جست‌وجوی استاد و پژوهشگر

Clifton Franklundمشاهده پروفایل

استاد

• Microbiology
• Bacterial Genetics
• Bioinformatics
• +۵ مورد دیگر

Clifton Franklund is a Professor of Microbiology in the Department of Biological Sciences at Ferris State University's College of Arts, Sciences and Education. He actively teaches microbiology courses while conducting research on bacterial genetics, anaerobic bacteria, and wastewater-based epidemiology, and contributes to student learning assessment and General Education program development. PhD, Medical College of Virginia/Virginia Commonwealth University, Microbiology and Immunology Certificate, James Madison University, Higher Education Assessment Specialist MS, North Dakota State University, Bacteriology BA, Concordia College, Biology, Minor in Philosophy Dr. Franklund has extensive expertise in anaerobic bacteria cultivation, bacterial genetics, bioinformatics, and reproducible research methods. His research bridges traditional microbiology with modern data science approaches, focusing on environmental monitoring and public health surveillance. He's particularly known for applying computational methods to biological problems and innovating in educational assessment practices. His teaching philosophy emphasizes hands-on learning and real-world applications of microbiological principles. Analysis of his recent publications (2021-2023) reveals a strong shift toward applied public health microbiology, with most work focused on wastewater-based SARS-CoV-2 surveillance and water quality testing for recreational environments. Earlier in his career (1990s-2000s), his research centered on bacterial genetics of Pseudomonas aeruginosa and bile acid metabolism in Eubacterium species. This evolution demonstrates his ability to adapt research focus to address contemporary health challenges while maintaining core microbiological expertise. Outstanding Course Assessment (2015) Top 25 STEM Professor in Michigan (2013) Ferris State University Distinguished Teacher of the Year (2012) Softchalk Higher Education Online Challenge Winner (2011) Exemplary On-Line Course, Web Enhanced (2007) Dr. Franklund actively mentors undergraduate researchers, with numerous students co-authoring his recent publications and presenting at regional conferences including the West Michigan Regional Undergraduate Research Symposium and Michigan Society of Toxicology meetings. His research program integrates classroom instruction with real-world public health applications, particularly in wastewater surveillance. He has developed innovative assessment tools and reproducible research methods that enhance both student learning and scientific rigor in his field. His laboratory work focuses on environmental microbiology applications for public health monitoring, particularly wastewater-based epidemiology. Through collaborations with local health departments and student researchers, his team has established ongoing surveillance systems for tracking SARS-CoV-2 variants and water quality indicators in West Michigan. His involvement with Software Carpentry and the Center for Open Science demonstrates commitment to open science practices and computational approaches in microbiological research.

Ryan Sengerمشاهده پروفایل

دانشیار

• Metabolic Engineering
• Synthetic Biology
• Systems Biology
• +۵ مورد دیگر

Ryan Senger serves as Associate Professor in the Department of Biological Systems Engineering at Virginia Tech's College of Engineering, with courtesy appointments in Chemical Engineering (Virginia Tech) and Surgery (Virginia Commonwealth University School of Medicine). His research bridges metabolic engineering, synthetic biology, and biomedical diagnostics through innovative applications of Raman spectroscopy. Education: Ph.D., Chemical Engineering, Colorado State University, 2005 M.S., Chemical Engineering, Colorado State University, 2002 B.S., Chemistry, Millikin University, 1999 Senger's research focuses on developing bio-based technologies for sustainable chemical production, disease detection through urine analysis, and synthetic biology containment. His lab pioneers Raman spectroscopy-based chemometric urinalysis (Rametrix) for detecting diseases including bladder cancer, chronic kidney disease, Lyme disease, and Long COVID. Current projects involve bioelectrical system engineering in microbes, electron harvesting from waste biomass, and secure synthetic genetic material containment. His work integrates AI/ML for spectral analysis and genome-scale metabolic modeling. His recent publications demonstrate strong trends in veterinary diagnostics (canine cancer detection), neurological disease monitoring, and renal dysfunction profiling using Raman chemometrics. The research consistently applies spectral fingerprinting to translate complex biological data into clinical diagnostic tools. Awards: 2022-24: Celebrating Innovation Recognition (x4) 2023: Dean’s List of Instructors 2016: VT Knowledgeworks Innovation Challenge Winner 2009: The Gaden Award for Metabolic Engineering Senger actively mentors students through laboratory research and teaches courses including Thermodynamics of Biological Systems, Metabolic Engineering, and Bio-Raman Chemometrics. His funding portfolio includes NSF grants for metabolic modeling workshops and commercial projects with Rametrix Technologies (where he serves as CTO), focusing on dialysis patient monitoring, renal disease management, and aquaculture feed development. He directs the Rametrix Technologies research team that develops urine-based diagnostic devices and spectral analysis software, maintaining strong industry-academic partnerships for technology commercialization.

Kebin Pengمشاهده پروفایل

استادیار

• Computer Vision
• 3D Computer Vision
• Depth Estimation
• +۷ مورد دیگر

Dr. Kebin Peng is an Assistant Professor in the Department of Computer Science at East Carolina University's College of Engineering and Technology. He joined ECU in 2024 after working as a Senior Software Engineer at MathWorks (2023-2024) and completing his Ph.D. at the University of Texas at San Antonio (2019-2023). Dr. Peng's research focuses on Computer Vision and Machine Learning, with particular expertise in 3D Computer Vision, Depth Estimation, Point Cloud Detection/Segmentation, and 3D Reconstruction. His work often explores unsupervised learning approaches to solve complex vision problems. He teaches graduate and undergraduate courses including Artificial Intelligence, Software Engineering, and Principles of Programming Languages. His publication record shows consistent contributions to top computer vision venues, with recent papers on monocular depth estimation in dynamic scenes, multi-view 3D reconstruction using transformers, and code clone analysis in VR software. Dr. Peng actively contributes to the research community as a reviewer for major conferences including NeurIPS, CVPR, ICCV, and AAAI. Dr. Peng serves on program committees for multiple prestigious conferences and journals, demonstrating his growing recognition in the computer vision and AI research community. His professional service includes reviewing for Neural Information Processing Systems (NeurIPS) 2024, AAAI Conference on Artificial Intelligence 2024, and multiple CVPR conferences. With his blend of academic research experience from UT San Antonio and industry experience at MathWorks and Samsung Research America, Dr. Peng brings a practical perspective to his teaching and research, connecting theoretical concepts with real-world applications in computer vision and artificial intelligence.

Ettore Merloمشاهده پروفایل

استاد

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

Ettore Merlo is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads research in cybersecurity, artificial intelligence, and software systems. He holds an M.Sc. from the University of Turin and a Ph.D. from McGill University. His affiliations include membership in the Institute for Data Valorization (IVADO), focusing on data science and AI innovation. His research integrates software engineering with AI, emphasizing: Software artifact analysis (static/dynamic/symbolic) AI-driven security solutions for clone detection, malware analysis, and access control Fairness and robustness in machine learning systems Evolutionary analysis of software vulnerabilities Recent publications (2022-2025) demonstrate a strong focus on ethical AI, including bias mitigation in neural networks, automated anomaly detection, and certification of safety-critical ML systems. His work frequently applies graph neural networks, unsupervised learning, and formal verification methods to industrial and cybersecurity challenges. Professor Merlo has supervised 25 graduate students (10 PhD, 15 Master's), with projects ranging from avionics software to phishing kit analysis. While no scientific awards are listed, his extensive publication record includes 136 works spanning journals, conferences, and technical reports. Collaborations include partnerships with industrial telecommunication firms and international academia. No dedicated lab is specified, but his research aligns with Polytechnique Montréal's 'New Frontiers in Information and Communications Technologies' center.

Baishakhi Rayمشاهده پروفایل

دانشیار

• Software Engineering
• Security
• Artificial Intelligence
• +۱ مورد دیگر

Baishakhi Ray is an Associate Professor of Computer Science at Columbia University's School of Engineering and Applied Science. Her work focuses on the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin and has established herself as a leading researcher in software engineering with AI applications. Dr. Ray's educational background includes a Ph.D. from the University of Texas, Austin. Her academic journey has led to a prominent position at Columbia University where she continues to advance research in software engineering. Dr. Ray's research spans several critical areas at the intersection of software engineering and artificial intelligence. Her work explores how machine learning techniques can improve software development processes, enhance security practices, and address challenges in program analysis. She has made significant contributions to understanding how large language models can be effectively applied to code generation, vulnerability detection, and software testing. Her research has practical implications for improving software reliability and security in real-world applications, particularly in safety-critical domains like autonomous systems. Analysis of Dr. Ray's recent publications reveals a clear trajectory toward leveraging AI for practical software engineering challenges. Her work has evolved from foundational program analysis techniques to cutting-edge applications of large language models in code understanding and generation. A notable trend is her focus on making AI-assisted software development more reliable, secure, and energy-efficient. Her research increasingly addresses the practical limitations of current AI approaches while developing novel methodologies to overcome them. IEEE TCSE Rising Star Award NSF CAREER Award IBM Faculty Award VMWare Faculty Award ICSME Most Influential Paper Award (2023) FSE'17 Distinguished Paper ASE'22 Distinguished Paper ISSTA'23 Distinguished Paper CACM Research Highlights Dr. Ray actively mentors graduate students and has built a productive research group focused on AI-driven software engineering solutions. Her research has been supported by prestigious grants including the NSF CAREER award. She has successfully guided numerous students through their research projects, with several of her advisees making significant contributions to publications in top-tier conferences. Dr. Ray also serves as an Amazon Visiting Academic, bridging academia and industry to address real-world software engineering challenges. Through her leadership in various research projects and collaborations, Dr. Ray has established a dynamic research environment that combines theoretical rigor with practical applications. Her work often involves interdisciplinary collaboration across computer science subfields, particularly connecting software engineering with security and AI research communities.

Eunjong Choiمشاهده پروفایل

دانشیار

• Software Engineering
• Software Maintenance/Evolution
• Program Comprehension
• +۴ مورد دیگر

Eunjong Choi is an Associate Professor at the Faculty of Information and Human Sciences, Kyoto Institute of Technology in Japan. She joined Kyoto Institute of Technology as a tenure-track assistant professor in 2019 and was promoted to associate professor in March 2024. Prior to this position, she served as an assistant professor at Nara Institute of Science and Technology (April 2016-March 2019) and at Osaka School of International Public Policy, Osaka University (April 2015-March 2016). Kyoto Institute of Technology (2019-Present) Nara Institute of Science and Technology (2016-2019) Osaka University (2015-2016) Dr. Choi's research focuses on software engineering with particular emphasis on software maintenance and evolution, program comprehension, software analytics, reused code management and detection, refactoring support, and test code generation. Her work bridges theoretical software engineering concepts with practical applications in industrial settings. She has investigated topics such as token-level SZZ algorithms, deep learning-based clone detectors, and modernization of large-scale industrial systems. Dr. Choi has held significant leadership roles in major software engineering conferences including serving as General Chair for ICPC 2020 and IWESEP 2017, Publicity Co-Chair for ICSE 2020, and Local Arrangement Co-Chair for SANER 2016. She has been actively involved in the IEEE Kansai Section Woman in Engineering Affinity Group, serving as Secretary (2016-2017) and Vice-Chair (2018-2021). Her teaching portfolio includes Software Mining and Analysis, Software Metrics, and Software Engineering courses at Kyoto Institute of Technology; Software Design and related courses at Nara Institute of Science and Technology; and Basic Information Literacy at Osaka University. Dr. Choi earned her Ph.D. in Information Science and Technology from Osaka University in 2015 and maintains an active research profile in the software engineering community.

Eric Boddenمشاهده پروفایل

استاد

• Secure Software Engineering
• Static Program Analysis
• Automated Program Analysis
• +۵ مورد دیگر

Eric Bodden is a Professor for Secure Software Engineering at Paderborn University and co-director of Fraunhofer IEM. He is also a member of the directorate of the Collaborative Research Center CROSSING at TU Darmstadt. As an ERC fellow, Bodden leads the Attract-Group on Secure Software Engineering at Fraunhofer IEM, where he develops code analysis technology for security in collaboration with leading national and international software development companies. Bodden is one of the leading experts in secure software engineering with a specialty in building highly precise tools for automated program analysis. His research spans static program analysis, Android security, taint analysis, and cryptographic API security. He has made significant contributions to the field through frameworks like FlowDroid for Android analysis, DroidBench benchmark suite, and as one of the chief maintainers of the Soot program analysis framework. His work bridges theoretical advances with practical applications, focusing on creating tools that can be effectively used by developers to enhance software security. His recent publications demonstrate a continued focus on advancing static analysis techniques, with increasing attention to modern challenges like C/C++ analysis, large language models in program analysis, and addressing security vulnerabilities in dependency management. His work shows a consistent trajectory of improving analysis precision, scalability, and usability for developers. Heinz Maier-Leibnitz-Preis (2014) ERC fellow BITKOM Management Club membership (2013) Bodden has advised numerous doctoral students whose theses cover diverse aspects of secure software engineering, including static analysis for Android applications, secure integration of cryptographic software, and information flow security engineering. His research group has received recognition through multiple dissertation awards including Summa cum laude distinctions and the Ernst Denert Software Engineering Award. The group has developed influential tools like FlowDroid, CogniCrypt, and SootUp that have shaped the field of secure software development. At Fraunhofer IEM, Bodden heads the Attract-Group on Secure Software Engineering, which collaborates with industry partners to develop practical security analysis solutions. His team has created several influential frameworks including FlowDroid for Android security analysis and the DroidBench benchmark suite. The group's work bridges academic research with real-world applications, focusing on making security analysis tools more precise, scalable, and usable for developers.

Istvan Davidمشاهده پروفایل

استادیار

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +۷ مورد دیگر

Istvan David is an Assistant Professor of Software Engineering at McMaster University's Faculty of Engineering, Department of Computing and Software, where he leads the Sustainable Systems and Methods Lab (SSM) and works as a researcher in the McMaster Centre for Software Certification (McSCert). His work bridges the gap between model-driven engineering, digital twins, and sustainability in systems engineering. Dr. David's research interests focus on digital twins , model-driven engineering , sustainability in computing, cyber-physical systems , and collaborative modeling . His lab develops novel reinforcement learning techniques , digital twin architectures , and modeling and simulation methods with a strong emphasis on sustainability as both a system characteristic and an engineering principle. His recent publications reveal a strong trend toward integrating digital twin technology with sustainability concerns, particularly in automotive systems, smart farming, and resource-efficient software engineering. The research spans from foundational modeling techniques to applied solutions addressing the four essential sustainability dimensions of technical systems: technical (long-term usage), economic (financial viability), environmental (reduced impact), and social (elevated utility). Scientific Awards: Best Practice Paper Award at MODELS Best Paper 2024 Runner-up in the Journal of Computer Languages Dr. David actively mentors students including PhD candidate Xiaoran (Sharon) Liu, and has supervised undergraduate researchers like Adwita Kashyap and Kyanna Dagenais. He has secured multiple research grants supporting work on sustainable systems and digital twin technologies, with applications in automotive engineering, smart farming, and resource-efficient computing. His lab collaborates with industry partners and academic institutions worldwide, including VU Amsterdam and University of Montréal. The Sustainable Systems and Methods Lab focuses on the vision of 'sustainable systems by sustainable methods,' addressing the growing problem of unsustainable systems engineering practices. The lab's work on bipartite sustainability aims to both build sustainable systems and develop systems using sustainable methods.

Yun Linمشاهده پروفایل

دانشیار

• Automatic programming
• Software debugging
• Software testing
• +۸ مورد دیگر

Yun Lin is an Associate Professor and Deputy Head of the Department of Computer Science and Technology at Shanghai Jiao Tong University's School of Computer Science. Prior to joining SJTU, Lin served as a Research Assistant Professor at the National University of Singapore working with Prof. Dong Jin Song. Lin leads the CoPhi ("Code Philia") research group, which focuses on the intersection of Software Engineering, AI, and Security. Lin's research spans three major areas: Automatic Programming (including code editing, software testing, and debugging), Explainable AI (focusing on representation interpretation and training data attribution), and Web Misinformation (particularly phishing and scam detection). The research has resulted in numerous tools including CoEdPilot for code editing recommendation, DeepDebugger for interactive debugging of deep classifiers, and Phishpedia for phishing webpage detection. Lin's recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models and vision language models, with traditional software engineering and security tasks. The work shows increasing sophistication in understanding project context, handling interactive nature of programming tasks, and addressing security challenges in the age of generative AI. Key themes include consistency-based approaches for anomaly detection, agent-based frameworks for complex tasks, and hybrid models that combine symbolic reasoning with neural approaches. ACM Distinguished Paper Award in ICSE'18 for "Towards Optimal Concolic Testing" Distinguished Reviewer Award in FSE'25 2nd prize Research Prototype Award in ChinaSoft'24 Lin advises a large team of PhD, Master's, and undergraduate students, with several publications co-authored with students appearing in top venues. Current research is supported by collaborations with National University of Singapore, particularly with Prof. Dong Jin Song, and includes projects on code editing, GUI testing, and phishing detection. The CoPhi group maintains active development of multiple research tools and datasets. The CoPhi research group under Lin's leadership focuses on building practical tools that bridge the gap between theoretical advances and real-world programming and security challenges. The group's work spans from fundamental program analysis techniques to applied security solutions, with an increasing emphasis on leveraging AI capabilities while maintaining explainability and reliability.

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.

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.

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.