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Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +3 more

Dimosthenis Kyriazis is a Professor at the University of Piraeus, affiliated with the Research Center. He currently teaches courses such as C Programming, e-Business, and Information Systems. His research focuses on service-oriented architectures, cloud/edge computing, data management, and healthcare informatics. Kyriazis holds a PhD from the National Technical University of Athens (NTUA) and has contributed to EU-funded projects like BigDataStack and CrowdHEALTH, addressing quality of service, workflow management, and IoT applications. He leads research on data management in cloud/edge environments, socially-enhanced IoT management, and big data applications in sectors like finance and e-health. His expertise spans distributed systems, software engineering, and interdisciplinary collaborations with industries and academia. He has participated in EU working groups on Future Internet Architecture and Cloud QoS&SLAs. Kyriazis actively seeks interns and collaborates on initiatives like AI-driven healthcare platforms (iHELP) and sustainable computing practices. His work emphasizes human-centric AI, data interoperability, and ethical AI frameworks such as AI4Gov for transparent governance. Education: Diploma in Electrical and Computer Engineering (NTUA, 2001), MSc in Techno-economics (NTUA/University of Athens/University of Piraeus, 2004), PhD in Service-Oriented Architectures (NTUA, 2007). Key research areas include federated data marketplaces (FAME), healthcare data integration (holistic health records), and AI applications in finance (DeepVaR). His publications address topics like explainable AI (XAI), defect detection in manufacturing, and environmental risk correlation with health. Grants and collaborations involve coordinating EU projects targeting data governance frameworks, energy-efficient mobility data spaces (Mobispaces), and AI for policy-making (e.g., OECD AI policy analysis). His contributions bridge technical innovation with societal impact through sustainable computing and ethical AI practices.

• Service-Oriented Architectures
• Data Management
• Cloud Computing
• +5 more

George Papagiannakis is a Professor of Computer Graphics at the University of Crete, Greece, and a Marie Sklodowska-Curie Fellow at FORTH's Institute of Computer Science. He holds visiting professorships at the University of Geneva and leads the Computer Graphics Group. His research focuses on mixed reality, GPU programming, and medical XR applications. He earned his PhD from the University of Geneva (2006), M.Sc. from Bristol, and B.Eng. from Manchester. His work includes pioneering projects like MAGES SDK (medical XR training platform) and pyGANDALF (educational framework). Key contributions span over 130 publications, 3400+ citations, and €8M in research funding. He actively participates in EU projects (HDRi, Hifi-PRINTER) and has been awarded the Marie-Curie Fellowship, Best Paper Awards, and NVIDIA grants. Teaching includes advanced courses on computer graphics and programming at the University of Crete. He advises students across 4 postdocs, 15 PhDs, and numerous MSc/BSc candidates. His lab develops cutting-edge tools for immersive technologies, including geometric algebra applications in 5G/6G XR environments.

• Mixed Reality
• Computer Graphics
• Virtual-Augmented Reality
• +6 more

Weihang Wang is a WiSE Gabilan Assistant Professor in the Department of Computer Science at the University of Southern California. His research focuses on building testing and analysis techniques to improve the reliability, security, and efficiency of complex software systems, with particular expertise in WebAssembly technologies. Wang received his Ph.D. in Computer Science from Purdue University in 2018. His educational background has provided a strong foundation for his research in software engineering and security, leading to numerous publications and awards in the field. His research spans multiple critical areas including WebAssembly analysis , software security , and program analysis . Wang's work addresses fundamental challenges in detecting and mitigating vulnerabilities in modern web applications and systems. His research group develops innovative approaches to static and dynamic analysis, bug detection, and security testing, with a strong emphasis on practical applications and real-world impact. Recent projects include developing frameworks for Spring analysis, WebAssembly function identification, and business flow tampering detection. Wang's publication record shows a clear trajectory of increasing impact in WebAssembly research, with multiple papers at top-tier conferences like WWW, ICSE, and FSE. His work spans from foundational analysis techniques to practical tools that address security and performance challenges in web technologies, demonstrating both theoretical depth and practical applicability. His scientific achievements have been recognized with prestigious awards including: N2Women Rising Stars in Networking and Communications (2024) University at Buffalo Exceptional Scholar - Young Investigator Award (2022) NSF CAREER Award (2021) Facebook Testing and Verification Research Award (2019) Mozilla Research Award (2019) Maurice H. Halstead Memorial Research Award (2018) Best Poster Award at ACSAC'22 Wang has successfully advised numerous graduate and undergraduate students, many of whom have published first-author papers at top conferences and secured positions at leading technology companies. He actively serves the research community through program committee roles at major software engineering and security conferences. His work has been supported by competitive grants from NSF, Facebook, and Mozilla, demonstrating the significance and potential impact of his research directions. At USC, Wang leads a vibrant research group focused on software engineering, security, and systems. The group is currently working on cutting-edge projects related to static/dynamic bug detection, program analysis for WebAssembly, attack investigation and detection, compiler testing, and performance profiling of modern web applications.

• Software Engineering
• Software Security
• Computer Systems
• +6 more

Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.

• Program analysis
• Program synthesis
• Program verification
• +4 more

Swarat Chaudhuri is a Professor of Computer Science at the University of Texas at Austin and Senior Staff Research Scientist at Google Deepmind (currently on leave). He directs the Trishul laboratory, focusing on neurosymbolic AI at the intersection of programming languages, formal methods, and machine learning. His research aims to develop reliable, transparent intelligent systems capable of complex reasoning beyond contemporary AI. Education: Ph.D. in Computer Science, University of Pennsylvania (2007) Bachelor's in Computer Science, Indian Institute of Technology, Kharagpur (2001) Research Interests: Neurosymbolic programming, program synthesis, automated reasoning, and AI applications in code generation, mathematics, systems engineering, and scientific discovery. Key focuses include interpretability, safety certification, and robustness in learning-enabled systems. Honors & Awards: Guggenheim Fellow (2025) NSF CAREER Award ACM SIGPLAN John Reynolds Dissertation Award Multiple ACM Distinguished Paper Awards Meta/Google Research Awards Leadership & Advising: Directs Trishul Lab with 9 current PhD students. Alumni hold positions at Meta, Google, Penn State, and UC Berkeley. Served as Program Chair for ICLR 2024 and CAV 2016. Affiliations: Core faculty in UT's Machine Learning Laboratory, Programming Languages/Formal Methods group, and Texas Robotics affiliate.

• Programming Languages
• Formal Methods
• Machine Learning
• +5 more

Ranjit Jhala is a Professor of Computer Science Engineering in the Jacobs School of Engineering at the University of California, San Diego. His research focuses on building reliable computer systems through programming languages and software engineering techniques. His primary research interests include Programming Languages, Formal Verification, and Software Engineering. He draws from and contributes to areas such as Type Systems, Model Checking, Program Analysis, and Automated Deduction, bridging theoretical foundations with practical implementations for real-world software development. Prof. Jhala's publication record shows a consistent trajectory in refinement type systems, evolving from Liquid Haskell to Flux for Rust, while also exploring neurosymbolic approaches to error repair and type error diagnosis. His work demonstrates a commitment to making formal verification techniques accessible to practitioners. He leads the Programming Systems Group at UCSD, mentoring graduate students and collaborating with researchers across the programming languages community. His service includes General Chair roles for POPL 2018 and PLDI 2022, reflecting his leadership position in the field. Prof. Jhala is also known for his mentoring activities, including talks on academic presentation skills and participation in ICFP's mentoring programs for students and early-career researchers.

• Programming Languages
• Formal Verification
• Software Engineering
• +3 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more