Ella Tamir is a Visiting Professor at the Department of Computer Science , School of Science , University of Helsinki. She concurrently holds a Doctoral Student position. Her research focuses on stochastic processes, neural networks, and sequential learning. Education: Master's degree in Natural Sciences, University of Helsinki (2017) Bachelor's degree in Natural Sciences, University of Helsinki (2016) Research Interests: She explores stochastic differential equations, function-space representations, and diffusion processes. Her work emphasizes applications in neural networks, data generation, and sequential learning. Recent studies involve diffusion generative models for particle smoothing and sparse parameterization techniques. Research Output: Her recent publications address topics like function-space neural networks, diffusion bridges for time series, and scalable inference methods. These contributions highlight advancements in both theoretical and applied machine learning domains. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: No lab affiliations or team collaborations are explicitly mentioned.
Kevin Michael Baird is a Professor in the Department of Accounting and Corporate Governance at Macquarie University. His research emphasizes the intersection of management accounting practices, organizational performance, and strategic decision-making. He has contributed to understanding how ethical leadership and human resource policies influence environmental management and organizational resilience, particularly during crises like the COVID-19 pandemic. Key research interests include Management Control Systems, Organizational Culture, Competitive Advantage, and the application of strategic management accounting. His work often explores mediation mechanisms, such as employee empowerment and budgetary participation, in achieving organizational goals. Recent studies highlight the role of Porter’s competitive forces and human resource management in enhancing resilience and performance. In 2014, he was awarded the 'Improving Management Accounting Curriculum to Enhance Students' Learning and Employability Skills' teaching prize. He collaborated on a 2012–2013 project examining institutional pressures on the adoption of the balanced scorecard. Baird has also presented at conferences, including a 2018 talk on CSR and competitive advantage, and contributed to media discussions on organizational structure changes.
Rajesh Manchanda is a Professor of Marketing at the Asper School of Business, University of Manitoba, and holds the title of The Associates Fellow in Sustainability. He has been affiliated with the university since 1997. His research focuses on two primary areas: the role of negative affect (e.g., guilt, embarrassment) in consumer behavior and sustainability/social marketing, including alternative frameworks to capitalism and environmental/social issues from consumer and organizational perspectives. He teaches sustainability, social marketing, and integrated marketing communications at both undergraduate and graduate levels (MBA and Ph.D.). Dr. Manchanda’s educational background includes a Ph.D. and M.S. from the University of Illinois at Urbana-Champaign, and an MBA and B.Com from the University of Bombay. His industry experience includes roles at Kellogg Company, Procter & Gamble, and J. Walter Thompson Advertising Agency. His research has been published in leading journals such as the Journal of Consumer Research and Journal of Consumer Psychology. His research trends emphasize interdisciplinary approaches, blending consumer psychology with sustainability, and exploring how emotions and ethical frameworks shape market behaviors. Articles often address contemporary challenges like data privacy, climate communication, and culturally rooted marketing strategies.
Ehsan Forootan is a Professor of Geodesy and Earth Observation at Aalborg University, Denmark. He holds a PhD in Geodesy from the University of Bonn and has held academic positions at Cardiff University, University of Hohenheim, and Robert Bosch GmbH. His research focuses on applying satellite geodetic techniques (GNSS, satellite gravity, altimetry) to monitor Earth System processes, including mass redistribution, hydrological cycles, and climate change impacts. He leads projects on flood awareness systems, digital twin Earth hydrology, and data assimilation frameworks. Forootan serves as vice-chair of the IAG's Sub-Commission 4.3 (Atmosphere Remote Sensing) and editor for Remote Sensing journals. Education: B.Sc. Civil-Surveying Engineering (Khaje Nasir Technical University, 2006), M.Sc. Geodesy-Hydrography (University of Tehran, 2008), PhD in Geodesy (University of Bonn, 2014). Postdoctoral fellowships include Curtin University and German Aerospace Center (DLR). Research interests include geodetic data assimilation, satellite remote sensing applications, and Bayesian methods for hydrological modeling. His work addresses UN SDGs related to climate action, water sustainability, and environmental monitoring. Key contributions include frameworks for GRACE/GRACE-FO data integration into hydrological models and thermospheric neutral density forecasting. Awards: ABC/J Young Academic Award (2015). Active in research networks (ESA, NASA) and editorial roles (Geodinamica Acta, Nature Geoscience). Projects include Space-based Flood Awareness System for Africa (SFAS) and Digital Twin Earth Hydrology initiatives.
Associate Professor Stuart Medley is an Honorary Lecturer in the School of Arts and Humanities at Edith Cowan University (ECU). He holds a Bachelor of Arts from Western Australian Institute of Technology and a PhD from ECU (2009). His primary research focuses on deliberate communication through visual mediums, including comics, graphic design, and service design. Medley is a co-founder and Chair of the Perth Comic Arts Festival and a founding partner of Hidden Shoal Recordings, a critically acclaimed record label. His professional affiliations include membership in the Australian Graphic Design Association (since 2022) and the Design Institute of Australia. He has received national and international recognition, including the DesignBoom Korea award (2010). Medley’s work spans academic research, creative projects, and industry collaborations, emphasizing the intersection of design, communication, and societal impact. Research interests include visual rhetoric in AI design, service design methodologies, and the application of comics in research and law. He has led or contributed to major grants, such as the 'Diagnosing Innovation' project (2021–2024) and the WA Healthy Children Program (2013–2022). Medley actively supervises doctoral and master’s students, focusing on topics like computational thinking visualization, graphic design significance, and disaster management interfaces. Key creative outputs include the book The Picture in Design (2012) and exhibitions like Vox Caldus (2014) and Becoming Catalogue (2013). His research bridges academic rigor with practical design solutions, addressing challenges in healthcare, disaster communication, and cultural representation.
Prof. Peter Lynn is a Professor at the University of Essex, serving as Director of Survey Futures and Chair of the European Social Survey Sampling and Weighting Panel. He is also the Associate Director for Methodology at Understanding Society. His research focuses on survey methodology, particularly longitudinal surveys, emphasizing sampling, weighting, non-response strategies, adaptive design, and mixed-mode methodologies. He has led numerous studies on improving survey response rates, addressing attrition, and enhancing data quality through innovative approaches like targeted incentives and sequential mixed-mode designs. Key roles include leading Survey Futures, a research group advancing survey methodologies, and contributing to high-profile initiatives such as the European Social Survey and Understanding Society. His work addresses challenges in maintaining sample representativeness, tackling non-response bias, and leveraging technology for efficient data collection. Recent projects include evaluating early bird incentives, optimizing incentive strategies, and exploring the impact of text messaging on web survey participation. Publications span methodological innovations, including frameworks for exclusion risk mitigation, pandemic-related attrition analysis, and technical reports on weighting strategies. His research often bridges theory and practice, informing policy and survey design globally.
Dyfrig Jones is a Senior Lecturer in Film at Bangor University's School of Arts, Culture and Language. He specializes in documentary filmmaking , media history , and media policy , with a focus on the historical development of public broadcasting in the USA during the mid-20th Century. Current roles: Senior Lecturer, PhD supervisor External positions: Chair of Partneriaeth Ogwen; former Chair of Neuadd Ogwen Management Board (2013-2023) Research Interests include: Public broadcasting history Media policy reform Surveillance and digital ethics Comic studies and post-9/11 media narratives Scientific Awards : Rockefeller Archive Center Research Stipend (2018) Recent Publications span topics from media reform to superhero comics , reflecting interdisciplinary interests in communication studies, digital ethics, and cultural representation.
Sami Najafi is an Associate Professor in the Information Systems & Analytics Department at the Leavey School of Business. He holds a Ph.D. in Management Science and Operations from London Business School (2011) and an M.Sc. in Industrial Engineering from Sharif University of Technology (2005). His research focuses on Service Operations, particularly analyzing how consumer loss-aversion impacts operational decision-making and developing analytic tools for web publishers in display ad revenue management. He has contributed to peer-reviewed publications and presented at international conferences. Dr. Najafi has extensive teaching experience, including the MBA core course 'Statistics for Management' at the University of Toronto’s Rotman School of Management, where he won a Teaching Award. He also instructed a graduate-level 'Queueing Theory' course in Industrial Engineering. His work bridges theoretical models with practical applications in digital advertising and consumer behavior analysis. He holds the Rotman MBA Teaching Award for his impactful pedagogical approach. Current research emphasizes optimizing operational strategies in digital ecosystems and understanding behavioral economics in marketing contexts. Collaborations and grants are not explicitly detailed in the provided texts.
Samiul Hasan is an Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), where he is also a member of the Future City Initiative. He leads the Urban Networks, Mobility, and Dynamics (UNMD) Lab, focusing on data-driven approaches for smart and resilient urban systems. His research is supported by funding from the National Science Foundation (NSF), Florida Department of Transportation (FDOT), and various University Transportation Centers. Education: Ph.D. in Transportation Engineering, Purdue University, 2013 M.S. in Civil and Transportation Engineering, Bangladesh University of Engineering and Technology, 2007 B.S. in Civil Engineering, Bangladesh University of Science and Technology, 2004 His research spans transportation data science, machine learning, hurricane evacuation modeling, disaster analytics, infrastructure resilience, and human mobility. He leverages large-scale datasets from mobile devices, social media, smart cards, and traffic sensors to model urban dynamics and emergency response systems. His work integrates deep learning, graph neural networks, and agent-based modeling to understand urban mobility, traffic congestion, and evacuation behavior. His recent publications reveal a strong focus on using real-time and social media data to predict evacuation traffic, assess crash risks during disasters, model community resilience, and enhance infrastructure robustness. Key themes include disaster preparedness, urban network modeling, and the application of AI in transportation. His lab develops tools for traffic forecasting, evacuation simulation, and resilience assessment, often using multi-source data fusion. Scientific Awards: Best Dissertation Award 2014 from INFORMS TSL Society for his Ph.D. work on “Modeling Urban Mobility Dynamics using Geo-location Data” Dr. Hasan has advised several graduate students who have gone on to careers in data science, transportation modeling, and energy sectors. His lab collaborates with institutions such as FIU, Virginia Tech, WVU, and SUNY Buffalo. Funded projects include ORDER-CRISP (NSF), disaster analytics with UTCs, arterial traffic management with FDOT, and transit smart card analysis. He actively promotes interdisciplinary research to build resilient urban infrastructure and improve emergency response through data science and network modeling. Labs and Teams: He directs the UNMD Lab at UCF, which brings together researchers in data science, civil engineering, and urban planning to develop innovative solutions for urban challenges. The lab emphasizes real-world applications in mobility, safety, and resilience, particularly during extreme events.
Catherine Forbes is a Professor and Deputy Head of the Department of Econometrics and Business Statistics (EBS) at Monash University, where she also serves as Director of Education. She holds an adjunct professorship at Sunway University, Malaysia, and is an Associate Editor of Applied Stochastic Models in Business and Industry . Her research focuses on Bayesian statistics, econometrics, and computational methods for analyzing financial risks and complex time series. She has led multiple research grants, including ARC and NHMRC-funded projects, and contributed to policy reforms, such as the Victorian Children, Youth and Families Act 2005. Her research interests span Bayesian modeling, financial econometrics, robust statistical methods, and nonparametric techniques. She has supervised 20 PhD students and 20 Honours projects, emphasizing mentorship in statistical methodologies. Notable collaborations include work with the Centre of Excellence in Child and Family Welfare on predictive analytics for youth outcomes. Dr. Forbes has led over 20 research projects, including grants focused on Bayesian empirical likelihood, financial risk dynamics, and health care analysis. Her recent work includes advancements in cross-validation methods and familial inference techniques, with applications in housing affordability and mental health linkages.
Leandro Soriano Marcolino is a Lecturer (Assistant Professor) in Data Engineering at Lancaster University's Computing and Communications Department. He holds a PhD from the University of Southern California (USC), advised by Milind Tambe, and completed his master's in Japan under a Monbukagakusho Scholarship and his undergraduate studies at Universidade Federal de Minas Gerais in Brazil, where he graduated with top honors. His research focuses on multi-agent systems, machine learning, and robotics, emphasizing teamwork, online learning, and real-world applications in domains like swarm robotics, computer games, and architectural design. Education: PhD in Computer Science, University of Southern California (USC), 2016 Master's Degree in Japan (Monbukagakusho Scholarship) Undergraduate Degree in Computer Science, Universidade Federal de Minas Gerais (Brazil), 2009 Research Interests: Multi-agent teamwork and coordination Online learning and planning Robotics (swarm robotics, real-time strategy games) Applications in computer vision, social networks, and bioinformatics Highlights: Recipient of the Best Dissertation and Best Research Assistant Awards at USC (2016, 2015) Best Paper Nomination at AAMAS 2011 and Best Undergraduate Paper in Brazil (2009) Active in PhD supervision, mentoring students in AI, multi-agent systems, and robotics Co-founder of the COLAB research group at Lancaster University Labs/Teams: Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRAS), LIRA - Fundamentals, SCC (Data Science).
Geir Olve Storvik is a Professor in Statistics and Data Science at the University of Oslo. His research focuses on computational statistics, Bayesian hierarchical modeling, Monte Carlo methods, spatio-temporal modeling, and dynamical processes. He has made significant contributions to Bayesian machine learning and its applications in biological and environmental domains. University: University of Oslo Department: Statistics and Data Science Academic Rank: Professor Storvik's research spans interdisciplinary applications of Bayesian methods in fields such as neural networks, DNA methylation, and infectious disease modeling. His recent work includes sparse Bayesian neural networks and sequential Monte Carlo approaches for epidemiology. Key projects include Bayesian methods in machine learning and BigInsight Statistical and machine learning methods for sensor data . He serves as an advisor to PhD students like Aliaksandr Hubin and Ivar Grytten, with completed theses in graph-based genomics and Bayesian variable selection. His work appears in journals like Transactions on Machine Learning Research , The Journal of Artificial Intelligence Research , and Journal of the Royal Statistical Society , covering topics from model sparsity to ecological forecasting.
Mareike Dressler is a Senior Lecturer (A/Professor, tenured) and ARC Discovery Early Career Research Fellow at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). She joined UNSW in February 2022 as a Lecturer (Assistant Professor, tenure-track) and was promoted to Senior Lecturer with tenure in July 2024. Her educational background includes: PhD in Mathematics from Goethe-Universität Frankfurt/Main (2018), supervised by Thorsten Theobald M.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2013) B.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2010) Mareike's research focuses on real and computational algebraic geometry and polynomial and convex optimization. She also works on problems intersecting with convex geometry, matrix and tensor computation, applied algebraic geometry, algebraic and geometric combinatorics, and real analysis. Her work particularly involves nonnegativity of polynomials, optimization methods, and ranks of matrices and tensors, with a special interest in sums of nonnegative circuits (SONCs) for sparse polynomials. Her research has strong applications in data science and machine learning. Mareike has received several prestigious awards including the ARC Discovery Project 2025 grant for "Quantifying Uncertainty of Risk-Aware Optimization for Safe Decision-Making," the J G Russell Award from the Australian Academy of Science, and the Early Career Impact Award from UNSW Science. She actively supervises research students, including PhD student Hongzhi Liao, Master's student Qi Wang, and recently supervised Moritz Schick to completion of his PhD in 2025. Mareike has secured significant research funding, including an ARC Discovery Early Career Researcher Award (DECRA) for 2024-2026. Mareike is an active member of the academic community, regularly organizing workshops and conferences such as "Optimization Days" at UNSW and minisymposia at major conferences like SIAM Conference on Applied Algebraic Geometry.
Ahmed Bouajjani is a Professor at Paris Diderot University (Univ. Paris 7) and a Senior member of the Institut Universitaire de France . He leads the Automata, Structures, and Verification (ASV) pole of the IRIF laboratory and is a member of the Modeling and Verification team. His work focuses on formal methods, program verification, and automata theory, particularly for concurrent and infinite-state systems. Research Interests : Formal specification and verification, program verification, concurrency, model-checking algorithms, verification of infinite-state systems, automata, and logics. Teaching : He teaches courses such as Introduction to Artificial Intelligence and Game Theory , Formal Methods for Verifying Systems , and Algorithmic Program Verification at the university. Scientific Contributions : His recent articles address weak memory models, robustness in distributed systems, and inter-procedural analysis of list-manipulating programs. Keywords include Computer Science , Formal Verification , and Concurrency . Awards : He is a Senior member of the Institut Universitaire de France , a prestigious academic recognition. Labs and Teams : He is affiliated with the IRIF laboratory and its Modeling and Verification team, contributing to collaborative research in automata and verification.
Dan Lizotte is an Associate Professor with appointments in the Department of Computer Science and the Department of Epidemiology and Biostatistics at Western University. He is also affiliated with the Schulich Interfaculty Program in Public Health, cross-appointed to the Department of Statistics and Actuarial Sciences, and serves as Associate Faculty in the Rotman Institute of Philosophy. His work bridges computer science, statistics, and public health, focusing on the application of advanced computational methods to healthcare decision-making. Dr. Lizotte earned his BCS from New Brunswick and completed his MSc and PhD in Computer Science at Alberta. His educational background in computer science forms the foundation for his interdisciplinary research that applies machine learning and statistical techniques to health data. Professor Lizotte's research aims to adapt and improve reinforcement learning, machine learning, and statistical techniques for application to health data, providing stakeholders with evidence for non-myopic health decision making. He is particularly interested in problems involving multiple outcomes, causal inference, and outlier detection in public health and primary health care. His work spans artificial intelligence, biostatistics, and public health, with a growing focus on health equity and intersectionality in AI applications. Analysis of Dr. Lizotte's recent publications reveals a strong focus on applying machine learning and statistical methods to healthcare challenges. His work spans predictive modeling for chronic diseases, AI implementation in primary care, intersectionality in health research, and reinforcement learning for decision support. Recent trends show increasing attention to health equity, the application of AI to marginalized populations, and methodological innovations in handling complex health data with multiple outcomes. Dr. Lizotte has secured significant research funding including: Machine learning methodology for sequential decision support from largescale longitudinal data (NSERC; 2018-2024) Reinforcement Learning Methodology for Decision Analysis and Support in Long-term Care (NSERC; 2021-2022) Beyond Supervised Learning: Artificial Intelligence Tools to Help Public Health Stakeholders Serve Marginalized Populations (CIHR; 2019-2023) Artificial Intelligence for Public Health (AI4PH) Training Platform [co-Applicant with lead Dr. Laura Rosella] (CIHR; 2022-2026) Dr. Lizotte leads the Biostatistical & Computational Methods research cluster, which focuses on developing and applying advanced statistical and computational techniques to health data. His team works at the intersection of computer science, statistics, and public health to create decision support tools that can handle complex, real-world healthcare scenarios with multiple outcomes and considerations.
