جست‌وجوی استاد و پژوهشگر

Gustavo Ovando-Montejoمشاهده پروفایل

استادیار

• Geographic Information Science (GIScience)
• Environmental and Natural Resource Management
• Human-Environment Interactions
• +۷ مورد دیگر

Gustavo Ovando-Montejo is an Assistant Professor in the Department of Environment & Society within the College of Natural Resources at Utah State University, based at the Blanding campus. He contributes to environmental and geospatial education and research, teaching undergraduate and graduate courses in GIScience, environmental data science, and natural resource management. Education: PhD in Geography (Geographic Information Science), Oklahoma State University, 2019 MS in Geography (Geographic Information Science), Oklahoma State University, 2015 BS in Geographic Information Systems, Brigham Young University, 2013 Research Interests: Dr. Ovando-Montejo's research centers on human-environment interactions, land cover change, and landscape ecology, with a focus on linking spatial patterns to ecosystem services. He explores issues of environmental equity, indigenous segregation, and applies advanced geospatial and data science techniques to environmental and natural resource problems. His work bridges technical GIScience with social and ecological applications. Publication Trends: His recent publication focuses on web-based visualization of large-scale geospatial data, emphasizing equitable access to advanced analytics. This reflects a growing trend in democratizing petascale computing for broader environmental research and education, combining technical innovation with social responsibility. Scientific Awards: QCNR Teacher of the Year, 2024 Advising and Grants: While no formal advisees or grants are listed in the provided text, Dr. Ovando-Montejo has been actively involved in teaching and extension since 2011. He teaches core courses such as Environmental Data Science, Geospatial Analysis, and Physical Geography, and contributes to USU Extension Sustainability efforts, indicating a long-standing commitment to outreach and applied environmental education. Labs and Teams: Specific research labs or collaborative teams are not mentioned in the available content. However, his work in geospatial analysis and environmental data science suggests potential affiliations with cyberinfrastructure or sustainability research initiatives at Utah State University.

• Digital Twins
• Smart Cities
• Blockchain Technology
• +۵ مورد دیگر

Noel Crespi is a Professor and Director of Studies at Telecom SudParis, part of Institut Polytechnique de Paris. He leads research in the NeSS group and is affiliated with SAMOVAR, a prominent research laboratory focusing on networks, systems, and services. His work spans multiple domains in telecommunications, networking, and smart systems. His primary research interests include digital twins for smart cities, blockchain technologies, Internet of Things (IoT) security and applications, 5G/6G networking, and machine learning applications in network management. He has published extensively on these topics, with over 100 publications in top-tier journals and conferences. His recent work shows a strong focus on digital twin applications for urban management, particularly in traffic and air quality monitoring. He has developed modular frameworks for smart city digital twins that integrate real-time data from multiple sources. His research also explores blockchain applications for network security, access control, and service provisioning in next-generation cellular networks. Dr. Crespi has received significant recognition for his work on digital twins, with his 2020 paper "Digital twin in the IoT context" in Proceedings of the IEEE being highly cited. His current research explores the integration of AI with digital twin technologies for sustainable urban development. He has supervised numerous PhD students and collaborates extensively with researchers across Europe and internationally. His work often involves interdisciplinary approaches, bringing together computer science, networking, urban planning, and environmental science. Dr. Crespi has been instrumental in developing frameworks for network digital twins, with applications in traffic management, air quality monitoring, and resource allocation in smart cities. His research demonstrates practical implementations in cities like Madrid, showing measurable improvements in urban management.

Haihua Chenمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Data Quality
• +۷ مورد دیگر

Haihua Chen is an Assistant Professor of Data Science in the Department of Information Science at the University of North Texas (UNT), with a co-affiliation in Health Informatics. They lead the Intelligent Data Engineering and Analytics (IDEA) Lab, focusing on interdisciplinary research in artificial intelligence, data science, and informatics. Chen earned a Ph.D. in Information Science (concentrating in Data Science) from UNT in 2022, an M.S. in Information Science from Wuhan University, and dual B.S. degrees in Information Science and English Literature from Central China Normal University. Research interests span applied machine learning, data quality evaluation, NLP, and informatics applications in legal and healthcare domains. Notable work includes developing frameworks for measuring scientific novelty, constructing high-quality legal and biomedical datasets, and leveraging AI for precision medicine and disaster response. Chen has secured over $499K in external grants, including NSF REU and HSI projects, and $20K+ in internal grants. Their work has been published in top journals like Journal of Informetrics , IEEE Transactions on Reliability , and Scientometrics , with a strong focus on innovation measurement and data-centric AI. Teaching includes courses on computational methods, data analysis, and AI in healthcare. Professional leadership roles include chairing ASIS&T SIG-STI and editorial roles for Journal of the Association for Information Science and Technology , Knowledge and Information Systems , and others. Awards include UNT’s Great Grads Award and the Linda Schamber Writing Award.

Stan Matwinمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Stan Matwin is a Professor and Director of the Institute for Big Data Analytics at the Faculty of Computer Science, Dalhousie University. He is also affiliated with the Institute for Computer Science of the Polish Academy of Sciences. Previously, he was a Distinguished University Professor at the University of Ottawa and has held academic positions at Warsaw University, University of Guelph, Acadia University, and visiting roles across the U.S., Europe, and Latin America. His educational background includes a Ph.D. from Warsaw University (1977) and a D.Sc. (habilitation) from the Polish Academy of Sciences (2010). Stan Matwin's research focuses on Artificial Intelligence , Machine Learning , Data Mining , Text Mining and Analytics , Big Data , and Data Privacy . He is internationally recognized for pioneering work in visual text analytics and the application of machine learning to real-world data challenges. His work bridges academic research and industrial innovation, particularly in technology transfer and data-driven applications. The publications linked from his profile are inaccessible due to a server restriction (403 Forbidden), but he is known to have authored or co-authored over 250 research papers in top-tier venues. Scientific Awards and Honors: Canada Research Chair (Tier 1) in Visual Text Analytics Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) Fellow of the Canadian Artificial Intelligence Association (CAIAC) Ontario Champion of Innovation Stan Matwin has secured significant research funding from organizations including the Canada Research Chairs program, Canada Foundation for Innovation (CFI) , NSERC , Ontario Centres of Excellence , and the Canadian Bureau for International Education . He has supervised numerous graduate students and led major research initiatives. He was former president of the Canadian Artificial Intelligence Association (CAIAC) and IFIP Working Group 12.2 on Machine Learning. He is also an active innovator, co-founding technology companies such as Distil Interactive Inc. and Devera Logic Inc. He leads the Big Data Analytics, AI & Machine Learning research cluster at Dalhousie and continues to be a key figure in advancing data science research and training through the Institute for Big Data Analytics.

Ralf Kellnerمشاهده پروفایل

استاد

• Economics
• Data Science
• Statistics
• +۲ مورد دیگر

Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de

Olusegun Michael Otunugaمشاهده پروفایل

استادیار

• Stochastic Modeling
• Mathematical Epidemiology
• Biostatistics
• +۷ مورد دیگر

Olusegun Michael Otunuga is an Assistant Professor in the Department of Mathematics at the College of Science and Mathematics and the Department of Population Health Science: Division of Biostatistics and Data Science at Augusta University. He holds a Ph.D. in Pure & Applied Mathematics from the University of South Florida and has been actively contributing to interdisciplinary research in stochastic modeling, epidemiology, biostatistics, and financial mathematics. Ph.D., Pure & Applied Mathematics, University of South Florida, 2014 M.S., Mathematics, Marshall University, 2009 B.Sc., Mathematics, Federal University of Agriculture, Nigeria, 2006 Dr. Otunuga's research focuses on the development and analysis of stochastic models to understand complex systems in biology, medicine, and economics. His work spans tumor growth dynamics under environmental stress, multi-strain epidemic modeling (including HIV, TB, measles, and COVID-19), vaccine breakthrough infections, and financial time series forecasting. He employs advanced techniques such as stochastic differential equations, parameter estimation, and first-passage-time analysis. The recent publications highlight a strong trend in applying mathematical rigor to public health challenges, particularly in modeling infectious disease spread with stochastic realism. His work bridges theoretical mathematics with practical applications in biostatistics and data science, often involving real-world data from U.S. states and global health contexts. The integration of time-varying parameters and random perturbations enhances model accuracy and predictive power. Scientific Awards: Excellence in Research Award, College of Science and Mathematics, Augusta University, 2024 Dr. Otunuga actively mentors students and serves as a faculty advisor for Pi Mu Epsilon and Goldwater Scholarship candidates. He has not received mention of specific grants in the provided text, but his sustained publication record and patent suggest active research funding. He contributes extensively to academic service through multiple committees at the department, college, and university levels, and has served as a reviewer for scholarly journals. He is involved in research teams and workshops such as the POPUPS Workshop at Augusta University, where he leads training on biostatistics and data analysis using R. His collaborative work includes joint research with the Ladde research group, culminating in a patented method for dynamic process modeling.

Jennifer Grayمشاهده پروفایل

پژوهشگر

• Materials Science
• Transmission Electron Microscopy
• Nanomaterials
• +۷ مورد دیگر

Jennifer Gray is an Assistant Research Professor at the Materials Characterization Lab within the College of Earth and Mineral Sciences at Pennsylvania State University. She is actively engaged in cutting-edge interdisciplinary research involving advanced materials characterization, with applications in energy, biomedicine, and planetary science. Her work leverages high-resolution electron microscopy and spectroscopy techniques to understand nanoscale structures and properties. Her research interests span a broad range of topics including Materials Science , Transmission Electron Microscopy , Nanomaterials , Thermoelectric Materials , Biomaterials , and Planetary Materials . She has made significant contributions to the analysis of asteroid Ryugu samples, studying space weathering, mineral alteration, and extraterrestrial organic matter. Her work also extends to energy materials, particularly thermoelectrics and magnetic composites, as well as biomedical applications such as nanoparticle delivery in cancer therapy. The recent publications highlight a strong trend toward interdisciplinary collaboration, especially in the characterization of complex materials using advanced electron microscopy techniques. Her work frequently appears in high-impact journals in materials science, planetary science, and biomedicine. The keywords across her articles reflect expertise in electron microscopy , nanoscale characterization , semiconductors , energy materials , and astrogeology . Scientific Awards: No awards explicitly mentioned in the text. Advising and Grants: Jennifer Gray collaborates extensively with large research teams, particularly in NASA-related asteroid sample analysis and energy materials research. While specific grants or students are not listed, her role as a research professor suggests involvement in funded projects and mentorship within the Materials Characterization Lab. She contributes to open science initiatives such as the STEP Initiative, promoting resource sharing in materials research. Labs and Teams: She is a key member of the Materials Characterization Lab at Penn State, which provides centralized facilities for advanced materials analysis. Her work is integral to interdisciplinary teams studying thermoelectrics, biomaterials, and extraterrestrial samples, often in collaboration with national and international researchers.

• Computer Graphics
• Virtual Reality
• Cultural Heritage Visualization
• +۷ مورد دیگر

Carlos Antonio Andújar Gran is a faculty member at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Science. He actively contributes to the academic community through teaching and supervising numerous student projects, particularly in advanced computing domains. His research interests center on the application of computer graphics, virtual reality, and artificial intelligence to real-world problems. Key areas include the 3D visualization and virtual reconstruction of cultural heritage, physically accurate rendering, intelligent segmentation of images, and the use of machine learning and reinforcement learning in sports analytics, particularly for padel. His work bridges technical innovation with practical applications in cultural preservation and sports technology. The recent publications under his supervision reflect a strong trend in digital cultural heritage, with multiple projects focused on tools for virtual visits, reconstruction, lighting simulation, and web-based 3D visualization of heritage models. Another significant trend is the application of AI to padel, including ball and player tracking, virtual coaching, and training virtual agents using reinforcement learning. These projects demonstrate a consistent focus on computer vision, interactive systems, and data-driven modeling. Carlos Antonio Andújar Gran has supervised a substantial number of students, guiding both undergraduate and master's theses. While specific grant information is not available in the text, his sustained supervision across many years suggests active involvement in research projects. He plays a key role in mentoring the next generation of computer scientists at UPC. His work is closely tied to the Department of Computer Science at FIB, where he collaborates with other faculty members such as Imanol Muñoz Pandiella, Núria Pelechano Gómez, and Mohammadreza Javadiha on various student projects, indicating a collaborative research environment focused on graphics, AI, and VR applications.

Zhuping Liuمشاهده پروفایل

دانشیار

• Marketing Analytics
• Machine Learning
• Big Data
• +۶ مورد دیگر

Zhuping Liu is an Associate Professor at the Allen G. Aaronson Department of Marketing & International Business within the Zicklin School of Business at Baruch College, City University of New York. She holds a PhD in Marketing from the University of Texas at Austin (2017), an MS in Statistics from the University of Connecticut (2012), an MS in Management Science from Beijing University of Aeronautics and Astronautics, and a BS in Management Information Systems from Qingdao University. Her research focuses on marketing analytics , machine learning applications , and consumer behavior in digital environments . Key areas include mobile advertising effectiveness, online consumer decision-making, crowdfunding dynamics, visual marketing analytics, and behavioral modeling in retail and sports contexts. She employs advanced methodologies such as Bayesian statistics, field experiments, and econometric modeling. Her publication portfolio demonstrates a strong focus on digital marketing trends, particularly mobile consumer engagement, online platform strategies, and visual attention analytics. Recent work explores push-notification advertising, eye-gaze effects in imagery, and omni-channel retail returns. Awards and Honors: MSI Alden G. Clayton Doctoral Dissertation Proposal Award (2017) SYLFF Doctoral Fellowships in Business (2016) University of Texas Graduate Continuing Fellowship (2016) AMA-Sheth Foundation Doctoral Consortium Fellow (2016) Grants and Funding: PSC CUNY grants totaling over $21,000 for projects including: Geo-messaging strategies (2021-2023) Mobile advertising congruency (2020-2022) Visual attention in marketing images (2022-2023) Push-notification effectiveness (2023-2024) She serves as Director of the Advertising and Marketing Communication Track and participates in multiple committees including the Zicklin Academic Standing Committee and Graduate Curriculum Committee. She also reviews for leading journals including Journal of Marketing and International Journal of Research in Marketing .

László Lengyelمشاهده پروفایل

استاد

• Model-Driven Engineering
• Internet of Things (IoT)
• Software Obfuscation
• +۷ مورد دیگر

László Lengyel is a Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics . His work bridges theoretical and applied computer science, focusing on industrial automation, IoT systems, and model-driven engineering. Research interests include Model transformations and domain-specific languages IoT device management and multi-domain integration Software obfuscation and cybersecurity Graph algorithms and distributed computing (MapReduce) Real-time data analysis in manufacturing Automotive sensor networks His recent publications reflect expertise in model-driven IoT architectures , granule manufacturing automation , and MapReduce-based graph analysis , with a focus on industrial and automotive applications. He contributes to open-source frameworks like SensorHUB and explores gamification in driver behavior systems.

• Process Mining
• Anomaly Detection
• Blockchain
• +۳ مورد دیگر

Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .

Harris Wuمشاهده پروفایل

استاد

• Technology data analytics
• Social media
• Cybersecurity
• +۳ مورد دیگر

Harris Wu is a Professor at Old Dominion University , affiliated with the Strome College of Business and the Department of Information Technology & Decision Sciences . His expertise spans technology data analytics, cybersecurity, text mining, and enterprise information systems. Expertise Areas: Technology data analytics, social media, cybersecurity, text mining, enterprise information systems, system integration. Research Trends : His recent publications focus on machine learning for author disambiguation using heterogeneous graphs (2024), cloud computing strategies involving pricing and guarantee compensation (2023), and optimization frameworks for cloud resource allocation (2022). These works integrate data analytics and business modeling to address complex IT challenges. Contact: Office: 2126 Constant Hall, Norfolk, VA 23529; Phone: 757-683-4460; Email: hwu@odu.edu .

Charlotte Changمشاهده پروفایل

استادیار

• Conservation Biology
• Social-Ecological Systems
• Quantitative Ecology
• +۵ مورد دیگر

Charlotte Chang serves as Assistant Professor of Biology and Environmental Analysis at Pomona College, where she has been faculty since 2020. Her dual appointment bridges the Department of Biology and Environmental Analysis program, with laboratory space in Seaver South. She maintains active affiliations including a Visiting Science Fellowship with The Nature Conservancy's One Conservancy program and collaborations with Centre for Wildlife Studies India and Wildlife Conservation Society. Ph.D. in Ecology and Evolutionary Biology, Princeton University Master of Philosophy, University of Cambridge Bachelor of Arts, Pomona College Chang's research integrates social-ecological systems analysis to advance conservation science, specializing in digital conservation constituencies, illicit wildlife hunting dynamics, and agroforest management for habitat specialists. Her lab employs innovative methodologies combining data science, quantitative ecology, and field methods to examine human-environment interactions across tropical systems. Current projects include LLMs for natural climate solutions evidence mapping and environmental social media analysis for conservation outreach optimization. Her publication record demonstrates consistent high-impact output across conservation biology, ecological modeling, and social science journals. Trends show increasing focus on digital conservation mobilization since 2020, with notable work on Twitter environmental discourse and platform migration patterns. Earlier research established foundational work on hunting motivation in Southwest China and coffee agroforest biodiversity in India's Western Ghats. David H. Smith Conservation Research Fellow National Science Foundation NIMBioS Postdoctoral Fellow IUCN Critical Ecosystem Partnership Fund Research Grant NSF GROW with USAID research fellowship Fulbright Fellowship Chang actively mentors early-career researchers across North America and Asia, with notable success in placing mentees in conservation organizations and graduate programs. Her lab receives funding from multiple NSF programs and conservation NGOs, supporting fieldwork in China, India, and Southeast Asia. Current grants focus on social media analysis for conservation outreach and natural climate solutions mapping. The Chang Lab operates as an interdisciplinary hub combining computational ecology, conservation social science, and field research. Her team collaborates with environmental practitioners, community leaders, and academic colleagues globally, with recent projects spanning the Western Ghats, Southwest China, and digital conservation networks worldwide.

• Self-regulated Learning
• Learning Analytics
• Online Education
• +۴ مورد دیگر

Dr. Jacqueline Wong is an Assistant Professor in the Department of Education and Pedagogy at the Faculty of Social and Behavioural Sciences, Utrecht University. Her academic work focuses on enhancing educational practices through evidence-based research in self-regulated learning and learning analytics, with particular applications in higher education contexts. Dr. Wong earned her Ph.D. (cum laude) from Erasmus University Rotterdam with a thesis entitled "Enhancing Self-Regulated Learning Through Instructional Supports and Learning Analytics in Online Higher Education," which received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. She completed her Master's degree (cum laude) in Educational Psychology, where she examined the use of movements to support text comprehension. Prior to joining Utrecht University, she was a postdoctoral researcher at Delft University of Technology (2020-2022) working with the Program of Innovation in Mathematics Education (PRIME). Dr. Wong's research program spans several interconnected areas: the application of learning analytics to support self-regulated learning, the design of instructional interventions that promote metacognitive awareness, and the investigation of how emerging technologies like generative AI can enhance educational experiences. Her work frequently examines how technology can be leveraged to support students' metacognitive processes and goal-setting behaviors in both traditional and online educational settings. She has made significant contributions to understanding how conversational agents and learning analytics can provide personalized support for students' self-regulated learning processes. Analysis of her recent publications reveals a clear trajectory toward integrating emerging technologies with established educational theories. Her 2024-2025 work increasingly focuses on the intersection of artificial intelligence and educational psychology, particularly examining how generative AI tools can enhance academic text accessibility and provide adaptive support for learning. She also continues to investigate cognitive aspects of learning in educational games and augmented reality environments, with implications for both educational practice and learning theory development. Among her notable achievements, Dr. Wong received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. This recognition highlights the significant contribution her doctoral work has made to the field of educational psychology and learning sciences. Her systematic reviews, including examinations of student engagement in mathematics education and gamification in MOOCs, have provided valuable syntheses of research in growing areas of educational technology. Dr. Wong has been an active research member of the Leiden-Delft-Erasmus Centre for Learning and Education (LDE-CEL) and the network for Self-Regulated Learning in Digitalised Schools (SeReLeDiS). Her collaborative approach to research is evident in her extensive co-authorship network across multiple institutions. Before joining Utrecht University, she worked with lecturers to promote student motivation and self-regulated learning in blended service mathematics courses and examined the effectiveness of technology tools for learning mathematics. Dr. Wong's work bridges theoretical frameworks with practical implementations, often involving close partnerships with educators to ensure research relevance to real-world classroom challenges. Her current research appears to be moving toward deeper integration of AI technologies with educational theory, particularly examining how generative AI can support metacognitive processes and self-regulated learning across diverse educational contexts.

Wayne Wobckeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.