جستجوی استاد راهنمای دانشگاهی

Jifeng Xuanمشاهده پروفایل

استاد

• Software Analysis and Testing
• Software Testing
• Software Debugging
• +۲ مورد دیگر

Jifeng Xuan is a Professor and Deputy Dean at the School of Computer Science, Wuhan University, China. He leads the CSTAR (Centre of Software Testing, Analysis and Reliability) research group and has made significant contributions to software engineering, particularly in software testing, debugging, and analysis. Dr. Xuan completed his bachelor's degree and PhD at OSCAR Lab, School of Software, Dalian University of Technology, China, under the supervision of Prof. He Jiang. He also conducted postdoctoral research at SPIRALS Team in INRIA Lille - Nord Europe, France, working with Dr. Martin Monperrus and Prof. Lionel Seinturier. His research focuses on Software Analysis and Testing , with specific interests in software testing and debugging, software data analysis, and search-based software engineering. Dr. Xuan's work bridges theoretical foundations with practical applications, addressing real-world challenges in software quality assurance. His research has led to numerous publications in top-tier software engineering venues and has influenced both academic research and industrial practices. Dr. Xuan's recent publications demonstrate a strong focus on innovative approaches to software testing, debugging, and analysis. His work spans multiple subfields including log analysis, fuzz testing, bug triage, and automated program repair. He has made significant contributions to understanding software crashes, improving bug report quality, and developing techniques for more effective software testing and analysis. 2025 ACM SIGSOFT Distinguished Paper Award 2025 IEEE TCSE Distinguished Paper Award 2024 CCF NASAC Youth Software Innovation Award 2018 ACM SIGSOFT Distinguished Paper Award 2015 Young Talent Development Program of CAST and CCF 2015 Luojia Young Scholar Program of Wuhan University 2014 Outstanding Doctoral Dissertation of CCF Dr. Xuan actively mentors PhD and master's students through his CSTAR research group at Wuhan University. He has served on numerous program committees for major software engineering conferences including ICSE, FSE, ASE, and ISSTA. His editorial service includes roles on the editorial boards of Empirical Software Engineering (EMSE), PLOS One, and Frontiers of Computer Science. Dr. Xuan has also organized several workshops and conferences, demonstrating his leadership in the software engineering community. As founder of CSTAR (Centre of Software Testing, Analysis and Reliability), Dr. Xuan leads a research team focused on advancing the state of the art in software quality assurance. The group's work spans theoretical foundations and practical applications, with strong connections to industry challenges. CSTAR has established collaborations with both domestic and international research institutions, contributing to a vibrant research ecosystem in software engineering.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

Mayur Naikمشاهده پروفایل

استاد

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +۵ مورد دیگر

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

Marian-Andrei Rizoiuمشاهده پروفایل

دانشیار

• Behavioral Data Science
• Misinformation and Disinformation Analysis
• Information Operations
• +۱۲ مورد دیگر

Marian-Andrei Rizoiu is an Associate Professor leading the Behavioral Data Science lab at the University of Technology Sydney's Data Science Institute, Faculty of Engineering and Information Technology. He holds concurrent appointments as an Honorary Lecturer at Australian National University and Honorary Research Scientist at Data61. Previously, he has held visiting professor positions at Imperial College London, Jean Monnet University, and Max Planck Institute for Software Systems. Associate Professor in Behavioral Data Science, UTS Data Science Institute (Jan 2024 - present) Senior Lecturer in Behavioral Data Science, UTS Data Science Institute (Jul 2021 - Jan 2024) Lecturer in Computer Science, UTS Faculty of Engineering and Information Technology (Feb 2019 - Jul 2021) Dr. Rizoiu's research focuses on interdisciplinary work crossing computer and social sciences, blending psycholinguistics, digital communication, and stochastic modeling to understand human attention dynamics online, the emergence of influence, and opinion polarization. His key contributions include developing theoretical models for online information diffusion that can account for complex social phenomena, and building skill-based real-time occupation transition recommender systems that link social media-predicted personality profiles with occupation skill requirements. His research outputs reveal strong trends in misinformation detection, social influence measurement, and online radicalization pathways. Recent publications demonstrate sophisticated modeling approaches including state space models for early misinformation prediction, multivariate Hawkes processes for analyzing partially interval-censored data, and ideology detection pipelines. His work spans computational social science, machine learning, and practical applications for countering harmful online content. Excellence Award and Academic of the Year at the 2023 Australian Defence Industry Awards ADMA'22 Best Application Paper Dr. Rizoiu has successfully secured over $2.9 million in research funding from selective funders including Meta Research, Defence Science and Technology Group, Department of Home Affairs, and Defence Innovation Network. He has supervised 3 PhD students to completion and more than 10 Honours students, most achieving High Distinction. His research has been applied in real-world contexts including serving as an expert for NSW government's Defamation Law Reform and providing evidence for Australian Federal Senate inquiry into media diversity. He leads the Behavioral Data Science lab which focuses on modeling human behavior in online environments, with particular emphasis on mis- and disinformation detection and labor market analysis. The lab has developed tools like TRACK, UTS OPEN's software for recommending personalized learning pathways, used by over 750 students and professionals.

David Corsarمشاهده پروفایل

مدرس ارشد

• Knowledge Representation
• Knowledge Reasoning
• Semantic Web
• +۱۱ مورد دیگر

Dr. David Corsar is a Principal Lecturer in Informatics at Robert Gordon University's School of Computing, Engineering and Technology. He is an active researcher in Artificial Intelligence with a focus on knowledge representation, reasoning methods, and explainable AI systems. His academic journey began with a Bachelor's Degree in Computing Science (Artificial Intelligence) from the University of Aberdeen (2000-2004), followed by a PhD in Computing Science (2004-2009) where he researched knowledge reuse through ontology mapping and ontology based knowledge acquisition. After his doctoral studies, he worked as a Research Fellow at the University of Aberdeen in interdisciplinary teams before joining RGU. Dr. Corsar's research interests span across multiple AI domains: Knowledge Representation and Reasoning Semantic Web and Knowledge Graphs Provenance in AI systems Explainable Artificial Intelligence (XAI) Case-Based Reasoning Integration of machine learning with symbolic AI Transparency and trustworthiness of AI systems His recent publication record (2023-2025) shows a strong emphasis on developing practical XAI solutions, particularly through the iSee platform which addresses the challenge of personalized explanations for diverse AI users. His work bridges theoretical AI research with practical applications in healthcare, regulatory compliance, and industrial domains like Oil and Gas decommissioning. Dr. Corsar has received research funding for multiple projects: iSee: Intelligent Sharing of Explanation Experience (2021-2024) Prophecy Project: Intelligent Decommissioning Materials Management (2019-2020) Intelligent System for assisted medical diagnosis for CARDIOvascular diseases (2020-2021) Building resilience in Health and Social Care students during Covid-19 (2020) As an educator, Dr. Corsar coordinates several technical modules including Native Android App Development, Internet of Things, Data Structures and Algorithms, and Knowledge Modelling and Reasoning. He is actively involved in supervising PhD students working on cutting-edge AI research topics and welcomes Honours and Masters projects aligned with his research areas. Dr. Corsar is a member of the Artificial Intelligence & Reasoning Research Group at RGU, contributing to the university's research theme 'Living in a Digital World.' His work on the W3C Provenance Working Group has helped develop standards for provenance information interchange on the Web, demonstrating his commitment to advancing foundational aspects of AI systems.

• Blockchain and smart contracts
• Open API technology and security
• Social big data analysis
• +۳ مورد دیگر

陳恭 is a Professor at the Department of Information Management, College of Business, National Chengchi University. With expertise spanning blockchain technology, information security, and software engineering, he has established himself as a leading academic in Taiwan's fintech research community. His work bridges theoretical computer science with practical business applications, particularly in digital finance and secure information systems. Dr. 陳恭 earned his PhD in Computer Science from Yale University (1989-1994), following Bachelor's and Master's degrees from National Taiwan University. His academic journey at National Chengchi University spans over two decades, progressing from Associate Professor to his current position as Professor, with significant administrative roles including Department Chair and Director of the Computer Center. Professor 陳恭's research primarily focuses on blockchain and smart contracts, Open API technology and security, social big data analysis, and programming languages. His work demonstrates a clear evolution from foundational programming language research to cutting-edge blockchain applications. He has made significant contributions to secure multi-party computation, aspect-oriented programming, and blockchain-based systems, with publications in top journals and conferences across computer science, public administration, and business innovation. An analysis of Professor 陳恭's recent publications (2016-2024) reveals a strong concentration on blockchain technology and its financial applications, particularly in smart contracts, consensus algorithms, and secure data dissemination. His research increasingly integrates with IoT systems and demonstrates practical applications in financial technology, showing a clear trajectory from theoretical foundations to real-world business solutions. His notable achievements include: Senior Excellent Teacher (20 years) from National Chengchi University Multiple Distinguished Professor appointments Special Outstanding Talent Award from the National Science Council Multiple Internationalization Excellent Research Awards Professor 陳恭 has secured numerous research grants from both governmental agencies like the Ministry of Science and Technology and private sector organizations including financial institutions and technology companies. His projects often focus on blockchain applications, fintech innovation, and information security, demonstrating strong industry-academia collaboration. He is actively involved in establishing National Chengchi University as a leading center for blockchain research in Taiwan, contributing to research centers focused on fintech innovation and digital transformation. His work has fostered significant collaborations between academia and industry in the rapidly evolving field of digital finance.

Elizabeth Iehlمشاهده پروفایل

مدرس

• Computer Science Education
• Educational Technology
• Crowdsourcing
• +۲ مورد دیگر

Elizabeth Iehl serves as an Adjunct Instructor in the Department of Computer Science at Northeastern Illinois University, teaching undergraduate courses including CS-100 Computers and Society and CS-109 First Year Experience. Her office is located in Room LWH 3050 with office hours on Tuesdays and Thursdays from 3:00-4:00 p.m. or by appointment. Education M.S. from Northeastern Illinois University Research Interests Her work centers on Computer Science Education, emphasizing skill development in problem-solving through emerging technologies. She integrates applications software programs and crowdsourcing methodologies to enhance student learning experiences, focusing on practical technological adoption in educational contexts. Scientific Awards No scientific awards were listed in the provided information. Advising and Grants No details regarding student advising, research grants, or laboratory affiliations were mentioned.

Masud Rahmanمشاهده پروفایل

دانشیار

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

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

Henner Gimpelمشاهده پروفایل

استاد

• Digital Health and Wellbeing
• Hybrid, artificial and collective intelligence
• Smart Sustainability and Digital Ethics
• +۵ مورد دیگر

Professor Henner Gimpel holds the Chair of Digital Management at the University of Hohenheim, where he serves as Vice Dean for Research and Knowledge Transfer at the Faculty of Business, Economics and Social Sciences. He leads the Digital Business Management program, directs the FIM Research Institute for Information Management, and serves as academic director of the Digital Leadership Academy. Additionally, he holds a leading position at Fraunhofer FIT's Business Informatics branch. Professor Gimpel's research focuses on the analysis and design of digitalization, particularly examining socio-technical information systems where people, information, and digital technologies interact. His work emphasizes human-centered and economically effective development of information systems, with special attention to individual acceptance and use of digital technologies across various roles including consumers, customers, patients, employees, and users. His key research areas include Digital Health and Wellbeing, Hybrid and Artificial Intelligence, Smart Sustainability and Digital Ethics, and IT adoption and usage. His research methodology combines theoretically grounded empirical studies with design-oriented and model-theoretical approaches, bridging business administration (particularly business informatics, marketing, and organization) with behavioral economics, psychology, and computer science. Professor Gimpel has international research experience from stays at IBM T.J. Watson Research Center in New York and the Center for Interuniversity Research and Analysis on Organizations in Montréal. Current Research Projects: Influence of ICT on Collective Intelligence of Human Groups (DFG) ABBA: AI for Business | Business for AI (BMBF) PALLADiUM: Palliative Care as a Digital Working World (bidt) Hospital 4.0: Lean digital-supported logistics in hospitals (BMBF) ForDigitHealth: Research network for healthy digital technology use (StMWK) PräDiTec: Prevention for safe and healthy work with digital technologies (BMBF) DeLLFi: Integrating digitalization in teaching, learning and research Professor Gimpel actively supports numerous companies across various industries in developing innovative solutions for digital transformation challenges, with clients including Allianz, BMW, Carl Zeiss, Deutsche Bank, IBM, Microsoft, Siemens, and multiple university hospitals. Prior to his academic career, he gained five years of practical experience as a management consultant at McKinsey & Company.

Panagiotis Manoliosمشاهده پروفایل

استاد

• Formal Methods
• Program Verification
• Theorem Proving
• +۵ مورد دیگر

Panagiotis (Pete) Manolios is a Professor in the Department of Computer Science within Northeastern University's College of Engineering in Boston. He leads the Northeastern University Formal Methods (NUFM) research group and maintains an active research program with multiple current PhD students. His primary affiliation is with the College of Computer Science (CCS) at Northeastern University, where he holds a tenured faculty position. Manolios' research focuses on formal methods with particular emphasis on program verification, theorem proving, and safety analysis of systems. His work spans several key areas including floating-point program analysis, resource-aware program verification, protocol verification, and safety-critical systems. He has developed significant tools and methodologies such as ACL2s (a powerful theorem prover) and pioneered approaches for analyzing numeric stability in compiler optimizations. His recent publications demonstrate substantial activity in formal verification, with particular focus on numeric stability analysis, invariant discovery through gamification, and model-based safety analysis of complex system architectures. These works reflect a consistent research trajectory toward making formal verification more practical and applicable to real-world systems, especially those with safety-critical requirements. Manolios has received recognition through multiple NSF grants including the SaTC: CORE: Medium collaborative project on bridging the gap between protocol design and implementation. His work on confidentiality and integrity of deep neural networks represents cutting-edge research at the intersection of formal methods and AI security. He has successfully mentored numerous PhD students who have gone on to positions at major technology companies including Google, Facebook, Intel, and MathWorks. His students' dissertations cover diverse topics within formal methods, from rank-polymorphic programming languages to resource-aware program analysis. Manolios directs several significant research projects including ACL2s (a powerful theorem prover), CID: Confidentiality and Integrity of Deep Neural Networks, Compilation-Dependent Security Properties of Software, and Platform Dependencies of Floating-Point Programs. These projects address fundamental challenges in making formal verification more practical and applicable to real-world systems.