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Prof. Dr. Andreas Dellnitz is a Professor of Quantitative Methods at Leibniz University of Applied Sciences, specializing in Operations Research and Computational Social Sciences. He also serves as Head of the Sustainable and Digital Transformation program and has been Vice President for Research since 2025. Education Dr. Dellnitz holds the following academic qualifications: Master of Science (Business Information Systems), FernUniversität in Hagen (2019) Doctor of Economics (summa cum laude), FernUniversität in Hagen (2015) Diploma in Economics (honors), Ruhr University Bochum (2010) Research Interests Dr. Dellnitz's research centers on multi-criteria optimization and (eco-)efficiency analysis , with applications in sustainability. His methodological focus includes linear and combinatorial optimization, algorithmic geometry, and entropy-driven analytics for social and power networks. He investigates sustainability-oriented production planning, energy systems, and social network structures. Publication Trends His recent work (2021-2025) integrates Data Envelopment Analysis (DEA) with machine learning for cost estimation and eco-efficiency assessment. Key applications include sustainable production planning under energy pricing schemes, gas-to-power demand response, and battery storage systems. His research bridges theoretical operations research with industrial sustainability challenges. Scientific Awards No scientific awards were mentioned in the available information. Grants and Advising Dr. Dellnitz has led third-party funded projects such as MaXFab I (2016-2018) and MaXFab II (2019-2021) on energy-flexible factories. He supports student projects and participates in study committees, emphasizing practical applications of operations research in business and sustainability.

• Operations Research
• Computational Social Sciences
• Multi-criteria Optimization
• +4 more

Dr. Salam Traboulsi is a Researcher at Stuttgart University of Applied Sciences (HFT Stuttgart), affiliated with the Competence Center for Digitalization in Research, Teaching & Economics since 2019. She holds a PhD in Computer Science from the University of Toulouse, France (2008). Her work bridges technology and urban innovation, with a focus on developing scalable solutions for modern cities. Research Focus: Dr. Traboulsi specializes in: Smart City ecosystems integrating IoT and 5G Precision technologies for urban positioning and navigation Data management frameworks for large-scale sensor networks Open-source IoT platforms for building efficiency and environmental monitoring Cloud and grid computing infrastructures Key Projects: She leads/contributes to: iCity 2: UDigiT4iCity – Developing urban digital twins using IoT building data and 5G sensor networks iCity 1 – Foundational research on intelligent urban infrastructure systems Publication Trends: Her recent work (2020-2024) emphasizes 5G-enabled urban solutions, including fleet management optimization, indoor positioning systems, and IoT analytics for smart buildings. Earlier research (2005-2013) focused on distributed computing, storage virtualization, and information retrieval systems, demonstrating consistent expertise in large-scale data infrastructure. Academic Engagement: She serves as a scientific reviewer for journals and conferences and teaches in the surveying study area at HFT Stuttgart.

• Smart City
• 5G Applications
• Indoor Positioning Systems
• +2 more

Matthias Kaschube is a Professor in the Faculty of Computer Science and Mathematics at Goethe University Frankfurt and a Senior Fellow at the Frankfurt Institute for Advanced Studies (FIAS). His research group focuses on understanding how the brain forms efficient representations of sensory environments and internal states through dynamic neural processes. He maintains active collaborations with leading neuroscience institutions including the University of Minnesota, Max Planck Florida Institute for Neuroscience, and Technion. Dr. Kaschube completed his physics studies at Goethe University Frankfurt and Georg-August-University Göttingen, graduating in 2000 and earning his doctoral degree in physics in 2005. His doctoral work was conducted at the Max Planck Institute for Dynamics and Self-Organization under Fred Wolf and Theo Geisel. He then held a Bernstein Fellowship before becoming a Theory Fellow at Princeton University's Lewis Sigler Institute from 2006-2011. In 2011, he joined Goethe University as Professor for Computational Neuroscience. His research spans four primary areas: the developmental emergence of cortical representations, flexible representations underlying learning and creativity, cognitive maps and representational spaces, and analysis methods for neural data. His group combines dynamic models of neural circuit function with neural data modeling techniques in close collaboration with experimental groups, creating an interdisciplinary interface between computer science, physics, biology, and AI. Notably, his work has revealed highly structured cortical networks prior to sensory experience that share similar architectural principles across sensory and association cortices. Analysis of his recent publications shows a consistent focus on understanding how endogenous neural activity patterns develop into reliable cortical representations through experience. His work spans multiple scales from molecular and cellular mechanisms to whole-brain functional organization, with particular emphasis on developmental processes in visual and auditory cortices. His methodological contributions include advanced techniques for analyzing chronic imaging data, tracking chromatophores in cuttlefish, and characterizing latent spaces in deep neural networks. Lewis Sigler Theory Fellowship (2006-2011) Bernstein Fellowship (2005) Professor Kaschube actively mentors a large group of PhD students including Lorenzo Butti, Jonas Elpelt, Santiago Galella, Deyue Kong, Maurycy Miekus, Ana Pamela Osuna Vargas, and Sigrid Trägenap. His research has been supported by multiple grants including NIH grants EY011488 and EY026273, Bernstein Focus Neurotechnology grant 01GQ0840, and BMBF project D-USA-Verbund: SpontVision. His group currently pursues three major research directions: the origin of distributed modular activity in neocortex, quantitative growth models for cuttlefish based on physical models, and the role of self-organization in linking endogenous cortical networks to sensory input.

• Cortex Functional Organisation and Development
• Neural Coding and Dynamics
• Neural Data Analysis
• +7 more

Claire Lelarge is a Professor of Economics at Paris-Saclay University and a Research Fellow at the Centre for Economic Policy Research (CEPR). Prior professional roles include: French Statistical Institute French Ministry for Industry Organisation for Economic Co-operation and Development (OECD) CREST (Insee) French Central Bank Her research specializes in firms' dynamics and organizational strategies, with core focus areas: Labour Economics (wage setting, minimum wage effects) Organizational Economics (internal firm structure, communication costs) Industrial Organization (technology adoption, firm responses) International Trade (input sourcing, productivity implications) Analysis of her 2015-2023 publications reveals consistent examination of how technological change, trade policies, and infrastructure investments reshape firm organization. Key themes include minimum wage impacts on productivity, robotics-driven labor market shifts, and communication technology effects on multi-plant firms, all analyzed through microeconomic frameworks and large-scale firm-level datasets to derive policy-relevant insights.

• Firms' Dynamics
• Organizational Strategies
• Labour Economics
• +2 more

Marco Francesconi serves as Professor of Economics at the University of Essex since 2004, concurrently maintaining a Research Fellowship at the university's Institute for Social and Economic Research (ISER). His international profile includes a Visiting Professorship at the Norwegian School of Economics since 2017 and research fellowships with CESifo, CEPR, and Torino's Centre for Household Income Labour and Demographic economics. This multi-institutional engagement facilitates cross-national labor economics research with European policy institutions. His educational foundation includes a PhD in Economics from New York University, establishing the methodological rigor evident in his empirical work. This training underpins his contributions to labor economics through sophisticated econometric approaches to complex socioeconomic questions. Francesconi's research program centers on labor economics with deep specialization in family economics, sociogenetics, and inequality measurement. His groundbreaking work on intergenerational wealth transfer revealed unprecedented patterns of unequal bequests, while current projects investigate tourism's localized labor market effects, business income measurement flaws in inequality statistics, and employment protection legislation impacts. This research consistently bridges theoretical frameworks with policy-relevant empirical analysis using large-scale demographic datasets. Recent publications (2024-2025) demonstrate thematic continuity in labor market analysis while expanding methodological scope. The tourism-labor market study examines spatial economic dynamics, the business income paper addresses fundamental measurement issues in inequality research, and the employment protection work analyzes institutional determinants of job security. Collectively, these contributions reflect his focus on empirically grounded policy analysis with implications for wealth distribution, tax systems, and labor market regulations. As Associate Editor of the European Economic Review and Co-editor of Labour Economics, Francesconi shapes scholarly discourse in his field. His editorial leadership complements active research fellowships that position him at the intersection of academic economics and European policy development, particularly through CESifo's labor and demographic economics network. His advising approach emphasizes methodological rigor and policy relevance, though specific student counts aren't documented. Current research is supported through institutional affiliations rather than individual grants, leveraging university resources and network collaborations for data-intensive projects. Francesconi's work occurs within Essex's Institute for Social and Economic Research ecosystem, which provides access to longitudinal datasets like the British Household Panel Survey. This infrastructure enables his signature analysis of intergenerational socioeconomic patterns while facilitating international collaborations through his Norwegian and Italian affiliations.

• Labor Economics
• Family Economics
• Sociogenetics
• +4 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.

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

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

• Software Engineering
• Self-Adaptive Systems
• Edge Computing
• +5 more

Dr. Yutian Tang serves as an Assistant Professor (UK Lecturer) and Principal Investigator at the School of Computing Science, University of Glasgow, where he supervises PhD students and leads research in AI-driven software engineering. His academic journey includes a PhD from The Hong Kong Polytechnic University's Department of Computing. His research spans AI+SE integration , particularly focusing on Large Language Models for program analysis, software testing, and Android security. Key areas include: LLM-assisted vulnerability detection and repair Empirical studies of real-world software systems Privacy protection mechanisms Configuration compatibility in mobile applications Smart contract security optimization His publication portfolio shows a clear trajectory toward AI-augmented software engineering , with recent work demonstrating how LLMs can enhance taint analysis, binary code similarity detection, and test generation. This evolution reflects the field's broader shift toward AI integration while maintaining rigorous empirical validation. Award highlights include: Best Industry Paper Award at ISSRE'18 Elevation to IEEE Senior Member (2024) Three Android OS defects confirmed by Google Security Team As an active researcher and community contributor, Tang serves on 40+ program committees including PLDI, ICSE, and FSE. His work receives funding from National Natural Science Foundation of China, Shanghai Science Commission, OpenAI, and Google. Current projects focus on automated bug localization and LLM-based testing frameworks, with recent grants from OpenAI Cybersecurity and Google Cloud programs. He leads research groups investigating Android security and AI-assisted program analysis, collaborating with institutions like Lund University.

• AI+SE
• Large-language Model+SE
• Program Analysis
• +5 more

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +4 more

Daniel R. Meyer is a Professor at the Sandra Rosenbaum School of Social Work and the Institute for Research on Poverty at the University of Wisconsin-Madison. His academic work bridges social work practice with rigorous policy analysis focused on family structure and economic security. Dr. Meyer's research centers on social policies affecting the well-being of lone parent families with children. His work examines custody arrangements, child support systems, and family complexity following divorce or separation. He investigates how legal frameworks and economic conditions shape parenting time, financial support, and children's living arrangements across different family structures. His research often employs sophisticated demographic methods to analyze large-scale datasets tracking family transitions over time. His recent publications reveal significant trends in joint physical custody arrangements both in Europe and the United States, showing how shared parenting time has increased substantially in recent decades. His work on child support compliance examines how social networks influence payment behavior, while other research explores how fathers' incarceration affects mothers' subsequent fertility patterns with new partners. Dr. Meyer's scholarly contributions primarily appear in top-tier demographic and social science journals including Demography and Social Science Research. His research has important implications for family policy, child welfare systems, and legal frameworks governing post-separation parenting.

• Social Policy
• Family Studies
• Poverty Research
• +4 more