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Dongdong ChenView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Image Processing
• +4 more

Dongdong Chen serves as an Assistant Professor in the Department of Computer Science at Heriot-Watt University's School of Mathematical and Computer Sciences in Edinburgh, United Kingdom. His research profile indicates active supervision of PhD students with full scholarships available, demonstrating his established position within the academic community. Current research outputs show consistent publication activity from 2020 through 2024 with increasing impact. Dr. Chen's research interests center on machine learning applications for imaging systems, with particular expertise in image processing, computer vision, computational imaging, and inverse problems. His work bridges theoretical machine learning with practical applications, especially in medical imaging contexts. The fingerprint analysis of his publications reveals strong connections to Deep Learning (31%), Unsupervised Learning (25%), and Inverse Problems (43%), indicating a cohesive research trajectory focused on unsupervised frameworks for imaging challenges. Analysis of his 15 most recent publications (2021-2025) shows a clear evolution toward unsupervised and equivariant learning approaches for inverse problems, with increasing focus on diffusion models and medical applications. The research demonstrates strong theoretical foundations combined with practical implementations, particularly in MRI and medical diagnostics. Citation metrics indicate significant impact, with several papers exceeding 40 Scopus citations. His notable scientific achievements include: IES Best Student Paper Award (2014) MICCAI'18 BIA Best Paper Nomination (2018) Dr. Chen actively mentors PhD students and has attracted research attention through media coverage of his ICCV paper on Equivariant Imaging. His research contributes to UN Sustainable Development Goals through imaging technology development. Current activities include recruiting PhD students for projects in Edinburgh with full scholarship support, indicating ongoing research expansion and team development.

Christoph CsallnerView profile

Professor

• Software Engineering
• Program Analysis
• Automated Bug Finding
• +5 more

Christoph Csallner is a Professor in the Computer Science and Engineering Department at the University of Texas at Arlington (UTA), where he leads research in software engineering, program analysis, and mobile software development. Prior to joining UTA, he worked at Google and Microsoft Research. He directs the SERC lab (Software Engineering Research Center) and has established himself as a leading researcher in automated bug finding, reverse engineering, and Simulink analysis. University of Texas at Arlington, Professor, Computer Science and Engineering Department SERC Lab Director Former researcher at Google and Microsoft Research Dr. Csallner earned his Diplom-Informatiker degree from Universität Stuttgart, Germany, and both his M.S. and Ph.D. in Computer Science from Georgia Tech. His educational background in both European and American institutions has informed his interdisciplinary research approach that bridges theoretical foundations with practical software engineering challenges. Dr. Csallner's research spans multiple areas of software engineering with particular expertise in program analysis, automated bug finding, and mobile software engineering. His work on reverse engineering mobile application user interfaces with REMAUI has been particularly influential in the field. His recent research focuses on Simulink analysis with projects like SLNET, ScoutSL, and EvoSL, which have created foundational datasets and tools for the model-based development community. His work on PSDoodle and D2S2 has advanced mobile app screen search through innovative sketch-based interfaces. His research consistently bridges theoretical program analysis with practical applications for real-world software development challenges. Dr. Csallner's publications demonstrate a consistent pattern of innovation in software testing, analysis, and reverse engineering. His recent work shows a strong focus on mobile application analysis, Simulink model processing, and the application of machine learning techniques to traditional software engineering problems. He has successfully built bridges between formal methods and practical software development tools. Best Paper Award - IEEE ISSRE 2010 ACM SIGSOFT Distinguished Paper Awards - ISSTA 2006, 2012 Best Paper Award - PPREW 2014 Best Paper Award - ASE 2007 ACM SIGSOFT Distinguished Paper Award - ASE 2015 Distinguished Referee Award - ASE 2019 Distinguished Reviewer Award - TOSEM 2011-2012 Dr. Csallner has successfully advised numerous Ph.D. and Master's students who have gone on to prominent positions at companies including Meta, Google DeepMind, BNSF Railway, Bloomberg, and Salesforce. His research has been supported by significant funding from the National Science Foundation, MathWorks, the Alzheimer's Association, and the Texas National Security Network. His work on fake news detection received media coverage from major outlets including the Dallas Morning News, NBC DFW, and WBAP/KLIF. Dr. Csallner directs the Software Engineering Research Center (SERC) lab at UTA, where his team develops innovative tools for software analysis and testing. Notable projects include JCrasher, Check 'n' Crash, DSD-Crasher, Pex/DySy, Dsc, and REMAUI/Pixel to App. His recent work focuses on PSDoodle, TreeVada, ScoutSL, and EvoSL, demonstrating his lab's continued innovation in software engineering research. The SERC lab maintains strong industry connections, particularly with MathWorks, and has produced numerous open-source tools that have influenced both research and practice in software engineering.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.