جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Advanced Machine Learning
• Conversational AI
• +۶ مورد دیگر

Neena Sathi is a Lecturer in the Department of Information Systems at the Paul Merage School of Business, University of California, Irvine (UCI). She brings over 30 years of industry experience in artificial intelligence, analytics, and automation, and contributes to both academic instruction and applied AI development. Education: MBA, University of Pittsburgh MS in Chemical Engineering, University of Illinois, Chicago MS in Physics, Northwestern University Her research and professional interests center on Artificial Intelligence , Advanced Machine Learning , Conversational AI , and Analytics . She focuses on translating AI prototypes into production systems across industries including healthcare, telecommunications, retail, and public services. Her work emphasizes practical implementation, integration architecture, and cognitive technologies. The selected publications reflect a strong trend in applied AI, spanning venues like AAAI, IEEE, IBM Developer Works, and World of Watson. These works cover subfields such as natural language processing, enterprise AI, cloud integration, and intelligent automation, demonstrating a consistent focus on deploying AI in real-world business and technical environments. Scientific Awards and Certifications: Master Certified Integration Architect (IBM and Open Group) Certified Project Management Professional (PMP) Certifications in Cloud and Cognitive Technologies Neena Sathi has held leadership roles in AI innovation, including as Director at KPMG Lighthouse Labs and Principal at the Applied AI Institute. She develops hands-on training content and interactive solutions for AI and automation topics. While no formal student advising is mentioned, her educational contributions through lectures and training materials indicate a strong pedagogical impact. She is actively involved in AI solution development, bridging academic knowledge with industry application, and drives initiatives in AI integration, digital transformation, and cognitive computing.

Hannes Dattaمشاهده پروفایل

دانشیار

• Econometrics
• Digital Media Consumption
• Music Streaming
• +۴ مورد دیگر

Hannes Datta is an Associate Professor in the Department of Marketing at Tilburg University, Netherlands. He holds dual roles within the Tilburg School of Economics and Management (TiSEM), focusing on advanced econometric models for digital media consumption and retailing. His research leverages large datasets and cloud computing, with notable contributions to understanding music streaming dynamics and marketing mix strategies. Datta has received prestigious awards including the MSI Young Scholar and NWO Veni grant. His career spans roles as Assistant Professor (2013-2018) and Associate Professor (2018-present) at Tilburg University. Teaching highlights include courses on social media research, digital marketing strategies, and data science methodologies. He has been recognized for teaching excellence with awards like Tilburg University's Teacher of the Year (2016). Research interests center on digital media's impact on consumer behavior, with projects exploring Spotify's playlist demand, web data scraping techniques, and cross-national market responses. He advises external organizations such as Stichting Onderhandelingen Thuiskopievergoeding (SONT) and Mobiel.nl. Datta actively engages in media commentary on streaming services' societal impacts and maintains a personal website (hannesdatta.com) with thesis resources (thesis.hannesdatta.com). Key contributions include over 14 peer-reviewed articles, datasets on music streaming and marketing science, and collaborations with institutions like Duke University. His work aligns with UN Sustainable Development Goals, emphasizing innovation and sustainable consumption patterns.

• Artificial Intelligence
• Natural Language Processing
• Knowledge Management
• +۷ مورد دیگر

Jorjeta Jetcheva is an Assistant Professor in the Computer Engineering Department at San José State University, part of the Charles W. Davidson College of Engineering. She brings extensive industry experience from leadership roles at Accenture, Fujitsu Laboratories of America, Itron, and Firetide, where she led innovations in AI, smart grids, and wireless networking. Her research focuses on Artificial Intelligence-based Personal Assistants, Natural Language Processing, and Knowledge Management . Her work bridges human-centered AI and enterprise applications, with emphasis on intelligent agents, knowledge platforms, and ethical AI systems. She explores how AI can enhance user productivity, decision-making, and system security across domains such as healthcare, energy, and customer service. The recent publications highlight a strong trend in Natural Language Processing applied to real-world problems like fake health news detection, agent assist systems, and knowledge graph integration. Her research also spans Smart Grid analytics, cybersecurity, and AI ethics , reflecting a multidisciplinary approach grounded in both theoretical rigor and practical deployment. Scientific Awards: Best Application Paper Award at IEEE Big Data Service 2022 Best Paper Award at SmartGridComm 2014 Top 10 Finalist, Fujitsu Next Generation Product Idea Contest 2016 ('Robo Butler') CSU STEM-NET Faculty Fellow (2023) Advising and Grants: Professor Jetcheva is the Principal Investigator of a $2.5 million NSF S-STEM grant titled 'Empowering Students to Succeed in Engineering and Computer Science'. She actively mentors graduate students, including Master’s student Garima Chaphekar, with whom she co-authored an award-winning paper on fake health news detection. She serves on the SJSU AI Advisory Committee and leads the AI Research Cluster, fostering interdisciplinary collaboration and student success. Labs and Teams: She leads the AI Research Cluster at SJSU, promoting innovation in artificial intelligence across departments. Her work involves close collaboration with students, industry partners, and interdisciplinary teams focused on deploying scalable, ethical AI solutions.

• Agent Technology
• Artificial Intelligence
• Autonomous Driving
• +۹ مورد دیگر

Prof. Dr.-Ing. habil. Dr. hc Sahin Albayrak is a distinguished academic and entrepreneur at the Technical University of Berlin , where he founded and directs the Distributed Artificial Intelligence Laboratory (DAI Laboratory) . He leads the Agent Technologies in Business Applications and Telecommunications research group and serves as founding member of Deutsche Telekom Laboratories (2004) and European Center for ICT (EICT) (2005). As initiator of Connected Living e.V. (2009) and managing director of German-Turkish Advanced Research Center for ICT (2012), he bridges international collaborations. He also founded IOLITE GmbH (2014) and other startups. Research Focus: Agent technology, autonomous driving, smart cities, cyber security, machine learning, and AI applications in energy systems Awards: Federal Cross of Merit (2014), multiple Best Paper Awards Leadership: Director of DAI Laboratory, head of research group at TU Berlin His 20+ recent publications (2022-2025) demonstrate expertise in agent-based architectures , smart mobility solutions , context-aware computing , and AI-driven security systems . Notable trends include integrating large language models into database interfaces, optimizing multi-agent coordination for logistics, and advancing explainable AI through feature attribution frameworks. Scientific Contributions: Recipient of Germany's Bundesverdienstkreuz for German-Turkish cooperation Best Paper Award at Smart Grid Architectures conference

• AI in Journalism
• Information Discovery
• Platform Dynamics
• +۵ مورد دیگر

Nick Hagar is a postdoctoral scholar at Northwestern University working on the Generative AI in the Newsroom Initiative and an incoming assistant professor at the University of Minnesota. His research focuses on collective attention dynamics in digital ecosystems, particularly examining how AI impacts journalism and media landscapes. Current Position: Postdoctoral Scholar at Northwestern University Upcoming Position: Assistant Professor at University of Minnesota Previous Experience: New York Times, Meta, Patreon Education: PhD from Northwestern University, Medill School of Journalism Hagar's research explores how people discover information online, why certain content becomes popular, and how AI systems affect news distribution. His work combines computational methods, data science, and network analysis to study platform dynamics in social media and news ecosystems. He has particular expertise in analyzing TikTok, Reddit, Facebook, and Substack as attention markets. His recent publications demonstrate a strong focus on the intersection of AI and journalism, with particular attention to local news systems, news recommendation algorithms, and how generative AI is transforming newsroom practices. His research shows how platform algorithms often demonstrate 'algorithmic indifference' toward news content, particularly on platforms like TikTok. Featured in Business Insider, The New Yorker, and Nieman Lab Regular contributor to media discussions on AI and journalism Active participant in academic conferences including ICWSM and ICA Hagar has developed several open-source tools including a Substack API, Common Crawl Genealogy project, and Archive Check CLI for collecting website data from various archives. His work bridges academic research with practical journalism applications, focusing on how computational methods can enhance news production and distribution.

Koushik Senمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Formal Methods
• +۳ مورد دیگر

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His academic career spans major contributions to software engineering and programming languages research through active participation in premier conferences including PLDI, ICSE, and ISSTA. His research focuses on Software Engineering , Programming Languages , and Formal Methods , with particular emphasis on developing software tools that enhance programmer productivity and software quality. Key research thrusts include automated test generation , symbolic execution , fuzzing techniques , and program synthesis . His work bridges theoretical foundations with practical tool development for real-world software verification challenges. Analysis of his publication record reveals consistent contributions to automated testing methodologies, with recent work integrating machine learning (particularly large language models) into traditional program analysis techniques. His research shows strong continuity in improving software reliability through innovative input generation and vulnerability detection approaches. As an active academic leader, he has served as General Chair for MAPL (2020), Program Chair for ISSTA (2017), and committee member for numerous top-tier conferences including PLDI, ICSE, and SPLASH across multiple years. His academic advising manifests through collaborative publications with students on topics like test corpus expansion (Bonsai Fuzzing), visualization synthesis (VizSmith), and smart contract auditing (ItyFuzz), though specific student names aren't listed in the source material. His research has been supported through conference participations and likely associated grants given his extensive publication record.

Yiling Louمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Programming Languages
• +۵ مورد دیگر

Yiling Lou is an incoming Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (starting Spring 2026), currently serving as a Pre-tenure Associate Professor at Fudan University. Previously a Postdoctoral Fellow at Purdue University under Prof. Lin Tan, Dr. Lou holds a Ph.D. and B.S. in Computer Science from Peking University supervised by Prof. Lu Zhang and Prof. Dan Hao. Research interests span Software Engineering synergized with Artificial Intelligence and Programming Languages , specifically focusing on LLM4Code, Agent&SE, Vulnerability Detection, and Software Testing/Debugging. Current projects include AgentIssue-Bench for agent system maintenance and INFERROI for enhancing static analysis with LLMs. Research trends show increasing integration of LLMs with traditional SE techniques, particularly in code generation (ClassEval, CodeGen4Libs), debugging (interactive runtime comparison), and vulnerability detection. Recent work emphasizes practical applications in agent systems and resource leak detection. ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) IEEE TCSE Distinguished Paper Award (ICSME 2021) Advises a large research group including 7 Ph.D. and 8 MS students at Fudan University, actively recruiting for UIUC starting Fall 2026. Leads the LLM4Code workshop series and serves on numerous program committees including ICSE, ASE, and FSE. Currently organizing research on Code Agents, Code LLMs, and AI&Security with strong industry relevance. Coordinates the Siebel School research group at UIUC focusing on the intersection of AI and Software Engineering, with particular emphasis on developing robust agent systems for code maintenance and security applications.

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.

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.

• Software Evolution
• Empirical Software Engineering
• Domain-Specific Languages
• +۲ مورد دیگر

Thomas Degueule is a researcher at CNRS (Centre National de la Recherche Scientifique) in France, actively contributing to software engineering research since 2015. He serves on program committees for major conferences including ASE, ICSE, and SLE, with primary research interests in Software Evolution, Empirical Software Engineering, and Domain-Specific Languages. His work focuses on breaking change analysis in APIs and libraries, client-library compatibility testing, and dependency management. He develops practical tools like Roseau for source-based breaking change detection and investigates semantic versioning impacts in ecosystems like Maven Central. His empirical approach leverages large-scale repository analysis to address real-world software maintenance challenges, particularly in Java ecosystems. Recent publications (2023-2025) show consistent contributions to breaking change analysis and compatibility testing, appearing in top venues like ASE, ICSE, and ISSTA. His research bridges theoretical insights with practical tooling for software evolution challenges, demonstrating strong empirical methodology and tool-oriented contributions. No scientific awards are documented in the available information. Degueule has advised no publicly listed students and holds no mentioned research grants. His organizational roles include Program Co-Chair for SLE 2023 and committee positions across multiple conferences, reflecting significant service to the software engineering community.

Hongyu Zhangمشاهده پروفایل

استاد

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +۴ مورد دیگر

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.

Song Wangمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Software Reliability
• +۵ مورد دیگر

Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering since May 2024. Previously, he served as an Assistant Professor at the same institution from July 2019 to May 2024. He earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan, an MS degree from the Chinese Academy of Sciences in June 2014 under Profs. Ye Yang and Wen Zhang, and BE and BHRM degrees from Sichuan University in June 2011. Dr. Wang's research focuses on the intersection of Software Engineering and Artificial Intelligence, with two main thrusts: 1) leveraging AI technologies to address software reliability challenges (AI for SE), and 2) developing software reliability techniques to improve AI infrastructure systems (SE for AI). His specific interests include software testing, program analysis, software reliability, and machine learning applications in software engineering. His research has led to tools that have detected hundreds of true bugs across various open-source projects. His recent publications reveal a strong focus on applying large language models to software engineering tasks, analyzing vulnerabilities in deep learning libraries, and developing techniques for software testing and reliability. The research spans multiple subfields including API testing, vulnerability detection, bias analysis in generated code, and automated assurance case generation. TOSEM Distinguished Reviewer Award 2023 APSEC'23 Distinguished Paper Award ACM SIGSOFT Distinguished Paper Award (ICPC 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2020) Best Paper Award at PROMISE 2019 Dr. Wang actively mentors students at all levels, currently supervising multiple PhD and MASc students. His research group has produced numerous publications in top-tier software engineering venues, including ICSE, FSE, ASE, and TOSEM. He also serves on the editorial board of ACM TOSEM and has been involved in organizing major conferences like ASE and CASCON.