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Marjorie ZielkeView profile

Research Professor

• Medical Simulation
• Virtual Reality
• Artificial Intelligence
• +3 more

Marjorie Zielke is a Research Professor and Director of the Center for Simulation and Synthetic Humans at the University of Texas at Dallas (UT Dallas). She specializes in virtual human systems, medical simulation technologies, and interdisciplinary research at the intersection of arts, technology, and healthcare. Her academic roles include leading the Center for Modeling and Simulation, overseeing projects funded by NSF, NIH, and other agencies, and directing collaborations with institutions like Southwestern Medical Foundation and Sam Houston State University. Her work has been recognized with multiple awards, including first place at the International Meeting of Simulation in Healthcare (IMSH) and IITSEC. Education: PhD in Aesthetic Studies (UT Dallas, 2007), MBA (UT Arlington, 1980), and BS in Journalism (University of Illinois, 1977). Research focuses on virtual patients, augmented reality in medical training, and game-based learning platforms. She has pioneered projects like the Emotive Virtual Patient and the Individual Nystagmus Simulated Training Experience (INSITE), addressing clinical communication and law enforcement training needs. Grants and Projects: Over $3M in funding for initiatives such as the NSF-funded Virtual Reality Graded Exposure (VRGE) program and the Texas Department of Transportation-supported INSITE project. She also leads the Virtual Dyslexia Therapist initiative under a Texas Scottish Rite Hospital grant. Teaching includes managing interactive media projects and graduate courses on cyberpsychology and autonomous technology design.

Yu XiangView profile

Assistant Professor

• Robotics
• Computer Vision
• 3D Environment Perception
• +5 more

Dr. Yu Xiang is an Assistant Professor of Computer Science at the University of Texas at Dallas (UT Dallas), leading the Intelligent Robotics and Vision Lab (IRVL) . He holds a Ph.D. in Electrical and Computer Engineering from the University of Michigan (2016) and prior roles include Senior Research Scientist at NVIDIA (2018–2021) and postdoctoral research at the University of Washington. Research Focus : His work centers on robotics and computer vision , particularly enabling robots to perceive 3D environments, plan actions, and interact autonomously in human-centric spaces. Key areas include unseen object segmentation, 6D pose estimation, manipulation trajectory optimization, and lifelong learning through robot-environment interaction. Key Contributions : Developed datasets like MultigripperGrasp and HO-Cap , and pioneered methods such as DeepIM for 6D pose estimation. His lab’s robot Ramp focuses on tasks like object manipulation and human-robot collaboration. Grants : NSF SMILE grant ($750K), DARPA Perceptually-enabled Task Guidance (co-PI), Sony Research Award (PI). Awards : NVIDIA Academic Grant (2024), Sony Research Award (2022), ECCV Best Paper (2018). Lab Activities : Engages in STEM outreach, including mentoring high school students in the 2024 Summer Bridge Camp. Current projects emphasize self-supervised learning and embodied AI for robotic systems.

Graziano FiorilloView profile

Assistant Professor

• Civil Engineering
• Structural Analysis
• Risk and Reliability Engineering
• +4 more

Dr. Graziano Fiorillo is an Assistant Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a Ph.D. from the City University of New York and M.Sc./B.Sc. from the University of Naples, Italy. His research focuses on structural reliability, bridge systems analysis, and risk assessment, incorporating machine learning and high-performance computing. He has contributed to probabilistic frameworks for infrastructure resilience, filovirus outbreak modeling, and bridge redundancy evaluation. Education: Ph.D. Civil Engineering, City University of New York, 2016 M.Sc. Building Engineering, University of Naples Federico II, 2003 B.Sc. Building Engineering, University of Naples Federico II Research Interests: Dr. Fiorillo specializes in structural analysis of bridges, risk-based design, and machine learning applications in infrastructure. He develops probabilistic models for bridge network reliability and flood risk assessment, with a focus on Manitoba's infrastructure resilience. His work integrates computational fluid dynamics (CFD) and energy efficiency solutions for buildings. Publications: His recent work emphasizes interdisciplinary approaches to infrastructure challenges, including CFD for sediment transport, EnergyPlus-based building efficiency studies, and MPI parallel computing for reliability analysis. His 2024 studies on flood-overload interactions and additive manufacturing in construction highlight emerging trends in civil engineering. Awards: He received the 2012 New York State Intelligent Transportation Society Award for best student paper. His research has been applied to truck weight regulation strategies and bridge importance factor calibration. Advising & Grants: Offers M.Sc. opportunities in CFD, building energy efficiency, and bridge structures. Positions require expertise in OpenFOAM, EnergyPlus, or structural analysis software. No specific grants mentioned in the text.

• Software Engineering
• Model Driven Engineering
• Software Product Lines
• +7 more

Jean-Marc Jezequel is a Professor of Software Engineering at University of Rennes , affiliated with CNRS , Inria , IRISA , and Institut Universitaire de France (IUF) . His research focuses on Model-Driven Engineering , Software Product Lines , Dynamic Adaptation , and Executable Meta-languages . Key Contributions : Pioneering work in aspect-oriented and model-driven approaches for software evolution Foundational research on model transformations (e.g., UMLAUT framework) Advances in testing and validation of distributed systems Research Trends from his recent publications include: Intelligent modeling assistance integrating machine learning Contextual variability modeling for complex systems Runtime model execution for self-adaptive systems Formal methods and constraint resolution for UML validation Collaborations include researchers from Luxembourg, Montreal, Colorado State University, and INRIA.

Joanna C. S. SantosView profile

Assistant Professor

• Software Engineering
• Software Security
• Program Analysis
• +4 more

Joanna C. S. Santos is an Assistant Professor at the University of Notre Dame's Department of Computer Science and Engineering. She leads the Security and Software Engineering research lab (S²E) and focuses on Software Engineering, Security, and Program Analysis. Her work bridges empirical studies with practical tool development. PhD in Computing and Information Sciences (Rochester Institute of Technology) M.Sc. in Software Engineering (Rochester Institute of Technology) B.Sc. in Computer Engineering (Federal University of Sergipe) Her research spans Software Security (vulnerability detection, ReDoS), Code Generation (LLM evaluation, benchmarking), and Program Analysis (taint tracking, call graphs). Recent articles show a strong focus on LLM-generated code quality and quantum computing applications. Scientific Awards : 2023 - Distinguished Reviewer (ESEC/FSE) 2020 - Research Pitch Winner (JOBS @MICRO) 2017 - Best Paper (ICSA) 2014 - CAPES Scholarship 2013 - ERBASE 3rd Place She actively contributes to conference committees (OOPSLA, ICSE, SCAM) and collaborates across institutions. Her lab S²E drives research in secure software development and empirical cybersecurity validation.

• mHealth Application Development
• Ontology
• Machine Learning
• +3 more

Daniel Campbell is a Lecturer in Web Development & Web AI at the Computer Science department of Edge Hill University. His work contributes to UN Sustainable Development Goals related to health and innovation. He is affiliated with the Centre for Intelligent Visual Computing and the Data and Complex Systems Research Centre. Education: He completed his Doctoral Thesis in 2018 titled 'An Ontology-Driven Approach To Personalised mHealth Application Development' under supervisors E. Pereira, G. McDowell, and C. Balakrishna. Research focuses on mHealth applications, ontology-driven frameworks, machine learning for health monitoring, and software engineering practices like bug prediction and open-source repository analysis. Recent projects include a Knowledge Exchange initiative with the water industry (2024-2026) as a Co-Investigator. His articles explore topics ranging from accelerometer-based elderly activity prediction to automated classification of software repository messages. Collaborations span institutions globally, with active engagement in topics like healthcare technology and user-centric design.

• Forest Inventory and Monitoring
• LiDAR Technology Applications
• Forest Regeneration Quantification
• +3 more

Tobias Ofner-Graff is a researcher at the Institute of Forest Growth within the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Based at Peter-Jordan-Straße 82, 1190 Wien, his work focuses on advanced forest monitoring technologies. His research interests include: LiDAR and remote sensing applications in forestry Automated forest inventory systems Forest regeneration quantification Airborne Laser Scanning (ALS) data analysis Sustainable forest harvesting planning Recent project contributions include: Leading lidar-based forest monitoring systems development Developing spatial forest growth models Implementing digital inventory workflows His publications demonstrate expertise in: Quantifying forest resources through 3D point clouds Advanced timber stack measurement techniques ALS data integration for forest modeling Mobile laser scanning applications Forest climate adaptation strategies

JIANG LingxiaoView profile

Professor

• Software Engineering
• Artificial Intelligence
• Data Science
• +7 more

Professor Lingxiao Jiang is a full-time faculty member at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU) , where he serves as Director of the Centre for Research on Intelligent Software Engineering (RISE) . His research and teaching focus on software engineering , program analysis , code search & reuse , and deep learning of code to enhance software quality, development productivity, and security. PhD, University of California, Davis (2009) Professor Jiang's research explores context-aware software deep learning , code clone detection , and security & privacy through tools like DECKARD (code clone detection), EqMiner (functionally equivalent code), and SmartEmbed (smart contract security). His work leverages distributed computing , symbolic execution , and neural program models for large-scale code analysis. Recent publications highlight trends in deep learning-based code analysis , smart contract vulnerabilities , and automated program transformation . Key themes include code clone detection , semantic patches , and cross-language API mappings . Scientific Awards ACM SIGSOFT Impact Paper Award (2018) for scalable code clone detection As advisor, Professor Jiang has mentored 14 graduate students, including Lucia (Ph.D. 2014) , Shaowei Wang (Ph.D. 2015) , and current advisees in intelligent software engineering. He leads multiple grants on code mining , security of digital platforms , and AI systems governance . RISE Lab at SMU develops tools like MANDO (smart contract analysis), iTiger (issue title generation), and TreeCaps (code processing with capsule networks). The lab actively recruits researchers in software engineering and AI.

Guanpeng LiView profile

Assistant Professor

• High-Performance Computing (HPC) Fault Tolerance
• Lossy Compression for HPC Data Reduction
• Safety of Autonomous Driving Systems
• +2 more

Guanpeng Li is an Assistant Professor in the Department of Computer Science at the University of Iowa since 2020. His research focuses on building dependable high-performance computing systems, with emphasis on fault tolerance, data reduction, and safety in autonomous systems. Ph.D., University of British Columbia (2019) Postdoc, University of Illinois Urbana-Champaign (2020) BASc, University of British Columbia (2014) Research interests include: HPC Fault Tolerance and Error Propagation Analysis Lossy Compression Techniques for Scientific Data Safety Assurance for Autonomous Driving Systems Dependability of Machine Learning Applications Recent publications reveal trends in GPU-based fault detection, error-bounded compression, and autonomous systems security. His team has contributed to IEEE/ACM SC, IPDPS, DSN, and ISSRE conferences. Scientific awards include: NSF CAREER Award (2025) IEEE TCHPC Early Career Researchers Award (2024) Multiple Best Paper Awards at SC, DSN, and ISSRE (2024-2018) IEEE Top Picks in Test and Reliability (2023, 2024) Guanpeng Li advises active PhD students and collaborates with institutions like the University of British Columbia and Intel. His work impacts real-time safety systems and deep learning frameworks.

Zeyu DingView profile

Assistant Professor

• Privacy
• Algorithmic Fairness
• Machine Learning
• +5 more

Zeyu Ding is an Assistant Professor in the School of Computing at Binghamton University, with a courtesy appointment in the Department of Mathematics and Statistics. He holds two PhDs: one in Computer Science from Penn State University and another in Mathematics from Binghamton University, along with a BS in Mathematics from Zhejiang University. Research Interests His work focuses on the intersection of privacy, security, machine learning, and algorithmic fairness. He investigates how to protect sensitive personal information through differential privacy mechanisms, formal verification, numerical optimization, and privacy-preserving statistical inference. Article Trends Ding's publications highlight advancements in differential privacy, including the Report Noisy Max with Gap Mechanism and the Permute-and-Flip approach. His research also addresses security challenges like reconstruction attacks and automated verification tools (e.g., Checkdp and DPGen), alongside mathematical explorations of automorphism group schemes and Barsotti-Tate groups. Scientific Awards CCS Outstanding Paper Award, 2018 Caper Bowden PET Award Runner-up, 2019 CCS Best Paper Award Runner-up, 2020 CCS Best Paper Award Runner-up, 2021 Research Award from Penn State University, 2019 Teaching Award from Penn State University, 2021 His research is supported by the NSF grant 2317233, underscoring his contributions to privacy-preserving computational methods.

• Aircraft Systems Engineering
• Avionics
• Integrated Modular Avionics (IMA)
• +12 more

Prof. Dr.-Ing. Frank Thielecke is a full Professor and the head of the Institute of Aircraft Systems Engineering (Flugzeug-Systemtechnik) at Technische Universität Hamburg (TUHH), Germany. His research is centered on advanced aircraft systems, avionics, flight control, and the integration of emerging technologies such as hydrogen and hybrid-electric propulsion. Institution: Technische Universität Hamburg Department: Institute of Aircraft Systems Engineering (Flugzeug-Systemtechnik) Email: frank.thielecke@tuhh.de Office: Neßpriel 5, Room 1.012, 21129 Hamburg His research interests include integrated modular avionics (IMA), model-based systems engineering (MBSE), aircraft load estimation, health monitoring, fault diagnosis, and sustainable aviation technologies. He leads a research group actively contributing to next-generation aircraft design, with a strong focus on digitalization, virtual testing, and system safety. The recent publications highlight a consistent trend in developing model-based tools and architectures for avionics and aircraft systems. Key themes include the design of IMA platforms, virtual integration, system validation, hydrogen aircraft systems, and control algorithms for UAVs and flexible aircraft. His work frequently appears in AIAA, DASC, DLRK, and CEAS conferences and journals. Prof. Thielecke has been involved in numerous collaborative research projects focusing on more-electric aircraft, fuel cell systems, and advanced actuation. He has contributed to the development of frameworks such as ASHLEY and SArA for avionics platform design and systems architecting. His team also works on noise reduction in hydraulic systems and condition monitoring for aircraft subsystems. He supervises a group of researchers and PhD students, many of whom co-author his publications. While specific student names are not listed, long-term collaborators like Oliver Luderer, Thimo Bielsky, Nils Külper, and Philipp Chrysalidis are likely doctoral candidates or postdoctoral researchers in his group. He has secured funding for projects related to hydrogen aircraft, hybrid propulsion, and digital avionics engineering. His lab, the Institute of Aircraft Systems Engineering, operates test benches for avionics, hydraulic systems, and flight control validation. The team uses advanced simulation, co-simulation (e.g., FMI), and hardware-in-the-loop techniques for virtual integration and testing. Ongoing work includes the development of tools for early validation of flight control platforms and automated requirement-based testing.

• Trustworthy AI
• Software Testing & Verification
• Requirements Engineering
• +5 more

Prof. Lionel C. Briand is a leading academic in software engineering and trustworthy AI, holding appointments at the University of Ottawa (EECS Department, Nanda Laboratory) and the University of Limerick (Lero - National Software Research Centre). He serves as Director of Lero and Scientific Director of the SnT software verification lab in Luxembourg. His research focuses on software testing, model-driven engineering, AI-driven quality assurance, and regulatory compliance. He has held the Canada Research Chair (Tier 1) since 2003 and led major institutions like Simula Research Laboratory (Norway) and Fraunhofer Institute (Germany). Education & Career: Full Professor at Carleton University (2008–2012) Head of Software Quality Engineering at Fraunhofer IESE (2000–2008) Research Scientist at NASA Software Engineering Lab (1990s) Research Interests: His work spans secure AI systems, automated legal compliance (e.g., GDPR), metamorphic testing, search-based software engineering, and safety-critical systems. He emphasizes practical applications, collaborating with industry partners globally. Awards & Recognition: IEEE Fellow (2010), ACM Fellow (2020) Harlan Mills Award (2012), ERC Advanced Grant (2016) Fellowships from Royal Society of Canada (2023) and Academia Europaea (2025) Grants & Labs: PEARL grant from Luxembourg FNR for SnT lab ERC Advanced Grant for software testing research Leadership roles in Lero and Nanda Lab Publications: Over 500+ papers on testing methodologies, AI ethics, and regulatory compliance. Notable tools include CompAI (GDPR compliance) and Teasma (DNN test adequacy).

• Biophotonics
• Nanotechnology
• Fluorescence Applications
• +7 more

Professor Ewa Goldys is a distinguished academic at the University of New South Wales (UNSW), serving as a Professor in the School of Engineering with a focus on Biomedical Engineering. She holds the prestigious position of Deputy Director at the ARC Centre of Excellence for Nanoscale Biophotonics (CNBP), where she leads partnerships, knowledge transfer, and research commercialization efforts. Her work spans interdisciplinary research connecting engineering, medicine, and biology, with significant contributions to biophotonics and nanotechnology applications in healthcare. Professor Goldys' research interests center around advanced imaging techniques, particularly autofluorescence characterization, which provides a non-invasive metabolic 'fingerprint' for distinguishing healthy from diseased cells. Her work has significant applications in cancer, diabetes, and neurodegenerative diseases. She has pioneered research in fluorescent and luminescent nanomaterials for biological applications, developing innovative approaches for high-contrast, background-free imaging using time-gating techniques. Her research portfolio also includes significant contributions to CRISPR-based biosensing, stem cell characterization, and non-invasive diagnostic approaches using multispectral imaging. Her publication record demonstrates a clear trend toward increasingly sophisticated applications of autofluorescence imaging combined with machine learning and molecular techniques. Recent work integrates hyperspectral imaging with transcriptomics, develops CRISPR-based point-of-care diagnostics, and applies autofluorescence techniques to diverse medical challenges from kidney disease diagnosis to immune cell characterization. The interdisciplinary nature of her work spans oncology, immunology, nephrology, and regenerative medicine. Professor Goldys has received notable recognition for her work, including: Eureka award in 2016 for Innovative Use of Technology Her research leadership has secured substantial funding, including directing the $23 million ARC investment in the CNBP (matched by $17 million from partners), establishing the $2 million ARC/NHMRC Network 'Fluorescence Applications in Biotechnology and Life Sciences,' and leading research that leveraged an additional $155 million in external funding. She has founded the Optical Characterisation Facility at Macquarie University and has been instrumental in establishing international research networks in biophotonics. Professor Goldys has made significant contributions to the international biophotonics community through conference organization, having chaired 11 conferences including SPIE 'Biophotonics Australasia' and serving as Track Chair for Nanobiophotonics at BIOS, the world's largest biomedical optics meeting. Her work has established foundational methodologies in autofluorescence characterization that continue to drive innovation in label-free medical diagnostics.

• Compilers
• Automatic Software Verification
• Compiler Optimization
• +7 more

Nuno Pereira Lopes is an Associate Professor at Instituto Superior Técnico , part of Universidade de Lisboa , and a researcher at INESC-ID . He also serves as an advisor at FuriosaAI , focusing on tensor contraction processors for AI workloads. Research Interests : Compilers, formal verification of LLVM optimizations, machine learning frameworks, undefined behavior exploitation, probabilistic model checking, blockchain security, and many-core code generation. Teaching : Compilers and Computer/Informatics Engineering projects. Funding : Supported by Google, Matter Labs, NLnet, Oracle, PRACE, RNCA, and Woven by Toyota. Recent Publications focus on LLVM backend validation , PyTorch pipeline parallelism , C++ dynamic cast optimization , undefined behavior in C/C++ , and AI tensor processors . His work bridges compiler design, formal methods, and AI hardware. Academic Service includes representing Portugal in ISO/IEC JTC 1/SC 22 (C++), organizing FLoC'26 , and serving on program committees for PLDI, EuroLLVM, and CGO.

• Software Engineering
• Cybersecurity
• Artificial Intelligence
• +5 more

Philipp Haindl is a lecturer at the Department of Computer Science and Security at St. Poelten University of Applied Sciences. His work focuses on software engineering, cybersecurity, and AI integration in education and manufacturing. Software Engineering Cybersecurity Artificial Intelligence DevOps Quality Assurance Research Interests: Dr. Haindl explores software metrics, inter-service security in microservices, and AI tools like ChatGPT in programming education. He investigates quality models and non-functional requirements in DevOps environments. Publications: Recent work includes studies on ChatGPT's impact in software engineering education, systematic reviews of microservice security, and frameworks for human-AI teaming in manufacturing.