جست‌وجوی استاد و پژوهشگر

Daniel Aloiseمشاهده پروفایل

استاد

• Data Mining
• Optimization
• Mathematical Programming
• +۵ مورد دیگر

Daniel Aloise is a Full Professor at the Department of Computer Engineering and Software Engineering, Polytechnique Montréal. He is a member of GERAD (Group for Research in Decision Analysis) and IVADO (Institute for Data Valorization), focusing on data science, optimization, and mathematical programming. His career spans institutions in Brazil and Canada, with significant contributions to clustering, classification, and operational research. Ph.D. in Exact algorithms for minimum sum-of-square clustering (HEC Montréal, 2009) Research Interests include data mining, optimization, mathematical programming, and algorithms. His work addresses challenges in big data, clustering algorithms, and classification models, applying these to diverse fields such as psychology, engineering, marketing, and disaster response. He explores polynomial-time algorithms for complex clustering problems and deep learning frameworks for unsupervised classification. Recent Articles emphasize optimization techniques (Benders decomposition, column generation), wireless signal prediction, bike-sharing inventory rebalancing, and serious games for disaster response data. These works integrate operations research, machine learning, and computational efficiency. Scientific Awards include the 2024 Omega Best Paper Award, 2023 CAPTRS Serious Games Award, and multiple CNPq Productivity Scholarships (2015–2018, 2012–2014). He received distinctions for his Ph.D. thesis and placement in international competitions. Supervision covers 7 Ph.D. and 12 Master's theses completed at Polytechnique Montréal, addressing topics like bug severity detection, anomaly analysis, and vehicle routing optimization. His lab collaborates with industry partners on real-time decision-making systems.

• Human Activity Recognition
• Process Mining
• Transfer Learning
• +۶ مورد دیگر

Dr. Ian McChesney serves as a Senior Lecturer in the School of Computing at Ulster University, based at the Belfast campus (Room BC-05-128) and Jordanstown Campus. His research spans Human Activity Recognition, Process Mining, and Transfer Learning with significant contributions to Autonomic Computing and Open Data initiatives. Affiliated with the Faculty of Computing, Engineering and Built Environment , he actively collaborates on projects like the PwC Advanced Engineering and Research Centre and the Connected Health Living Lab. His research interests focus on Human Activity Recognition (91% fingerprint match), Process Mining (57%), and Transfer Learning (45%), with applications in smart homes, business processes, and healthcare. Key methodologies include semi-Markov models for IoT device management, synthetic data generation for autonomic systems, and semantic enrichment of HAR datasets. His recent work shows increasing emphasis on educational frameworks for competency-based computing education in the UK. Among his 49 research outputs and 3 datasets, notable contributions include the InSync dataset and research on dyslexia in programming. He received the Best Paper Award ICAS 2025 for work on synthetic data generation. His projects include the TRAXX Fusion consultancy (2014-2016) and current involvement in the PwC Advanced Engineering Centre (2021-2026). Supervision: Mentored 4 students through supervised research work Grants: Contributed to KTP Programme with MJM Marine Limited (2022-2025) and Connected Health Living Lab (2018) His work supports UN Sustainable Development Goals through applications in healthcare optimization, educational innovation, and industrial process improvement. Current projects focus on few-shot learning, large language models, and human activity recognition in connected health environments.

Hussain Jamilمشاهده پروفایل

استادیار

• User Experience
• User Research
• Social Media Analysis
• +۶ مورد دیگر

Hussain Jamil is an Assistant Professor at Sejong University 's Department of Data Science , specializing in User Experience (UX) evaluation , Machine Learning , and Medical Informatics . His work bridges Artificial Intelligence with Healthcare Applications , particularly in DXA image analysis and social media-based mental health detection . Education: Ph.D. in Biomedical Engineering (Kyung Hee University, 2019) MS in Biomedical Engineering (Kyung Hee University, 2014) BCs in Computer Science (Kohat University, 2010) Research interests focus on Machine Learning for Medical Imaging , User Experience , and Text Mining . Key projects include AI-driven osteoporosis diagnosis , depression detection via SNS analysis , and multimodal UX platforms . Recent publications span 2024 research on respiratory disease prediction , Arabic text analysis for pandemic monitoring , and multimodal emotion recognition , alongside foundational work in 2023-2018 on UX evaluation frameworks and knowledge-based systems .

Iman Rusmanaمشاهده پروفایل

استاد

• Environmental Microbiology
• Microbial Community Ecology
• Nitrogen Cycling
• +۴ مورد دیگر

Professor Iman Rusmana is a distinguished faculty member in the Department of Biology, Faculty of Mathematics and Natural Sciences at IPB University (Bogor Agricultural University), Indonesia. His research spans environmental microbiology with strong expertise in microbial community ecology and ecophysiology. His primary research focuses include: Nitrogen cycling metabolisms in tropical estuaries and agricultural systems Methanotrophic and methanogenic bacteria in rice fields and aquatic ecosystems Bacterial probiotics for aquaculture Molecular studies of bacterial anti-quorum sensing mechanisms Professor Rusmana's publication record demonstrates consistent contributions to microbial ecology, particularly in tropical environments. His recent work (2023-2024) shows strong emphasis on bioelectrochemical systems, microbial fuel cells, nitrogen cycle dynamics in estuaries, and mangrove ecosystem biogeochemistry. His research integrates metagenomics, microbial metabolism analysis, and molecular techniques to address environmental challenges. His work has been supported by major Indonesian funding agencies including Kemenristek DIKTI through various projects such as CRC Start Up and other research grants focused on sustainable agriculture and aquaculture. Professor Rusmana actively collaborates with researchers across Indonesia and has contributed to significant projects including the CRC project group B02 on rainforest transformation impacts on soil prokaryotic communities in Sumatra.

Simone Diniz Junqueira Barbosaمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Information Visualization
• Model-based Interactive Systems Design
• +۷ مورد دیگر

Simone Diniz Junqueira Barbosa is an Associate Professor at the Informatics Department of Pontifical Catholic University of Rio de Janeiro (PUC-Rio). She leads the IDEIAS-SERG lab, focusing on Human-Computer Interaction (HCI) and Information Visualization research. Her work spans model-based interactive systems design, visual analytics, digital storytelling, and AI techniques for improving system usability and accessibility. Her research explores: Model-based approaches for interactive systems Data science and visual analytics for complex datasets Digital storytelling techniques Accessibility and usability enhancement through AI Semiotic engineering foundations for HCI Publication analysis reveals strong emphasis on HCI methodologies, data visualization literacy, ethical AI development, and applications in legal informatics/industrial automation. Recent works focus increasingly on explainable AI, visualization cognition, and ethical ML practices. Awards & Honors: IFIP TC13 Pioneer Award (2019) Outstanding HCI Career Award (Brazilian Computer Society, 2020) Selo de Inovação SBC Innovation Award (2022) IFIP Fellow Award (2024) She has advised over 45 graduate students (PhD/MSc) and coordinates multiple labs including DasLab, ExACTa, and Americanas Futuro Lab. Funded projects include CNPq, FAPERJ, Microsoft Research, and Petrobras collaborations focusing on HCI innovations and data visualization tools. Leads the IDEIAS-SERG research group merging semiotic engineering with interaction design. Serves on steering committees for ACM CHI and IFIP TC13, previously co-edited ACM Interactions Magazine (2016-2019).

Ronny Bochمشاهده پروفایل

پژوهشگر

• Carbonates
• Tunneling
• Geothermal Energy
• +۵ مورد دیگر

Ronny Boch is a Researcher & Lecturer at the Graz University of Technology and a Research Associate at the University of Vienna . His work bridges carbonate geochemistry , tunnel drainage systems , and geothermal energy to address challenges like mineral scaling , microbial processes , and material alteration .

Clara Schumacherمشاهده پروفایل

پژوهشگر ارشد

• Learning Analytics
• Self-Regulated Learning
• Feedback
• +۶ مورد دیگر

Clara Schumacher is a Post-doctoral researcher in the LA-DIVA project at Humboldt University of Berlin's CATALPA research cluster since April 2020, specializing in educational technologies and learning analytics. Her work bridges pedagogical theory with data-driven systems to enhance learning support for students and instructors. Her academic foundation includes: Doctoral degree (Dr. rer. Pol.) on "Cognitive, Metacognitive, and Motivational Perspectives on Learning Analytics. Synthesizing Self-regulated Learning, Assessment, and Feedback with Learning Analytics" from University of Mannheim (2020) Diploma in educational science (Diplom-Pädagogin) with focus on adult education, psychology and work psychology from University of Koblenz-Landau (2014) Research focuses on learning analytics , self-regulated learning , and feedback systems within educational technologies, emphasizing how algorithmic approaches impact collaborative learning and student diversity. Her interdisciplinary methodology integrates computer science with educational theory to develop practical tools for real-world learning environments. Recent publications (2021-2024) reveal a clear trajectory toward ethical implementation of learning analytics, with growing emphasis on algorithmic grouping transparency, student perception of automated systems, and equity considerations in data-driven education. This evolution highlights her commitment to socially responsible edtech development. As spokesperson for the DELFI working group on learning analytics, she actively shapes research standards while contributing to the LA-DIVA project's pedagogical framework at CATALPA.

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.

Anja Møller Pedersenمشاهده پروفایل

پژوهشگر ارشد

• Privacy Law
• Data Protection Law
• EU Law
• +۴ مورد دیگر

Anja Møller Pedersen serves as a Postdoc and Research Fellow at the Centre for Information and Innovation Law (CIIR) within the Faculty of Law at the University of Copenhagen, where her work on EU privacy frameworks and digital surveillance technologies is co-funded by the university and the Danish Institute for Human Rights. Her research critically examines tensions between security imperatives and fundamental rights in algorithmic governance. Her academic foundation includes: Master of Laws (LL.M.) from the University of Copenhagen (2011) Studies at Paris-Panthéon-Assas University (2009-2010) Bachelor in Laws from the University of Copenhagen (2008) Dr. Pedersen's scholarship focuses on the legal ramifications of AI-driven policing, facial recognition systems, and data retention policies within European human rights frameworks. She investigates how surveillance capitalism and predictive analytics challenge constitutional privacy guarantees, with particular attention to Danish implementation gaps and democratic oversight mechanisms. Her interdisciplinary approach bridges legal theory, criminology, and technology policy to develop rights-preserving regulatory models for emerging digital tools. Analysis of her recent publications reveals three dominant thematic currents: the erosion of legal certainty in police adoption of biometric surveillance, the conflict between data-driven security measures and proportionality principles under EU law, and the need for transnational regulatory harmonization in information law. Her work consistently emphasizes empirical assessment of technological deployments against fundamental rights benchmarks rather than purely theoretical critiques. Scientific Awards No major scientific prizes or fellowships are documented in current public records. Her research is institutionally supported through University of Copenhagen-Danish Institute for Human Rights funding, with significant knowledge exchange via visiting positions at NYU Grossman School of Medicine (2017) and University of Amsterdam (2017). While formal student supervision isn't explicitly listed, her advisory role at the Danish Institute for Human Rights (2014-2016) and board membership in the Digital Security Council (2016-2018) demonstrate mentorship capacity in policy contexts. She operates within CIIR's collaborative ecosystem at the Faculty of Law while contributing to national discourse through the Digital Security Council (Rådet for Digital Sikkerhed). Her media engagements—including 16 press appearances since 2018 on topics like unlawful logging and PET surveillance practices—showcase active translation of academic research into public policy debates, particularly regarding facial recognition legality and predictive policing accountability.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +۵ مورد دیگر

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Associate Professor Biplob Ray serves as Head of Course for Postgraduate ICT Courses at the School of Engineering and Technology, Central Queensland University. With over 15 years of academic experience, he has progressed from ICT Lecturer (2015-2019) to Senior Lecturer (2020-2022) and currently holds the position of Associate Professor (2023-Present). His research is centered at the Centre for Intelligent Systems within the Institute for Future Farming Systems, where he leads multidisciplinary projects with significant industry and government funding. Dr. Ray holds a PhD from Deakin University (2015), Master of Information Technology from University of Ballarat (2008), and Graduate Certificate in Education Tertiary Teaching (2013). His academic journey began with a BSEng in Computer Engineering, followed by professional experience as Analyst Programmer at Telstra and Systems Programmer in the Philippines. His research focuses on the intersection of Artificial Intelligence, Internet of Things, and Cybersecurity, with practical applications spanning smart farming, environmental monitoring, and energy systems. His multidisciplinary approach has secured over $3 million in research funding from Australian federal government and industry sources since 2016, including significant projects like the $1.28 million 'Intelligent, Adaptable, and User-Friendly Weed Management system' and the $513,268 'AI-SSPCAS' project with CSIRO Data61. Dr. Ray's publication record shows consistent high-quality output with approximately 15-20 papers annually, primarily in IEEE and Elsevier journals. His work demonstrates strong translational impact, with multiple projects featured in media outlets including ABC News, Channel 7, and Australian Tree Crop magazine. His research has directly contributed to practical implementations such as smart irrigation systems for Cairns Regional Council and cybersecurity solutions for small businesses. 2025 Australian Awards for University Teaching (AAUT) 2024 Vice-Chancellor's Award of Commendation for Outstanding Researcher (Mid-Career) 2023 Vice-Chancellor's Award for Exemplary Practice in Learning and Teaching 2021 Vice-Chancellor's Award for Exemplary Practice in Learning and Teaching (Tier 1) 2019 Vice-Chancellor's Award for Exemplary Practice in Learning and Teaching 2016 'RISING STAR of 2016' award from CQUniversity Dr. Ray currently supervises fifteen research students across PhD and Master's programs, with active grants supporting multiple research positions. His leadership extends to professional service as Vice-Chair of IEEE Victorian Section (2024-2025), former Chair of IEEE Victoria IoT Community, and member of the Engineering Institute of Technology Course Advisory Committee. The Centre for Intelligent Systems fosters interdisciplinary collaboration with regular seminars, industry partnerships, and international research connections, providing students with rich opportunities for professional development.

Alireza Kasaieمشاهده پروفایل

استادیار مهمان

• Operations Research
• Optimization
• Simulation
• +۴ مورد دیگر

Alireza Kasaie, Ph.D., serves as Visiting Assistant Professor of Business Administration at Fort Lewis College's Katz School of Business, located in EBH 148 with contact (970) 247-7641 and skasaiesharifi@fortlewis.edu. His academic foundation includes a Ph.D. in Industrial and Systems Engineering from the University of Missouri-Columbia (2023), M.S. in Industrial Engineering from Khatam University (2017), and B.Sc. in Industrial Engineering from Azad University (2015). Educational Background: Ph.D. in Industrial and Systems Engineering, University of Missouri-Columbia (2023) M.S. in Industrial Engineering, Khatam University (2017) B.Sc. in Industrial Engineering, Azad University (2015) Dr. Kasaie's research centers on healthcare analytics and equitable resource allocation through optimization , simulation , and machine learning methodologies. His work addresses critical gaps in public health operations, including psychiatric clinic efficiency, blood supply chain resilience, and pandemic-driven nursing workflow disruptions. He prioritizes data-driven solutions for health equity in underserved communities. His 2022-2024 publications reveal a concentrated focus on healthcare operations research, with 80% targeting psychiatric and intensive care settings. Key trends include AI-driven scheduling optimization (40% of works), pandemic impact analysis on clinical workflows (40%), and supply chain modeling under uncertainty (20%), demonstrating consistent application of operations research to real-world healthcare crises. Scientific Recognition: Industrial Engineering Honor Society (Alpha Pi Mu) Dr. Kasaie teaches business analytics and systems simulation courses, leveraging industry partnerships with Loyola University Chicago and DePaul University. While specific grant details aren't documented, his conference presentations at INFORMS and Human Factors Society indicate active research funding. He previously contributed to health informatics projects as a Research Associate at Loyola's Department of Health Informatics and Data Science. His collaborative work spans ICU workflow analysis teams and psychiatric clinic optimization groups, with recent projects involving electronic medical record log data mining and fatigue-recovery simulation modeling for critical care nursing staff.

Graham Shawمشاهده پروفایل

استاد

• Medical Education
• Higher Education Leadership
• Biochemistry
• +۳ مورد دیگر

Dr. Graham Shaw serves as Professor of Biomedical Sciences at Barry University's School of Podiatric Medicine, leveraging over three decades of academic experience spanning biochemistry research, medical education innovation, and higher education leadership. His current role builds upon foundational work as a Postdoctoral Research Assistant at Aston University and continuous contributions to healthcare professional training. His educational trajectory includes: BSc in Applied Biology (First Class Honors) from Bristol Polytechnic (1979-1983) Ph.D. in Biochemistry from Aston University (1983-1987) MBA from Barry University (2017-2019) Dr. Shaw's research program centers on transformative medical education, with emphases on pandemic-responsive teaching strategies, student engagement optimization, and biochemistry curriculum development. His work bridges clinical science with pedagogical innovation, particularly examining how technological integration affects non-native English speakers in medical training and how leadership frameworks can navigate institutional crises. The 2019 publication of his textbook "Biochemistry for Health Professionals" established a foundational resource in the field. Analysis of his 15 most recent publications reveals a decisive pivot toward educational leadership during global crises, with 80% of recent work addressing pandemic impacts on higher education. Simultaneously, he maintains dual research threads in public health (focusing on South Florida's AIDS epidemic disparities) and cardiovascular-diabetes comorbidities, demonstrating interdisciplinary range. His scholarly recognition includes: Barry University Outstanding Scholarship Award (2003-2008) Outstanding Business Student Award (2019) School of Podiatric Medicine Pre-Clinical Sciences Faculty Award (2023) As a dedicated academic citizen, Dr. Shaw shapes national standards through his decade-long role as Biochemistry question writer for the National Board of Podiatric Medicine Exam and as editorial reviewer for medical education journals. His leadership extends to developing evidence-based admission rubrics and computerized student-patient log systems that transform clinical training. The Beta Gamma Sigma Honor Society membership reflects his cross-disciplinary excellence spanning business and biomedical sciences. While no formal laboratory is described, Dr. Shaw leads collaborative educational innovation teams focused on lecture capture implementation, asynchronous discussion networks, and computer literacy assessment systems that directly impact podiatric medical student training.

Jie Liuمشاهده پروفایل

استاد

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +۳ مورد دیگر

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Pinjia Heمشاهده پروفایل

استادیار

• Software Engineering
• Natural Language Processing
• Systems
• +۴ مورد دیگر

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.