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James FouldsView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Ethics in AI
• +4 more

James Foulds is an Associate Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). His research focuses on socially conscious machine learning and artificial intelligence, emphasizing fairness, privacy, and computational social science using probabilistic models and Bayesian inference. He holds an NSF CAREER Award and a runner-up prize from the SoCal Machine Learning Symposium (2016). His work includes developing fair AI systems, privacy-preserving techniques, and Bayesian modeling approaches for intersectional fairness. Dr. Foulds earned his postdoctoral training at UC San Diego and UC Santa Cruz, and completed his PhD at UC Irvine. He teaches courses in artificial intelligence, ethical AI, and data mining. His research has been supported by grants such as the NSF CISE Research Initiation Initiative (CRII) and the NSF EAGER grant for healthcare resource fairness. Notable contributions include the Data Mining textbook (under revision), the Stochastic-CVB0 Julia package for topic modeling, and collaborations on AI fairness frameworks like the IEEE DEB Special Issue. He advises students on projects in fair machine learning, including equitable healthcare resource allocation and bias mitigation in resume filtering systems.

Sheri MarkoseView profile

Professor

• Financial Market Modelling
• Computational Economics
• Systemic Risk Analysis
• +5 more

Professor Sheri Markose is a Professor of Economics at the University of Essex, leading the Centre for Computational Finance and Economic Agents (CCFEA). She specializes in financial market modeling, systemic risk, and agent-based computational economics (ACE). Her research bridges complex adaptive systems theory and practical policy design, with notable contributions to central bank digital currencies (CBDCs), financial stability, and FinTech innovation. She serves on the Bank of England's CBDC Academic Advisory Group and has advised institutions like the Reserve Bank of India and the BIS/BCBS. Education: PhD in Economics from the London School of Economics (LSE). She teaches computational market microstructure for FinTech programs and has pioneered postgraduate curricula in FinTech and algorithmic trading. Her work integrates Gödelian formal mathematics to analyze strategic innovation and market dynamics, recognized by awards such as the 2017 Eubank Prize from Rice University. Key Research Areas: Systemic risk modeling, agent-based simulations, digital financial systems, and cross-border FinTech trends. Grants & Funding: Led the COMISEF project (EU FP6) and advised the Reserve Bank of India on financial network modeling. Publications span over 50 articles, focusing on FinTech ecosystems, systemic risk, and digital economy dynamics. Her recent work explores UK-India FinTech trade, CBDC frameworks, and genomic intelligence applications in complex systems. She is an Associate Editor of Frontiers in Robotics and AI: Computational Intelligence.

Tai Tan MaiView profile

Assistant Professor

• Data Mining
• Learning Analytics
• Process Mining
• +3 more

Dr. Tai Tan Mai is an Assistant Professor at Dublin City University's School of Computing. He holds a PhD from DCU funded by the Irish Research Council and an MSc in Business Information Systems from University College Cork (2016) where he graduated as top-performing student. His research integrates data mining, learning analytics, and complex systems theory with applications in educational technology and business process management. Research interests focus on: Educational data mining and learning analytics Cryptocurrency market analysis using graph-based methods AI applications in education and societal risk assessment Complex systems approaches to programming education Prior to academia, he developed Business Process Management solutions for Vietnam's banking/financial sector.

• Database Systems
• Data Management
• Data Mining
• +4 more

Michael Grossniklaus is a Professor at the University of Konstanz, Germany, specializing in database systems, data management, and information systems. His research focuses on query optimization, social media data streams, and object-oriented databases. He has contributed to the development of C-SPARQL for RDF stream processing and frameworks for adaptive data management. Grossniklaus has collaborated extensively with researchers in areas such as trajectory data modeling, cloud database optimization, and context-aware systems. His work emphasizes practical applications in education technology, mobile computing, and semantic web technologies. Affiliation: University of Konstanz, Germany Key Research Areas: Database Systems, Data Mining, Social Media Analysis, Query Optimization Notable Projects: C-SPARQL, Sahara (cloud database optimization), LPLM (neural cardinality estimation) Recent publications highlight advancements in neural models for cardinality estimation, trajectory data models, and memory optimization in cloud databases. His interdisciplinary approach bridges theoretical foundations with real-world applications in education, social media, and mobile systems.

• Machine Learning
• Algorithm Optimization
• Neural Networks
• +4 more

Kyungmi Joanne Lee is a Senior Lecturer at James Cook University (JCU), Australia, specializing in Computer Science and Artificial Intelligence. She holds a PhD in Computer Science from Griffith University (2007) and previously served as a Lecturer at Charles Sturt University (2007–2008). Her research focuses on machine learning, algorithm optimization, neural networks, data mining, and applied AI. Key projects include developing real-world scheduling systems for mining operations, ultrasonic signal classification, and spatio-temporal trajectory analysis. Teaching responsibilities include modules like Database Modelling, where she coordinates and lectures. She currently supervises 5 PhD students, with 3 nearing completion. Her work spans diverse applications, such as emergency management through geospatial data analysis and curriculum development via text mining. With 58 publications across journals and conferences, her contributions highlight interdisciplinary approaches to data-driven solutions.

Martin CarnoyView profile

Professor

• Labor Economics
• Political Economy of Education
• Comparative Education
• +4 more

Martin Carnoy is the Lemann Foundation Professor at Stanford University's Graduate School of Education. His academic background includes a BS in Electrical Engineering from the California Institute of Technology (1960), MA in Economics from the University of Chicago (1961), and PhD in Economics (1964). His research focuses on the political economy of education, comparative educational systems, and the intersection of education with socioeconomic factors. Key areas of study include international testing analysis, educational policy in Brazil and Latin America, and the impact of privatization on public services. Dr. Carnoy's work emphasizes equity and systemic barriers, often using longitudinal data and comparative methodologies. His research has addressed topics such as achievement gaps in Brazil, the effects of educational subsidies, and the role of machine learning in evaluating educational systems. He critiques the misuse of international test scores for policy decision-making and advocates for context-aware educational reforms. His recent publications highlight trends in global education systems, including the unintended consequences of privatization, the gendered outcomes of virtual education, and the persistent racial and socioeconomic divides in academic performance. Carnoy’s analyses often bridge economics and sociology, offering interdisciplinary insights into education’s role in societal development. Though no scientific awards are explicitly mentioned in the provided text, his contributions to comparative education and critical policy analysis are widely recognized in academic circles. His advisory work and grant activities remain unspecified in the source material.

Emily PostanView profile

Senior Lecturer

• Bioethics
• Data Ethics
• AI Ethics
• +5 more

Dr. Emily Postan serves as Senior Lecturer and Chancellor's Fellow in Bioethics at the University of Edinburgh's Edinburgh Law School, where she also holds the position of Deputy Director of the Mason Institute for Medicine, Life Sciences and the Law with lead responsibility for policy engagement. Her academic leadership extends to Director of Postgraduate Academic Guidance within the Law School. Her educational background includes a PhD from the University of Edinburgh (2017), LLM (Distinction), MA(Hons), and MLitt by research in Philosophy from the University of Edinburgh and University of Stirling. Prior to her academic career, she spent a decade in policy management at the Scottish Government. Postan's research centers on the intersection of biomedical technologies, personal information, and identity formation, with particular focus on how health informatics shapes our self-conception. Her work critically examines ethical governance frameworks for bioinformation, emphasizing the narrative dimensions of identity. She explores how AI-driven health data classification impacts belonging and social categorization, particularly through her current project 'Identity by Algorithm: Ethical Impacts of Categorisation by Health AI'. Her scholarly output reveals consistent engagement with neuroethics, genomic regulation, and AI ethics, with publications spanning from foundational identity theory to practical policy recommendations. The trajectory shows increasing focus on algorithmic governance in healthcare, culminating in the 2025 international consensus paper on AI trust frameworks. 2018 Rising Star lecture at International Neuroethics Society Annual Meeting Project leadership for Nuffield Council on Bioethics’ 2013 Novel Neurotechnologies report Key contributions to Scottish policy through Mason Institute submissions on organ donation, human tissue legislation, and Adults with Incapacity Act reforms As a doctoral supervisor, she mentors researchers in bioethics and medical law, currently guiding Gokce Kolukisa's work on 'Genome Editing in International Law'. Her Wellcome Trust-funded project 'Confronting the Liminal Spaces of Health Research Regulation' (2017-2021) established her as a leading voice in health research governance. Postan actively engages with media and policymakers on bioethics issues, bridging academic research and real-world application through the Mason Institute's policy portfolio.

Greg RidgewayView profile

Professor

• Criminology
• Statistics
• Data Science
• +3 more

Greg Ridgeway serves as the Rebecca W. Bushnell Professor of Criminology at the University of Pennsylvania's School of Arts and Sciences, with a dual appointment in the Department of Statistics and Data Science. He holds multiple leadership roles including Co-director of the Data Driven Discovery Initiative and Co-editor-in-chief of the Journal of Quantitative Criminology. His affiliations span the Quattrone Center for the Fair Administration of Justice, Penn Injury Science Center, Center for Causal Inference, and Population Studies Center. His educational background includes a Ph.D. in Statistics from the University of Washington (1999), where his dissertation focused on Bayesian inference for massive datasets under advisors David Madigan and Thomas Richardson. Additional degrees include an M.S. in Statistics (1997) and B.S. in Statistics (1995) from the University of Washington and California Polytechnic State University respectively. Ridgeway's research centers on statistical methods for crime analysis and justice system improvement, with major contributions in police use-of-force analysis, racial profiling detection, and justice system benchmarking. His work bridges criminology and data science through innovative applications of propensity scoring, causal inference, and predictive modeling to real-world criminal justice challenges. He has developed methods implemented by police departments in Cincinnati, Los Angeles, and New York City, as well as Federal Public Defender Organizations. His 15 most recent publications demonstrate consistent focus on police behavior analysis, sentencing disparities, and environmental crime prevention. Key trends include the development of conditional likelihood models for officer shooting analysis, benchmarking systems for judicial accountability, and rigorous evaluation of place-based interventions like vacant lot remediation. His methodological contributions span criminology, statistics, and public health with strong emphasis on practical policy applications. Fellow of the American Society of Criminology (2025) Fellow of the Academy of Experimental Criminology (2024) Fellow of the American Statistical Association (2013) ASA Outstanding Statistical Application Award (2007) RAND Gold Medal Award (2007) 8 granted US patents in medical treatment hypothesis testing and resource pre-fetching Ridgeway has secured over $20 million in research funding from entities including Arnold Ventures, National Institute of Justice, and Neubauer Family Foundation. His advisory work includes directing the Master of Science in Criminology program and mentoring students through Penn's Graduate Groups. As former Acting Director of the National Institute of Justice (2013-2014), he led an 80-person agency with a $250M budget, implementing reforms like a $75M school safety research program. Current service includes chairing the American Statistical Association's Committee on Law and Justice Statistics. His leadership extends to directing RAND's Safety and Justice Program and Center on Quality Policing, where he managed 50-person teams and $10M in annual research. Current institutional roles include Co-director of the Data Driven Discovery Initiative, which he launched to develop data science for social good programming, seed grants, and a data science minor.

Ignacio De Castro ArribasView profile

Lecturer

• Social Computing
• Internet Economics
• Network Analysis
• +5 more

Dr. Ignacio De Castro Arribas is a Lecturer in Data Analytics at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on social computing, internet economics, internet measurements, and web systems. He chairs the Research and Analysis of Standardisation Processes (RASP) Research Group at the IRTF. Teaching responsibilities include the postgraduate Cloud Computing module, covering virtualization technologies, service paradigms, and ethical/security considerations. He is affiliated with the Centre for Networks, Communications and Systems, contributing to interdisciplinary research on decentralized systems and online moderation. Research highlights include studies on Fediverse migrations, Mastodon social network dynamics, and content moderation in decentralized platforms. He has received grants from EPSRC for projects like 'Understanding Transitions when Tackling Online Harms' and 'Fediverse Observatory.' Grants: £339k EPSRC grant (2022–2025), £15k Fediverse Observatory grant (2024) Key Projects: Analysis of IPFS storage optimization, toxicity detection in decentralized web platforms, and editor curation in news ecosystems Affiliations: Centre for Networks, Communications and Systems

David LeslieView profile

Professor

• Digital Ethics
• Algorithmic Accountability
• AI Governance
• +5 more

Professor David Leslie is a Professor of Ethics, Technology, and Society at Queen Mary University of London and Director of Ethics and Responsible Innovation Research at The Alan Turing Institute. His research focuses on digital ethics, algorithmic accountability, and the societal impacts of AI, synthetic biology, and geoengineering. He has contributed to UK government AI ethics guidelines and international frameworks like the UNESCO Ethics of AI Recommendation. Education background: Previously taught at Princeton, Yale, and Harvard with notable teaching awards. Leadership roles: Co-authored key documents such as the ICO’s 'Explaining Decisions Made with AI' and advised the Council of Europe on AI governance. Research emphasizes cross-cultural and intercultural perspectives on data justice, with projects involving global collaborations. His work bridges academia, policy, and industry, addressing ethical challenges in AI deployment.

Sahar SoltaniView profile

Research Fellow

• Computational Design Sustainability
• Social Sustainability
• Industrialised Building
• +6 more

Dr. Sahar Soltani is a Research Fellow at the Department of Architecture, Monash University, affiliated with the Future Building Initiative lab and the Australian Research Centre for Interactive and Virtual Environments (IVE). She holds a PhD in Architecture from the University of South Australia (2021), complemented by a Bachelor and Master of Architectural Engineering from Iran. Her research focuses on computational sustainability, industrialised construction, and Industry 4.0 technologies, with emphasis on bridging academic and industry needs through collaborative projects. Her expertise spans spatial analysis for social sustainability, digital transformation in construction (e.g., BIM, Mixed Reality), and education innovation. Key projects include: Leading Building 4.0 CRC initiatives for construction productivity and skills training Evaluating regional construction hubs and modern methods of construction (MMC) Developing frameworks for AI integration in technical education Dr. Soltani collaborates with institutions like RMIT, University of Adelaide, and Technical University of Denmark, and actively engages with industry partners to ensure practical outcomes. Her work addresses challenges in sustainable built environments, digital collaboration, and workforce upskilling. Research outputs include over 18 peer-reviewed articles and conference papers, focusing on topics like mixed reality in education, Industry 4.0 applications in transportation, and spatial configuration for social equity. She supervises graduate research students and reviews for journals such as Buildings , Architectural Science Review , and Smart and Sustainable Built Environment . Future work continues to explore AI-driven construction solutions, digital twin technologies, and the socio-technical dimensions of industrialised building systems.

Gianna Del CorsoView profile

Associate Professor

• Quantum Computing
• Numerical Linear Algebra
• Spectral Analysis
• +3 more

Gianna Del Corso is an Associate Professor at the Department of Computer Science, University of Pisa. Her research interests span Quantum Computing, Numerical Linear Algebra, and Spectral Analysis. She has advised PhD students in quantum computing topics such as quantum algorithms and machine learning. Her teaching includes courses on Numerical Calculus, Parallel Scientific Computing, and Introduction to Quantum Computing, delivered across multiple academic years at the University of Pisa. She has also contributed to advanced courses in the PhD program in Computer Science. Her research focuses on quantum algorithms for machine learning, quantum walks, eigenvalue computation of structured matrices, and spectral techniques for web analysis. Recent work includes applications of quantum k-means clustering and variance estimation subroutines. Her publications span quantum computing, numerical methods, and scientific citation models. Her articles highlight contributions to PageRank computation, matrix factorization for recommendation systems, and analysis of citation-based research evaluation models. She has actively engaged in international conferences, presenting on topics such as orthogonal iterations for nonlinear eigenvalue problems and quantum hitting time algorithms. Labs/Teams: Research conducted within the Department of Computer Science at the University of Pisa, focusing on quantum computing and numerical methods.

James CaverleeView profile

Professor

• Machine Learning
• Recommender Systems
• Information Retrieval
• +7 more

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.

• Predictive Analytics
• Machine Learning
• Biologically Inspired Knowledge Discovery
• +7 more

Anasse Bari is a Clinical Assistant Professor of Computer Science at New York University’s Courant Institute of Mathematical Sciences. His research focuses on predictive analytics, machine learning, biologically inspired knowledge discovery, and data science. He leads the AI and Predictive Analytics research group and lab, which develops specialized AI solutions for healthcare, finance, politics, and societal challenges. Bari is an award-winning educator and author of Predictive Analytics for Dummies , and a Fulbright Scholar. Education: Ph.D. in Computer Science from George Washington University (2012). Research Interests: Bari’s work spans predictive analytics applications in diverse fields, including fake news detection, climate change modeling, vaccine hesitancy analysis, and cultural impact studies. He emphasizes interdisciplinary approaches, leveraging machine learning for societal and organizational challenges. Awards: Recipient of the Golden Dozen Award for research contributions and a Fulbright Scholar designation. His work has been featured in Science , CNBC , The Washington Post , and France24 . Labs/Teams: Head of the AI and Predictive Analytics Lab at NYU, which collaborates with global organizations to design AI solutions. He advises on data strategy for leading international entities and delivers keynote speeches on AI’s role in decision-making.

Srinath MaheshView profile

Researcher

• Environmental Engineering
• Transportation Sustainability
• Emission Modeling
• +3 more

Dr. Srinath Mahesh is a postdoctoral researcher at the DiTTlab within the Department of Transport and Planning at Delft University of Technology’s Faculty of Civil Engineering and Geosciences. Previously, he held a postdoctoral position at Trinity College Dublin’s Department of Civil, Structural, and Environmental Engineering. His research focuses on modeling the environmental impact of transportation systems, particularly quantifying vehicle emissions via real-world measurements (e.g., PEMS, remote sensing) and integrating emission models into traffic simulations. Current work involves the TULIPS project, advancing airport sustainability and aviation decarbonization. Education: PhD in Civil Engineering from Indian Institute of Technology Madras (IIT-M) M.Tech and B.Tech in Civil Engineering with a Transportation specialization Research Interests: Quantifying emissions from motorcycles, cars, buses, and trucks Integrating emission models into traffic simulation frameworks Sustainable airport landside transport systems Decarbonization strategies for aviation and urban freight Key Projects: TULIPS: Enhancing airport sustainability through decarbonization REDMAP: Roadside emissions analysis in Dublin Advising & Grants: While no student advisees are listed, his research contributions inform policy and sustainable infrastructure development. His work has been supported through institutional collaborations and project grants. Labs & Affiliations: Active in DiTTlab and affiliated with TU Delft’s Transport & Planning department, contributing to interdisciplinary research on sustainable mobility solutions.