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Rita HartelView profile

Senior Lecturer

• Data Compression
• Bioinformatics
• Graph Databases
• +4 more

Dr. Rita Hartel serves as a Senior Lecturer and Research Associate in the Department of Databases and Electronic Commerce at the University of Paderborn's Institute of Computer Science, while also fulfilling the critical role of Academic Advisor for the Computer Science Student Office where she guides undergraduate and graduate students. Her research forms two distinct yet complementary pillars: cutting-edge data compression algorithms for bioinformatics (specializing in Burrows-Wheeler Transform optimizations for DNA sequence data) and graph databases (developing grammar-based compression for knowledge graphs), alongside innovative digital humanities work applying OCR and semantic analysis to comics historiography with a focus on German traditions. This interdisciplinary approach bridges computational rigor with cultural scholarship. Dr. Hartel demonstrates consistent research productivity with five publications from 2021-2025 in premier venues including the Data Compression Conference and specialized workshops. Her work reflects strong international collaboration patterns and likely benefits from active grant funding given the technical resources required for bioinformatics compression research. As an Academic Advisor, she provides essential student support while her dual research focus creates unique opportunities for students interested in either computational methods or digital humanities applications.

Ding LiView profile

Assistant Professor

• Program Analysis
• Energy Optimization
• Mobile Applications
• +2 more

Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)

Jingyue LiView profile

Professor

• Software Engineering
• Software Security
• Software Engineering and AI
• +2 more

Jingyue Li is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She earned her Ph.D. in Software Engineering from NTNU in 2006 and has extensive industrial experience including positions at IBM and DNV Research and Innovation. Her educational background includes: Ph.D. in Software Engineering from NTNU (2006) Professor Li's research spans several key areas in software engineering with a focus on both theoretical and practical applications. Her work bridges traditional software engineering practices with emerging technologies, particularly in the domain of security and blockchain systems. She has conducted extensive empirical research and applied design science methodologies to develop innovative tools and approaches. Her recent publications demonstrate a strong trend toward blockchain technologies, software security, and the intersection of AI with software engineering. The research shows increasing focus on practical applications of blockchain in decentralized autonomous organizations, consensus protocols, and security vulnerabilities in smart contracts. There's also significant work on integrating security practices into DevOps (DevSecOps) and applying machine learning techniques to software engineering problems. Professor Li has received recognition through her leadership roles in major conferences: General Chair for FSE 2025 (The ACM International Conference on Foundations of Software Engineering) Member of the EASE (International Conference on Evaluation and Assessment in Software Engineering) steering committee In terms of research leadership, Professor Li has served as Principal Investigator (PI) or Key Scientist on numerous research projects including TRACE4EU (2023-2025), PaaSforChain (2020-2023), CyberSmart (2017-2020), and several others focused on blockchain, security, and software engineering. She has also conducted research visits to institutions including University College London, University of Washington, Hiroshima University, and Peking University. Her research group appears to be actively engaged in both theoretical and applied research, with strong industry connections through projects like CyberSmart and SecureCyber that address real-world challenges in cybersecurity and smart city infrastructure.

Emmanouil AthanasiadisView profile

Research Fellow

• Bioinformatics
• Computational Biology
• Single Cell Genomics
• +3 more

Dr. Emmanouil Athanasiadis is a bioinformatician at the University of Cambridge's Department of Haematology, with dual affiliations at the Wellcome Trust Sanger Institute and Medical Research Council (MRC) Stem Cell Institute. His research integrates computational biology with medical applications, focusing on single-cell genomics, cancer biology, and cardiovascular disease mechanisms since 2016. His educational foundation includes: BSc in Biomedical Engineering from Technological Educational Institution of Athens (2004) MSc in Medical Physics from University of Patras (2006), funded by State Scholarships Foundation of Greece PhD in Medical Physics from University of Patras (2010), supported by National State Scholarship Foundation Athanasiadis specializes in developing computational frameworks for biological data interpretation. His work spans single-cell RNA sequencing analysis in haematopoiesis, medical imaging algorithms for cancer diagnostics, and drug repurposing pipelines. Key contributions include SPNsim for pulmonary nodule simulation and ChemBioServer for chemical compound analysis, demonstrating his dual expertise in algorithm development and biomedical application. Publication analysis reveals a trajectory from medical imaging (2007-2012) to genomic network analysis (2013-2016), culminating in current single-cell and spatial transcriptomics research. His work consistently bridges computational innovation with clinical questions in oncology, haematology, and cardiovascular disease, with strong emphasis on open-source tool development. His scientific recognition includes: Computational award from Greek Research and Technology Network for 'GRAND' project Multiple presentation prizes at national medical conferences Continuous academic distinctions from State Scholarships Foundation of Greece K. Karatheodoris research scholarship He has secured EU funding through FP7 projects including 'PIK3CA Oncogenic Mutations' and 'NOISEPLUS', and maintains active collaborations across Cambridge, UCL, and Greek research institutions. His teaching contributions include lecturing in biomedical engineering programs at Technological Educational Institution of Athens (2010-2016). Current work centers on single-cell RNA sequencing analysis within Cambridge's haematology research ecosystem, particularly investigating transcriptional dynamics in blood cell development and cancer evolution through collaborative projects with Sanger Institute.

David KleijnView profile

Professor

• Biodiversity
• Ecology
• Conservation Biology
• +5 more

David Kleijn is a Full Professor at Wageningen University & Research, where he works in the Plant Ecology and Nature Conservation Group. With over 300 publications and 166,062 reads on ResearchGate, his research has significantly contributed to our understanding of biodiversity conservation, particularly in agricultural landscapes. His expertise spans ecology, conservation biology, and the interactions between agricultural practices and ecosystem services. Professor Kleijn's research primarily focuses on: Biodiversity conservation in agricultural landscapes Pollinator ecology and conservation Ecosystem services in farming systems The impacts of land use change on biodiversity Development of practical biodiversity assessment tools His work centers on understanding how agricultural landscapes can be managed to support biodiversity while maintaining productivity. Kleijn has pioneered the use of UAVs and machine learning for biodiversity monitoring, examining relationships between landscape structure, habitat quality, and biodiversity, with particular emphasis on pollinators. His recent publications (2024-2025) reveal a strong focus on technological innovation in ecological monitoring, habitat restoration effectiveness, and the socio-economic dimensions of biodiversity conservation in farming systems. His research bridges ecological theory with practical conservation applications, making significant contributions to evidence-based policy development in agricultural biodiversity conservation. Professor Kleijn has collaborated extensively with researchers across Europe, contributing to major initiatives like the EU Pollinator Monitoring Scheme. His work on pollinator conservation has directly informed agricultural policy discussions at the European level, particularly regarding the effectiveness of Common Agricultural Policy measures for supporting wild pollinators. His research group focuses on applying ecological principles to agricultural systems, with particular expertise in pollination ecology, landscape ecology, and biodiversity assessment methods. Current research directions include investigating trade-offs between biodiversity conservation and agricultural production, developing practical tools for non-experts to assess biodiversity, and understanding how farmers perceive the costs and benefits of biodiversity-friendly farming practices.

Philipp BruneView profile

Professor

• Artificial Intelligence
• Facial Emotion Recognition
• Cybersecurity
• +5 more

Professor Dr. Philipp Brune serves as a prominent academic leader at Neu-Ulm University of Applied Sciences (HNU), holding multiple key positions including Academic Head of TTZ Günzburg, Head of the Master of Artificial Intelligence and Data Analytics program, Head of the Centre for Secure IT Applications and Infrastructures, and Head of the Institute of Agile Product and System Development within the Faculty of Information Management. His career demonstrates a notable transition from physics research (evident in his early 2000-2004 publications) to his current focus on cutting-edge information systems and artificial intelligence. Dr. Brune's research program spans several interconnected domains with particular emphasis on facial emotion recognition systems, legacy system modernization, and secure IT infrastructure development. His work investigates fundamental challenges in AI interpretability, data quality for emotion recognition, and practical approaches to modernizing legacy applications. He has made substantial contributions to understanding the limitations of current facial expression recognition technologies and developing more robust, trustworthy alternatives. Analysis of his recent publications reveals a clear research trajectory toward explainable and trustworthy AI systems, with increasing sophistication in addressing data quality issues and model interpretability challenges. His work bridges theoretical computer science with practical applications across healthcare, marketing, and smart city technologies, demonstrating strong translational research capabilities. As an educator, Dr. Brune has developed innovative pedagogical approaches specifically tailored for non-computer science majors, with research examining student motivation in programming courses and effective methods for teaching cybersecurity concepts. His leadership in the Master of Artificial Intelligence and Data Analytics program shapes curriculum development for next-generation AI professionals. Dr. Brune directs multiple research initiatives including the Centre for Secure IT Applications and Infrastructures, which addresses critical cybersecurity challenges in modern IT environments, and the Institute of Agile Product and System Development, which explores innovative software development methodologies. His collaborative research approach is evident through extensive co-authorship networks spanning multiple institutions and disciplines.

Sabine Arndt-LappeView profile

Professor

• Linguistics
• Morphology
• Phonology
• +5 more

Sabine Arndt-Lappe is a Professor of English Linguistics at the University of Trier , Germany. She previously held research positions at the Universities of Marburg, Siegen, and Düsseldorf. As a structural linguist, her work bridges empirical and theoretical approaches to morphology and phonology. Research Focus : Variation and productivity in English lexicon, formal and semantic aspects of word-formation, prosodic morphology, analogy-based and usage-based grammatical models, computational analogical modeling, and Optimality Theory. Projects : Leads the DFG-funded AdvAnTheMorph project (2025–2028) and co-leads the DFG-ANR ERSaF project (2022–2025). Spokesperson for the Trier Center for Language and Communication research group on Patterns/Musterhaftigkeit (2020–2023). Publications span topics like name blending, stress assignment in English verbs, clipping mechanisms, affix rivalry, and cross-linguistic morphological analysis. Her work employs computational models (e.g., Naive Discriminative Learning) and corpus-based methodologies. Editorial Contributions : Editor-in-Chief of Morphology (2022–present), former Editor of Journal of Linguistics (2017–2021), and editorial board member for multiple journals including English Language and Linguistics and Journal of Linguistics . Teaching includes courses like LING 101: Introduction to Linguistic Studies , LING 801: Words in the Mind , and LING 804: Statistics for Linguists . She also mentors doctoral candidates and supervises software development for morphological research (e.g., M2E - Morph2Excel ).

Janina KneippView profile

Professor

• Surface-Enhanced Raman Scattering
• Plasmonic Catalysis
• Nanoscale Spectroscopy
• +4 more

Prof. Dr. Janina Kneipp is a Professor (W3) of Physical Chemistry at Humboldt-Universität zu Berlin, where she has led an active research group since 2012. She previously held positions as Assistant Professor at HU Berlin/BAM (2008-2012), Junior Researcher at BAM (2005-2008), and research appointments at Harvard Medical School, Princeton University, and Erasmus Universiteit Rotterdam. Education: Dr. rer. nat. (summa cum laude), Freie Universität Berlin (2002) Undergraduate/Graduate Studies in Biology & Physics, Freie Universität Berlin (1992-1998) Research Focus: Her interdisciplinary work bridges physical chemistry and biospectroscopy, with particular emphasis on: Surface-enhanced Raman scattering (SERS) for complex sample analysis Plasmonic catalysis and hot electron chemistry Multiphoton-excited vibrational spectroscopy Nanoscale biochemical mapping in plant and animal systems Development of advanced plasmonic substrates Publication Trends: Recent work demonstrates strong focus on multimodal spectroscopy applications, with studies combining SERS, hyper-Raman, IR, and synchrotron techniques to address questions in catalysis, nanoparticle-cell interactions, plant biochemistry, and biosensing. Publications frequently incorporate advanced nanomaterials, electrochemical methods, and machine learning-assisted spectral analysis. Scientific Awards: Fellow, European Academy of Sciences (2020) Caroline von Humboldt Professorship (2019) Wilhelm Ostwald Fellow, BAM (2012) Bunsen-Kirchhoff Award, GDCh (2010) ERC Starting Grant (2010) Academic Leadership: Currently advises 5 PhD students and leads multiple collaborative initiatives. Serves as Board Member of Einstein Center Catalysis (since 2019), Head of Chemistry Department (2014-2016), and Speaker of Graduate School SALSA (since 2012). Secured funding through DFG, EU networks, and ERC grants supporting spectroscopy infrastructure development. Lab & Team: Leads the KneippLab research group with 2 postdoctoral researchers, 5 graduate students, and technical staff. Research focuses on developing spectroscopic methods for interrogating biological and chemical processes at nanoscale resolution using plasmonic enhancement strategies.

Andreas WübbekeView profile

Professor

• Software Engineering
• Agile Development Models
• Digitalization in Agriculture
• +2 more

Prof. Dr. Andreas Wübbeke is a Professor of Software Engineering at the Faculty of Electrical Engineering, University of Applied Science Southern Westphalia (since 2022). With extensive industry experience in tech leadership roles at CLAAS E-Systems and Wincor Nixdorf, his research bridges academic rigor and practical applications. Research Focus: His work spans Agile methodologies, VR application testing, digital agriculture, and cloud systems. Recent projects include AI-driven classification of agricultural operations and sustainable energy solutions. He leads the South Westphalia Software Engineering Lab, mentoring thesis students through structured programs. Professional Engagement: Active in academic communities, he serves on committees for conferences (PROFES, GIL) and journals. His invited keynotes address topics like smart farming connectivity and agile transformations. Education & Background: PhD in Computer Science (Databases/Information Systems), University of Paderborn (2010) Business Computer Science degree, University of Paderborn (2007)

Thomas KopinskiView profile

Professor

• Machine Learning
• Deep Learning
• AI Safety
• +6 more

Prof. Dr. Thomas Kopinski is a Professor at the Faculty of Engineering and Economics, South Westphalia University of Applied Sciences in Meschede, Germany. He leads the AI Safety and Collective Intelligence Lab, focusing on cutting-edge research in machine learning applications for industrial and automotive systems. His work bridges academic research and industry collaborations, notably with BMW AG. Research Focus: His team explores: Deep learning architectures for real-time gesture recognition and automotive HMI AI safety protocols and collective intelligence frameworks Industrial applications including predictive maintenance and anomaly detection 3D programming and sensor fusion techniques Team & Students: Current advisees include PhD candidates working on: Bayesian deep learning for predictive maintenance (Felix Neubürger) Generative models for image synthesis (Yasser Saeid) Object recognition in crash test videos (Daniel Gierse) Key Projects: Actively directs WiTraPres and Core Transformer initiatives, with upcoming R&D in AI Safety launching in 2025. Industrial collaborations focus on automotive safety systems and manufacturing optimization.

Maura JohnView profile

Researcher

• Bioinformatics
• Genome-wide Association Studies
• Machine Learning in Biology
• +4 more

Maura John serves as a Research Associate at the Chair of Bioinformatics at Hochschule Weihenstephan-Triesdorf's Straubing Campus for Sustainable Resource Use. Her research focuses on developing advanced computational methods for biological data analysis, with particular expertise in genome-wide association studies and protein structure prediction. Her primary research interests include: Genome-wide association studies with permutation-based significance thresholds that preserve population structure Development of bioinformatics tools like permGWAS2 and easyPheno Protein thermostability prediction using machine learning approaches Genomic selection methodologies for crop breeding applications Dr. John's recent publications demonstrate a strong focus on methodological improvements in computational biology, particularly addressing limitations of traditional approaches in handling skewed phenotype distributions and population structure. Her work bridges theoretical statistical methods with practical biological applications across plant genomics and protein science. Notable contributions include: permGWAS2: An improved method that maintains population structure during permutations ProLaTherm: A protein language model-based thermophilicity predictor outperforming existing methods easyPheno: A comprehensive Python framework for phenotype prediction model comparison Her research program demonstrates strong collaborative efforts with Dominik Grimm's group and other bioinformatics researchers, focusing on developing open-source tools that address critical challenges in genomic data analysis. The work has practical applications in plant breeding, protein engineering, and understanding genotype-phenotype relationships.

Jan-Hinrik SchmidtView profile

Research Fellow

• Social Media
• Digital Communication
• Political Communication
• +4 more

PD Dr. Jan-Hinrik Schmidt is a Senior Researcher for Digital Interactive Media and Political Communication at the Leibniz Institute for Media Research | Hans-Bredow-Institut (HBI) in Hamburg, where he has worked since November 2007. Since 2019, he has headed the Hamburg section of the Research Institute Social Cohesion (RISC), focusing on the role of media in social cohesion. He habilitated at Universität Hamburg in June 2020 with a thesis on 'Practices and Social Order of the Social Web.' His educational background includes sociology studies at Otto-Friedrich-University Bamberg and West Virginia University Morgantown, USA. His doctoral work focused on the institutionalization of local online offerings (2004), followed by postdoctoral research on online networking practices. Dr. Schmidt's research primarily examines social media's impact on information circulation, opinion formation, and science communication. His work explores how digital platforms transform public discourse, with particular interest in social cohesion, public service media, and the societal implications of algorithmic curation. Recent projects include the Social Media Observatory database and research on public perceptions of ZDF's educational function. His scholarly output shows a clear trajectory from early work on Web 2.0 developments toward contemporary research on AI in social media, fake news, and the contribution of public service media to social cohesion. The publications reveal an interdisciplinary approach combining communication sociology, political science, and media studies to understand digital transformation's societal impacts. At HBI, he contributes to multiple research initiatives including the Social Media Observatory, which provides systematic observation of the media-based public sphere, and projects examining public service media's integration-related functions. His work often involves collaborative research with colleagues like Uwe Hasebrink and Dieter Storll.

Xin ZhangView profile

Assistant Professor

• Programming Languages
• Software Engineering
• Program Analysis
• +6 more

Xin Zhang serves as an Assistant Professor in the Department of Computer Science and Technology within the School of Electronics Engineering and Computer Science at Peking University. His academic profile demonstrates deep engagement with programming languages and software engineering research communities through active participation in major conferences including ASE, SPLASH/OOPSLA, PLDI, and ICSE. Dr. Zhang's research focuses on the synergistic relationship between program analysis and machine learning. He investigates how ML/AI techniques can enhance traditional program analysis methods while simultaneously developing program analysis approaches to improve the interpretability, fairness, robustness, and safety of AI systems. His work spans probabilistic program analysis, abstraction refinement techniques, Bayesian modeling for program semantics, and applications of graph neural networks to static analysis problems. His publication record shows consistent contributions to top venues from 2016 through 2025, with recent work emphasizing Bayesian program analysis, abstraction refinement methods, and the intersection of formal methods with machine learning. The trajectory of his research demonstrates increasing sophistication in combining traditional program analysis techniques with modern AI approaches. Dr. Zhang actively contributes to the academic community as a program committee member for major conferences including ASE, SAS, PLDI, and SPLASH. His service includes reviewing, session chairing, and committee participation across multiple venues, reflecting his standing in the programming languages and software engineering communities.

Xiao YuView profile

Researcher

• LLMs Data Governance and Evaluation
• Intelligent Software Engineering
• Software Security and Reliability
• +2 more

Xiao Yu is a Research Fellow (Assistant Research Professor) at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Previously, they were a Postdoctoral Researcher at Huawei under Prof. Xin Xia. They hold dual PhD degrees: from Wuhan University's School of Computer Science (December 2020) supervised by Prof. Jin Liu, and from City University of Hong Kong's Department of Computer Science (March 2021) supervised by Prof. Qing Li and Prof. Jacky Wai Keung. Research focuses on three interconnected domains: LLMs Data Governance and Evaluation addressing hallucination phenomena and task-specific LLM evaluation in software engineering; Intelligent Software Engineering leveraging deep learning for code generation, annotation, and maintenance; and Software Security and Reliability investigating vulnerability detection, log anomaly identification, and security bug classification. Their work bridges theoretical advancements with industrial applications, particularly in blockchain and data security contexts. Recent publications demonstrate strong trends in realistic LLM evaluation (RealisticCodeBench), vulnerability detection using semi-supervised learning, and industrial anomaly detection. Key thematic areas include effort-aware defect prediction, code smell detection, and the practical application of large language models in software engineering tasks, with increasing emphasis on data quality and privacy considerations. Xiao Yu actively contributes to the academic community through extensive service roles including journal reviewing for ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Dependable and Secure Computing, and serving on program committees for major conferences like APSEC 2025 and ASE 2025. They have supervised numerous graduate students as evidenced by authorship patterns in publications. Based at Zhejiang University's State Key Laboratory of Blockchain and Data Security, their research operates at the intersection of academic rigor and industrial relevance, with strong collaborations spanning multiple institutions including Huawei, Wuhan University, and City University of Hong Kong.

Xiaoning DuView profile

Senior Lecturer

• Software Engineering
• Artificial Intelligence
• Cybersecurity
• +3 more

Xiaoning Du is a Senior Lecturer (equivalent to U.S. Associate Professor) at the Department of Software Systems and Cybersecurity within the Faculty of Information Technology at Monash University, Australia. She was promoted to this position effective July 1, 2025, having previously served as a Lecturer (Assistant Professor) since joining Monash in February 2021. Her research bridges the gap between theory and practical applications of program analysis and formal methods in evaluating traditional and AI-assisted software systems. Dr. Du's educational background includes: PhD from Nanyang Technological University (2020) Bachelor's degree from Fudan University (2014) Dr. Du specializes in software engineering, artificial intelligence, and cybersecurity , with particular focus on SE4AI (Software Engineering for AI), software analysis and testing . Her research has made significant contributions to the security and quality assurance of intelligent software systems, especially intelligent software engineering tools. She is best known for her work on Devign , BigCodeBench , DeepStellar , and SimPy , which have advanced the fields of code generation, program analysis, and AI security. Her approach consistently bridges theoretical foundations with practical applications to improve software quality and security. Dr. Du's recent publications demonstrate a strong focus on the intersection of software engineering and AI, particularly examining how large language models interact with source code. Her work addresses critical challenges in code generation efficiency, security vulnerabilities in AI-assisted development, and fairness in AI systems. She has made significant contributions to benchmarking frameworks like BigCodeBench and has pioneered research on watermarking techniques to protect code datasets from misuse by neural code completion models. Dr. Du has received numerous prestigious awards and recognitions: 2024 Google Research Scholar Award in Software Engineering ACM SIGSOFT Distinguished Paper Award (ISSTA'24) ICLR Oral presentation (2025) 2024 FIT Dean's Early Career Researcher of the Year Award Multiple FIT ECR Seed Grants (2021-2023) Dr. Du actively mentors PhD students, with notable successes including Terry (2024-2025 IBM PhD Fellowship Award recipient) and Zhensu (2024 Bytedance Scholarship Award recipient). She is currently seeking self-motivated PhD students with strong programming skills and relevant research experience, offering full scholarship support. Her research has been supported by multiple grants including the Google Research Scholar Program award and several FIT ECR Seed Grants that have enabled her team to pursue innovative research in software security and AI-assisted development. Dr. Du leads a research group focused on intelligent software systems security and quality assurance. Her team has developed several influential tools and benchmarks including Devign, BigCodeBench, DeepStellar, and SimPy. These resources have become important assets for researchers and practitioners working at the intersection of software engineering and artificial intelligence, particularly in the areas of code generation, program analysis, and security testing of AI systems.