Shueng-Han Gary Chan is a faculty member in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), within the College of Engineering. He is actively engaged in research and mentoring, with a strong publication record in mobile computing, indoor localization, and AI for pervasive systems. His research focuses on indoor localization using Wi-Fi, geomagnetic, and inertial signals , sensor fusion , crowd counting with deep learning , domain adaptation , and efficient mobile AI systems . His work bridges theoretical innovation with real-world deployment, as seen in systems for missing person search and indoor navigation. Recent publications (2023–2025) show a consistent trend toward self-supervised and domain-agnostic learning , efficient model design for mobile devices , and robust signal fusion in noisy environments . His team leverages transformer architectures, graph neural networks, and novel optimization techniques to solve real-world challenges in urban and indoor spaces. He has advised numerous graduate students, including Jierun Chen, Zhuoxuan Peng, and Tianlang He, who have contributed as first authors to joint publications. His collaborations span institutions and include work on large-scale system deployments and mobile AI. He leads a research group focused on mobile and pervasive computing , with projects involving IoT-based contact tracing, indoor navigation (e.g., DeepNavi, SiFu), and real-time localization systems. The team emphasizes practical deployment and system robustness.
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.
Dr. Brian Jalaian is an Associate Professor in the Department of Intelligent Systems and Robotics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is also a research scientist at the Institute for Human-Machine Cognition (IHMC), blending academic and applied research in artificial intelligence. His work is deeply rooted in robust, safe, and resilient AI systems with applications in defense and complex real-world environments. Education: Ph.D. in Electrical Engineering, Virginia Tech M.S. in Industrial Systems Engineering, Virginia Tech M.S. in Electrical Engineering (Communication & Network Systems), Virginia Tech Dr. Jalaian's research focuses on advancing the reliability and trustworthiness of AI systems. Key areas include robust machine learning, uncertainty quantification, adversarial machine learning, neuro-symbolic AI, and Bayesian deep learning. His work aims to develop AI systems that are not only powerful but also safe, explainable, and resilient to real-world uncertainties and attacks. He has led cross-functional teams at the Army Research Laboratory and the Department of Defense’s Joint Artificial Intelligence Center, contributing to foundational knowledge in risk-aware AI. His research has been published in top-tier conferences such as NeurIPS, ICML, AAAI, and in prestigious IEEE journals, highlighting the impact and rigor of his contributions. While specific publications are not listed, the breadth of his research suggests a strong emphasis on both theoretical and applied aspects of machine learning and AI assurance. Scientific Awards: Dr. Jalaian has extensive experience in leading research initiatives and interdisciplinary teams. He previously served as the AI Test & Evaluation Tools Lead at the DoD Joint Artificial Intelligence Center (now CDAO) and led critical research on AI robustness at the Army Research Laboratory. His leadership has driven innovation in the Internet of Battlefield Things Collaborative Research Alliance, resulting in multiple knowledge products and influencing DoD priorities in trustworthy AI. Although specific grants are not mentioned, his government affiliations indicate significant funding and project leadership. He is affiliated with the Institute for Human-Machine Cognition (IHMC), a leading research institute in human-centered AI and robotics, further enhancing his research ecosystem and collaborative opportunities.
Gaurav Nanda serves as an Assistant Professor in the School of Engineering Technology at Purdue University, where he leads research at the intersection of artificial intelligence and human-centered systems. His work develops intelligent decision support frameworks applicable across critical domains including occupational safety, smart manufacturing infrastructure, healthcare analytics, and educational technology. Education Background Ph.D. in Industrial Engineering, Purdue University Dual Degree: B.Tech. and M.Tech. in Agricultural and Food Engineering (Major) with Electrical Engineering Minor, Indian Institute of Technology Kharagpur His research program integrates applied machine learning and natural language processing to solve complex problems in safety analytics (injury surveillance systems), Industry 4.0 (IoT-enabled manufacturing), healthcare (breast cancer prediction models), and STEM education (MOOC feedback analysis). Current projects emphasize human-AI collaboration, with growing focus on ethical AI implementation and social justice integration in engineering contexts. The INDESS Research Group he directs develops systems that balance algorithmic precision with human factors considerations. Recent publications (2023-2025) demonstrate accelerating adoption of large language models and vision-language systems across application domains, particularly in safety analytics and educational technology. Key trends include human-in-the-loop validation frameworks, explainable AI interfaces, and multimodal data integration (eye-tracking, text, sensor data). His work increasingly addresses fairness considerations in AI deployment, especially regarding diversity in engineering education and workplace safety systems. Dr. Nanda actively mentors the next generation of engineers through the INDESS Research Group , advising Ph.D. candidates Madhumathi Ponnusamy and Shuning Yin, while previously supervising Master's graduates including Srushti Vichare and Meet Suthar. His research receives support through Purdue-affiliated institutes including ICON (Control/Optimization Networks), RDE (Digital Enterprise), and FWL (Future Work/Learning). He maintains active service roles as Editorial Board Member for the International Journal of Industrial Ergonomics and as reviewer for leading publications including IEEE Transactions on Learning Technologies and Safety Science. The research group maintains strong industry connections through the Purdue School of Engineering Technology, with projects spanning manufacturing automation, healthcare informatics, and educational technology platforms. Current initiatives focus on real-time anomaly detection systems, ethical AI frameworks for safety-critical applications, and inclusive curriculum development for engineering education.
Li Li is a Professor of Software Engineering at Beihang University , China. Previously, he served as an ARC DECRA Fellow and Senior Lecturer at Monash University , leading the SMart software Analysis and Trustworthy computing (SMAT) research lab at the Department of Software Systems and Cybersecurity. His academic journey includes a Ph.D. in Software Engineering from the University of Luxembourg (2016), supervised by IEEE Fellow Prof. Yves Le Traon and Dr. Jacques Klein. Research Interests Li's research focuses on Mobile Software Engineering (Mobile Security, Quality Assurance) and Intelligent Software Engineering (SE4AI, AI4SE). He applies static code analysis , dynamic program testing , and machine/deep learning to enhance software security and reliability. Key areas include Android API evolution, automated patch validation, and multi-language code analysis frameworks like Scalpel for Python. Scientific Recognition ARC DECRA Fellowship Rising SE Research Star Top-5 Most Impactful Early Career SE Researchers (2020, 2017) 5 Best/Distinguished Paper Awards across PLDI, WWW, ASE, MSR, and SANER Academic Contributions He has contributed to foundational Android analysis tools (e.g., AndroZoo++, DroidRA) and developed scalable systems for distributed program analysis (Seads). His work appears in top venues like ICSE, ESEC/FSE, ASE, ISSTA, POPL, and TSE.
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. George Fitzmaurice is a Research Fellow at Autodesk, leading the Human Computer Interaction and Visualization Research group. With over 120 publications and 95 patents, his work spans 25 years of innovation in interactive systems, focusing on technology-assisted learning , 3D visualization , and novel input techniques . His notable contributions include the Maya 1.0 UI and SketchBook Pro design, as well as pioneering Graspable UIs and Spatially-Aware Displays . Education : MIT (B.Sc. Math/CS), Brown (M.Sc. CS), Toronto (Ph.D. CS) His research explores immersive visualization and generative AI applications in design workflows, with recent work focusing on VR/AR tools like TimeTunnel for motion editing and WhatIF for AI-assisted narrative design. Current projects examine the intersection of large language models , 3D design systems , and collaborative environments . Key article themes include: Generative AI integration (3DALL-E, WorldSmith) Immersive motion analysis (AvatAR, VideoPoseVR) Creative workflow optimization (MoodCubes, Immersive Sampling) Privacy-aware VR systems (Vice VRsa) Scientific Recognition: 2019 - Inducted into ACM CHI Academy 2024 - Awarded ACM Fellow for computing contributions He has developed foundational interaction techniques like ViewCube™ and SteeringWheels™ , and his work continues to shape modern 3D UI paradigms and spatial computing approaches through projects like DreamSketch and Tesseract.
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.
Susan Murray , ScD, is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She has made significant contributions to survival analysis methodology, particularly in lung allocation systems and quality-of-life-adjusted survival models. Education: ScD in Biostatistics (Harvard, 1994), MS in Biostatistics (Harvard, 1992), BA in Statistics and English (Rice, 1990) Roles: Biostatistical Editor for American Journal of Respiratory and Critical Care Medicine , member of Cystic Fibrosis Foundation's Data and Safety Monitoring Board Her research focuses on nonparametric survival analysis with informative censoring, group sequential methods for censored data, and quality-of-life-adjusted survival models. She pioneered Lung Allocation Score development and dependent censoring methodology. Recent collaborative work with pulmonary researchers has advanced CT parametric response mapping for COPD progression, machine learning applications in proteomics, and spatially localized lung disease analysis. Her publications show strong emphasis on survival modeling , censored data handling, and transplantation outcomes . Scientific Awards: University of Michigan School of Public Health Excellence in Teaching Award (2003) On Job/On Campus Master's Program Teacher of the Year (2001) Professor Murray maintains active collaborations with University of Michigan Medical School pulmonary researchers and national institutions. She has served on editorial boards for Biometrics and Lifetime Data Analysis , and currently contributes to the American Journal of Respiratory and Critical Care Medicine as Biostatistical Editor.
Marsha Chechik is a Professor in the Department of Computer Science at the Faculty of Arts and Science, University of Toronto. She previously served as Department Chair from 2019-2022 and as Acting Dean in the Faculty of Information from July-December 2022. Her academic career spans numerous research contributions and leadership roles within the software engineering community. Professor Chechik's primary research interests focus on software engineering with emphasis on formal methods to enhance software quality. Her work encompasses scalable automated verification techniques including model-checking and theorem-proving, formal specification languages, verification of protocols, non-classical logics, and reasoning under inconsistency. She has made significant contributions to model management, software product lines, safety and security assurance, and automotive safety systems. Her research bridges theoretical foundations with practical applications, particularly in managing uncertainty in software models and developing techniques for automotive safety verification. Her recent publications demonstrate a strong focus on model management and transformations, software product lines and variability analysis, safety and security assurance cases, and semantic analysis of software evolution. The integration of formal methods with practical software engineering challenges, especially in safety-critical domains like automotive systems, represents a consistent theme throughout her work. Professor Chechik has been recognized with multiple prestigious awards including a Best Paper Award at RE'12, a SIGSOFT Distinguished Paper Award at ICSE'12, a Best Student Paper Award at CASCON'07, and a Distinguished Paper Award at ICSE'07, highlighting the impact and quality of her research contributions. She actively supervises graduate students and has successfully guided numerous Ph.D. candidates to completion. Her group has produced graduates who predominantly pursue research careers in both academic institutions and industrial research labs. She currently leads several funded projects including the Automotive Safety project (in collaboration with General Motors) and the Software Evolution project, focusing on practical applications of her research interests. Professor Chechik leads the Software Engineering Lab at the University of Toronto, where innovative projects like Matchmakers (a serious game for software engineering) are developed. Her collaborative network extends across institutions, with notable partnerships including Julia Rubin at the University of British Columbia, demonstrating her commitment to interdisciplinary research and academic collaboration.
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.
