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Yong ZhengView profile

Associate Professor

• Data Science
• Machine Learning
• Artificial Intelligence
• +7 more

Dr. Yong Zheng is an Associate Professor and tenured faculty member in the Department of Information Technology and Management (ITM) at the College of Computing, Illinois Institute of Technology. He holds a Ph.D. in Computer and Information Sciences from DePaul University. His research focuses on Data Science, AI, Recommender Systems, and Human-Centric Computing. He directs the Center for Decision Making and Optimization (DMO) and co-directs the ITM PhD program. Dr. Zheng’s academic roles include service as General Chair for ACM HT 2025, Track Co-Chair for multiple conferences, and editorial roles at journals like Education and Information Technologies (EAIT) and Frontiers in Big Data. He has received awards such as the Springer Nature Editorial Contribution Award (2025) and NSF grants for conference participation and research. His research spans multi-objective optimization in finance, context-aware recommendations, educational technologies, and FinTech. He has developed open-source libraries like CARSKit, DeepCARSKit, and MOPO-LSI. His work has been published in top venues including ACM RecSys, IUI, and SIGIR. Dr. Zheng teaches courses in Data Science, AI, and Applied Data Analytics. He advises numerous PhD and master’s students, focusing on topics like multi-criteria recommendations and financial systems. His industry collaborations include roles at Pandora Media and Morningstar, Inc.

Zachary K. CollierView profile

Assistant Professor

• Causal Machine Learning
• Structural Equation Modeling
• Educational Measurement
• +3 more

Zachary K. Collier is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut. He holds a Ph.D. in Measurement and Statistics from the University of Florida (2018) and a B.S. in Special Education from Winthrop University (2013). His research integrates Causal Machine Learning and Structural Equation Modeling with critical perspectives to advance equitable data analysis methods. He developed the CritSEM framework to examine how social and structural inequalities shape measurement constructs. Founded the MUDD Lab (Methods for Unstructured and Difficult to use Data) in 2020 Secured over $9 million in external funding His work emphasizes fairness, data ethics, and methodological transparency across education, public health, and social sciences. Selected publications demonstrate innovative applications of machine learning to educational measurement challenges. Scientific recognition includes: Emerging Scholar Featured Mathematician

Pallavi GuhaView profile

Associate Professor

• Anti-rape and sexual harassment media activism
• Social media and activism
• #Metoo activism
• +3 more

Pallavi Guha serves as an Associate Professor in the Department of Mass Communication within Towson University's College of Fine Arts and Communication. Her research centers on anti-rape and sexual harassment media activism, with particular focus on #MeToo movements in India and global south contexts, examining intersections of social media, politics, and gender-based violence. Dr. Guha earned her academic credentials through: Ph.D. in Journalism, University of Maryland College Park Graduate Certificate in Women's Studies, University of Maryland Her scholarly work bridges communication theory, gender studies, and digital activism, with primary emphasis on how media platforms shape anti-rape movements and political discourse. Secondary research explores visual framing techniques and big data applications in media analysis, consistently contextualizing findings within postcolonial and feminist frameworks. Analysis of her 2019-2023 publications reveals consistent thematic focus on Indian sociopolitical contexts, examining #MeToo's evolution through news media and social platforms, sexual assault narratives during elections, and pandemic impacts on activism. Her interdisciplinary approach connects communication studies with gender politics, digital humanities, and social movement theory. Key recognitions include: Center for Community News Faculty Champion (2024) Solutions Journalism Network Student Media Challenge Award (2024) DEFCon Teaching Fellowship (2022) Howard County Commission for Women's 'So She Did' recognition (2021) AEJMC Nafziger-White-Salwen Dissertation Award (2019) Dr. Guha actively mentors graduate researchers on topics including Black women's media representation, digital grief practices, and intimate partner violence framing. Her grant portfolio demonstrates strong institutional support for research on gender-based violence and innovative teaching methods, including the Kirwan Commission's Open Education Resource grant. She contributes to broader academic ecosystems through leadership roles including StarTUp Entrepreneur Faculty Fellow, White House Task Force research stakeholder for online harassment initiatives, and advisory council chair for Girls Report, Inc.'s Nepal-based gender empowerment journalism program.

• IoT
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Hemlata Sharma is a Senior Lecturer in Digital Analytics and Technology at the School of Computing and Digital Technologies, College of Business, Technology and Engineering. With over 14 years of combined industry and academia experience, she holds a PhD in Computer Science Engineering from Banasthali Vidyapith (2019). Her research focuses on IoT, Artificial Intelligence, Machine Learning, Data Science, Text Mining, and NLP. She has published extensively in top-tier journals/conferences and delivered expert talks on Machine Learning, Tableau visualization, and Industry 4.0. Education: PhD in Computer Science Engineering (Banasthali Vidyapith, 2019). Research Interests: Her work emphasizes practical applications of AI and data science in areas such as recommendation systems (matrix factorization variations), sentiment analysis for EVs and mental health, generative AI in education, and IoT optimization challenges. She also explores macroeconomic factors' impact on financial sectors and predictive modeling in real estate through XGBoost. Key Contributions: Over 20 publications in journals like Entropy, IEEE Access and conferences like ICRITO. Authored a book chapter on IoT optimization and guest-edited a special issue on cyber-physical systems. Her work bridges theoretical advancements with real-world applications across healthcare, education, and finance.

• Programming Languages
• Concurrency
• Embedded Systems
• +4 more

Dr. Emil Sekerinski is a Professor in the Department of Computing and Software at McMaster University. He holds the position of Undergraduate Advisor for Computer Science (Jan 1, 2025 – June 30, 2025). His expertise spans programming logic (verification, refinement), concurrency (components, implementation), embedded systems (modelling, analysis), and programming languages (run-time, compilation). He is actively involved in research clusters focused on Digital & Smart Systems. His research interests emphasize formal methods in computer science, concurrency theory, parallel algorithms, and embedded systems design. He has contributed to advancing methodologies for software quality assurance and system reliability through formal specifications and automated verification techniques. Recent work includes efficient synchronization mechanisms for object-oriented programming languages and formal development of delay-tolerant multicast protocols for wireless sensors. Dr. Sekerinski has organized and participated in major conferences such as FMTea'24 (Co-Chair) and ICTAC'24 (Program Committee). His teaching includes advanced courses like CAS 781 Advanced Topics: Advanced Compiler Design and Optimization and SFWR ENG 3BB4 Concurrent System Design . He maintains an active presence in academic outreach through LinkedIn, Instagram, and other platforms. His work bridges theoretical foundations with practical applications, including environmental sensor data analysis and holistic embedded system development. Despite no explicit mention of awards, his contributions to formal methods education and research are widely recognized in his field.

Gene Moo LeeView profile

Associate Professor

• Artificial Intelligence (AI) in Business
• Information Systems
• Cybersecurity
• +6 more

Gene Moo Lee is an Associate Professor in the Accounting and Information Systems Division at the UBC Sauder School of Business. He holds a PhD in Computer Science from UT Austin. His research focuses on AI in business contexts, cybersecurity, and data analytics, with notable contributions to understanding AI strategies, IT platforms, and workforce transformations. His work has been published in top journals like MIS Quarterly , Information Systems Research , and Journal of Management Information Systems . Education: BS (Korea University), MA (UT Austin), PhD (UT Austin). He has held roles at Samsung, Intel, Goldman Sachs, and others, and holds over 10 patents in mobile technologies. His Erdős number is 3. Research interests include AI's impact on industries, cybersecurity defense mechanisms, and the integration of robotics and service operations. His recent studies explore AI-driven innovation, mobile device management in remote work, and the effects of service robots on labor dynamics. He has supervised over a dozen PhD and Master’s students, including Jaecheol Park (2025 dissertation) and Myunghwan Lee (2023 proposal). Awards include AIS Distinguished Member (2021) and Reviewer of the Year (MISQ/ISR 2021). He serves as an AE for ISR and SE for Decision Support Systems . Key projects include the Data Analytics & AI Research Group, cyber risk assessment frameworks, and studies on generative AI in journalism and video content creation. His work bridges theory and practice, addressing real-world challenges in digital transformation and AI ethics.

Jürgen PfefferView profile

Professor

• Computational Social Science
• Big Data Analysis
• Social Media Dynamics
• +4 more

Prof. Jürgen Pfeffer is a Professor of Computational Social Science & Big Data at the Technical University of Munich (TUM), affiliated with the TUM School of Social Sciences and Technology. His research bridges social and computer science, focusing on large socio-technical systems, algorithmic challenges, and dynamics like online firestorms and hate speech. He holds a doctorate from Vienna University of Technology and has postdoctoral experience at Carnegie Mellon University. Key research areas include computational social science, network analysis, and critical data studies. He leads interdisciplinary projects on online misogyny, disaster communication, and AI ethics. His work addresses methodological rigor in handling big data, visualization of complex networks, and policy implications of social media dynamics. Pfeffer has authored numerous publications in top journals like Science and IEEE, and serves on editorial boards for Frontiers in Big Data and EPJ Data Science. He actively participates in global conferences, advocating for ethical AI and responsible data practices. His research projects span collaborations with institutions worldwide, addressing societal challenges through data-driven insights.

Samuel C BooneView profile

Lecturer

• Geology
• Tectonics
• Thermochronology
• +4 more

Dr Samuel C Boone is a Lecturer in Geology at the School of Geosciences, University of Sydney, and Honorary Fellow at the University of Melbourne. His research focuses on continental tectonics, thermochronology, and the integration of geochemical Big Data with numerical geodynamic models. He holds a PhD in Geology from the University of Melbourne (2019) and has held postdoctoral roles at Joannes Gutenberg University, University of Adelaide, and University of Melbourne. Education: BSc in Physics (University of Massachusetts, 2009), MSc in Geology (University of Melbourne, 2013), PhD in Geology (University of Melbourne, 2019). Research interests include the thermal and tectonic evolution of continental crust, with a focus on rift systems (East Africa, New Zealand), supercontinent breakup, and the interplay between tectonics, climate, and resource preservation. He leads the Melbourne Thermochronology Research Group and co-developed the AusGeochem platform for global geochemical data synthesis. Recent articles emphasize the application of thermochronology to rift dynamics, continental breakup mechanisms, and the development of geospatial data platforms. Awards include the 2021 University of Melbourne Early Career Research Grant and DAAD fellowships. Advising: Supervises PhD and MSc students across multiple institutions, including projects on Australian tectonic evolution, monazite fission-track techniques, and Cobar Basin mineral exhumation. Grants include leadership in AuScope infrastructure projects and IODP collaborations. Labs/Teams: EarthByte Group (Sydney), Melbourne Thermochronology Research Group, University of Adelaide Tectonics and Earth Systems Group, and the AuScope Geochemistry Network.

• Violent Extremism
• Community Resilience
• Counter-Radicalization
• +3 more

Professor Michele Grossman AM holds the Research Chair in Diversity and Community Resilience at Deakin University's Alfred Deakin Institute. She co-convenes the AVERT Research Network and directs the Centre for Resilient and Inclusive Societies (CRIS), focusing on community contributions to countering violent extremism. Her research examines how communities build resilience against violent extremism through bystander reporting mechanisms, youth resilience measurement, and cross-cultural trust relations. Recent projects include international studies on intimate bystander reporting thresholds in Canada, the UK, and Australia. Publications demonstrate methodological innovation in terrorism research ethics, mixed-methods approaches to extremist discourse analysis, and arts-based interventions for radicalization prevention. Scientific Awards: Member of the Order of Australia (AM) for service to tertiary education and social welfare initiatives Walter McRae Russell Prize for 'Entangled Subjects: Indigenous Cross-Cultures of Talk, Text and Modernity' She leads major grants including $875,247 from the Australian Research Council for 'Understanding the drivers and motivators of extremist violence' and $210,123 from the US Department of Homeland Security for bystander reporting research. Current doctoral supervision focuses on clinical assessment of high-risk individuals.

George AzzopardiView profile

Associate Professor

• Pattern Recognition
• Image Processing
• Machine Learning
• +5 more

George Azzopardi is an Associate Professor in Pattern Recognition at the University of Groningen, affiliated with the Bernoulli Institute. He leads the PRISMA team, focusing on robust AI, medical imaging, and interdisciplinary applications in astronomy and forensics. His roles include coordinating the Applied AI theme at the Jantina Tammes School and leading WG4 of the EU GLITSS COST Action. Education: PhD in Computer Science (University of Groningen, 2013), MSc in Advanced Methods in Computer Science (Queen Mary University London, 2008), BSc in Computer Science (Goldsmiths College London, 2006). Research Interests: Brain-inspired algorithms, image processing, predictive analytics, and applications in radio astronomy, forensics, and medical diagnostics. His work emphasizes robustness in AI models and cross-domain collaborations. Publications span advancements in COSFIRE filters, deep learning robustness, and interdisciplinary applications like radio galaxy classification and biometric systems. Recent trends include enhancing model explainability and combating adversarial attacks. Awards: Ben Feringa Impact Award (2023), ENLIGHT Impact Award (2024) Advising: Supervises PhD students in AI-driven medical imaging, radio astronomy, and computer vision. Collaborates with institutions like ASTRON, UMCG, and industry partners Ziel NL and KAIOS. Labs/Teams: PRISMA team develops robust AI tools for medical and astrophysical data analysis, with projects on biostructure segmentation and camera forensics.

• Computational Geometry
• Geometric Algorithmics
• GIScience
• +5 more

Prof. Marc van Kreveld is a Professor and Head of the Department of Computer Science at Utrecht University since February 2023. His research focuses on computational geometry, geometric algorithmics, and their applications in GIScience, cartography, and puzzle design. He leads the Geometric Computing group and has made significant contributions to algorithms for trajectory data, geometric similarity, and spatial data analysis. Education: PhD in Computer Science from Utrecht University (1992). Research Interests: Computational geometry, GIScience, graph drawing, LIDAR point cloud reconstruction, and geometric puzzle analysis. His work bridges theoretical foundations and real-world applications, such as route data analysis and environmental modeling. Publications: Over 290 peer-reviewed papers, including foundational work on Fréchet distance algorithms, geometric spanners, and trajectory grouping. Recent contributions include advancements in collision detection for modular robots and mobility data science. Awards & Editorial Roles: Editor-in-Chief of Computing in Geometry and Topology , and editorial board member of Journal of Computational Geometry . Active in program committees for conferences like GIScience and SoCG. Teaching: Specializes in computational geometry and its applications, with a focus on algorithm design and geometric computing. Labs & Teams: Leads the Geometric Computing group at Utrecht University, collaborating on projects like braided river network modeling and automated puzzle generation.

Rui HenriquesView profile

Assistant Professor

• Data Mining
• Machine Learning
• Bioinformatics
• +4 more

Rui Henriques is an Assistant Professor at the Department of Electrical and Computer Engineering (DEI), Instituto Superior Técnico (IST), University of Lisbon. He is also an Associate Researcher at INESC-ID. His research focuses on data mining, machine learning, bioinformatics, and urban analytics, with applications in healthcare, transportation systems, and pattern recognition. He has advised numerous PhD and MSc students, contributing to impactful projects in areas like healthcare decision support, mobility optimization, and environmental data analysis. Education: PhD in Learning from High-Dimensional Data (2016) from IST, University of Lisbon. Research interests include developing algorithms for high-dimensional data analysis, integrating spatiotemporal data for urban planning, and applying machine learning to biomedical challenges. Notable contributions include tools like BicPAMS for biological data analysis and software frameworks for transportation analytics. His work spans over 100 publications in top journals and conferences, addressing topics from triclustering algorithms to fraud detection in online tests. He leads or contributes to projects such as ILU (city mobility optimization) and Data2Help (emergency medical services optimization). His research emphasizes interdisciplinary approaches, combining data science with real-world applications in health, transport, and sustainability.

• Cybersecurity
• Cybercrime Analysis
• Digital Forensics
• +3 more

Nigel Phair is a Professor in the Department of Software Systems & Cybersecurity at Monash University, specializing in cybersecurity, cybercrime analysis, and digital forensics. His research focuses on global cybersecurity trends, corporate cybersecurity practices, and the intersections of law enforcement with digital platforms. Phair’s work frequently addresses critical issues such as cybercrime indices, boardroom cybersecurity competency, and the societal impact of digital technologies. He has contributed to national cybersecurity strategy discussions in Australia and explored challenges in law enforcement’s adaptation to digital crime landscapes. His publications highlight innovative methods like Automated Social Media Intelligence Analysis (ASMIA) for detecting illicit activities online and emphasize the disconnect between corporate cybersecurity claims and actual practices. He has also analyzed encryption’s role in balancing national security and privacy rights. While no awards or grants are explicitly listed, his extensive output reflects a commitment to advancing cybersecurity policy, education, and technical solutions. His research extends to global cybersecurity comparisons, digital financial service vulnerabilities, and the evolving threat landscapes in cyberspace.

Trisalyn NelsonView profile

Professor

• GIScience
• Urban Planning
• Transportation Planning
• +5 more

Trisalyn Nelson is a Professor and Department Chair in the Department of Geography at the University of California, Santa Barbara (UCSB), holding the Jack and Laura Dangermond Endowed Chair of Geography. Previously, she served as Director of Arizona State University's School of Geographical Sciences and Urban Planning (2016–2020) and Lansdowne Research Professor at the University of Victoria, Canada. Her leadership emphasizes fostering inclusive academic cultures, innovation through industry partnerships, and advancing diversity in STEM. Her research focuses on active transportation, urban planning, and big data applications, with a particular emphasis on bicycle safety and infrastructure equity. She pioneered BikeMaps.org, a crowdsourced platform mapping cycling collisions and near-misses, and developed methodologies using Strava fitness app data to analyze ridership patterns. Nelson also explores spatial ecology, including grizzly bear behavior and salmon ecosystems, and co-leads the Spar Lab at UCSB. Recent work includes evaluating bicycle network connectivity, equity in cycling infrastructure access, and the impacts of bike theft on ridership. Her articles highlight themes like data-driven urban design, transportation equity, and geospatial education innovation. Awards include the prestigious Dangermond Chair, recognizing her contributions to geographic sciences. Nelson's leadership during her ASU tenure included doubling research revenue, expanding student diversity by over 200%, and generating $2M in new revenue through partnerships. She advises five graduate students and collaborates with over a dozen industry partners. Key projects include the INTERACT urban intervention study and WalkRollMap.org for mobility barriers.

Christine YuenView profile

Assistant Professor

• Data Science
• Statistics Education
• Programming for Data Science
• +3 more

Christine Yuen is an Assistant Professor (Education) in the Department of Statistics at the London School of Economics and Political Science (LSE) and serves as the BSc Data Science Programme Director. She earned her PhD from LSE in 2021 and has been teaching since 2016. Her roles include lecturing in Programming for Data Science (ST101) and Managing and Visualising Data (ST115), as well as serving as a teaching assistant for courses like Distributed Computing for Big Data (ST446). Her expertise focuses on statistics education and data science pedagogy, reflecting her contributions to curriculum development and teaching innovation. She has overseen the BSc Data Science programme since her appointment, ensuring alignment with industry and academic standards. No scientific awards or grants were explicitly mentioned in the provided text.