Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.
Dr. Ara J. Schmitt is a Professor of School Psychology and Associate Dean for Research and Faculty Advancement at Duquesne University's School of Education. He holds a Ph.D. from Illinois State University (2001) and a B.S. from the University of Illinois at Urbana-Champaign (1996). His research focuses on neuropsychological assessment, evidence-based interventions for learning disabilities, school-based chronic illness management, and bullying dynamics. Dr. Schmitt has authored numerous peer-reviewed articles, book chapters, and textbooks, including Patterns of Learning Disorders (2006) and Theory and Cases in School-Based Consultation (2020). Education: Ph.D., School Psychology, Illinois State University (2001); B.S., Psychology, University of Illinois (1996) Professional Roles: Associate Dean for Research, School Psychology Program Director, Adjunct Faculty Collaborator His research interests emphasize applied interventions in schools, including neurocognitive assessments, concussion management, and bullying prevention. He has pioneered studies on Taped Problems Intervention adaptations for remote learning and explored cognitive predictors of bullying behavior in adolescents. Recent work addresses socially responsive evaluation practices in health psychology and disparities in concussion awareness among athletes and parents. Dr. Schmitt has advised over 20 graduate students and contributed to national initiatives like the CCTC 2020 guidelines for health psychology training. His work bridges clinical practice and education, particularly in supporting students with chronic illnesses and neurodiverse needs.
Christian Newman is an Associate Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and has expertise in software engineering methodologies, refactoring techniques, and source code analysis. His research focuses on improving code quality, developer practices, and automated documentation. Education: Newman holds a BS, MS, and Ph.D. from Kent State University. His academic background aligns with his current research in software engineering and empirical studies. Research Interests: His work emphasizes identifier naming standards, technical debt management, refactoring strategies, and code reuse. He explores how developers perceive and implement refactoring tools, as well as the role of large language models (LLMs) in programming education and code generation. Publications: Newman's recent work includes studies on identifier semantics, part-of-speech tagging for code analysis, and the performance of LLMs in introductory programming tasks. His research often combines empirical studies with tool development, such as SATDBailiff for technical debt tracking and TSDetect for test smell detection. Teaching & Advising: He teaches courses like SWEN-250 (Personal Software Engineering), SWEN-331 (Engineering Secure Software), and graduate-level thesis supervision. His courses emphasize secure development, software design principles, and team-based projects. Tools & Contributions: Newman has developed tools like srcSlice (static slicing), srcType (type resolution), and SCALAR (identifier analysis). These tools support software evolution, code comprehension, and empirical research in the field.
Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Professor Efthymios Pavlidis is a faculty member in the Department of Economics at Lancaster University Management School (LUMS). He holds the rank of Professor and specializes in macroeconomics, international finance, and time series econometrics. His research focuses on housing market dynamics through collaborations like the International Housing Observatory (with the Federal Reserve Bank of Dallas) and the UK Housing Observatory. He is a Fellow of the Higher Education Academy, reflecting his commitment to academic excellence in teaching and research. His research interests include speculative bubble detection, real estate price forecasting, and testing parity conditions in financial markets. Pavlidis actively supervises PhD students in applied time series econometrics, emphasizing practical applications in financial markets and housing economics. He is involved in numerous academic activities, including organizing conferences and workshops such as the Dynare Conference and the Lancaster Economics Seminar. Key contributions include developing econometric methods for detecting market exuberance and analyzing real exchange rates. His work bridges theoretical econometrics with practical policy implications, particularly in housing and energy markets. Pavlidis collaborates internationally, evidenced by his participation in global academic networks and institutions like the European Economic Association and the Royal Economic Society. His teaching includes the course ECON222 Intermediate Macroeconomics I, and he maintains an office in the Management School (B015), with weekly office hours on Tuesdays. A comprehensive overview of his research and projects is available at his personal webpage: https://sites.google.com/view/etpavlidis/ .
Andrea Bunt is a Full Professor and Associate Head (Graduate) in the Department of Computer Science at the University of Manitoba, where she co-directs the HCI lab. She has established herself as a leading researcher in human-computer interaction with significant contributions to software learnability, rural computing, and technologies for children and families. Her work bridges theoretical and practical aspects of HCI, with strong community engagement through student supervision and collaborative projects. Dr. Bunt completed her B.Sc. at Queen's University, followed by an M.Sc. in 2001 and Ph.D. in 2007 at the University of British Columbia. Prior to joining the University of Manitoba, she was a Postdoctoral Fellow at the University of Waterloo in the Human-Computer Interaction Lab. This educational trajectory has provided her with a strong foundation for her interdisciplinary research approach. Her research interests span multiple areas within human-computer interaction, with particular focus on software learnability for diverse user groups, improving computing experiences in rural and remote communities, and designing technologies specifically for children and families. Her work on explainable AI, gender inclusivity in technology, and collaborative learning dynamics represents cutting-edge contributions to the field. Recent projects include Stream Assistant for live streamers, digital interventions for adolescent tech disengagement, and gender-inclusive approaches to online question-and-answer platforms. Analysis of her recent publications reveals a strong emphasis on user-centered approaches to technology design, particularly focusing on vulnerable or underserved populations. Her work consistently bridges theoretical HCI principles with practical applications, demonstrating how technology can be made more accessible, inclusive, and effective for diverse user groups. There's a clear trajectory toward addressing societal challenges through HCI, with increasing focus on ethical considerations in AI systems. CS-Can | Info-Can Young Researcher Award (2018) NSERC Accelerator Supplement (2015-2018) Multiple Best Paper Awards at premier conferences including CHI, Graphics Interface, and FDG Consistent recognition for methodological innovation and impactful research contributions Dr. Bunt actively mentors a diverse group of students at all levels, from undergraduate research assistants to Ph.D. candidates. Her lab receives funding from NSERC Discovery Grants and other sources to support research on intelligent interactive systems. She has successfully guided numerous students through their academic journeys, with many going on to impactful careers in academia and industry. Her collaborative approach extends to interdisciplinary partnerships across computer science, education, and social sciences. The HCI lab she co-directs serves as a vibrant research hub focusing on real-world applications of human-computer interaction principles. Current projects address critical challenges including technology use in rural communities, digital wellbeing for adolescents, and inclusive design practices. The lab fosters a collaborative environment where students and researchers work together to develop innovative solutions to complex HCI problems.
Prof. Christoph Lütge holds the Peter Löscher Endowed Chair for Business Ethics at the TUM School of Social Sciences and Technology and directs the Institute of Ethics in Artificial Intelligence . He earned a PhD from TU Braunschweig (1999) and habilitation from LMU Munich (2005). Notable roles include a Heisenberg Fellowship (2007) and Distinguished Visiting Professorship at the University of Tokyo (2020–) . His research focuses on AI ethics, business ethics, and ethical challenges in autonomous systems. Education : Doctorate: TU Braunschweig (1999) Postdoctoral Qualification (Habilitation): LMU Munich (2005) Research interests span AI ethics , autonomous driving , and business ethics . He explores ethical frameworks for emerging technologies, regulatory governance, and societal impacts of AI. Recent work includes AI in healthcare, surveillance technologies, and hybrid work models. Awards : Heisenberg Fellowship (2007) Best Scientific Article Award (2020) Academic Membership at Tsinghua University (2021–) His advising and grant activities are not detailed here, but he collaborates with global institutions like AI4People and the German Ethics Commission for Automated Driving. His lab, the Institute of Ethics in AI, drives interdisciplinary research on ethical AI integration.
Michael P. O'Brien is an Associate Professor of Information Management at the Department of Management & Marketing, Kemmy Business School, University of Limerick. He teaches undergraduate and postgraduate modules in Information & Knowledge Management, Business Analytics Simulation, and Technical Communication, serving as Course Director for the MA in Business Management programme. PhD in Computer Science (University of Limerick) MSc in Computer Science (by research and thesis, University of Limerick) BSc (Hons) in Information Systems His research bridges Data Analytics , Software Evolution , and Educational Psychology , focusing on empirical studies of programmers, instructional design, and gamification in education. Recent publications explore experiential learning and gamification for strategic thinking . Michael supervises Masters students in topics ranging from Cloud Computing Security to Blockchain in Finance , with a focus on Big Data and AI Impacts . His advising style emphasizes practical applications and technology-driven solutions. 2000 : AGB Dwyer Memorial Award for Excellence in Education 2017 : KBS Seed Funding Competition He actively contributes to academic networks like the Irish Learning Technology Association and Psychology of Programming Interest Group , aligning his work with UN Sustainable Development Goals.
Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.
Prof. Dr. Poldi Kuhl is a Professor of Educational Psychology at Leuphana University , Lüneburg, since 2021. Affiliated with the Institute of Psychology in Education (IPE) and the Center for Empirical Research on Language and Education (ERLE) , Kuhl specializes in educational psychology, developmental psychology, and inclusive education. Their research focuses on data-driven decision-making, digital learning platforms, academic language demands, and teacher professional development. Education: Diploma in Psychology (2003) and PhD in Philosophy (2008) from Freie Universität Berlin. Kuhl’s recent work examines how academic language features affect learning outcomes, digital data utilization in primary education, and mental health literacy among teachers. Their publications span topics from virtual reality training tools to inclusive teaching strategies in mathematics. Kuhl’s career includes leadership roles at the Research Data Center (FDZ) at the Institute for Quality Improvement in Education (IQB) and a Junior Professorship at Leuphana University. They have collaborated with institutions like the Universitat Oberta de Catalunya and the Max Planck Institute for Human Development .
Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.
Tanya Gupta is a Lecturer in the Department of Chemistry and Biochemistry at the University of Oregon , College of Arts and Sciences. With a PhD in Chemical/Science Education from Iowa State University, she specializes in student-centered inquiry-based teaching and technology integration in chemistry education. Education : PhD (2012) and MEd (2007) in Science Education from Iowa State University; MSc (2000) in Inorganic Chemistry and BSc (1998) in Chemistry Honors from Indian institutions Tanya’s research focuses on enhancing student retention through inquiry-based pedagogy , simulations , and collaborative learning . Her work addresses Diversity, Equity, Inclusion & Access (DEIA) in STEM education, with expertise in instructional design models like ADDIE, SAM, and Kirkpatrick. She has taught at multiple institutions, including Iowa State University and Grand Valley State University, delivering both large-enrollment and graduate-level courses through face-to-face, hybrid, and distance education platforms. Tanya’s publications and book chapters highlight her contributions to technology integration in chemistry education, game-based learning , and social media applications for student engagement. Her work spans curriculum development, educational research, and professional development for science educators.
Professor Joseph Wood is a Professor of Visual Analytics at City St George's, University of London, where he serves as a founding member of the giCentre. His academic career spans over three decades, with continuous contributions to Geographic Information Science and visualization since 1990. He previously served as Head of Department for Computer Science at City University between 2014 and 2017. Professor Wood's educational background includes a PhD in Geographical Information Science from the University of Leicester (1996), an MSc in the same field from the University of Leicester (1990), and a BSc in Physical Geography & Geology from the University of Sheffield (1989). His academic progression shows steady advancement from Research Scholar at the University of Leicester (1990-1992) through various lecturer and senior positions to his current professorship. His research interests center on visual analytics and data visualization, with particular expertise in geographic information science and terrain analysis. Professor Wood has developed innovative methods bridging GI Science, Data Visualization, and education domains. His specific interests include narrative of visual analytic design, computational thinking in pedagogy, and novel visualization design for geographic data. His work demonstrates a consistent focus on making complex spatial data understandable through innovative visualization techniques. Analysis of Professor Wood's recent publications reveals a strong emphasis on practical applications of visualization techniques across diverse domains including transportation, epidemiology, sports analytics, and historical migration patterns. His work shows an evolution from foundational geographic information science toward broader applications in visual analytics, with increasing focus on narrative structures, responsive design, and accessibility considerations in visualization. The interdisciplinary nature of his research is evident in collaborations spanning computer science, geography, urban planning, and public health domains. Professor Wood has been actively involved in the academic community, serving on organizing and program committees for major international conferences including IEEE Infovis and VAST, Eurovis, GIScience, Spatial Accuracy, and Geomorphometry. His contributions to the field have been recognized through invitations to deliver keynote talks at prestigious venues ranging from GeoComputation to TEDx, where he presented on topics such as visualizing movement behavior of cyclists. As an advisor, Professor Wood has supervised numerous PhD and Master's students, with current supervision of Julia Crossley (Student conceptualisation of abstraction in computer science) and Jude Nzemeke (Understanding student misconception in recursive algorithmic thinking). His extensive supervision history includes completed PhDs on topics ranging from cycling behavior to spatio-social relations in photographic archives. His academic leadership extends to software development, with contributions to tools like litvis, elm-vega/el-vegalite, giCentre Utils, handy, and LandSerf GIS. Professor Wood is an active member of professional organizations including IEEE (2007-present), Association of Computing Machinery (ACM) (2007-present), and Association of Geographic Information (AGI) (1997-present), demonstrating his commitment to interdisciplinary collaboration across computer science and geographic information domains.
Kathryn Stolee is an Associate Professor in the Department of Computer Science at North Carolina State University. She received her Ph.D. in Computer Science from the University of Nebraska-Lincoln under Sebastian Elbaum after graduating from the Jeffrey S. Raikes School of Computer Science and Management. Her research spans multiple perspectives in software engineering: technical (program analysis), human (human aspects of software engineering, software product management), and educational (comparative comprehension of algorithms). Notable contributions include work on regular expression refactoring for improved comprehension, constraint solvers for code reuse identification, and cross-language code-to-code search to aid developers learning new languages. Dr. Stolee has secured over $2,000,000 in federal grants, including an NSF CAREER award. Her research combines analysis techniques (refactoring, semantic code search, code-to-code search) with human factors (comprehension, reuse, learning). Her scholarly contributions have been recognized with a National Science Foundation Faculty Early CAREER Award (2018) and a Best Paper Award at the International Symposium on Empirical Software Engineering and Measurement (ESEM, 2011). Actively engaged in the software engineering research community, Dr. Stolee serves as an author, reviewer, and organizer at top conferences. She is committed to mentoring the next generation of computer scientists and has developed educational interventions focused on software testing and code comprehension.
Dr. Andrew Hardie is a Reader in Linguistics at Lancaster University, holding a position equivalent to Associate Professor. He is affiliated with the Faculty of Humanities, Arts and Social Sciences and the Department of Linguistics and English Language within the School of Social Sciences. His primary research specialism focuses on corpus-based methodology and its applications across various disciplines: Corpus design and construction methodologies Development of corpus analysis software tools Applications in grammar of English and other languages Extensions to discourse analysis, language teaching, and other humanities and social sciences fields Dr. Hardie's work spans both theoretical and applied linguistics, with particular emphasis on adapting corpus methods for social scientists and humanists in fields including Psychology, Geography, History, and English Literature. His expertise extends to multiple languages, with special focus on South Asian languages such as Nepali, Urdu, and Hindi. He has developed several significant software tools for corpus linguistics, including Corpus Workbench (as a lead developer), CQPweb (as creator), Unicodify, and Unitag, which have substantially advanced the field of corpus linguistics methodology. His research portfolio includes major projects like the ESRC Centre for Corpus Approaches to Social Science (where he serves as Deputy Director) and UCREL (where he is Chair), demonstrating his leadership in the field. His work bridges theoretical linguistics, computational methods, and practical applications across multiple disciplines. Dr. Hardie actively supervises PhD students working on diverse topics from collocational errors in language learning to sociolinguistics of swearing in Arabic, reflecting the breadth of his research interests and influence across linguistic subfields.
