Burak Güneralp is an Associate Professor in the Department of Geography at Texas A&M University. His research focuses on urbanization, global environmental change, and sustainability, with a particular emphasis on systems analysis and geospatial methodologies. He holds a Ph.D. in Natural Resources and Environmental Sciences from the University of Illinois at Urbana-Champaign (2006), and M.S. and B.S. degrees in Industrial Engineering from Bogazici University, Turkey (2000 and 1997). Dr. Güneralp’s work addresses human-environment interactions, urban land change, and biodiversity conservation. His projects often integrate interdisciplinary approaches, including systems dynamics modeling and remote sensing, to analyze urban expansion impacts. Notable contributions include global forecasts of urban land use and assessments of flood risk under climate change. He has received awards such as the Chinese Academy of Sciences Fellowship (2012) and multiple recognitions from the System Dynamics Society. His research has informed policy discussions through contributions to the IPCC’s Fifth Assessment Report and publications in high-impact journals like *PNAS* and *Global Environmental Change*.
Professor Anna Giacomini is a leading academic in Rock Mechanics and Civil Engineering at the University of Newcastle. She holds a PhD from the University of Parma, Italy, and has been at the University of Newcastle since 2005. Her roles include Director of the Priority Research Centre for Geotechnical Science and Engineering and Deputy President of the Academic Senate (Research). She specializes in rockfall hazard analysis, mine geotechnics, and numerical modeling of geomechanical systems. Her research focuses on improving safety in mining and civil environments, with over $7.5M in funding and 140+ publications. Key areas include rockfall trajectory analysis, energy absorption in safety barriers, and drapery systems. She has led 20 major projects through ACARP and pioneered low-cost photogrammetric monitoring systems for rock slopes. Professor Giacomini is also a co-founder of HunterWiSE, promoting women in STEM. She has received prestigious awards such as the 2022 NSW Premier’s Engineering Prize and the 2019 John Booker Medal. Her administrative roles include membership in the ARC College of Experts and leadership in gender equity initiatives. Her technical contributions span experimental and numerical rock mechanics, including advancements in discrete element modeling (DEM) and stochastic approaches for discontinuity shear strength prediction. She collaborates internationally with institutions like the Colorado School of Mines and the University of Bologna.
Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.
Boris Gutman is an Assistant Professor of Biomedical Engineering at Illinois Institute of Technology , affiliated with the Armour College of Engineering . He holds a Ph.D. and B.S. in Biomedical Engineering and Applied Mathematics respectively from the University of California, Los Angeles (UCLA) .
Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.
Kangkang Yin is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on computer animation, computer graphics, humanoid robotics, machine learning, and multimedia analysis. He teaches courses such as Computer Animation and Scientific Computing, and holds a PhD from the University of British Columbia (2007), MSc from Zhejiang University (2000), and BSc from Zhejiang University (1997). His work bridges robotics and animation through projects like physics-based character controllers, motion diffusion models, and robotic manipulation. Key contributions include the SIMBICON biped locomotion framework and research into emotion-driven dance animation. Recent efforts emphasize reinforcement learning applications in motion synthesis and robust visual navigation for unmanned ground vehicles. Yin's publications span over two decades, addressing challenges in motion control, physics-based simulation, and machine learning applications. His lab contributes to both academic advancements and practical robotics solutions. Current research trends show strong emphasis on combining generative AI with traditional animation techniques, as seen in recent work on auto-regressive motion models (AAMDM) and physics-augmented reinforcement learning (PARC).
Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.
Sara van de Geer is a Full Professor at the Seminar for Statistics within the Department of Mathematics at ETH Zürich since 2005. She previously held academic positions at the University of Leiden, Université Paul Sabatier (Toulouse), and others. She earned a Master's (1982) and Ph.D. (1987) in Mathematics from Leiden University. Her research focuses on high-dimensional statistics, empirical processes, and mathematical foundations of machine learning. Van de Geer has received prestigious recognitions including the Van Wijngaarden Award (2016), Knight in the Order of Orange-Nassau (2015), and membership in Leopoldina (2013). She served as President of the Bernoulli Society (2015–2017) and Chair of the Seminar for Statistics at ETH Zürich. Her contributions include landmark works on statistical learning theory and high-dimensional inference, with key publications in top journals like Annals of Statistics and SIAM/ASA Journal on Uncertainty Quantification. Her academic leadership includes organizing Saint Flour Lectures, Wald Lectures (2016), and delivering plenary lectures globally. Her research bridges theoretical statistics with applied methodologies, emphasizing rigorous mathematical frameworks for modern data analysis challenges.
Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.
Dr Ronald Ting Tai Chan is a Senior Lecturer and Education Focused Academic in the School of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW). He holds a pivotal leadership role as Postgraduate Coursework Coordinator, focusing on curricula development and accreditation processes, including leading the 2021 Engineers Australia (EA) accreditation of the Master of Engineering program. Education: PhD in Manufacturing and Management Engineering (UNSW), BE (Hons I) in Aerospace Engineering (UNSW), and BSc in Mathematics and Statistics (UNSW) His research interests center on leveraging educational technology to enhance learning experiences, particularly through VR/AR/MR integration, gamification, and data-driven methodologies like PLS-PM for industrial systems analysis. He advocates for critical thinking and methodological approaches in solving open-ended engineering challenges. Dr Chan teaches advanced courses such as Engineering Management, Design and Analysis of Product Process Systems, and Reliability Engineering. His academic contributions span statistical process improvement methodologies applied in industries like healthcare and beverage manufacturing. He actively participates in international conferences, presenting work on PLS regression models and team performance analysis at the 8th International Conference on Partial Least Squares (Paris, 2014).
Dr. Tharindu P. De Alwis is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is actively engaged in teaching and research, with a focus on high-dimensional data analysis and machine learning applications. Ph.D. in Mathematics (Statistics), Southern Illinois University Carbondale M.S. in Mathematics, Southern Illinois University Carbondale B.Sc. in Statistics and Operations Research, University of Peradeniya, Sri Lanka His research centers on dimension reduction techniques, particularly Sufficient Dimension Reduction (SDR), envelope methods, and their applications in multivariate time series and spatial-temporal data. He integrates deep learning and neural networks into statistical modeling, with recent work on stacking-based deep neural networks and Fourier-based SDR methods. His work bridges statistical theory with practical machine learning applications in complex datasets. His recent publications (2021–2024) demonstrate a strong focus on developing innovative statistical and machine learning methods for time series and high-dimensional regression. Key themes include nonlinear modeling, dimension reduction, R package development, and AI-augmented reliability analysis. These works reflect interdisciplinary applications in engineering, data science, and systems safety. Dr. De Alwis has presented his research at national academic conferences in the USA and has publications in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety , as well as preprints on arXiv and software on CRAN. He teaches a variety of courses including Precalculus with Trigonometry, Linear Algebra, Applied Statistics, and Data Science. While no formal advisees or grants are mentioned, his active publication record suggests ongoing research mentorship and scholarly engagement. He previously served as a post-doctoral scholar at Worcester Polytechnic Institute before joining UWF.
Elena Tuzhilina is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, specializing in machine learning, applied statistics, and computational biology. Her research focuses on statistical tools for chromatin 3D spatial structure reconstruction and analyzing emotional disorders' impact on brain function. Ph.D. in Statistics from Stanford University Specialist's degree from Moscow State University Two-year Data Science program at Yandex School Her research spans high-dimensional data analysis , dimension reduction , and statistical modeling in biological contexts. She has developed novel algorithms for chromatin conformation reconstruction and pandemic trajectory modeling. Recent publications focus on canonical correlation analysis , low-rank matrix approximation , and 3D genome architecture , with applications in computational biology and neuroscience. Dorothy Shoichet Women Faculty in Science Award JSM Student Travel Award Outstanding Teaching Assistance at Stanford Stanford Teaching Assistant Award Elena supervises PhD students and postdoctoral fellows across disciplines including statistical sciences, biochemistry, and applied mathematics. She has secured multiple grants including a NSERC Discovery Grant and University of Toronto Accelerator Grant .
Aatishya Mohanty is a Lecturer in Economics at the University of Aberdeen Business School, where she contributes to research and teaching in economic development and cultural economics. She joined the university in 2023 and is affiliated with the Department of Economics, actively publishing in high-impact interdisciplinary journals. Ph.D. in Economics, Nanyang Technological University, Singapore (2023) M.Sc. in Applied Economics, Nanyang Technological University, Singapore Her research centers on the interplay between culture and economic outcomes, particularly in development and environmental contexts. She investigates how cultural norms affect public health behaviors, responses to pandemics, environmental regulations, and disaster resilience. Her work combines rigorous econometric methods with interdisciplinary insights from sociology, public health, and environmental science. The analysis of her recent publications reveals a strong thematic focus on the socio-economic dimensions of global crises, especially the COVID-19 pandemic. Her research consistently explores how cultural and institutional factors mediate policy effectiveness and environmental outcomes. She employs large-scale datasets and cross-national comparisons to draw robust conclusions, contributing significantly to the fields of cultural economics and development policy. Aatishya Mohanty has not been publicly recognized with any scientific awards or fellowships based on the available information. There is no publicly available information about her advising graduate students or securing research grants. Her teaching responsibilities include courses such as Environmental Economics, Applied Economic Policy Evaluation, Industrial Economics, and The Global Economy, indicating a strong engagement with both undergraduate and postgraduate education. No specific laboratory or research team affiliation is mentioned in the provided texts. However, her work appears to be collaborative, involving researchers from various institutions, suggesting participation in broader academic networks and research consortia.
Clifford M. Hurvich is a Professor of Statistics and Research Professor of Information, Operations, and Management Sciences at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 1986. He currently serves as the Doctoral Coordinator for the TOPS-Statistics program, reflecting his leadership in graduate education. His academic training includes a Ph.D. in Statistics from Princeton University (1985), an M.A. in Statistics from Princeton (1982), and a B.A. in Mathematics from Amherst College (1980). Professor Hurvich's research centers on time series econometrics , model selection , and forecasting . His work has had substantial practical impact, notably in the development of the X-12 seasonal adjustment method used by the U.S. Census Bureau. He co-authored foundational research on measuring mean reversion in time series, a key concept in financial econometrics for understanding equilibrium behavior in interest rate spreads and other financial variables. His recent focus includes the forecastability of stock returns and volatility , bridging statistical theory with financial applications. His publications appear in leading journals such as the Journal of Econometrics , Econometric Theory , Journal of Financial and Quantitative Analysis , Biometrika , Stochastic Processes and their Applications , and the Journal of the American Statistical Association . Although specific recent articles are not listed, his body of work consistently contributes to the advancement of statistical methodology in economics and finance. Notable honors include: Fellow of the American Statistical Association Recipient of a National Science Foundation research grant for work in statistical model selection As Doctoral Coordinator, Professor Hurvich plays a central role in mentoring PhD students in statistics. He teaches courses in forecasting, regression, and statistical methods for business control across undergraduate, MBA, and PhD programs. There is no indication of part-time status, and his ongoing administrative and research roles confirm his active faculty status. He is associated with the Department of Technology, Operations, and Statistics at Stern and contributes to interdisciplinary research at the intersection of statistics, operations, and financial economics.
Dr. Mirzet Šeho is a Senior Lecturer at the School of Business, Monash University Malaysia, where he contributes significantly to teaching, research, and academic leadership. With nearly fifteen years of combined industry and academic experience, he has established himself as a leading voice in Islamic finance, fintech, and financial economics. He plays a pivotal role in developing innovative fintech curricula that blend academic rigor with real-world industry insights. PhD in Islamic Finance, International Centre for Education in Islamic Finance (2018) Dr. Šeho's research centers on Islamic finance and banking, corporate finance, financial economics, and fintech. His work explores critical issues such as the stability of dual-banking systems, the impact of interest rates on Islamic financial instruments, and the role of finance in energy justice and sustainable development. He actively investigates how diversification strategies influence bank risk and returns, particularly in mixed financial environments. His recent publications, primarily from 2020 to 2024, reflect a strong trend toward empirical and policy-relevant research in Islamic and conventional banking systems. These works frequently appear in high-impact journals such as the Pacific Basin Finance Journal and International Review of Finance , with a methodological emphasis on econometric modeling, including GMM techniques. The interdisciplinary nature of his research is evident in contributions linking finance with energy justice and sustainable development goals. Dr. Šeho has been honored with three major scientific awards: Best Paper Award by the Journal of Muamalat and Islamic Finance Research (2016) Pacific-Basin Finance Journal Best Paper Award (2018) Pacific-Basin Finance Journal Best Paper Award (2019) He is actively involved in academic advising, currently accepting PhD students, and has contributed to research grants and projects through collaborative international research. His academic service includes peer review for journals like Applied Finance Letters and Journal of International Financial Markets, Institutions and Money , editorial responsibilities, and participation in major conferences such as the 14th Financial Markets and Corporate Governance Conference 2024, where he served as both speaker and session chair. Dr. Šeho is affiliated with research networks focusing on Islamic finance and fintech, collaborating with scholars from Malaysia, Bosnia and Herzegovina, and beyond. His work is disseminated not only in scholarly outlets but also through press and media features, demonstrating his commitment to public engagement and policy impact.
