP Lab, which operates at the intersection of education, computer science, and linguistics. The lab follows a three-pronged approach: gathering evidence through data science using NLP models, developing algorithms through co-design with domain experts, and piloting solutions that practitioners can use in real educational settings. Current projects include M-Powering Teachers, which provides automated feedback to educators, and tools for adapting mathematics curricula to support students with diverse learning needs.

پروفایل رسمی دانشگاه

Iñaki Odriozola Larrañagaمشاهده پروفایل

پژوهشگر

University of CopenhagenDenmark

Iñaki Odriozola Larrañaga is a Postdoctoral Researcher at the University of Copenhagen's Globe Institute, specifically within the Section for Hologenomics. His research focuses on advanced genomic techniques to study host-microbe interactions across diverse species. His research interests span hologenomics, metagenomic analysis, environmental DNA applications, and microbiome dynamics. Dr. Odriozola Larrañaga specializes in developing and implementing genomic methodologies to understand how environmental factors influence microbial communities in various organisms, from mammals to fish. His work bridges computational biology with field ecology to address fundamental questions about host-microbe coevolution. Analysis of his recent publications reveals a strong emphasis on methodological development in hologenomic research, with particular attention to DNA extraction protocols, data generation standards, and functional inference techniques. His research shows consistent collaboration with the Alberdi and Gilbert research groups, focusing on comparative studies across vertebrate species and environmental contexts. Dr. Odriozola Larrañaga maintains active research collaborations across multiple institutions, as evidenced by his co-authorship patterns and international publication record. His work has generated significant academic interest, with multiple publications receiving substantial attention on academic social networks and research platforms.

پروفایل رسمی دانشگاه