Patrick Brown is an Associate Professor at the University of Toronto , affiliated with the Department of Statistical Sciences and cross-appointed to the Centre for Global Health Research and St. Michael's Hospital . His research focuses on spatio-temporal data modeling , Bayesian inference , and non-parametric methods for spatial epidemiology and environmental sciences. Fields of Interest : Spatial Statistics, Cancer Statistics, Statistical Software Education : PhD from University of Lancaster His methodological work encompasses Bayesian inference for non-Gaussian spatial data, Gaussian Markov random fields, and computational techniques like INLA and MRA. Applied research themes include disease mapping, environmental risk assessment, and public health surveillance using real-world data sources such as electronic health records and wastewater monitoring . He has developed key R packages (mapmisc, geostatsp, diseasemapping) supporting spatial statistical applications. Current collaborative projects span diverse fields: Ultra-diffuse galaxy detection with astrophysical applications Multi-pollutant mortality studies in Canadian cities SARS-CoV-2 seropositivity tracking Homelessness population estimation using EHR Geospatial cancer risk tools for Nova Scotia His work bridges statistical innovation with global health challenges , emphasizing computationally efficient solutions for large-scale spatiotemporal datasets.
Krishna Jagannathan is a full-time Professor in the Department of Electrical Engineering at the Indian Institute of Technology Madras (IIT Madras), India. He specializes in stochastic modeling, communication networks, information theory, and queuing theory. He obtained his B.Tech from IIT Madras in 2004, followed by S.M. and Ph.D. degrees from MIT in 2006 and 2010, respectively. After post-doctoral positions at Caltech and MIT, he joined IIT Madras in 2011. Education: B.Tech in Electrical Engineering, IIT Madras (2004) S.M. in Electrical Engineering and Computer Science, MIT (2006) Ph.D. in Electrical Engineering and Computer Science, MIT (2010) Research Interests: His research focuses on stochastic modeling and analysis of communication networks , information theory , and queuing theory . He has made significant contributions to understanding network performance, resource allocation, and risk-aware decision-making in complex systems. He leads the Networks and Stochastic Systems lab at IIT Madras, mentoring a large cohort of Ph.D. and M.S. students working on cutting-edge problems in networking, optimization, and stochastic systems. Scientific Awards: Best Paper Award at WiOpt 2013, Tsukuba, Japan Young Faculty Recognition Award for Excellence in Teaching and Research, IIT Madras (2014) Teaching & Mentorship: He has taught a wide range of courses including Probability Foundations , Stochastic Modeling and Queuing Theory , Convex Optimization , and Signals & Systems , consistently receiving high teaching evaluations. He has supervised over 15 Ph.D. and M.S. students to completion and continues to guide several active researchers.
Daniel B. Neill is a Professor of Computer Science, Public Service, and Urban Analytics at New York University (NYU), jointly appointed across the Courant Institute of Mathematical Sciences, Robert F. Wagner Graduate School of Public Service, and the Center for Urban Science and Progress (Tandon School of Engineering). He also serves as the Director of the Machine Learning for Good Laboratory (ML4G) and is affiliated with NYU's Center for Data Science and Tandon Department of Computer Science and Engineering. Education: Ph.D. in Computer Science, Carnegie Mellon University M.S. in Computer Science, Carnegie Mellon University M.Phil. in Computer Speech, Cambridge University Research Interests: Dr. Neill's research focuses on developing novel machine learning methods for social good, with applications in disease surveillance (e.g., early outbreak detection), healthcare (e.g., anomalous care patterns), and urban analytics (e.g., predicting citizen needs). He also explores algorithmic fairness , causal inference , and pre-syndromic surveillance using unstructured data. His work bridges theoretical machine learning with real-world policy challenges, collaborating with health departments, hospitals, and city governments to deploy data-driven tools that enhance public health, safety, and security. Scientific Awards & Honors: NSF CAREER Award NSF Graduate Research Fellowship IEEE Intelligent Systems' "Top Ten AI Researchers to Watch" Yelp Dataset Challenge Winner Hidden Signals Challenge Runner-Up (DHS) Grants & Funding: He has received significant funding from the National Science Foundation (NSF), including grants on fairness in AI (IIS-2040898), bias in urban analytics (IIS-1926470), and others. He also acknowledges support from UPMC, MacArthur Foundation, and Richard King Mellon Foundation. Laboratory & Leadership: He directs the Machine Learning for Good Laboratory (ML4G) at NYU, focusing on AI for social impact. He previously co-directed NYU's Urban Initiative (2019-2022) and led the Event and Pattern Detection Laboratory at Carnegie Mellon University.
Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.
Thomas E. Bittner serves as an Associate Professor in the Department of Philosophy at the University at Buffalo, specializing in formal and applied ontology with particular focus on spatial and temporal reasoning. His academic work bridges philosophy, computer science, and geographic information systems. His research interests encompass Formal and Applied Ontology , Ontology of Space, Time and Spatial Entities , Spatio-Temporal Reasoning , and Bio-Medical Ontology . He investigates fundamental questions about spatial relations, vagueness in geographic boundaries, and quantum-inspired models for geographic information systems, developing rigorous formal frameworks applicable to both philosophical and computational domains. His publication record demonstrates consistent engagement with cutting-edge topics in ontology, particularly exploring quantum geography concepts and computational implementations of formal ontologies. Key themes across his work include the representation of spatial vagueness, quantum-inspired geographic modeling, and verifiable computational ontology systems. Bittner holds a PhD from the Technical University of Vienna and teaches courses including Logic and Spatial Ontology. His contact information lists office location at 109 Park Hall on UB's North Campus with phone (716) 645-5149.
Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for effective and responsible analytics, leveraging techniques from causal inference, data management, theoretical computer science, machine learning, and human-computer interaction to address challenges in trustworthy system design including robustness, explainability, and fairness. Education: Postdoc: University of Chicago PhD: University of Massachusetts Amherst (supervised by Barna Saha) BTech: Indian Institute of Technology Delhi (IIT Delhi) (supervised by Prof. Amitabha Bagchi) Research Interests: Dr. Galhotra's research spans several interconnected areas in data science and artificial intelligence. His work primarily focuses on Responsible Data Science , where he develops methods to ensure that data-driven systems operate fairly and transparently. Within this broad area, his specific interests include: Causal Inference techniques for understanding cause-effect relationships in complex data Algorithmic Fairness approaches to mitigate bias in machine learning systems Explainable AI methods that make black-box models more interpretable Data Management systems for efficient and reliable data processing Entity Resolution techniques for integrating data from multiple sources Trustworthy System Design that addresses robustness, explainability, and fairness His recent publications demonstrate a clear trend toward developing frameworks that combine causal reasoning with practical data management systems, particularly focusing on how to make data-driven decisions more transparent and equitable. The intersection of database systems with fairness considerations appears to be a particularly active area of his research. Scientific Awards: Rising Star in Data Science at the Data Science Institute, UChicago (Oct 2021) Computing Innovation Fellowship Award Recipient (by CRA, CCC and NSF) (Apr 2021) DAAD AInet Fellow (Feb 2021) ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist (May 2020) Most reproducible paper award in SIGMOD 2018 and 2019 (Jun 2019) First recipient of Krithi Ramamritham Computer Science Scholarship (Jun 2019) Best paper award in SIGSOFT FSE 2017 (May 2017) Dr. Galhotra is actively seeking students to collaborate with on his research projects. His work has been supported by various fellowships and awards, including the prestigious Computing Innovation Fellowship. He has mentored several students through his research projects, with a focus on developing the next generation of data scientists who can build responsible and trustworthy systems. His research group appears to focus on the intersection of database systems and responsible AI, developing tools like HypeR for causal reasoning, Ver for view discovery, and Nexus for correlation discovery in spatio-temporal data. This work suggests a cohesive research agenda centered around making data systems more transparent, fair, and user-friendly.
Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.
Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.
Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.
Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.
Professor Jason Dykes is a leading figure in the field of information and geovisualization at City, University of London, where he holds the position of Professor in the Department of Computer Science and co-directs the giCentre , a renowned research centre in visualization. He is affiliated with the School of Mathematics, Computer Science and Engineering and maintains an active research and teaching profile. His academic journey includes a PhD in Geography from the University of Leicester and extensive leadership in both research and education. Education: PhD in Geography, University of Leicester, 2000 MSc in Geographic Information Systems, University of Leicester, 1991 BA/MA in Geography, University of Oxford, 1989 Jason Dykes' research is centered on designing visual methods and tools for exploring, analyzing, and presenting information, with a strong emphasis on geographic data. His work integrates cartography, information visualization, GIScience, and human-computer interaction , leading to the development of innovative techniques such as geowigs, ODmaps, BallotMaps, and AttributeSignatures. He has published extensively in top-tier journals like IEEE Transactions on Visualization & Computer Graphics, with over 20 papers in the last decade, and co-authored the seminal book Exploring Geovisualization (2005). His research is supported by major funders including EPSRC and the EU, with projects like RAMP VIS (Covid-19 response) and VALCRI (criminal intelligence). The most recent articles highlight a consistent trend in applied and human-centered visualization , focusing on responsive design, education, pandemic modeling, and novel visual metaphors for complex data. His work increasingly emphasizes methodological rigor, design exposition, and the role of visualization in interdisciplinary and emergency contexts. Scientific Awards and Recognition: National Teaching Fellow, Higher Education Academy (2005) Best Paper Awards at GIS Research UK (consecutive years) Honorable Mentions, IEEE InfoVis (2009, 2010, 2016, 2018) Security Innovation Commercialisation Award (EU, 2022) Research Supervisor of the Year, City Student Union (2020) Innovations in Teaching Award and multiple teaching grants at City Jason Dykes has supervised eight PhD students to completion and advised many others, including notable researchers like Roger Beecham, Sarah Goodwin, and Susanne Bleisch. His teaching includes modules such as Visualizing Society and Data Presentation. He has received significant grant funding from UK research councils and the EU for projects like DIVA, VALCRI, and RAMP VIS. His service to the community includes leadership roles in IEEE VIS, ICA Commission on GeoVisualization, and editorial positions at IEEE TVCG and the Journal of Visualization and Interaction. He leads the giCentre , a dynamic research group that fosters innovation in visualization, and has been instrumental in establishing the field’s educational and methodological foundations through participation in Dagstuhl seminars and publications on visualization pedagogy.
Yangyang Zhao is a Research Fellow at Princeton University, affiliated with the Resplandy Research Group. His work focuses on climate science and ocean biogeochemistry, particularly coastal hypoxia and nitrous oxide dynamics in the Northern Indian Ocean. Research Interests Interactive marine carbon, nitrogen, and oxygen biogeochemistry Coastal and ocean deoxygenation processes Impact of climate change and human activities on oceanic oxygen dynamics Regional biophysical ocean modeling for nitrous oxide variability Publications Overview Yangyang’s publications span 2012–2025, emphasizing coastal hypoxia, nitrous oxide dynamics, CO2 fluxes, and marine environmental changes. His work integrates observational data with high-resolution physical-biogeochemical models, addressing climate-ocean interactions and anthropogenic impacts. Laboratory Affiliation Yangyang is part of the Resplandy Research Group at Princeton University, which investigates climate science and ocean biogeochemistry through multidisciplinary approaches.
Veronica J. Berrocal is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. Her work focuses on developing statistical methods for spatial, spatio-temporal, and longitudinal data with applications in environmental health, atmospheric sciences, and medical fields including rheumatology and reproductive endocrinology, contributing to public health protection through research and EPA advisory roles. Her educational background includes: PhD in Statistics from the University of Washington (2007) MSc in Statistics from Michigan State University (2002) Dr. Berrocal specializes in creating statistical models for dependent data structures, particularly spatial and spatio-temporal frameworks. Her research addresses environmental determinants of health such as air pollution, weather patterns, built environment, and socio-economic factors, with direct applications in atmospheric sciences, environmental epidemiology, and medical domains like rheumatology and reproductive health. She develops hierarchical models for environmental risk prediction, calibrates geophysical models, and leverages complex data sources including social media for exposure assessment. Her recent publications (2016-2019) demonstrate consistent methodological innovation in spatial statistics applied to critical public health challenges. Key themes include nonstationary spatial prediction for environmental resources, distributed lag modeling of pollutant interactions, and advanced spatio-temporal frameworks for fMRI and urban pollution mapping. Her work bridges statistical theory with practical health impact assessments across atmospheric science, environmental epidemiology, and medical imaging domains.
Bärbel Finkenstädt Rand is a Senior Tutor at the Warwick Medical School , University of Warwick, with extensive research contributions at the intersection of statistics, machine learning, and biomedical sciences. Her work focuses on developing advanced methodologies for analyzing temporal and spatio-temporal data, particularly in circadian rhythms and disease dynamics. Research Themes : Bayesian inference, Hidden Markov Models, circadian rhythm stability, transcriptional bursting, and wearable sensor data analysis. Collaborations : Chronotherapy Group at Warwick, Université Paris-Saclay, and interdisciplinary teams across medicine, genetics, and computational biology. Publications reveal a strong emphasis on circadian health monitoring, gene expression dynamics, and epidemic modeling using stochastic frameworks. Her recent work prioritizes personalized medicine applications through telemonitored biomarkers and IoT platforms . Methodological Innovations include spline-based HMMs, distributed delay systems, and harmonic modeling for nonstationary time series. Applications span oncology, sleep medicine, and population ecology.
Huaizu Jiang is an Assistant Professor at Khoury College of Computer Sciences, Northeastern University. His research bridges computer vision, graphics, and natural language processing to develop AI systems that understand and reconstruct 3D visual environments. Prior to joining Northeastern, he was a Postdoc Researcher at Caltech and Visiting Researcher at NVIDIA. He holds a Ph.D. from UMass Amherst (advised by Prof. Erik Learned-Miller), and M.E./B.E. degrees from Xi'an Jiaotong University. His research focuses on fundamental challenges in 3D scene understanding, including geometry reconstruction, semantic interpretation, novel view synthesis, motion generation, and optical flow estimation. Core interests span video processing, human-object interactions, multimodal reasoning, and efficient edge-device implementations. Recent publications emphasize diffusion models for motion/scene generation, transformer-based 3D perception, and video interpolation. Key trends include multi-view consistency techniques, text-to-3D synthesis, and efficient real-time algorithms for robotics applications. Awards & Honors: Winner of the VQA Challenge 2020 He advises 15+ graduate students on projects spanning 3D reconstruction, motion synthesis, and vision-language models. His group collaborates with institutions like NVIDIA and Caltech, focusing on generative AI for dynamic scene understanding.
