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Andreas VogelsangView profile

Professor

• Requirements Engineering
• Model-Based Systems Engineering
• Empirical Software Engineering
• +1 more

Andreas Vogelsang is a full Professor of Software Systems Engineering at the University of Duisburg-Essen and board member of paluno - the Ruhr Institute for Software Technology. His research focuses on requirements engineering, model-based systems engineering, empirical software engineering, and AI for software engineering, with significant contributions to the software engineering community through conference leadership and publications. His research interests center on improving requirements engineering processes using AI techniques, addressing model-based systems engineering challenges in embedded systems, and conducting empirical validation of software engineering practices. He investigates industrial adoption barriers, quality requirements interpretation, and the application of large language models to automate requirements tasks, emphasizing practical relevance and empirical evidence. Recent publications reveal a strong trend toward leveraging AI—particularly large language models—for requirements elicitation, traceability, and code generation. His work bridges theoretical advancements with industrial applications, focusing on empirical validation in domains like automotive systems and research software, while exploring explainability in AI-assisted development. Scientific awards include: Junior Fellow of the German Informatics Society (GI) (2018) Young Scientist of the Year by academics and the DHV (2019) As a board member of paluno, he drives interdisciplinary research initiatives and industry collaborations, fostering innovation in software technology through academic-industrial partnerships and contributing to the institute's strategic direction in software engineering research.

Yulei SuiView profile

Associate Professor

• Program Analysis
• Software Engineering
• Machine Learning
• +7 more

Yulei Sui is an Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW), where he conducts research at the intersection of programming languages, software engineering, and machine learning. His work focuses on developing open-source frameworks for static analysis and verification to enhance software reliability and quality. He currently serves as Program Chair for LCTES 2024, SAS 2025, and ISSRE 2025, and is a program committee member for numerous top-tier conferences including PLDI, OOPSLA, and ICSE. His educational background includes a PhD from UNSW, where he has been a faculty member since completing his studies. His research interests span program analysis, software verification, machine learning applications in software engineering, and the intersection of programming languages with natural language processing and code LLMs. He leads the development of SVF (Static Value-Flow), an open-source framework for code analysis and verification, which has gained significant traction in both academic and industrial settings. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques, particularly in areas like context-free language reachability, pointer analysis, typestate analysis, and vulnerability detection. His work often bridges theoretical advances with practical applications, resulting in tools that address real-world software reliability and security challenges. Several of his papers have received distinguished paper awards at top conferences including ICSE and FSE. ICSE Distinguished Paper Award (2025) FSE Distinguished Paper Award (2024) OOPSLA 2022 Distinguished Artifact Award Australian Research Council Future Fellowship Fellow of Engineers Australia (FIEAust) Professor Sui actively mentors PhD students and undergraduate thesis candidates, with numerous students having completed their research under his supervision. He has secured significant research funding including ARC Discovery Projects and an ARC Future Fellowship. His community service includes editorial roles for IEEE Transactions on Software Engineering and IEEE Transactions on Reliability, demonstrating his standing as a leader in the software engineering research community.

Iftekhar AhmedView profile

Associate Professor

• Software Engineering
• Software Testing
• Machine Learning for Software Engineering
• +3 more

Iftekhar Ahmed is an Associate Professor in Informatics at the Donald Bren School of Information and Computer Science, University of California, Irvine. His research focuses on software engineering, particularly combining software testing, static analysis, and machine learning to develop better tools and techniques for software quality assurance. His educational background includes: PhD in Computer Science (2018) from Oregon State University, advised by Carlos Jensen BSc in Computer Science & Engineering (2007) from Shahjalal University of Science and Technology Dr. Ahmed's research interests center on software testing, static analysis, and the application of machine learning to software engineering problems. He has made significant contributions to mutation analysis, particularly in scaling this technique for real-world software systems. His work often bridges theoretical advances with practical applications, focusing on how to make software testing more effective and efficient for developers. He leads the STAIRS (Software Engineering & Testing Using Artificial Intelligence for Reliable Software) research group at UCI, where his team explores innovative approaches to software reliability through AI and machine learning. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software engineering practices. There's a clear focus on applying machine learning to code analysis, commit message generation, and bug detection, while maintaining rigorous empirical validation through studies of real-world software projects and developer practices. His work spans multiple domains including web accessibility, quantum computing, and Jupyter notebooks, showing both depth in core software engineering topics and breadth across application areas. Dr. Ahmed has received several prestigious awards: IBM Ph.D. Fellowship for academic year 2016-2017 Graduate School tuition relief Scholarship for academic year 2016-2017 IBM Ph.D. Fellowship for academic year 2017-2018 Actively involved in the academic community, Dr. Ahmed serves on program committees for major software engineering conferences including ASE, ICSE, and ESEC/FSE. He is currently accepting PhD students into his research group and emphasizes mentorship and professional development. His research has been supported by various grants that enable his team to explore innovative approaches to software testing and analysis. Dr. Ahmed leads the STAIRS research group at UCI, which focuses on developing AI-powered techniques for software testing and reliability. The group collaborates with industry partners and academic institutions to ensure their research addresses real-world challenges in software development. Current projects include improving mutation testing scalability, analyzing code smells in emerging domains like quantum computing, and developing tools for accessibility testing.

René PeinlView profile

Professor

• Multimodal Artificial Intelligence
• Resource-efficient AI
• Speech Recognition
• +7 more

René Peinl is a Professor of Business Informatics at the Institute for Information Systems (iisys) within the Faculty of Computer Science at Hof University of Applied Sciences. He serves as the Institute Director and leads the research group on Multimodal Artificial Intelligence. His academic career spans over 15 years of teaching in master's programs including 'Master Informatik' and 'Master Applied Research in Computer Science', with a focus on resource-efficient AI for text understanding since 2021. Dr. Peinl's research spans multiple cutting-edge domains in AI and information systems. His primary focus is on multimodal artificial intelligence systems that integrate various data modalities. He specializes in resource-efficient AI models that can operate without cloud resources, with particular emphasis on German language processing for speech recognition, text understanding, and speech synthesis. His work extends to practical applications in Industry 4.0 through customizable manufacturing execution systems (HiCuMES) and immersive learning applications using virtual reality technology for fields like electrical engineering and healthcare training. Analysis of his recent publications reveals a strong trend toward practical, efficient AI implementations with a focus on German language processing and multimodal systems. His work addresses critical challenges in model compression, bias mitigation in generative AI, and multimodal integration of text and image understanding. A significant portion of his research focuses on making AI more accessible and practical for small and medium enterprises through resource-efficient implementations that don't require cloud infrastructure. Professor Peinl actively participates in academic service and industry collaboration. He serves as a member of Hof University's senate and supervisory board. His research is supported through EFRE projects including 'Wirtschaft im Mittelstand 4.0' and 'Digitale Transformation des Mittelstands mit KI'. He frequently engages with industry through speaking engagements, consulting, and collaborative projects with companies across various sectors. At Hof University, Peinl leads the Institute for Information Systems and the Multimodal Artificial Intelligence research group. His team works on developing privacy-friendly speech assistance systems that function without cloud resources, highly customizable manufacturing execution systems for Industry 4.0, and immersive VR learning applications for domains that are expensive, dangerous, or difficult to replicate in physical reality.

Heidi SchuhbauerView profile

Professor

• Knowledge Management
• Social Network Analysis
• Industry 4.0
• +3 more

Prof. Dr. Heidi Schuhbauer serves as a Professor at Nuremberg University of Applied Sciences, where she maintains active teaching and research responsibilities in Business Informatics, Knowledge Management, and Social Network Analysis. Her office (HQ.317) and contact details (phone: +49 (0)911 5880-1662; email: heidi.schuhbauer@th-nuernberg.de) reflect her current institutional affiliation, with consultation hours arranged via email correspondence. Her research spans Knowledge Management, Social Network Analysis, and Industry 4.0 applications, with particular emphasis on educational technology innovations. Current projects investigate gamification mechanisms, AI integration in curricula, and sustainable literacy development through storytelling techniques. She examines how digital escape rooms, agile learning frameworks, and generative AI tools impact student motivation and learning outcomes in economic and business education contexts. Analysis of her 2022-2025 publications reveals three dominant trajectories: (1) gamification elements in e-learning platforms (digital escape rooms, Eduscrum frameworks), (2) social network analysis applied to political communication and enterprise systems, and (3) generative AI implementation challenges in undergraduate business education. Her work consistently bridges theoretical knowledge management concepts with practical educational applications, often focusing on STEM diversity and motivational dynamics. No scientific awards were documented in the source material. The text provided no details regarding student advising activities, grant funding, research laboratories, or collaborative teams. Her publication record indicates active research output without mention of future project directions or completed educational programs.

Yingzhen LiView profile

Senior Lecturer

• Machine Learning
• Probabilistic Modelling
• Bayesian Deep Learning
• +7 more

Yingzhen Li is a Senior Lecturer in Machine Learning at the Department of Computing, Imperial College London, within the Faculty of Engineering. She holds a Turing Fellowship at The Alan Turing Institute (2024). Her research focuses on building reliable machine learning systems through probabilistic methods, emphasizing uncertainty quantification, robustness, explainability, and adaptive techniques. Key areas include trustworthy ML models, generative modeling (especially sequential and spatiotemporal data), and approximate inference applied to Bayesian deep learning. Education: PhD in Engineering from the University of Cambridge, supervised by Prof. Richard E. Turner. Previously a senior researcher at Microsoft Research Cambridge and intern at Disney Research. Research interests span uncertainty-aware AI, causal representation learning, and scalable inference techniques. Her work has influenced industrial applications in deep learning frameworks like TensorFlow Probability and Pyro. Notable contributions include tutorials on approximate inference at NeurIPS 2020 and organizing conferences like AABI and AISTATS. Her recent publications address challenges in generative AI, calibration of vision-language models, and energy-based models. Awards include the Turing Fellowship recognizing her leadership in foundational AI research. Collaborations include affiliations with The Alan Turing Institute and prior industry experience. She actively mentors students and advises on adaptive learning systems through her academic and industrial networks.

• Cybersecurity
• Blockchain
• Distributed Systems
• +2 more

Sheng Wen is a Professor at Swinburne University of Technology's School of Science, Computing and Emerging Technologies, where he serves as Director of the Blockchain Innovation Lab and Deputy Director of the Swinburne Cybersecurity Lab. His research spans cybersecurity, blockchain, and distributed systems, with over $7 million in research funding from ARC and industry partners. He leads a team focused on vulnerability detection, AI security, and privacy-preserving technologies. Research interests include adversarial machine learning, blockchain consensus security, IoT device vulnerabilities, and federated learning applications. His publications consistently address emerging threats in AI systems and cross-chain blockchain security. Awards include the 2021 SCET Research Excellence Award and the 2015 Scientists Meet Senators recognition. Grants support projects in optimized energy management, railway signaling verification, and healthcare AI security. Supervision includes 36 PhD students working on blockchain applications, fuzzing tools, and cybersecurity AI. His lab collaborates with CSIRO and industry partners on critical infrastructure protection.

Hsiao-Ping HsuView profile

Assistant Professor

• Generative AI in Education
• Computational Thinking
• Virtual Reality Learning
• +2 more

Hsiao-Ping Hsu is Assistant Professor in Digital Learning at Dublin City University's Institute of Education. His research integrates emerging technologies like generative AI, VR, and game-based learning into educational contexts. Current projects examine how generative AI transforms lesson planning and computational thinking development. His work emphasizes practical applications of immersive technologies for environmental education and digital literacy. AECT Nova Southeastern Award for Instructional Design Reimagine Education Bronze Award (VR/AR category) Foxconn Education Foundation Research Fellowship

Nikolas MartelaroView profile

Assistant Professor

• Human-Computer Interaction
• Interaction Design
• Human-AI Collaboration
• +7 more

Nikolas Martelaro is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he leads the Augmented Design Capability Studio. His work focuses on developing tools and methods that augment designers' capabilities through the integration of human-centered design principles and advanced computational technologies. Dr. Martelaro received his Ph.D. in Mechanical Engineering from Stanford's Center for Design Research, where he was co-advised by Larry Leifer and Wendy Ju. He also holds a B.S. in Engineering Design from the Franklin W. Olin College of Engineering. Before joining CMU, he worked as a researcher in the Digital Experiences group at the Accenture Technology Labs in San Francisco. His research interests center around human-AI collaboration to support deeper design thought, with specific focus on reflective practice, designerly questioning, diagrammatic thinking, and analogical thinking. He develops tools to help creatives work better, with emphasis on systems for generating design artifacts and support for running successful creative businesses. His work also explores collaborative design methods to engage end-users throughout design and engineering processes, particularly in the context of automated physical systems. Current research spans automotive user experience, interaction with conversational music systems, semi-automated farm equipment, and new mobility technologies. Recent publications demonstrate a strong trend toward leveraging AI to augment the design process, with particular emphasis on human-AI co-creation, analogical thinking, and supporting designers' reflective practices. His work spans multiple domains including visual design, engineering design, and human-robot interaction, showing how computational tools can enhance rather than replace human creativity and expertise. Best Paper Honorable Mention at CHI 2025 for 'BioSpark: Beyond Analogical Inspiration to LLM-augmented Transfer' Dr. Martelaro actively mentors graduate students including Howie Wang, Frederic Gmeiner, David Chuan-en Lin, and Zeda Xu. His research is supported by collaborations with industry partners including the Toyota Research Institute, and he regularly presents his findings at leading conferences such as CHI, CSCW, and DIS. He teaches courses including 'The AI Augmented Designer' and 'Design of AI Products & Services,' emphasizing critical evaluation of AI tools in design practice. He leads the Augmented Design Capability Studio, which brings together researchers from diverse backgrounds to explore how emerging technologies can augment human design capabilities. The studio's work focuses on developing interactive systems that enable designers to understand people, prototype new devices, and embed intelligence into products. Current projects include Inkspire (supporting design exploration with generative AI), VideoMap (reimagining video editing), Soundify (enhancing video sound design), and Jigsaw (prototyping multimodal applications by assembling AI foundation models).

Phil WoodlandView profile

Professor

• Speech Recognition
• Speaker Diarisation
• Machine Learning
• +7 more

Professor Phil Woodland is a faculty member at the University of Cambridge, affiliated with the Department of Engineering. He serves as a Director of Studies in Engineering and is a Fellow. His research focuses on speech recognition, speaker diarisation, and the integration of large language models (LLMs) into speech processing systems. His recent work explores end-to-end training frameworks, multimodal safety benchmarks (e.g., CASE-Bench), and advanced attention mechanisms like Multi-head Temporal Latent Attention. He investigates topics such as speech prompt learning, emotion recognition, and handling class imbalance in emotion detection tasks, often leveraging transformer architectures and self-supervised learning techniques. Key trends in his publications include the fusion of speech and LLM technologies, robust audio event detection, and parameter-efficient adaptation strategies. His research also addresses biomedical applications, such as depression and Alzheimer's detection via speech analysis. Scientific Award: 1st place solution to the Odyssey Emotion Recognition Challenge Task 1 (2024).

• Big Data
• Data Mining
• Machine Learning
• +2 more

Xiang Zhang is an Associate Professor at the College of Information Sciences and Technology, The Pennsylvania State University, focusing on big data, data mining, machine learning, and biomedical informatics. His work develops algorithms for analyzing large-scale social, biological, and medical datasets using graph-based methods. Education: Ph.D. in Computer Science (2011), University of North Carolina at Chapel Hill Research Interests: Xiang Zhang's research spans graph neural networks, dynamic graph analysis, and biomedical data mining. He specializes in explainable AI, robust learning, and topology-aware models for imbalanced data. His group investigates security in graph models, synthetic data generation, and cross-domain adaptation. Publications Trends: Recent work emphasizes explainable dynamic graph neural networks, adversarial attacks on LLMs, and robust contrastive learning. His team explores topology-level imbalances, prompt attacks, and causal relation modeling in temporal systems. Scientific Awards: Best Research Paper Award at SIGKDD 2008 Best Student Paper Award at ICDE 2008 Best Paper Candidate at ICDM 2018 and ICDM 2012 SIGKDD Dissertation Award Honorable Mention (2012) Advising: He mentors a group of Ph.D. students including Zongyu Wu, Minghua Lin, and Junjie Xu, with alumni securing faculty positions at Florida International University and North Carolina State University.

Hong ShenView profile

Assistant Professor

• Social Computing
• Human-Centered AI
• Responsible Computing
• +5 more

Hong Shen is an Assistant Professor in the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University's School of Computer Science, where he directs the CARE (Collective AI Research and Evaluation) Lab and participates in the CMU-NIST Cooperative Research Center on AI Measurement Science & Engineering (AIMSEC). Education: PhD from University of Illinois at Urbana-Champaign Research Interests: Dr. Shen is an interdisciplinary scholar at the intersection of human-computer interaction, artificial intelligence, and public policy. His work examines the social, ethical, and policy implications of digital platforms and algorithmic systems, with core focus areas in Social Computing , Human-Centered AI , Responsible Computing , Ethics and Policy , Research through Design , and Tools and Programming . His research emphasizes community-centered approaches to AI development and governance. Publication Trends: His 2025 publications reveal strong interdisciplinary convergence across AI ethics, participatory governance, and real-world AI deployment. Key themes include public involvement in algorithmic decision-making, reparative justice frameworks for AI harms, risk management in AI content work, and cross-cultural studies of algorithmic labor. The work consistently bridges technical AI development with social science methodologies and policy implications, particularly in community-university partnerships and healthcare contexts. Scientific Awards: Best Paper Award at HCOMP'24 (2024) for "Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination" Google Research Scholar Award in Human-Computer Interaction (2024) Microsoft Research AI & Society Fellow (2024) for "Supporting the Responsible AI Red-Teaming Human Infrastructure" Best Paper Award at CHI'23 (2023) for "Understanding Frontline Workers’ and Unhoused Populations’ Perspectives on AI Used in Homeless Services" Advising and Funding: Dr. Shen advises graduate students Jini Kim, Ningjing Tang, Alice Qian Zhang, and Qing Xiao. His research is supported by significant grants including multiple National Science Foundation awards (including July 2025 funding for participatory AI design with behavioral health services), Amazon Research, Cisco Research, Google Research, Microsoft Research, The Public Interest Technology University Network, Block Center for Technology and Society, and CyLab. Recent grants focus on community-centered AI toolkits and mental health organization partnerships. Labs and Teams: He leads the CARE Lab, which develops collective approaches to AI research and evaluation, and actively contributes to AIMSEC's mission of advancing human-centered AI measurement science through CMU-NIST collaboration. His lab emphasizes participatory methods and infrastructure for ethical AI development.

Zhuang LiView profile

Lecturer

• Natural Language Processing
• Machine Learning
• Data-efficient Learning
• +5 more

Dr. Zhuang Li is a Lecturer at RMIT University's School of Computing Technologies, specializing in Natural Language Processing (NLP), machine learning, and trustworthy AI. He earned his PhD from Monash University (2023), focusing on semantic parsing in low-resource conditions, and previously worked at Microsoft on Cortana and Bing. His research bridges NLP, computational social science, and cultural alignment of AI systems, emphasizing data-efficient training and LLM safety. Education: PhD in NLP, Monash University (2019–2023) M.Comp in AI, Australian National University (2014–2015) B.Eng in Electrical Engineering, Wuhan University of Science and Technology (2009–2013) Research Interests: Data-efficient learning, culturally-grounded AI, large language models, computational social science, and ethical AI deployment. Collaborations: Active in industry partnerships and academic collaborations with Monash University, TU Darmstadt, Microsoft Research India, and Ant Group. Recent work includes ACL 2025 papers on LLM safety and peer review analysis. Supervision: Open to PhD students in low-resource languages and culturally-aligned LLMs. Collaborates with senior faculty at RMIT and Monash.

• Neuroscience
• Molecular Biology
• Neurodevelopmental Disorders
• +3 more

Hong Li is an Associate Research Scholar in the Department of Neuroscience at Yale University's Yale School of Medicine. Their research focuses on molecular mechanisms of cortical developmental malformations in neurodevelopmental and psychiatric disorders, employing animal models and integrative methodologies including molecular biology, genetics, morphology, and transcriptomics. Dr. Li holds a DPhil from the University of Helsinki (2007) and an MD from Anhui University (1997). Their work aims to advance understanding of neuropsychiatric diseases and develop therapeutic strategies. Educational Background: DPhil in Neuroscience, University of Helsinki (2007) MD, Anhui University (1997) Research Interests: Investigating molecular pathways underlying cortical development abnormalities, with emphasis on translational research to bridge basic science and clinical applications. Techniques include advanced genetic engineering, transcriptomic profiling, and cross-disciplinary approaches combining neuroscience and molecular biology. Advising & Grants: No specific grants or trainees listed in available materials, though their publications suggest collaborative research in quantum computing, embedded systems, and telecommunication networks. Recent work includes cross-domain contributions to quantum error correction systems and LLM-based robotic applications. Professional Contributions: Active in systems engineering, publishing on operating systems design, FPGA-based quantum decoders, and secure memory management. Their work bridges biomedical research with computational and hardware innovations.

• Machine Learning
• Deep Neural Networks
• Video Analytics
• +4 more

Daniel Kang is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois. His research focuses on machine learning systems, cybersecurity for AI models, and database optimization for unstructured data. He specializes in developing robust systems for large language models (LLMs), including defenses against adversarial attacks and benchmarking frameworks for AI agents. His work bridges machine learning and systems research, addressing challenges such as prompt injection vulnerabilities, zero-day exploit mitigation, and privacy-preserving inference via zero-knowledge proofs. He has contributed to tools like LEAP for processing unstructured data and AIDB for ML-driven databases. Key collaborations include studies on AI safety, vulnerability exploitation, and ethical AI evaluation. His research outputs emphasize practical applications of ML systems in cybersecurity, data engineering, and interdisciplinary domains like social science analytics.