Sébastien Mosser is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering since January 2022. He previously held the same position at Université du Québec à Montréal (2019-2021) and Université Côte d’Azur (2012-2022). His research focuses on scalable software composition, domain-specific languages, and modeling, applied to cloud computing, cyber-physical systems, and micro-services architecture. PhD (2010) and MSc (2007) in Computer Science from Université Nice – Sophia Antipolis Developed the Abstract Composition Engine (ACE) for software composition automation Active in industry collaboration with technology transfer experience Registered P.Eng. license in Québec (since December 2021) His work addresses safety-critical software challenges through formal methods and industrial partnerships, particularly in the McMaster Centre for Software Certification (McSCert). As Undergraduate Advisor for Software Engineering, he combines academic leadership with technical innovation.
Hongxin Hu is a Professor and Associate Chair in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York (SUNY). His research spans security, networking, and machine learning, with publications across top conferences including security (S&P, CCS, USENIX Security, and NDSS), networking (SIGCOMM and NSDI), machine learning (NeurIPS, ICML, and EMNLP), and human-computer interaction (CHI and CSCW). His work has been funded by NSF (SaTC, CNS, IIS, OAC, SOC), USDOT, VMware, Amazon, Google, and Dell. Dr. Hu earned his PhD in Computer Science and Engineering from Arizona State University in 2012. His academic journey has led him to become a prominent researcher in cybersecurity with a strong publication record and significant research impact. Dr. Hu's research interests encompass a wide range of topics at the intersection of security, networking, and artificial intelligence. His work focuses on Emerging Network Technologies and Security (5G/Future-G, NFV, SDN, Edge computing), Machine Learning for Security and Privacy , Security and Privacy in IoT and Cyber-Physical Systems , and AI for Social Good (addressing online abuse, unsafe children's games, and cyberbullying). His interdisciplinary approach has enabled him to tackle complex security challenges through innovative solutions that combine networking expertise with machine learning techniques. His recent publications demonstrate a strong trend toward applying large language models and advanced machine learning techniques to security challenges, particularly in content moderation, vulnerability detection, and privacy protection. The research spans multiple domains including voice assistant security, IoT security, network security, and social media safety, showing a consistent pattern of addressing real-world security problems with cutting-edge technical approaches. IEEE Big Data Security Senior Research Award (2025) ACM SACMAT Test-of-Time Award (2024) NSF CAREER Award (2019) Multiple Best Paper Awards from ACM ASIACCS (2022), ACSAC (2020), IEEE ICC (2020), and ACM SIGCSE (2018) Amazon Faculty Research Award (2022) First Place Award in ACM SIGCOMM 2018 Student Research Competition Dr. Hu has successfully advised multiple PhD students, including Nishant Vishwamitra who joined UT San Antonio as a tenure-track Assistant Professor. His research has been generously funded by major agencies and industry partners. As an active member of the academic community, he serves as Associate Editor for IEEE Transactions on Dependable and Secure Computing and Computers & Security, and has held numerous leadership roles in major security conferences including TPC Co-Chair for ASONAM 2025 and IWSPA 2024/2025. Dr. Hu leads a vibrant research group that has produced significant contributions in network security function virtualization, intrusion detection systems, and privacy-preserving technologies. Current projects include developing LLM-assisted vulnerability detection systems, defenses against jailbreak attacks on large language models, and security mechanisms for emerging networking technologies. His team's work on IoT security, voice assistant applications, and online content moderation has received wide recognition and press coverage.
Jürgen Giesl is a Professor at the Teaching and Research Area Computer Science 2 within the Department of Computer Science at RWTH Aachen University , Germany. He leads research in programming languages, formal verification, automated deduction, and term rewriting systems. Research Interests: Automated Termination and Complexity Analysis of Programs Dependency Pairs and Term Rewriting Systems Verification of Probabilistic and Integer Programs Static Analysis and Symbolic Execution Model Checking and Constrained Horn Clauses Development of Automated Tools (AProVE, LoAT) His recent research, reflected in the latest publications, focuses on termination and complexity analysis for probabilistic programs, polynomial loops, and integer programs, using advanced techniques such as dependency pairs, loop acceleration, and semiring semantics. He also contributes to SMT solving and transitive relation learning for infinite-state model checking. Scientific Awards: Best Tool Paper Award at iFM 2017 Silver Medal (Second Best Paper) at SEFM '16 Best Paper Honourable Mention at IJCAR 2024 Best Student Paper Honourable Mention at IJCAR 2024 Advising and Grants: Giesl has supervised numerous PhD and Master’s students, including prominent researchers such as Fabian Frohn, Jens Hensel, Nils Lommen, and Marcel Hark. He leads a large research group focused on automated verification and has contributed extensively to international verification competitions. His work is supported by ongoing research grants and collaborations with leading institutions in formal methods. Labs and Teams: He leads the Programming Languages and Verification research group at RWTH Aachen, which develops and maintains the AProVE and LoAT tools. These tools are central to automated termination and complexity analysis and are regularly submitted to international competitions such as TERMCOMP and VBS.
Wolfgang Stammer is a PostDoc researcher in the Machine Learning Group at TU Darmstadt's Computer Science Department. His work focuses on making AI models more interpretable and interactive, particularly in explainable AI (XAI), neuro-symbolic architectures, and systematic compositionality challenges in neural networks. He completed his Ph.D. in Machine Learning at TU Darmstadt (2019–2025), an M.Sc. in Computer Science at Goethe University Frankfurt (2016–2018), and a B.Sc. in Cognitive Science at the University of Osnabrück (2011–2015). Research Interests : Stammer's research bridges gaps between human understanding and AI capabilities. Key areas include: Explainable AI (XAI) and interactive machine learning (XIL) Neuro-symbolic integration for logical reasoning and visual concepts Mitigating shortcut learning and confounding factors in datasets Concept discovery and program synthesis for interpretable models Publications : His work spans foundational contributions to AI benchmarks (e.g., V-LoL, SLR-Bench) and frameworks (Neural Concept Binder, Revision Transformers). Recent studies highlight AI's limitations in systematic generalization and propose solutions for aligning reinforcement learning agents with human values. Grants & Labs : He contributes to the Machine Learning Lab at TU Darmstadt and co-organized workshops like the Interactive Machine Learning Workshop @ AAAI 2022. His research bridges theoretical advances with practical applications in healthcare and ethical AI systems.
G. Caltais is an Assistant Professor in the Formal Methods and Tools (FMT) group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Previously, they worked as an independent junior scientist at the University of Konstanz (Chair for Software and Systems Engineering) and as a post-doctoral researcher at ETH Zürich (Chair of Software Engineering). They hold a PhD from Reykjavík University and Radboud University. Research Focus: Formal modeling of computer systems, emphasizing automata theory, concurrency, causal/knowledge models, and software-defined networks (SDNs). Current projects include DyNetKAT (formal SDN analysis), Zorro (zero downtime knowledge models), and exploring cyclic structures in software correctness. Committees: Served on program committees for CALCO 2023, ESOP 2023, FORTE 2022-2023, FSEN 2023-2025, IEEE NFV-SDN 2020-2022, SPIN 2022-2024, and others. Organized events like CREST@ETAPS 2019/2023 and EXPRESS/SOS 2023. Student Projects: Offers B.Sc./thesis proposals on DyNetKAT visualization, causal analysis of SDN safety violations, and learning formal network models from real datasets. Contact for custom project ideas.
Xin (Eric) Wang is an Assistant Professor in the Computer Science Department at the University of California, Santa Barbara (UCSB) , and serves as Head of Research at Simular AI. His research focuses on Multimodal and Embodied AI Agents , blending methodologies from machine learning, computer vision, natural language processing, and robotics. Education: Ph.D. in Computer Science, UC Santa Barbara B.Eng. in Computer Science, Zhejiang University Research Interests: Natural Language Processing Computer Vision Multimodal AI Embodied AI Trustworthy AI Systems His work emphasizes agents that collaborate with humans in complex environments, addressing ethical design and generalizable reasoning. Awards: Best Paper Awards at CVPR 2019 and ICLR 2025 Google Faculty Research Award, 2022 eBay & Cisco Faculty Awards (2022–2024) Amazon Alexa Prize Awards (multiple years) Advising & Grants: Supervised students Dr. Xuehai He and Dr. Jing Gu. Secured grants from Microsoft, Adobe, eBay, and Snap. Organized workshops on vision-language research and embodied AI. Labs/Teams: Leads the ERIC Lab at UCSB, focusing on multimodal agent systems and ethical AI design.
Dr. Karla Badillo-Urquiola is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame's College of Engineering. She directs the EPOCH Research Lab, focusing on technology-driven solutions for youth and marginalized populations. Ph.D. and M.Sc. in Modeling and Simulation (University of Central Florida) B.Sc. in Psychology (University of Central Florida) Her research integrates human-centered and participatory design methods to address adolescent online safety, particularly for teens in foster care. She advocates for diversity, inclusion, and equity in computing through her work with SIGCHI's Latin American HCI Community. Recent publications span topics like AI ethics, digital resilience, and cross-cultural design. Her awards include the McKnight Doctoral Fellowship and the Order of Pegasus Induction (UCF, 2020). Dr. Badillo-Urquiola collaborates with institutions like Notre Dame’s Lucy Family Institute for Data and Society and partners in Mexico on health monitoring systems. She mentors underrepresented students in STEM while balancing motherhood to two daughters.
Daphna Harel (she/her) serves as Associate Professor of Applied Statistics and Director of the A3SR MS Program within the Department of Applied Statistics, Social Science, and Humanities at New York University's Steinhardt School of Culture, Education, and Human Development. She holds leadership roles including PI of the NYU QUEER data lab and membership on the steering committee for the DEPRESSD project. Education: PhD in Mathematics and Statistics from McGill University Harel's research focuses on measurement challenges in survey methodology, particularly for self-reported questionnaires in health and social sciences. Her work bridges theoretical statistics with practical applications in LGBTQIA+ data collection, differential item functioning, and patient-reported outcomes. She develops methodological frameworks for polytomous Item Response Theory and creates guidelines for statistical analysis of complex survey data. Her recent work emphasizes improving statistical practice for LGBTQIA+ populations and advancing queer data collection methods. Her publication portfolio demonstrates consistent focus on measurement validity across diverse health contexts, with recent work expanding into LGBTQIA+ data science. Her research shows increasing emphasis on methodological innovations for gender and sexuality measurement, with multiple publications presented at major conferences including AAPOR 2024 and LGBTQ+ Health Conference 2024. Research Leadership: PI of NYU QUEER data lab (founded Fall 2022) Steering committee member for DEPRESSD project Collaborator with Scleroderma Patient-centered Intervention Network Recipient of NIH R21 funding and NYU intramural grants Harel mentors a diverse team of graduate students through the Applied Statistics for Social Science Research program, focusing on inclusive research methods for marginalized populations. Her grant portfolio includes investigations into transgender voice training, LGBTQIA+ survey methodology, and healthcare access for gender-affirming services. Laboratory Leadership: As founder and PI of the NYU QUEER data lab, Harel leads research projects examining survey question design for gender/sexuality measurement, content moderation effects on hate speech, and accessibility of transgender voice training. The lab operates through collaboration between NYU Steinhardt and the NYU BITS lab, with research assistants drawn from the Applied Statistics MS program.
Bruno Vallespir is a Professor at Universite de Bordeaux, affiliated with the Production Engineering research group and MEI team. His work focuses on lean manufacturing, industry 4.0 integration, and enterprise interoperability using simulation frameworks. Key research themes: Lean techniques evaluation Co-simulation for manufacturing systems Organizational interoperability Safe work activity design His recent publications address: Combining lean methods with Industry 4.0 technologies Human-machine interaction in production environments Verification of collaborative processes Performance metrics for dynamic industrial contexts Collaborations include institutions like IMS Bordeaux , INCOSE , and industrial partners such as STMicroelectronics , Thales , and Stellantis .
Dr. M M Manjurul Islam is a Research Associate in Artificial Intelligence for Smart Manufacturing at Ulster University's School of Computing, Engineering and Intelligent Systems. His research focuses on applying advanced AI techniques to solve critical challenges in manufacturing systems, with particular expertise in fault diagnosis, predictive maintenance, and semiconductor production optimization. His research interests span Artificial Intelligence , Smart Manufacturing , Fault Diagnosis , Machine Learning , Deep Learning , Predictive Maintenance , and Semiconductor Manufacturing . He has made significant contributions to the application of convolutional neural networks, support vector machines, and generative adversarial networks in industrial settings, particularly for bearing fault diagnosis and wafer defect classification. Dr. Islam's recent publications (2023-2025) demonstrate a strong focus on practical AI applications in manufacturing, with multiple chapters in the Springer Series in Advanced Manufacturing. His work shows an evolving trajectory from traditional machine learning approaches to more sophisticated deep learning and explainable AI techniques, with increasing emphasis on semiconductor manufacturing challenges and trustworthy AI systems. His research contributes to UN Sustainable Development Goals, particularly in industrial innovation and infrastructure. He is an active member of professional organizations including IEEE and Advance HE, serving as Chair for both networks. According to Scopus data, Dr. Islam has accumulated 1,706 citations with an h-index of 16, reflecting the impact of his research in the field. His publication record shows consistent productivity, with research outputs spanning from 2015 to anticipated publications in 2025.
Prof. Dr. Fadi AL-TURJMAN serves as the founding Dean of the Faculty of AI and Informatics at Near East University (NEU), Cyprus. He holds multiple leadership roles including Head of the Software Engineering Department and Director of the AI and Robotics Institute and the International Research Center for AI and IoT. With a PhD in Computer Science from Queen’s University (2011), he specializes in AIoT systems, wireless networks, and blockchain integration. Affiliation: Near East University Leadership: Founding Dean for AI and Informatics, Director of AI & Robotics Institute Research Focus: His work bridges Artificial Intelligence of Things (AIoT) , Blockchain Applications , and Smart Networking . He explores cybersecurity frameworks for smart cities, quantum state optimization techniques, and novel AI-driven solutions for healthcare, agriculture, and energy systems. Key Article Trends: Recent publications emphasize Transformer models for environmental monitoring, blockchain-enabled security protocols , and evolutionary algorithms for resource optimization. His research spans interdisciplinary domains including medical diagnostics, vehicular networks, and sustainable infrastructure. Scientific Awards: Lifetime Golden Award of Dr. Suat Gunsel (2022) Multiple Best Research Awards at International Venues Labs & Teams: Directs the International Research Center for AI and IoT at NEU, leading multidisciplinary teams in developing advanced networking technologies and AI-driven solutions for global value chain applications.
Haoyu Wang is a Researcher in the Computer and Information Science department at the University of Pennsylvania . He previously held research positions at Shanghai Jiao Tong University and interned at Google DeepMind , Amazon AWS , ByteDance , AI2 , Tencent AI Lab , and Goldman Sachs . Education : PhD in Computer and Information Science (2021–Present), MS in Computer and Information Science (2019–2021), BS in Computer Science (2015–2019). His research focuses on Event-Centric NLP/NLU , LLM Reasoning and Planning , Knowledge Graph , and Pose Estimation in Computer Vision . His work includes event causality identification, semantic classification in context, and synthetic control for temporal reasoning. He has contributed to multimodal hallucination analysis and safety in reasoning models through projects like RESIN-11 and Devil's Advocate . His publications span venues like EMNLP , EACL , and ACL . His recent articles analyze LLM limitations in NP-hard problems , clinical trial prediction , event causality , and hallucination in vision-language models . He has served as PC Member for conferences including ACL , NAACL , NeurIPS , and EMNLP since 2019.
Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.
Gérard Berry (born December 25, 1948) is a distinguished French computer scientist currently serving as Professor at the Collège de France, holding the permanent chair Algorithmes, machines et langages (Algorithms, Machines, and Languages) since 2012. He previously held the Informatique et sciences numériques chair (2009-2010) and the Technological Innovation Liliane Bettencourt chair (2007-2008) at the same institution. Before joining Collège de France full-time, he served as Director of Research at INRIA Sophia Antipolis (2009-2012) and at École des Mines de Paris (1977-2001). His research spans over 30 years in three main fields: lambda calculus and functional programming, parallel and real-time programming languages, and design automation for synchronous digital circuits. He is particularly renowned for developing the Esterel programming language. His work bridges theoretical computer science with practical industrial applications. Berry's research has evolved to include current work in Hop and HipHop for Web programming, formal verification of compilers, and languages for computer music. His publications demonstrate consistent contributions to programming language theory, formal methods, and their applications in hardware and software systems. Gold Medal of CNRS (2014) Chevalier de l'Ordre de la Légion d'Honneur (2012) Member of French Academy of Sciences (2002) Member of Academia Europaea (1993) Monpetit Prize of Académie des sciences (1990) Berry has advised 17 PhD students and reviewed numerous theses. His industrial experience includes serving as Chief Scientist Officer of Esterel Technologies (2000-2009), where he directed the implementation of the Esterel v7 compiler. He has also held significant leadership roles including President of the Scientific Council of IRCAM and membership on the Scientific Council of the National Education. His teaching at Collège de France has covered topics ranging from the foundations of computation to the societal impact of digital technology, with courses including The Informatics of Time and Events and Proving Programs: Why? When? How? His laboratory work has focused on developing practical applications of theoretical computer science concepts.
Zhiyuan Li is a Professor in the Department of Computer Sciences at Purdue University's College of Engineering. His primary research and teaching focus on program analysis, transformation, and run-time management for high-performance computing and multicore systems, as well as reliable software for networked embedded systems. Professor Li teaches graduate-level courses including CS502: Compiling and Programming Systems and CS591RS1: Research Seminar for First-year Graduate Students. Office: LWSN 3154H Contact: li@cs.purdue.edu Phone: +1 765-494-7822 Professor Li's research spans multiple areas within computer science, with particular emphasis on compiler design, program analysis, and parallel computing. His work addresses fundamental challenges in enabling efficient execution of applications on modern parallel architectures, including multicore processors and large-scale distributed systems. He has made significant contributions to techniques for data dependence analysis, loop parallelization, array privatization, and memory optimization in compilers. His research also extends to reliable software development for embedded and sensor network systems, where resource constraints and reliability requirements present unique challenges. Professor Li's publication record demonstrates consistent contributions to top-tier conferences and journals in computer science, particularly in the areas of parallel computing, compiler optimization, and high-performance numerical methods. His work shows a progression from foundational compiler techniques to applications in scientific computing domains such as computational fluid dynamics for jet engine noise simulation. This interdisciplinary approach connects low-level program analysis with real-world engineering applications requiring petascale computing resources. Principal Investigator for NSF/PetaApps project on jet engine noise simulation Principal Investigator for Intel-sponsored research on data dependence profiling Extensive service on program committees for major conferences including ICS, PPoPP, and LCTES Professor Li has been actively involved in mentoring graduate students through research projects and course instruction. His jet engine noise simulation project specifically mentions training three Ph.D. graduate students and involving undergraduate research assistants. As coordinator for the first-year graduate research seminar, he plays a significant role in guiding new students through the transition to graduate research work in computer science. His laboratory work focuses on developing compiler techniques and runtime systems for parallel and high-performance computing. The research infrastructure includes implementations in GCC for fast data dependence profiling and support for SIMD/SSE instructions, demonstrating practical applications of theoretical compiler techniques.
