Dr. H.V. Jagadish is a Professor in the Computer Science and Engineering department, focusing on database systems, data science, and ethical AI. He emphasizes mentorship and independence in research, guiding students to become colleagues through collaborative growth. Research Focus His work spans Data integration and fairness in machine learning Query processing and knowledge graph analysis Reproducibility and ethical data management Advising and Research Dr. Jagadish encourages open communication, frequent feedback, and proactive student-driven research. He supports internships and conference attendance, prioritizing SIGMOD and VLDB for presentations.
Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Béla Bollobás is a renowned mathematician affiliated with the University of Memphis as the Jabie Hardin Chair of Excellence in Combinatorics and the University of Cambridge as a Fellow of Trinity College and Honorary Professor at the Centre for Mathematical Sciences. His work spans combinatorics, probability theory, and graph theory, with significant contributions to percolation and random graphs. Dr. Rer. Nat. (Budapest, 1967) Ph.D. (Cambridge, 1972) Sc.D. (Cambridge, 1984) Bollobás pioneered extremal graph theory, random graphs, and probabilistic combinatorics. He introduced novel graph polynomials and advanced bootstrap percolation models, impacting both theoretical mathematics and statistical physics. His research includes inhomogeneous random graphs and cellular automata in random environments. His selected publications reveal a focus on percolation thresholds, graph invariants, and stochastic processes. Notably, he derived sharp thresholds for bootstrap percolation and defined critical probabilities for Voronoi percolation. Senior Whitehead Prize (2007) Fellow of the Royal Society (2011) Foreign Member, Hungarian Academy of Sciences (1990) Foreign Member, Polish Academy of Sciences (2013) Honorary Doctorate, Adam Mickiewicz University (2013) Szechenyi Prize (2017) Bollobás has supervised over 50 Ph.D. students and authored over 450 publications, including 10 books. He co-founded the journal Combinatorics, Probability and Computing and served on eight editorial boards. He organized numerous conferences, including Bill Tutte and Paul Erdős events.
Guanglei Hong is a Professor at the University of Chicago, holding tenure in the Comparative Human Development Department and the Committee on Education. She chairs the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences and the Committee on Education. Her research focuses on causal inference methodologies for evaluating educational and social policies, particularly mediation and moderation effects in multi-level longitudinal studies. She developed the RMPW and MMWS methods, widely used in causal mediation analysis. Hong holds a Master’s in Applied Statistics and a Ph.D. in Education from the University of Michigan. Education: Ph.D. in Education, University of Michigan, 2004 Master's in Applied Statistics, University of Michigan, 2002 Research Interests: Hong’s work centers on causal moderation and mediation, spillover effects, and sensitivity analysis in policy evaluation. She applies these methods to assess impacts of educational programs, contextual changes, and socioeconomic factors on child/youth development. Her monograph *Causality in a Social World* (2015) is a foundational text in the field. Awards: John Simon Guggenheim Fellowship (2021–2022) William T. Grant Scholar Award (2009–2014) NAE/Spencer Postdoctoral Fellowship (2006–2007) AERA Mary Catherine Ellwein Dissertation Award (2005) Teaching & Training: Hong teaches advanced quantitative methods courses, including causal inference and mediation analysis. She leads the NSF-funded SIARM for STEM institute, training researchers in computational methods for education research. Notable courses include *Advanced Topics in Causal Inference* and *Mediation, Moderation, and Spillover Effects*. Grants & Leadership: Hong leads major grants from NSF, IES, and private foundations. Her current projects include methodological advancements for multisite trials and sensitivity analysis in mediation. She has co-authored over 60 peer-reviewed articles and edited volumes, and serves on editorial boards of leading journals. Labs/Teams: Hong directs the Quantitative Methods Group at the University of Chicago, fostering interdisciplinary collaborations in causal inference and policy evaluation. Her work integrates statistical innovation with real-world applications in education and health sciences.
Dr. Wenjing Zhang is an Assistant Professor in the School of Computer Science at the University of Guelph, Canada. She holds a Ph.D. in Computer Science from the University of Guelph (2024) and was a visiting research scholar at the University of Arizona's Department of Electrical and Computer Engineering (2016–2018). Her research focuses on cybersecurity in AI/ML, including security threats to models, privacy-preserving techniques, and data privacy in generative AI. She leads projects on robust defenses against adversarial attacks, secure federated learning, and privacy-preserving prompt engineering for LLMs. Dr. Zhang’s research areas include: Security in AI/ML (e.g., poisoning, evasion, prompt injection attacks) Model Privacy (protection of internal parameters) Data Privacy (synthetic data generation, privacy-preserving prompt engineering) She has secured a five-year NSERC Discovery Grant (2025–2030) for her research on enhancing security, privacy, and fairness in generative AI. Her work has been published in top-tier venues such as NeurIPS, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. She is a recipient of the 2022 Westin Scholar Award and serves on technical committees for IEEE conferences including CNS 2025 and ICC 2025. Her team collaborates on interdisciplinary projects involving federated learning, information theory, and reinforcement learning. She actively seeks partnerships in security, privacy, and generative AI applications.
Frederick A. A. Kingdom is a Professor in the Department of Ophthalmology at McGill University's Faculty of Medicine, focusing on Perception, Cognition and Cognitive Neuroscience . His research explores the interplay between early visual feature detection (edges, bars) and intermediate stages forming contours, textures, and surfaces through spatial vision, color vision, stereopsis, texture perception, brightness/lightness perception, and transparency studies . Email: fred.kingdom@mcgill.ca Key research domains include: Perceptual Mechanisms : Lateral inhibition, contrast normalization, spatial bandpass filters, and their role in brightness/lightness perception and illusions like simultaneous brightness contrast. Color Vision : Red-green vs blue-yellow system distribution, chromatic contrast requirements for stereopsis, color-based depth processing limitations, and color-shading effects that parse surfaces vs illumination. Texture Analysis : Detection thresholds for orientation/frequency/contrast modulated textures, co-circularity in texture perception, and texture statistical sensitivity (e.g., kurtosis importance). Shape Processing : Shape-frequency/shape-amplitude aftereffects, global vs local shape coding, and contour inflection adaptation. His work combines psychophysics , fMRI , image processing , and computational modeling to dissect visual system architecture, particularly how color and luminance signals are integrated/separated in early cortical processing.
Professor Donald Robertson is a faculty member at the University of Cambridge , holding the position of Professor of Economics and Director of Graduate Studies and PhD Programme within the Faculty of Economics . He is affiliated with Pembroke College and contributes to econometric research and graduate education. Research Interests : His work focuses on Econometrics , Applied Macroeconomics , and Financial Economics , with methodological expertise in Time Series Analysis , Panel Data Analysis , and Predictive Modeling . His publications address topics like cross-sectional dependence, unit root testing, and instrumental variable estimation. Teaching : He instructs modules such as Introduction to Probability and Statistics , Time Series Methods , and MPhil Prep Course - Statistics . Publications : Recent contributions include work on R² bounds for predictive models, factor residuals in panel data, and fiscal fatigue in debt ratios, reflecting his focus on econometric theory and macroeconomic applications. Contact : Email dr10011@cam.ac.uk or phone +44(0)1223 335270. Office hours by email appointment in Room 70.
Dennis W. Duke is a Professor of Physics at Florida State University, with a joint appointment in the Department of Physics within the College of Arts and Sciences and the School of Computational Science. He has been a faculty member at FSU since 1979, progressing from Visiting Assistant Professor to his current position as full Professor since 1992. Duke has also held significant leadership roles including Director of the Supercomputer Computations Research Institute (1993-1998) and Director of the San Diego Supercomputer Center (1996-1997). Duke's research spans multiple disciplines with primary focuses on the history of ancient astronomy, computational physics, and high-performance computing. His work in ancient astronomy includes detailed studies of Greek, Babylonian, and Indian astronomical traditions, with particular emphasis on planetary models, trigonometry development, and historical measurement techniques. In computational science, he has made contributions to nonlinear dynamics, chaos theory, and statistical process control. Duke is particularly known for creating educational computer animations of ancient planetary models that have become valuable teaching tools. Analysis of Duke's 15 most recent publications reveals a consistent focus on historical astronomy, with approximately 75% of his work dedicated to analyzing ancient astronomical systems from Greek, Babylonian, and Indian traditions. The remaining publications address statistical methods and computational techniques. His historical research often combines mathematical analysis with historical scholarship to reconstruct and understand ancient astronomical practices. COFRS Award, The Florida State University (1985) COFRS Award, The Florida State University (1986) Developing Scholar Award, The Florida State University (1986) President's Continuing Education Award, The Florida State University (1992) Duke has been extensively involved in professional service, serving on numerous national committees related to supercomputing and organizing major conferences including multiple Supercomputing 'XY events from 1988 through 2006. His teaching extends beyond traditional academic boundaries through public outreach initiatives such as chairing the Board of Directors for Tallahassee Free-Net, Inc. and serving on the Leon County Schools Information Services Advisory Board. He has supervised numerous graduate students in computational physics and the history of science, though specific student names are not documented in the provided materials. Duke maintains an active research laboratory focused on computational ancient astronomy, where he develops mathematical models and computer animations to visualize historical astronomical concepts. His team has created an extensive collection of interactive animations demonstrating planetary models from Ptolemy through Copernicus, which are widely used in educational settings. Current projects include further analysis of ancient star catalogs and the development of more sophisticated visualization tools for historical astronomical concepts.
John F. Rudge is a Professor of Geodynamics at the Bullard Laboratories, Department of Earth Sciences, University of Cambridge , and a Fellow and Dean of Trinity College. His research focuses on the dynamics of Earth's interior, including mantle convection, magma transport, and the geochemical evolution of the planet. He employs continuum mechanics, numerical analysis, and statistical methods to address questions about melt dynamics, mantle heterogeneity, and the surface expressions of geophysical processes. Education includes a mathematics undergraduate degree and a PhD in Earth Sciences and Applied Mathematics from Cambridge. He held postdoctoral positions at institutions like ETH Zürich, Yale, and Columbia University before joining Cambridge as a University Lecturer in 2010 and being promoted to Professor in 2022. Key research areas include: Transport of melts and volatiles in the mantle Evolution of Earth's interior from accretion to present Dynamic topography and inner core dynamics Publications span over two decades, with recent works addressing melt rheology, mantle mineralogy, and interdisciplinary applications in oncology. He has supervised numerous PhD students and postdocs, including David Rees Jones, Isarapong Eksinchol, and Laura Alisic. Awards include the Philip Leverhulme Prize and Geological Society President’s Award. Teaching responsibilities include courses on geophysics, magma dynamics, and supervisions in Natural Sciences at Trinity College. Research tools include finite element modeling and collaborations with computational scientists.
Jonathan Santo is a Professor of Psychology at the University of Nebraska at Omaha (UNO), where he serves as Director of the Graduate Developmental Psychology Program and as a faculty member in the Office of Latino/Latin-American Studies. His research explores peer relations, cultural differences in self-esteem, and classroom-level influences on child and adolescent development, with a focus on improving school environments and fostering positive social interactions. ORCA Faculty Fellow His research spans broad areas including Developmental Psychology , Social Support Systems , and Cross-Cultural Peer Dynamics , with specific attention to peer victimization , self-continuity , school climate , and developmental trajectories in Brazilian and Colombian youth . Recent publications analyze topics like hikikomori experiences , friendship security , and HPA axis dysregulation in adolescents. Scientific awards include the ORCA Faculty Fellow recognition. His work often involves longitudinal assessments of peer relationships cross-cultural comparisons (Brazil, Colombia, China, Nigeria, Singapore, U.S.) policy-focused blog posts on family separations and school interventions
Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.
Dr. Nemanja Stanišić is a Full Professor at Singidunum University's Faculty of Business, with a distinguished academic career spanning over 15 years. He holds a Ph.D. in Corporate Finance from Singidunum University (2010), an MBA in Finance from Lincoln University (2007), and a Bachelor's in Accounting from the University of Belgrade (2005). His expertise focuses on Corporate Finance, Banking, Audit, and Applied Statistical Analysis. His research integrates quantitative methods with economic theory, addressing topics such as audit opinion prediction using AI, tourism destination competitiveness, financial distress dynamics, and air pollution health impacts. He co-authored textbooks including Contemporary Exchange and E-business (2010) and Financial Statement Analysis (2024), and served as Editor-in-Chief of The European Journal of Applied Economics . He teaches courses from Financial Accounting to Advanced Financial Engineering at undergraduate, master's, and Ph.D. levels. The 15 most recent publications highlight his interdisciplinary approach: 7 in Finance/Audit, 5 in Tourism/Hospitality, and 3 in Environmental Health. Key trends include applying machine learning to audit quality (2023), multilevel modeling for hospitality satisfaction (2015-2019), and air pollution mortality analysis (2016). His work appears in high-impact journals like Tourism Management (IF 10.125) and Environmental Health (IF 4.986). He held administrative roles including Rector (2020-2021) and Vice President of Singidunum University. He served as Vice Dean for Student Affairs (2010-2011) and participated in TEMPUS projects for educational reform. He mentors graduate students extensively, advising 100+ bachelor's, 57 master's, and 4 doctoral theses, including international candidates. His visiting professorship at Bangkok's ICO NIDA and teaching in Austria-Singidunum joint programs reflect global engagement. Professional development includes advanced training at Utrecht University (Bayesian Modeling, 2019), Stanford (Mentoring, 2010), and NYU (Valuation, 2012). He reviews for top journals like Annals of Tourism Research and Cornell Hospitality Quarterly , with 1017 Google Scholar citations and 349 Scopus citations. Current research involves the Science Fund of Serbia's TOURCOMSERBIA project evaluating tourism competitiveness models.
Gavan J. Fitzsimons is the Edward S. & Rose K. Donnell Distinguished Professor of Marketing and Psychology at Duke University's Fuqua School of Business , with a secondary appointment in the Department of Psychology & Neuroscience. He is a Faculty Network Member of the Duke Institute for Brain Sciences . His research bridges consumer psychology, behavioral decision-making, and social cognition, focusing on nonconscious influences on consumption patterns. Education: Ph.D., Columbia University (1995) Key research themes include subconscious consumer behavior , brand relationships , health-related consumption , and social dynamics in purchasing decisions . Recent work examines financial stress effects on purchase satisfaction, secret consumer behaviors in relationships, and pandemic-related decision-making. Notable trends in his 2023-2025 publications involve Marketing's subconscious influence (2025: Quality-Quantity Tradeoffs) Brand teasing as relationship-building (2025: Humor in Branding) Financial constraint effects on consumer happiness (2024: Opportunity Cost Analysis) Crisis behavior during pandemics (2024: Prosociality Across 39 Countries) Health behavior spillovers in families (2024: Parental Food Choices) Scientific Contributions include Foundational work on nonconscious consumer psychology (2008 JCP editorial) Methodological innovations in moderated regression analysis (2013 JMR ) Behavioral economics of brand sincerity effects (2015 JCR )
Jan de Gier is a Professor at the School of Mathematics and Statistics, The University of Melbourne . He is also the Founding Director of MATRIX , Australia’s residential research institute in the mathematical sciences, and a former Deputy Director and Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . Additionally, he co-founded the Australian and New Zealand Association for Mathematical Physics (ANZAMP) in 2011 and served as its inaugural Chair. His research focuses on solvable lattice models at the intersection of mathematical physics and statistical mechanics . Key areas include the application of quantum integrability , algebraic structures like the Yang-Baxter equation, Hecke algebras, and quantum groups, as well as analytical methods such as complex analysis and elliptic curves. His work bridges pure and applied mathematics through connections between enumerative combinatorics , representation theory , and real-world phenomena like traffic flow modeling via exclusion processes . The 15 most recent articles reflect his expertise in integrable systems , non-equilibrium statistical mechanics , and algebraic combinatorics . Topics span Macdonald polynomials , stochastic duality , quantum spin chains , and traffic modeling , with methodologies involving matrix product forms , exact solutions , and critical phenomena analysis. He has contributed to editorial efforts through the AustMS Gazette and MATRIX Annals, and has been involved in public science communication via opinion pieces on mathematics funding and applications. His work emphasizes the importance of fundamental research in driving technological innovation, as highlighted in media articles discussing pi calculation , zero-knowledge proofs , and mathematics education .
