جستجوی استاد راهنمای دانشگاهی

Yang Liمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +۵ مورد دیگر

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

• Scientific Visualization
• Computer Graphics
• Visual Analytics
• +۵ مورد دیگر

Christoph Müller is a Doctoral Researcher at the Visualization Research Center (VISUS) at the University of Stuttgart, working within the Weiskopf Group and Sedlmair Group. With over 19 years of research experience documented through publications from 2006 to 2025, he has established himself as a significant contributor to the scientific visualization community. Dr. Müller's research spans multiple domains of visualization including scientific visualization, computer graphics, visual analytics, cybersecurity visualization, molecular graphics, and energy-efficient visualization techniques. His recent work demonstrates a strong focus on practical applications, particularly in cybersecurity analysis through projects like AlertSets for exploratory analysis of cybersecurity alerts and VITALflow for network traffic analysis. He has also made important contributions to understanding and reducing the energy consumption of visualization systems, with recent papers on foveated volume visualization and comprehensive energy measurement approaches. His publication record shows consistent contributions to top visualization venues, with recent work appearing in IEEE Transactions on Visualization and Computer Graphics, Journal of Visualization, and proceedings of major conferences including IEEE VIS, Eurographics, and VizSec. His research often involves interdisciplinary collaboration, particularly with cybersecurity experts and domain scientists. Active research in cybersecurity visualization for security operations Pioneering work on energy measurement and reduction in visualization systems Contributions to scientific visualization for cosmic evolution and manufacturing Development of visualization frameworks like MegaMol Exploration of novel platforms including gaming consoles for scientific visualization Dr. Müller's work bridges theoretical visualization research with practical applications, making significant contributions to both the academic community and real-world visualization challenges across multiple domains.

Maximilian Freyenfeldمشاهده پروفایل

استاد

• Digital Tax Consulting
• Cryptocurrency Taxation
• AI in Tax Consulting
• +۶ مورد دیگر

Professor Dr. Maximilian Freyenfeld serves as Professor of Tax Consulting and Digitalization at the Technical University of Applied Sciences Augsburg (THA), School of Business since August 2023. He is based in room W 2.22 and can be reached at +49 821 5586-2904 or maximilian.freyenfeld@tha.de. His academic journey includes law studies at the University of Augsburg (2011-2015), legal traineeship (2015-2017), doctoral studies at the University of Augsburg (2015-2020), and an LL.M. in Business and Tax Law from Ruhr University Bochum (2020-2022). Professor Freyenfeld specializes in the digital transformation of tax consulting, with research focusing on cryptocurrency taxation, AI applications in tax practice, tax procedural law in digital contexts, real estate tax law, and corporate taxation. His work bridges theoretical tax law with practical consulting challenges in the digital age. His recent publications and presentations reveal a strong trend toward examining how emerging technologies like blockchain, AI, and virtual environments impact traditional tax frameworks. He has published extensively on cryptocurrency taxation and AI applications in tax consulting in leading tax journals. Specialist Lawyer for Tax Law (awarded 2022) Regular speaker at major tax conferences including Swiss Future Forum Legal Tech and Tax Technology Conference in Frankfurt Active participant in professional discourse on digital transformation of tax profession Professor Freyenfeld maintains an active dual career as both academic and practitioner. He supervises Bachelor's and Master's theses while also serving as partner at ALPHYN Rechtsanwälte since 2024 and member of the management board of Cryptolight GmbH since 2023. His YouTube channel 'Digitaxation' demonstrates his commitment to digital knowledge dissemination in tax law.

• Competition Law
• Antitrust Regulation
• Digital Markets
• +۲ مورد دیگر

Rupprecht Podszun is a Professor at the Faculty of Law, Heinrich Heine University Düsseldorf, specializing in competition law and digital market regulation. His research examines the legal frameworks governing digital platforms, with particular expertise in the EU's Digital Markets Act (DMA), antitrust enforcement in technology sectors, and the reform of competition policy for digital ecosystems. Primary research domains include: Digital market regulation and platform governance frameworks Abuse of dominance doctrines in technology markets Implementation challenges for the Digital Markets Act Intersection of competition law with data governance and AI Comparative analysis of international digital regulation approaches His recent publications demonstrate a strong focus on reforming competition law for digital markets, with articles analyzing DMA enforcement mechanisms, redefining market dominance in platform economies, and addressing regulatory gaps in emerging technologies. The work shows consistent thematic development from theoretical foundations of competition to practical implementation challenges in digital regulation. Professor Podszun actively contributes to policy development through expert consultations with the German Parliament and European regulatory bodies, particularly regarding digital market legislation and antitrust reform. His research collaborations span multiple international institutions including the Max Planck Institute, Yale University, and University of Cambridge.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

Xin Xiaمشاهده پروفایل

استاد

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +۵ مورد دیگر

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

Xiao Yuمشاهده پروفایل

پژوهشگر

• LLMs Data Governance and Evaluation
• Intelligent Software Engineering
• Software Security and Reliability
• +۲ مورد دیگر

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.

Zhiyuan Wanمشاهده پروفایل

دانشیار

• Software Engineering
• Software Security
• Programming Languages

Zhiyuan Wan is an Associate Professor in the College of Computer Science and Technology at Zhejiang University, China. His academic career spans multiple prestigious institutions across North America and Asia, with a focus on advancing software engineering practices through empirical research and tool development. Dr. Wan's educational background includes: Ph.D. in Computer Science from Zhejiang University (2014) His postdoctoral journey featured positions at: University of British Columbia, Canada (2019-2020) Singapore Management University (2018) Zhejiang University (2016-2020) Lehigh University, United States (2014-2015) Dr. Wan's research program centers on empirical software engineering with particular expertise in blockchain technologies and software security. His work bridges theoretical insights with practical tool development, focusing on: Smart contract security and vulnerabilities in cryptocurrency ecosystems Code search and recommendation systems for developer productivity Empirical studies of developer practices and challenges Impact of machine learning on software development workflows His approach combines rigorous empirical methods with practical tool building to address real-world challenges faced by software practitioners. Analysis of Dr. Wan's recent publications reveals a strategic evolution toward blockchain security research, beginning around 2020 with studies on smart contract security and expanding to cover NFT ecosystems, Solana blockchain transactions, and cross-chain vulnerabilities. His work consistently applies empirical methods to uncover practical insights while developing tools that directly address identified challenges in software development. Dr. Wan actively contributes to the software engineering community through service on program committees for major conferences including ASE, ICSE, ESEC/FSE, and ISSTA. His academic leadership extends to mentoring relationships with students and collaborators across international institutions, though specific advisees are not documented in the provided materials.

Christoph Treudeمشاهده پروفایل

دانشیار

• Software Engineering
• Empirical Studies
• Natural Language Processing in Software Development
• +۵ مورد دیگر

Christoph Treude is an Associate Professor of Computer Science at Singapore Management University. His academic journey includes senior lecturer positions at the University of Melbourne and the University of Adelaide, and postdoctoral research at McGill University, the University of São Paulo, and the Federal University of Rio Grande do Norte. Treude's research focuses on improving software quality and developer efficiency through better access to relevant information. His methodology combines empirical studies with tool development that considers natural language artifacts in software repositories. His work spans several key areas of software engineering: Empirical studies of developer behavior and practices Integration of large language models in software development Human-AI collaboration frameworks Software documentation quality and maintenance Reproducibility in scientific software Security practices in code reviews His recent publications demonstrate a strong focus on the intersection of artificial intelligence and software engineering, examining how AI technologies can enhance developer productivity while maintaining code quality and security. His research often involves large-scale empirical studies across various software ecosystems and developer communities. Treude has received significant recognition for his contributions: ARC Discovery Early Career Research Award (2018-2020) Four best paper awards, including two ACM SIGSOFT Distinguished Paper Awards He serves on the Editorial Boards of IEEE Transactions on Software Engineering and Springer's Empirical Software Engineering journal, and holds the role of Open Science Editor for Elsevier's Journal of Systems and Software. Treude has chaired major conferences including ICSME 2020, ICPC 2023, and TechDebt 2023, and regularly participates in software engineering conference program committees. His research has been funded by industry leaders including Google, Facebook, and DST, and he has authored over 150 scientific articles with more than 250 co-authors.

Maxime Lamotheمشاهده پروفایل

استادیار

• Empirical software engineering
• Mining software repositories
• Software APIs
• +۵ مورد دیگر

Maxime Lamothe is an assistant professor at Polytechnique Montreal specializing in empirical software engineering and mining software repositories. His research focuses on software APIs, build systems, and the intersection of AI and software engineering. Previously, he was a postdoctoral researcher at the University of Waterloo's Software REBELs Lab under Prof. Shane McIntosh. Dr. Lamothe's educational background includes: Ph.D. in Software Engineering from Concordia University (2020) M.Eng from Concordia University (2017) B.Eng from McGill University (2013) His research interests center around empirical studies of software engineering practices, with particular focus on API design and evolution, software build systems, and performance analysis. Dr. Lamothe investigates how developers interact with APIs, how build systems operate in practice, and how AI techniques can enhance software engineering processes while maintaining human oversight of critical decisions. Dr. Lamothe's publication record shows a consistent focus on empirical approaches to understanding software engineering practices. His work frequently examines API usage patterns, code review processes, and continuous integration systems. A notable trend is his growing interest in applying AI techniques to software engineering challenges while maintaining empirical validation of proposed solutions through rigorous case studies and longitudinal analyses. Dr. Lamothe actively serves the academic community as a reviewer for top journals including Transactions on Software Engineering (TSE), Empirical Software Engineering (EMSE), and Journal of Systems and Software (JSS). He has served on program committees for major conferences including ASE, ICSE, ESEC/FSE, MSR, and SANER across multiple years, with particular involvement in the NIER Track, Research Papers track, and Tool Demonstration tracks. Currently seeking Masters and Ph.D. students, Dr. Lamothe leads research at the intersection of traditional software engineering practices and emerging AI techniques. His work combines rigorous empirical methods with practical applications to solve real challenges in software development, with implications for improving API design, enhancing code review processes, optimizing build systems, and developing trustworthy AI-assisted software engineering tools.

Simone Scalabrinoمشاهده پروفایل

استادیار

• Software Quality
• Software Testing
• Software Security
• +۲ مورد دیگر

Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.

Qing Huangمشاهده پروفایل

دانشیار

• Software Engineering
• Knowledge Graphs
• Human-Computer Interaction
• +۵ مورد دیگر

Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.

• Management Accounting
• Financial Controlling
• Sustainability Accounting
• +۴ مورد دیگر

Christoph Binder is a Professor of Management Accounting and Controlling at ESB Business School, Reutlingen University. He serves as Head of Career Center & Alumni, Programme Director for MA European Management Studies, and Liaison Officer for the German National Academic Foundation. Previously, he served as Dean of ESB Business School from 2019-2024. German-French dual diploma (IPBS) from ESB Reutlingen (1996-2000) Semester at University of Texas at Austin (1997) Management consultant at McKinsey & Company (2000-2008) Doctorate in Management Accounting & Control at European Business School (2004-2006) Professor at Reutlingen University since 2008 Binder's research focuses on management accounting systems with particular emphasis on sustainability controlling, carbon accounting, and geopolitical risk management. His work bridges traditional accounting practices with emerging challenges in risk assessment and sustainable business operations. He has developed frameworks for structured risk identification in SMEs and value-oriented performance measurement systems. His publication record shows consistent contributions to controlling literature since 2014, with increasing focus on geopolitical risks (2023-2025) and sustainability accounting. The articles demonstrate evolution from foundational controlling concepts toward contemporary challenges in risk management and sustainable business practices, reflecting growing industry relevance of these topics. Member of Board of Trustees, International Controller Association Member of Advisory Board, Dieter von Holtzbrinck Stiftung Binder maintains active industry engagement through advisory roles while leading academic programs. His consulting background informs practical applications of controlling frameworks, particularly in energy, logistics, and telecommunications sectors. Current initiatives focus on integrating sustainability metrics into traditional controlling systems and developing risk management tools for volatile geopolitical environments.