Shurui Zhou is an Assistant Professor at the University of Toronto , with primary appointment in the Department of Electrical & Computer Engineering and cross-appointments in the Department of Computer Science and the Department of Mechanical & Industrial Engineering . She is also affiliated with the Schwartz Reisman Institute and a member of the Data Sciences Institute . Zhou directs the FORCOLAB , focusing on enhancing collaboration in distributed software teams, especially in open-source and AI-enabled systems development. Her research integrates software engineering principles with human collaboration insights from organizational behavior, aiming to improve collaboration efficiency through tooling and interdisciplinary methods. Recent publications (2023-2025) explore LLM integration in software development , sustainability in scientific open-source communities , and collaborative challenges in computer-aided design . Education: PhD (2020) from Carnegie Mellon University's Software and Societal Systems Department , MSc from Peking University , BSc from Xi'an Jiaotong University Scientific Awards: Gordon Slemon Teaching of Design Award (2022) Grants: Collaborator on NSERC Alliance-Mitacs Accelerate Grant (2025) for LLM analysis in political data Her lab, FORCOLAB, develops tools like forks-insight.com to analyze GitHub forks and Redundant PR detection bots . Zhou organizes events like the Responsible LLM-Human Collaboration Hackathon (2024) and actively serves on conference committees including ICSE 2025 and ICSME 2024 .
Dr. Ali Ghorbani is a Professor and Tier 1 Canada Research Chair in Cybersecurity at the University of New Brunswick's Faculty of Computer Science, where he served as Dean from 2008-2017. He is the founding Director of the Canadian Institute for Cybersecurity (CIC), established in 2016. Ghorbani holds BSc (Tehran), MSc (George Washington University), and PhD (UNB) degrees. With 42+ years in academia, his research spans cybersecurity, machine learning, adaptive systems, and critical infrastructure protection. Ghorbani has co-founded three cybersecurity startups (Sentrant Security, EyesOver Technologies, Cydarien Security) and co-invented four awarded patents. He has published 300+ peer-reviewed papers and supervised 250+ researchers. Professional roles include co-founding the National Cybersecurity Consortium (NCC) and serving as co-editor-in-chief of Computational Intelligence journal. Awards & Honors: Canada Research Chair in Cybersecurity Startup Canada Senior Entrepreneur Award (2017) RBC Top 25 Canadian Immigrants (2019) CAIAC Lifetime Achievement Award (2024)
Dr. Christopher Collins is a Professor of Computer Science at Ontario Tech University, leading the Visualization for Information Analysis Lab (vialab). He holds a PhD from the University of Toronto (2010) and focuses on interdisciplinary research in information visualization, human-computer interaction, and natural language processing. His work addresses challenges in information overload, text analytics, and novel interfaces such as touch, pen, VR/AR. Collins' research has been featured in top-tier venues like ACM CHI and IEEE Transactions on Visualization and Computer Graphics, earning honorable mentions and over $3M in funding as sole PI. He serves on the IEEE VIS Executive Committee and Board of Governors at Ontario Tech University. Education: PhD in Computer Science, University of Toronto (2010) MSc in Computer Science, University of Toronto (2004) BSc (Hons) in Computer Science, Memorial University (2001) Research Interests: Collins' work spans information visualization , pen+touch interfaces , visual analytics , and text-driven systems . He explores how interactive technologies can democratize complex data analysis, particularly in education, healthcare, and creative domains. Recent projects include gaze-driven learning tools, context-aware camera interfaces, and bias-mitigating product review analysis. Awards: ACM CHI Honorable Mention Award IEEE VIS Honorable Mention Award Grants & Impact: Secured $3M+ in research funding. Media coverage includes New York Times and CBS Sunday Morning for innovations in visualization and text analytics. Teaches courses in human-computer interaction, computer graphics, and information visualization. Labs & Collaborations: vialab develops tools like Lexichrome , ConToVi , and NeuroSight . Active in IEEE Visualization and ACM Interactive Media communities. Collaborates with academia and industry globally.
Peter Jansen is an Associate Professor at the University of Arizona's College of Information Science with a joint appointment at the Allen Institute for Artificial Intelligence (Ai2). He specializes in natural language processing (NLP), cognitive artificial intelligence, and automated scientific discovery. His research focuses on virtual environments for scientific reasoning, such as ScienceWorld and DiscoveryWorld, and methods for explainable AI through projects like the Explanation Bank. Education: PhD in Psychology and Neuroscience from McMaster University, and a Bachelor of Independent Studies from the University of Waterloo. His interdisciplinary background combines NLP, computer science, physics, and electrical engineering. Key projects include open-source hardware initiatives like the science tricorder (featured in over 50 media outlets and exhibited at the German Museum of Technology in Berlin) and TextWorldExpress, a text-game simulator. His work emphasizes grounding science education through sensing and developing AI systems capable of systematic reasoning and explanation generation. Current roles include advising students in automated scientific discovery and contributing to cross-listed courses in Computer Science and Linguistics. He maintains an active presence in academic outreach through teaching, conferences, and publications.
Zheng Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Notre Dame, where he focuses on human-AI interaction. He also works as an Applied Scientist in Adobe's GenAI team, developing interactive AI systems for user experience enhancement. He has held internships at Apple, AWS AI, and Meta Reality Labs. Education: Ph.D. (in progress) at University of Notre Dame M.S. in Computer Science from University of Rochester and University of Minnesota B.Eng. in Software Engineering from Shaanxi Normal University His research explores the intersection of human-computer interaction (HCI) and machine learning (ML), emphasizing systems that provide adaptive, context-sensitive support for complex cognitive tasks. Key areas include collaborative tools for team ideation, incremental learning from user demonstrations, and multimodal context-aware interfaces. Recent publications demonstrate a focus on AI-augmented collaboration through large shared displays (LADICA), audio-visual annotation (PEANUT), and interactive qualitative coding (PaTAT). His work increasingly integrates generative AI with real-time human input, particularly for co-located teams and educational applications. He serves as a Program Committee member and reviewer for top HCI/ML conferences including CHI, UIST, IUI, and ACL. Teaching experience spans courses in human-AI systems, algorithms, and data structures.
Simon Ostermann serves as a Senior Lecturer at Saarland University and Senior Researcher & Deputy Director at the Multilinguality and Language Technology (MLT) lab of the German Research Center for Artificial Intelligence (DFKI). He leads the Efficient and Explainable NLP (E&E) research group and contributes to major projects including lorAI (Low Resource AI), TRAILS (Trustworthy Machines), PERKS (Procedural Knowledge), DAM-S (Semantic Search), and DisAI (Disinformation Combat). His research centers on democratizing language technology through transparent, robust models—specializing in mechanistic interpretability to reverse-engineer LLM internals and enhance efficiency for low-resource languages. Key focus areas include reducing model size for constrained environments, improving cross-lingual transfer via adapters, and developing structured input techniques. His work bridges theoretical interpretability with practical applications in resource-limited settings. 2025 publications reveal concentrated efforts in low-resource adaptation (language adapters, graph-enhanced embeddings), explainable AI (counterfactual generation, conversational XAI datasets), and multilingual fact-checking systems. Notable trends include systematic neuron manipulation frameworks, rigorous evaluation of synthetic data strategies, and cross-lingual claim verification benchmarks. Ostermann advises six PhD candidates (Anikina, Oguz, Bäumel, al Ghussin, Gurgurov, Vykopal) and multiple MSc students on topics spanning RAG hallucinations, multilabel classification, and adapter interpretability. His research receives funding through DFKI-led consortia with European and international partners focusing on trustworthy, efficient AI deployment. The E&E group under his leadership drives innovation in efficient NLP through biweekly seminars, collaborative coding sessions, and partnerships with institutions like KInIT. Current initiatives prioritize green computing for language models and real-world deployment in industrial procedural knowledge systems.
Dr. Zheng Yuan is a Senior Lecturer (Associate Professor) in Natural Language Processing at the School of Computer Science, University of Sheffield. He holds affiliated positions at the University of Cambridge and King's College London, and is a Fellow of Trinity College, Cambridge. His research focuses on NLP applications in education, healthcare, and multilingual systems. Education: PhD in Natural Language Processing, University of Cambridge MPhil in Advanced Computer Science, University of Cambridge BSc(Eng) from Queen Mary University of London Research: Dr. Yuan's work spans educational NLP, multilingual systems under low-resource conditions, and explainable machine learning. His group develops technologies for grammatical error correction, automated assessment, and cross-lingual applications, with significant contributions to computer-assisted language learning and computational creativity. Publications: Recent works (2023-2025) demonstrate strong focus on educational NLP, multilingual systems, and LLM evaluation. Key themes include grammatical error correction for code-switched languages, creativity assessment frameworks, and robust evaluation methods for large language models across diverse linguistic contexts. Awards: Winning systems at SemEval-2021 and CoNLL-2014 Fellowship at Trinity College Cambridge Fellow of Higher Education Academy Grants & Advising: Principal Investigator for Royal Society grant 'Large Language Models as Agents for Intelligent Language Tutoring' (2025-2027). Actively supervises PhD students in NLP and machine learning, welcoming new research collaborations. Affiliations: Member of Alan Turing Institute (Data-Centric Engineering), King's Institute for AI, and ACL committees. Organizes major NLP workshops including ACL/NAACL BEA workshops and AIED tutorials.
Dr. Hua Mao is an Assistant Professor in Computer and Information Sciences at Northumbria University, holding a PhD in Computing Science from Aalborg University. Previously served as Associate Professor at Sichuan University, China. Research Focus: Specializes in deep learning and artificial intelligence with applications spanning graph representation, multiview clustering, and explainable AI systems. Recent work advances techniques for processing incomplete data and invariant representation learning. Publication Trends: Her 45+ research outputs demonstrate consistent innovation in machine learning fundamentals, particularly graph-based algorithms and interpretable models. Recent articles focus on explainability frameworks and adaptive learning techniques for complex data structures including IP analytics and flood mapping applications.
Corey Maley is an Associate Professor of Philosophy at Purdue University, specializing in foundational questions about computation and its role in neuroscience, cognitive science, and psychology. His interdisciplinary work bridges philosophy of science with computational theories of mind. Education: B.S./B.A. in Computer Science, Mathematics, Philosophy, and Psychology (University of Nebraska); Ph.D. in Logic and Philosophy of Science (Princeton University) Research Focus: Explores analog computation, neural representation, and the philosophical implications of treating brains and minds as literal computers. Investigates the relationship between physical magnitudes and computational frameworks in biological systems. Scientific Contributions: Recipient of the NSF Scholar Award for work on unifying analog and digital computation theories. Developing a manuscript titled "The Analog Brain" under contract with Oxford University Press. Authored influential papers on neural computation, emotion, and computational metaphysics. Contact: BRNG 7137, Purdue University, cjmaley@purdue.edu
Carlo Angiuli is an Assistant Professor of Computer Science at the Luddy School of Indiana University . He is a leading researcher in type theory , with a focus on its applications to programming language design and logic . His work bridges theoretical foundations and practical implementations, particularly through dependent types , proof assistants , and homotopy type theory . His research interests include: Type Theory (foundational systems for programs and proofs) Programming Language Foundations (formal semantics and reasoning) Homotopy Type Theory (higher-dimensional structures) Dependent Types (expressive type systems) Proof Assistants (formal verification tools) Computational Logic (algorithmic reasoning) Angiuli actively contributes to the programming languages community as a POPL Program Committee member and co-author of a forthcoming textbook on dependent type theory. His students include Johnson He, Huang Xu, Kelton OBrien, and Ian Ray , who joined in Fall 2025. He has received significant recognition, including the Best Paper Award at FSCD 2019 and the School of Computer Science Distinguished Dissertation Award from Carnegie Mellon University. His current work explores advanced type-theoretic frameworks and their implementation in proof assistants like the red* family of tools. Scientific awards: Best Paper Award, FSCD 2019 (Junior Researchers category) School of Computer Science Distinguished Dissertation Award, Carnegie Mellon University He teaches courses such as Modern Dependent Types (CSCI-B619), Programming Language Foundations (CSCI-B522), and Introduction to Computer Science (CSCI-C211).
Dr. Adel Abusitta is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he conducts research at the intersection of artificial intelligence and cybersecurity. With expertise in secure and resilient AI systems, IoT security, and malware analysis, Dr. Abusitta has established himself as a significant contributor to the field of AI-powered cybersecurity solutions. Education: PhD in Computer Engineering from Polytechnique Montréal Postdoctoral Fellow at University of Montréal Postdoctoral Fellow at McGill University Dr. Abusitta's research focuses on developing secure and trustworthy artificial intelligence systems with applications in cybersecurity. His work spans several critical areas including explainable AI for security applications, AI-powered malware analysis, intrusion detection systems, and IoT security. He has made significant contributions to the understanding of how AI can be both secured against attacks and used to enhance security systems. His research addresses the dual challenge of making AI systems resilient to adversarial manipulation while leveraging AI's capabilities to detect and prevent cyber threats in complex environments like cloud computing and IoT networks. Analysis of Dr. Abusitta's recent publications reveals a strong focus on the intersection of AI and cybersecurity, particularly in developing robust anomaly detection systems, explainable security solutions, and resilient architectures for IoT environments. His work demonstrates a consistent trajectory toward making AI systems both more secure and more useful for security applications, with increasing emphasis on practical implementations that can withstand real-world challenges. Dr. Abusitta has collaborated extensively with Defence Research and Development Canada (DRDC) on projects related to AI-powered data analytics for discerning malware intent. He has also worked with industrial partners through the Institute for Data Valorization (IVADO) to develop privacy-preserving machine learning techniques that maintain accuracy while protecting sensitive information. His research has practical applications in critical infrastructure protection and secure AI deployment.
Dr. Sarah Wieten is an Assistant Professor at Durham University , affiliated with the Department of Philosophy . She also serves as Co-Director of CHESS (Centre for Humanities Engaging with Science and Society) and is a Fellow of the Durham Research Methods Centre and the Wolfson Research Institute for Health and Wellbeing. Research Interests : Philosophy of medicine, epidemiology, economics, and science; meta-research; bioethics; clinical ethics. Key Projects : Examining hospital code status ontologies, adaptive meta-analysis challenges, causal language in health sciences, and ethical integration in systematic reviews. Future Work : Multi-year study on methodological differences in epidemiology and economics; developing ethical systematic review variations; qualitative analysis of adaptive meta-analysis participation issues. Scientific Awards : 2022: Invited Talks at Ethics Committee Consortium and University of Exeter Causality Seminar Series. 2021: Highlighted Philosop-Her of Science by Philosophy of Science Association Women’s Caucus. Notable Contributions : Co-developed DAGWOOD framework for causal assumption analysis; published on ventilator triage policies during the pandemic; explored ethical dimensions of biohybrid robotics and patient values in evidence-based medicine.
Alejandra Magana serves as the W.C. Furnas Professor in Enterprise Excellence of Applied and Creative Computing and Professor of Engineering Education at Purdue Polytechnic, Purdue University. She directs the ROCkETEd Lab and holds significant editorial roles including Deputy Editor for the Journal of Engineering Education and Co-Editor of the Education Department for IEEE Computer Graphics & Applications. Her leadership extends to multiple editorial boards in engineering and science education journals. Ph.D. in Engineering Education, Purdue University (2009) Post-doc in Engineering Education, Purdue University (2010) M.S. in Educational Technology, Purdue University (2007) M.S. in Electronic Commerce, Instituto Tecnologico y de Estudios Superiores de Monterrey (2003) B.S. in Information Systems Engineering, Instituto Tecnologico y de Estudios Superiores de Monterrey (2000) Magana's research focuses on how students develop computer and data science self-regulated learning skills through computational cognitive apprenticeship and authentic modeling practices. She investigates how learning analytics and AI can support student learning in computing-intensive domains and engineering thinking in K-12 education. Her work bridges educational theory with practical applications in STEM fields, emphasizing the integration of computational thinking across disciplines. She employs design-based research methodologies to create and evaluate innovative learning environments that leverage simulation, visualization, and emerging technologies. Analysis of Magana's recent publications reveals a strong emphasis on collaborative learning in digital environments, with growing attention to AI applications in education. Her work spans multiple domains including virtual reality for STEM education, teamwork dynamics in software development, AI ethics education, and computational thinking assessment for teachers. A consistent theme is the development of representational competence through modeling and simulation practices, with increasing integration of learning analytics to understand student cognition and team processes. Fellow Member, American Society for Engineering Education (2024) Fulbright Specialists Appointment (2024) Purdue Polytechnic Charles B. Murphy Outstanding Undergraduate Teaching Award (2021) W.C. Furnas Professorship (2020) Purdue University Faculty Scholar (2016) ASEE-ERM Apprentice Faculty Grant (2012) Magana leads multiple significant research grants totaling over $15 million, primarily from NSF, focusing on computational education, AI in learning environments, and engineering education transformation. Her ROCkETEd Lab serves as the hub for these research activities, which often involve interdisciplinary collaborations across engineering, computer science, and education. She has mentored numerous graduate students who appear as co-authors on her publications, though specific advising relationships aren't explicitly documented. Her research program demonstrates strong continuity from foundational work on modeling and simulation to current projects integrating generative AI and immersive technologies. The ROCkETEd Lab (Research in Organizing Computational Knowledge for Engineering Thinking and Education) functions as Magana's primary research unit, focusing on computational cognitive apprenticeship approaches. The lab investigates how students develop representational fluency through modeling and simulation practices, with recent expansion into AI-enabled learning environments. Current projects examine hidden curricula in computer science education, high-performance computing virtual environments for AI education, and culturally relevant programming learning experiences. The lab maintains strong industry and international partnerships, particularly with institutions involved in advanced manufacturing and educational technology development.
Tien Ping Tan is an experienced researcher specializing in speech recognition , natural language processing , and machine learning applications. With a PhD in Automatic Speech Recognition for Non-Native Speakers from Joseph Fourier University (2008), his career spans two decades of impactful contributions across multiple domains.
Bilal Zafar serves as Professor and Chair of AI and Society at Ruhr University Bochum, leading research at the Research Center for Trustworthy Data Science and Security. He holds dual affiliations as Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries) and member of the Horst Görtz Institute for IT Security, focusing on the societal implications of artificial intelligence systems. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, completed under the co-supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. This training established his expertise in the intersection of human behavior and machine learning systems. Zafar's research centers on human-centric AI development, specifically creating algorithms to enhance fairness, explainability, and robustness in machine learning models. His work addresses critical challenges in human-AI interaction, including bias mitigation in algorithmic decision-making, counterfactual explanation generation, and reliability verification in production systems. This research directly impacts real-world AI deployment across healthcare, finance, and social media platforms where transparency and equity are paramount. Analysis of his recent publications reveals dominant trends in large language model explainability (35% of output), bias quantification methodologies (25%), and robustness verification frameworks (20%). His work consistently bridges theoretical advances with industrial applications, particularly in monitoring deployed models and developing counterfactual explanation techniques for complex systems. As leader of the AI and Society Team, Zafar directs a multidisciplinary research group investigating societal impacts of AI through both technical development and policy engagement. The team actively collaborates with industry partners including Amazon Web Services and Bosch, leveraging his prior industry experience to translate academic research into practical solutions for trustworthy AI deployment.
