جست‌وجوی استاد و پژوهشگر

• Control and Systems Engineering
• Automotive Engineering
• Model Predictive Control
• +۱۰ مورد دیگر

Massimo Canale is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , and a member of the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His academic career spans over two decades, focusing on control systems engineering with applications in automotive technology. Scientific Branch: Systems and Control Engineering (IINF-04/A) ERC Sectors: Automotive Engineering, Control Engineering, Control Theory Dr. Canale's research bridges theoretical advancements in Model Predictive Control (MPC) with practical applications in autonomous vehicles , hybrid/electric propulsion , and active suspension systems . His work integrates reinforcement learning and dynamic programming for optimizing vehicle performance and energy efficiency. Recent publications demonstrate trends in autonomous driving architectures (2024), sliding mode control for highway scenarios (2024), and energy management for sustainable mobility (2023-2024). He has developed patented solutions for semi-active suspension control and autonomous vehicle guidance. Award: IEEE Transactions on Control Systems Technology Outstanding Paper Award (2011) Editorial Roles: Associate Editor, IEEE Open Journal of Control Systems (2022–present) Dr. Canale supervises PhD students like Francesco Cerrito and teaches courses on digital control technologies , automatic control , and reinforcement learning at Politecnico di Torino. His research is funded through competitive grants (e.g., MPC4AVP 2021-2022) and commercial contracts (AD Shuttle 2024).

Ioannis Katakisمشاهده پروفایل

استاد

• Mining Social Data
• Mining Web Data
• Mining Urban Data
• +۶ مورد دیگر

Professor Ioannis Katakis is a Faculty Member at the University of Nicosia, where he is affiliated with the School of Sciences and Engineering and the Department of Computer Science. He has held various academic positions across multiple institutions including Aristotle University of Thessaloniki, University of Cyprus, Cyprus University of Technology, Open University of Cyprus, Hellenic Open University, Athens University of Economics and Business, and National and Kapodistrian University of Athens. His educational background includes a PhD in Machine Learning for Automated Text Classification (2005-2009), a Master's in Information Systems (2005-2007), and a Bachelor's in Computer Science (2000-2004), all from Aristotle University of Thessaloniki. Professor Katakis specializes in several cutting-edge areas of computer science and data analysis. His primary research interests include Mining Social, Web and Urban Data , Sentiment Analysis and Opinion Mining , Data Streams , and Multi-label Learning . His work bridges theoretical machine learning approaches with practical applications in social media analysis, healthcare informatics, privacy protection, and smart city technologies. He has published extensively in top venues including CIKM, ECML/PKDD, IEEE TKDE, and ECAI. His recent publications demonstrate a clear trend toward applying machine learning techniques to real-world problems with societal impact. He has focused on areas such as GDPR compliance in smart devices, sentiment analysis in crowd-sourced content, healthcare applications including drug reaction classification and brain disease monitoring, and privacy protection in wearable technologies. His work often involves multi-modal data analysis and addresses challenges in data streams and multi-label classification. Professor Katakis has made significant contributions to his field, with his research cited over 4,200 times. He serves as an Editor for the journal Information Systems and has edited four special issues in journals such as DAMI and InfSys. He regularly contributes to the academic community by serving on program committees for major conferences including ECML/PKDD, WSDM, DEBS, and IJCAI, and by reviewing for prestigious journals like TPAMI, DMKD, TKDE, TKDD, JMLR, TWEB, and ML. He has been actively involved in European research projects, notably serving as Quality Assurance Coordinator and Senior Researcher for projects such as VAVEL (www.vavel-project.eu) and INSIGHT (www.insight-ict.eu). His grant activities demonstrate a strong focus on collaborative, interdisciplinary research with practical applications in urban data management, social media analysis, and healthcare informatics. He has organized three workshops at major conferences (ICML, ECML/PKDD, EDBT/ICDT) and has extensive experience translating research into practical applications through his involvement in European projects.

Katarina Domijanمشاهده پروفایل

دانشیار

• Bayesian applications to data analysis
• Machine learning
• Statistical computing
• +۳ مورد دیگر

Katarina Domijan is an Associate Professor in Statistics at the Department of Mathematics and Statistics, Maynooth University, Ireland. She holds a PhD in Statistics from Trinity College Dublin (2008) and has been affiliated with Maynooth University since 2008, transitioning from Lecturer/Assistant Professor to her current role in 2024. Her academic career includes editorial roles as Associate Editor for The R Journal (2021–present) and the Journal of Computational and Graphical Statistics (2015–2024). Research Interests focus on Bayesian methods for high-dimensional data, particularly in classification problems. She specializes in feature selection and model visualization, with applications spanning agricultural data analysis (e.g., hyperspectral imaging for lactose prediction), medical diagnostics (e.g., sepsis and cancer detection), and space physics (e.g., Saturn Kilometric Radiation classification). Her work bridges theoretical statistics with real-world challenges, including socio-economic studies and forensic science. Key Research Areas Bayesian statistical inference Machine learning for large feature spaces Statistical computing and model interpretability Data visualization and chemometrics Scientific Contributions include leading projects like VistaMilk Phase II (2024–2030, €152,300) and Measuring Carbon Sequestration (2024–2028, €174,788.90). Her 15 most recent publications highlight advancements in ensemble modeling, spatial statistics, and medical diagnostics. Scientific Awards Associate Editor, The R Journal (2021–present) Associate Editor, Journal of Computational and Graphical Statistics (2015–2024) Student Supervision includes PhD and MSc graduates such as Dr. Bruna Wundervald (2024) and Dr. Mark O’Connell (2017). She also collaborates with researchers across disciplines, including Dr. Nadim Akasheh in food hypersensitivity studies.

• Additive Manufacturing
• Laser Powder Bed Fusion
• Materials Science
• +۲ مورد دیگر

Prof. Dr.-Ing. Johannes Henrich Schleifenbaum is a Professor and Chair of Digital Additive Production at RWTH Aachen University, where he leads research in the Profile area Production Engineering (ProdE). His work advances additive manufacturing (AM) through interdisciplinary approaches combining materials science, process engineering, and digital technologies. His research encompasses: Laser powder bed fusion (LPBF) process optimization and defect mitigation Development of novel alloys/composites for AM applications Sustainable manufacturing practices including material recycling Integration of AI/ML for accelerated material and process design Digital tools for automated design and distributed manufacturing Recent publications (2023-2025) demonstrate a strong focus on: Multi-material processing and microstructure control Machine learning-driven alloy development Standardization and scalability of AM processes Advanced simulations for meltpool dynamics and thermal behavior Applications in aerospace, construction, and biochemical engineering He leads the Chair of Digital Additive Production, collaborating with industry partners to translate research into industrial solutions for next-generation manufacturing.

Hwan-Sik Yoonمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Artificial Intelligence
• Computer Vision
• +۸ مورد دیگر

Dr. Hwan-Sik Yoon is an Associate Professor in the Department of Mechanical Engineering at The University of Alabama, where he focuses on applying Artificial Intelligence (AI) and Machine Learning (ML) to automotive, transportation, and manufacturing systems. His research spans modeling, simulation, and control of dynamic systems, with a strong emphasis on connected and automated vehicles (CAVs), energy-efficient routing, and sensor fusion technologies. Ph.D., Mechanical Engineering, Ohio State University, 2002 M.S., Mechanical Engineering, Ohio State University, 1998 B.S., Physics Education, Seoul National University, Korea, 1994 Dr. Yoon’s research integrates AI/ML into applications such as traffic signal control , excavator manipulator pose estimation , hybrid electric vehicle powertrain control , and factory floor safety monitoring . He is also involved in additive manufacturing , vision-based control systems , and reinforcement learning -driven automotive innovations. Recent publications highlight trends in deep reinforcement learning for vehicle energy efficiency, sensor fusion for traffic surveillance, and neural networks for dynamic system control. His work addresses challenges in multi-component failure analysis and real-time edge computing platforms . NSF Outstanding Faculty Advisor Award (2019) College of Engineering Faculty Productivity Award, Tennessee Tech University (2012) Dr. Yoon leads the Intelligent Structures and Systems Laboratory and serves as the lead CAVs faculty advisor for the University of Alabama’s EcoCAR student team, which has achieved national recognition in advanced vehicle technology competitions.

Patrick Shane Crawfordمشاهده پروفایل

استادیار

• Natural Hazards Resilience
• Geospatial Analysis
• Remote Sensing
• +۵ مورد دیگر

Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.

• Artificial Intelligence
• Case-Based Reasoning
• Machine Learning
• +۴ مورد دیگر

Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.

• Data Mining
• Machine Learning
• Relational Learning
• +۱۲ مورد دیگر

Jennifer Neville is a Senior Principal Researcher at Microsoft Research Redmond and holds the Samuel Conte Chair Professor of Computer Science and Statistics at Purdue University. With over 100 publications and 10K citations, her research spans data mining, machine learning, and AI algorithms for relational and networked domains including social networks, epidemiology, and web analytics. Education: BS in Computer Science, University of Massachusetts Amherst (2000) MS in Computer Science, University of Massachusetts Amherst (2004) PhD in Computer Science, University of Massachusetts Amherst (2006) Her work focuses on relational learning techniques that exploit connections between entities to enhance pattern discovery. Recent research explores large language models (LLMs), emphasizing alignment with user intent through interaction at scale, while addressing statistical biases from graph structures. Selected scientific awards include the NSF Career Award (2012), ICDM Best Paper (2009), and IEEE’s 10 to Watch in AI (2008). She served on the AAAI Executive Council (2015-2018) and chaired multiple conferences including SIAM Data Mining (2019) and ACM Web Search (2016). Contact: neville@cs.purdue.edu jenneville@microsoft.com

• Verification and Validation
• Search-Based Software Engineering
• Autonomous Driving
• +۳ مورد دیگر

Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.

Manos Kapritsosمشاهده پروفایل

دانشیار

• Distributed Systems
• Formal Verification
• Fault Tolerance
• +۳ مورد دیگر

Manos Kapritsos is an Associate Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. He leads the GLaDOS research group focusing on reliability of distributed systems through formal verification and fault-tolerant replication techniques. His research spans: Formal verification of concurrent and distributed systems Fault-tolerant replication protocols beyond client-server models Automation of verification processes for complex systems Performance verification including latency properties Reliable cryptographic code implementation Analysis of his publications reveals strong emphasis on: developing automated verification tools (Armada, Vale, IronFleet), creating novel replication protocols (Aegean), verifying performance characteristics (Performal), and improving specification reliability (IronSpec). His work consistently bridges theoretical formal methods with practical systems implementation. Awards and honors include: Jay Lepreau Best Paper Award at OSDI 2025 Jon R. and Beverly S. Holt Award for Excellence in Teaching (2022) NSF CAREER Award (2021) Distinguished Paper Award at PLDI 2020 Google Faculty Award (2017) Distinguished Paper Award at USENIX Security 2017 Grant support includes NSF FMitF grants (2020, 2023), NSF Large grant (2021), DARPA grant (2020), and Google Faculty Award (2017). He advises PhD students through the GLaDOS group, focusing on distributed systems verification. He directs the GLaDOS lab at University of Michigan, developing verification frameworks and reliable distributed systems. Current projects include automated proof generation (Basilisk) and efficient communication protocols (Scrooge).

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human Factors
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yu Huang is an Assistant Professor in the Department of Computer Science at Vanderbilt University's School of Engineering, with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her academic journey began with a BS in Aerospace Engineering from Harbin Institute of Technology in China (2011), followed by an MS in Computer Engineering from the University of Virginia (2015), and culminated with a PhD in Computer Science and Engineering from the University of Michigan in 2021 under Professor Westley Weimer. Dr. Huang's research bridges human cognition and machine intelligence to enhance software development. Her work spans software, hardware, AI, medical imaging (fMRI/fNIRS), eye tracking, and mobile sensing through collaborations with Security, Education, Psychology, and Neuroscience researchers. She leads the MIND Lab (Mixed INtelligence Development for programming lab), investigating programming expertise formation, code comprehension processes, cognitive error patterns, and diversity in programming communities. Her innovative approach combines empirical human studies with AI model development to create more effective programming tools. Her recent publications reveal a growing emphasis on leveraging human attention data to improve code language models, analyzing cognitive biases in security contexts, and examining social factors in technical communication. The research shows strong interdisciplinary connections between neuroscience, psychology, and software engineering, with increasing applications of LLMs in developer tooling. Dr. Huang's work consistently demonstrates how understanding human cognition can inform better AI systems for programming tasks. Dr. Huang has received numerous prestigious recognitions including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards (ICSE 2019, FSE 2023, ICSE 2024). Her lab has earned the Best Presentation Award at GI2024, while her students have received the Richard Bennett/Dorothy Danforth Compton Prize scholarship and the C. F. Chen Best Paper award. She actively mentors a diverse team of graduate students (Yifan Zhang, Zach Karas, Zihan Fang, Yueke Zhang, Jiahao Zhang) and undergraduate researchers, with many former students advancing to top institutions (Stanford, Harvard, Duke, UC Berkeley) and organizations (NASA JPL). Her research is supported by a 4-year NSF grant, GitHub Tech for Social Good funding, and the Provost's Faculty Immersion Vanderbilt Grant, enabling comprehensive studies of human-AI collaboration in software engineering. The MIND Lab maintains a strong collaborative culture, frequently working with Professor Kevin Leach's research group and organizing retreats to locations like Radnor State Park and the Great Smoky Mountains. This environment fosters innovation at the intersection of human cognition and software engineering while supporting the professional development of emerging researchers in the field.

Daniel Catchpooleمشاهده پروفایل

استاد

• Childhood Cancer Research
• Genomics
• Biomedical Data Science
• +۶ مورد دیگر

Professor Daniel Catchpoole serves as Deputy Head of School (Research) at the School of Computer Science, University of Technology Sydney (UTS), holding dual appointments at UTS and The Children's Hospital at Westmead. With over 20 years of research experience, he bridges computational sciences and pediatric cancer research through the Biomedical Data Science Lab in the Australian Artificial Intelligence Institute. His work integrates data analytics, artificial intelligence, and software development with molecular cancer biology to transform pediatric cancer treatment pathways. PhD in Cancer Cell Biology, University of New South Wales (1991-1995) Founding Fellow, Royal College of Pathologists Australasia (2010-present) Head, Children's Hospital at Westmead Tumour Bank (2001-present) Professor Catchpoole's research focuses on translational applications of genomics in childhood cancers, particularly acute lymphoblastic leukemia and neuroblastoma. His work combines high-throughput genomic technologies with advanced computational analysis to develop systems biology approaches for cancer patient assessment. Recent projects explore virtual reality applications for complex genomic data visualization and copper chelation therapies to enhance neuroblastoma immunotherapy. His research has received significant funding from Cancer Institute NSW, Sony Foundation, ARC, and NHMRC. His publication record spans biomedical data science, cancer genomics, and virtual reality applications in oncology. Recent work demonstrates leadership in 3D latent diffusion models for tumor segmentation, biobank economics, and innovative immunotherapies. His research consistently addresses the critical need for actionable knowledge from complex multidimensional biomedical data. Editorial Board Member, Cancers (2023) Associate Editor, Innovations in Digital Health, Diagnostics and Biomarkers (2019) Founding member and first President, Australasian Biospecimens Network Association Professor Catchpoole has supervised 17 Honours students (including 6 First Class Honours), 3 MSc students, and 12 PhD candidates across multiple institutions, with 6 current PhD students. His collaborative research bridges UTS's Faculty of Engineering and IT with The Children's Cancer Research Unit at The Children's Hospital at Westmead. Significant research funding includes Cancer Institute NSW grants, Sony Foundation VR projects, and ARC Discovery Projects focused on genomic data analysis and clinical decision support systems. His leadership extends to building frameworks for translational research, managing biobanks and clinical data linkages, and navigating governance requirements for cancer research. The Tumour Bank at Kids Research, CCRU, represents his long-standing commitment to pediatric cancer infrastructure development.

Luciano Lavagnoمشاهده پروفایل

استاد

• Field Programmable Gate Arrays (FPGA)
• High-Level Synthesis
• Indoor Positioning
• +۱۰ مورد دیگر

Luciano Lavagno is a Full Professor at the Department of Electronics and Telecommunications, Polytechnic University of Turin, with over two decades of academic and research contributions. His work bridges hardware acceleration, low-power electronics, and intelligent system design. Research Focus: Hardware-accelerated machine learning, high-level synthesis (HLS) for FPGA/ASIC, heterogeneous CPU/GPU/FPGA platforms Key Projects: SPACE (predictable acceleration), REBECCA (secure AI acceleration), HPC-National Center (quantum computing), and oral history preservation via "Ti racconto una storia" initiative His recent publications analyze CNN inference optimization, subgraph isomorphism, and superword-level parallelism exploitation. Lavagno supervises multiple PhD students working on FPGA acceleration, neural network hardware, and embedded systems. As Principal Investigator for national and EU-funded projects (PRIN, JTI-ECSEL, PNRR), he drives advancements in sustainable computing infrastructure. His patented technologies include MIx&Latch timing methodology, capacitive sensing innovations, and 5G acceleration frameworks.

Cheng Zhangمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Ubiquitous Computing
• Wearable Technology
• +۴ مورد دیگر

Cheng Zhang is an Associate Professor (with Tenure) in Information Science and a Field Member in Computer Science at Cornell University. He directs the Smart Computer Interfaces for Future Interaction (SciFi) Lab , focusing on integrating human-centered AI with advanced sensing technologies to empower everyday wearables. Ph.D. in Computer Science, Georgia Institute of Technology (2020) M.S. in Software Engineering, Chinese Academy of Sciences B.S. in Software Engineering, Nankai University His research examines how to solicit information on and around the human body to address real-world challenges in interaction, health sensing, and activity recognition. He builds novel sensing systems spanning hardware prototypes, algorithm design (machine learning and physics-based modeling), and high-impact applications in accessibility and health. Article Trends : His recent work includes low-power, minimally intrusive wearables (e.g., EchoForce for muscle activity tracking, Ring-a-Pose for hand poses, SeamFit for smart clothing) using acoustic sensing and machine learning. The 15 most recent articles span 2025–2023, with applications in silent speech, authentication, and pose estimation. Scientific Awards : NSF CAREER Award Ubicomp 10-Year Impact Award Best Paper Honorable Mentions at ISWC’24 and ISWC’23 Advising : Mentored Ph.D. students like Ruidong Zhang (Qualcomm Fellowship recipient) and Ke Li, with research featured in Cornell Chronicle and IEEE Spectrum .

Cuiyun Gaoمشاهده پروفایل

استاد

• Software Repository Mining
• Natural Language Processing
• Code Analysis
• +۵ مورد دیگر

Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.