Alvise SPANO' is a Researcher at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He is also part of the Research Institute for Complexity. His academic focus includes Blockchain Programming Languages, Programming Languages, and Information Systems. Research projects include ALGOMOVE for Algorand and the RINmaker bioinformatics tool. He has taught courses like Object-Oriented Programming and Introduction to Programming at both undergraduate and doctoral levels. Research interests span smart contract analysis, type systems, and cybersecurity in distributed systems. He contributed to projects like secure RPL for mobile networks and Android-LEGO interoperability. Funding includes the CEVID 2016 project (Role: LD), collaboratively managed with researchers like Agostino Cortesi and Salvatore Orlando. His work bridges theoretical foundations (e.g., COBOL code typing) with practical applications in wearable systems and bioinformatics. Collaborations include the Blockchain Programming Languages research group with Lorenzo Benetollo and Sabina Rossi.
Ziyang Li is an Assistant Professor of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. He holds a Ph.D. in Computer Science from the University of Pennsylvania (2025) and dual bachelor's degrees in Computer Science and Mathematics from UCSD (2019). Research Areas: Neurosymbolic Programming, AI4Code His research bridges programming languages and machine learning, focusing on neurosymbolic methods that combine symbolic reasoning with learning-based techniques. Applications span software security, computer vision, natural language processing, bioinformatics, and clinical decision-making. He developed Scallop , a neurosymbolic programming language, and Lobster , a GPU-accelerated framework for neurosymbolic applications, with impacts in cybersecurity and biomedical domains. Recent publications highlight neurosymbolic approaches for RNA structure prediction, Long COVID modeling, and safety-critical systems. His work emphasizes data-efficient learning, weak supervision, and hybrid AI for scalable reasoning. Scientific Awards : AWS Fellowship (2023) KPCB Fellows, Engineering (2018) NIH L3C Honorable Mention Award Li has mentored students including Jason Liu, Felix Zhu, and Eric Zhao, and served as Teaching Assistant for courses at UPenn and UCSD. He co-organized the TACPS Workshop and reviewed for NeurIPS, ICLR, and ICML.
Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.
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.
Ling Zheng serves as Associate Professor and Chair of the Department of Computer Science and Software Engineering at Monmouth University, a position she has held since joining in August 2018. She earned her Ph.D. in Computer Science from the New Jersey Institute of Technology and an M.S. in Biomedical Engineering from Zhejiang University, China. Her educational background includes: Ph.D., Computer Science, New Jersey Institute of Technology M.S., Biomedical Engineering, Zhejiang University Dr. Zheng's research integrates Biomedical Informatics , Ontology Engineering , and Data Science with focus areas in biomedical ontology summarization, genotype-phenotype correlation analysis, drug-drug interaction discovery, and semantic data mining. Her work develops quality assurance methodologies to enhance biomedical knowledge representation and discovery. Analysis of her 2018-2021 publications reveals consistent innovation in ontology auditing across major biomedical terminologies (UMLS, SNOMED CT, NCIt), pioneering structural anomaly detection and machine learning applications for ontology enrichment, notably using convolutional neural networks for SNOMED CT improvement. Award highlights: 2016: Fellowship, International Conference on Biological Ontology and BioCreative 2012: Excellent Postgraduate Students Award, Zhejiang University 2009: Excellent Postgraduate Students Award, Southern Medical University 2008: National Scholarship, Ministry of Education of China As department chair and instructor of courses including Advanced Object-Oriented Programming and Applied Machine Learning, her leadership spans academic administration and curriculum development, though research grants and student advising details were not specified in the source material.
Thomas P. Jensen is a Researcher at INRIA Rennes , France, specializing in program analysis , software security , and abstract interpretation . With a Cand. scient. in Computing and Mathematics from the University of Copenhagen (1990) and a PhD from Imperial College, University of London (1992), he has led research teams at INRIA, including the Celtique project-team (2010-2022) and currently the Epicure project-team (since 2022). He holds a Habilitation à diriger des recherches from Université Rennes 1 (1999). Research Focus Program analysis with type-based and big-step semantics approaches Certification of static analysis tools for embedded systems Software fault isolation and language-based security Information flow control through hybrid static-dynamic analysis His work spans Java security (including Java Card and mobile telephony) and formal verification of compilers and sandboxes. Notable projects include JavaSec , AJACS , and the CominLabs cybersecurity network. He received a Best Paper Award at GPCE 2018. Publications & Editorial Recent publications focus on algebraic data types , automata-based verification , and control-flow analysis with applications in cybersecurity . He has contributed to the Strategic research and innovation roadmap for SPARTA (2022) as editor. His work appears in top venues like POPL , PLDI , ICFP , and ESOP . Leadership Director of Laboratoire d'Excellence CominLabs (since 2022) Co-chair of VMCAI 2026 and member of PriSC 2024 Program Committee
Derek Dreyer serves as Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS) and holds the position of Honorarprofessor (Honorary Professor) of Computer Science at Saarland University's Saarland Informatics Campus. With a PhD from Carnegie Mellon University, he has established himself as a leading researcher at the intersection of programming language theory and practical software verification. Dreyer's research focuses on developing formal methods that bridge theoretical foundations with real-world systems programming challenges. His work has significantly advanced the theoretical understanding of programming languages, particularly in the areas of type systems, separation logic, and concurrency. He is renowned for his contributions to the formal verification of the Rust programming language, including the influential RustBelt project. His recent publications demonstrate a consistent focus on making formal verification practical for industrial-strength codebases. The research trajectory shows increasing sophistication in handling complex systems properties while maintaining theoretical rigor. His work spans from foundational logical frameworks to applied verification techniques for specific language features and system components. As an academic leader, Dreyer has served as Program Chair for major conferences including POPL and ICFP, and has mentored numerous students and postdocs. He is known for his insightful commentary on academic life, including a widely-read blog post addressing impostor syndrome in research careers. Dreyer leads a vibrant research group at MPI-SWS that collaborates extensively with both academic and industrial partners. His team's work has influenced both theoretical developments in programming languages and practical verification tools used in industry.
Konstantinos Kallas serves as Assistant Professor of Computer Science at the University of California, Los Angeles (UCLA), commencing his appointment in January 2025. Previously affiliated with the University of Pennsylvania as evidenced by his 2020 PLDI contribution, his research bridges theoretical formal methods with practical systems engineering across multiple high-impact conferences including PLDI, POPL, and SPLASH. His research program centers on enhancing computational efficiency and correctness in systems software, with three flagship projects defining his trajectory: PaSh for automatic shell script parallelization, Durable Functions for stateful serverless computing semantics, and DiffStream for differential testing of stream processing. These efforts consistently target the intersection of programming language theory and real-world systems constraints, particularly in parallelism, concurrency, and cloud-native environments where correctness guarantees are challenging to implement. Analysis of his publication history since 2020 reveals a methodological pattern: developing formal semantic models to enable practical optimizations in distributed systems. His work increasingly focuses on serverless architectures and data-intensive pipelines, with recent contributions emphasizing automated verification techniques. The evolution from shell script optimization (2020-2021) to serverless state management (2021-2022) demonstrates strategic expansion into cloud computing's hardest problems. Dr. Kallas actively contributes to the academic community through program committee service for PLDI (2022, 2025), POPL (2021, 2022, 2023), and SPLASH (2020-2023), including leadership roles as Publicity Co-Chair for PLDI 2025 and 2026. His June 2024 announcement confirms recruitment for Fall 2025 students at UCLA, targeting researchers interested in systems, compilers, and programming languages who can advance his work on correctness-preserving parallelization and serverless computing.
Yiling Lou is an incoming Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (starting Spring 2026), currently serving as a Pre-tenure Associate Professor at Fudan University. Previously a Postdoctoral Fellow at Purdue University under Prof. Lin Tan, Dr. Lou holds a Ph.D. and B.S. in Computer Science from Peking University supervised by Prof. Lu Zhang and Prof. Dan Hao. Research interests span Software Engineering synergized with Artificial Intelligence and Programming Languages , specifically focusing on LLM4Code, Agent&SE, Vulnerability Detection, and Software Testing/Debugging. Current projects include AgentIssue-Bench for agent system maintenance and INFERROI for enhancing static analysis with LLMs. Research trends show increasing integration of LLMs with traditional SE techniques, particularly in code generation (ClassEval, CodeGen4Libs), debugging (interactive runtime comparison), and vulnerability detection. Recent work emphasizes practical applications in agent systems and resource leak detection. ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) IEEE TCSE Distinguished Paper Award (ICSME 2021) Advises a large research group including 7 Ph.D. and 8 MS students at Fudan University, actively recruiting for UIUC starting Fall 2026. Leads the LLM4Code workshop series and serves on numerous program committees including ICSE, ASE, and FSE. Currently organizing research on Code Agents, Code LLMs, and AI&Security with strong industry relevance. Coordinates the Siebel School research group at UIUC focusing on the intersection of AI and Software Engineering, with particular emphasis on developing robust agent systems for code maintenance and security applications.
Dr. Shiqiang Tao is an Associate Professor at McGovern Medical School, The University of Texas Health Science Center at Houston. His academic journey includes prior roles as an Assistant Professor at the University of Kentucky's Department of Internal Medicine, Lead Developer at Kentucky's Institute of Biomedical Informatics, and Research Associate at Case Western Reserve University's Center for Clinical Investigation. His research centers on neuro-informatics with two primary thrusts: Ontology-driven Clinical Data Management : Involving data integration, cross-cohort querying, visualization, and temporal analysis for healthcare datasets. Deep Learning in Healthcare : Applied to substance use disorder prediction, sleep spindle detection/sub-typing, and postictal generalized EEG suppression (PGES) identification. Dr. Tao's publications (2014–2018) demonstrate consistent focus on medical informatics innovations, including sleep data repositories, clinical trial automation, SNOMED CT mining, and neurotechnology interfaces. His work emphasizes scalable data solutions and ontology-based frameworks.
Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.
Jie Lu is an Associate Professor at the Institute of Computing Technology of the Chinese Academy of Sciences (ICT, CAS), where he leads research in software security and program analysis. His work focuses on developing advanced program analysis techniques to improve software reliability and security, with applications in cloud systems, distributed environments, and modern web applications. Dr. Lu's research interests include: Software Security: Focusing on vulnerability detection and prevention in open-source software Program Analysis: Specializing in static/dynamic analysis techniques and context-sensitive pointer analysis Cloud Systems: Researching distributed system security, crash-recovery, and concurrency bug detection His recent publications demonstrate a strong focus on practical security solutions for real-world systems. The research spans Kubernetes ecosystems, PHP applications, Linux kernel security, Java web applications, and Windows IPC systems. A notable trend is the development of precise static analysis techniques that balance efficiency with accuracy, addressing the longstanding challenge in program analysis. His work often bridges theoretical advances with practical implementations that have been adopted by industry. Dr. Lu has received several prestigious awards: ACM SIGSOFT Distinguished Paper Award 2025 Best Paper Honorable Mention at CCS 2022 Chinese Academy of Sciences Outstanding Doctoral Dissertation 2021 Chinese Academy of Sciences President's Special Award 2020 ICT New Hundred Stars 2020 Dr. Lu actively mentors students and researchers, recruiting PhD candidates, Master students, and research interns interested in software security and program analysis. His research has been supported by the National Natural Science Foundation of China, CCF-Huawei Innovation Research Plan, and CCF-Ant Research Fund. The Program Analysis Group (ICT-PAG) at the National Key Laboratory of Processor has successfully identified numerous errors and vulnerabilities in popular open-source applications, with over 200 severe bugs confirmed by the open-source community and assigned more than 100 CVE numbers. His research group, the Program Analysis Group (ICT-PAG), is based in the National Key Laboratory of Processor at ICT, CAS. The group has achieved significant impact through both academic publications in top venues (SOSP, CCS, USENIX Security, NDSS, OOPSLA, ISSTA, FSE, ASE, TSE) and practical applications in leading IT companies and government organizations.
He Ye serves as an Assistant Professor at University College London (UCL), specializing in AI-driven software engineering solutions. His work bridges academic research and industry applications through EuniAI, a startup transforming research into developer tools. Research focuses on code agents for automating software tasks, with three core thrusts: Codebase context retrieval to enhance LLM capabilities Automated issue resolution systems Code agent memory construction His publications (2021-2025) demonstrate consistent innovation in fault localization and program repair. Current advising includes PhD students Zhaoyang Chu and Xiang Li (starting Fall 2025), alongside research assistants Yue Pan, Jiayi Xu, and Han Li. He co-founded EuniAI to commercialize research solutions for practical developer challenges. He actively shapes the field through workshop organization ( LMPL@SPLASH 2025 , APR@ICSE 2025 ) and program committee roles across major conferences including ASE, ICSE, and ESEC/FSE.
