International Day Of Women in Statistics and Data Science: Full Schedule

Registration opens August 15th at www.idwsds.org.

You will be able to validate your attendance (Check-In to sessions) using your personal schedule.

arrow_back View All Dates

08:00 UTC

S400 - Building Future Leaders in Statistics and Data Science Japan's Collaborative Model from School Education to Society

Tuesday October 6, 2026 08:00 - 09:00 UTC

Zoom Room #4

As artificial intelligence and data-driven decision-making become integral to society, developing future
leaders in statistics and data science has become an international priority. Beyond teaching statistical
methods or programming skills, education must cultivate statistical thinking, inquiry, evidence-based
reasoning, and the ability to connect data with real-world decision-making throughout learners'
educational journeys.

Japan has been developing an inquiry-based learning framework that progressively connects statistical
and data science education from primary school to university. This framework encourages learners to
investigate authentic questions using data while strengthening statistical literacy through curriculum
reform, inquiry-based learning, student competitions, and university education and research. It is
supported through collaboration among educational institutions, professional societies, public
organizations, and non-profit organizations.

This session introduces four complementary perspectives on this framework. First, it presents how
long-term educational support by a non-profit organization has contributed to nurturing future
statistical talent. Second, it explores how national and international statistical competitions, including the
Statistical Graph Contest and the ISLP International Poster Competition, inspire students and foster
statistical thinking. Third, it shares innovative practices in inquiry-based data science education in
Japanese secondary schools. Finally, it discusses how statistics and data science education can prepare
hybrid talent capable of integrating disciplinary expertise with AI and emerging technologies across
secondary education, higher education, and lifelong learning.

Through these perspectives, the session aims to share Japan's inquiry-based educational framework
with the international community and to promote dialogue on how statistics and data science education
can cultivate future leaders capable of addressing increasingly complex societal challenges.

Speakers

Kiwa Tomaru

Mathematics Teacher, Nagoya University Junior and Senior High School

Kiwa Tomaru is a mathematics teacher at Nagoya University Junior and Senior High School, where she has designed and taught the compulsory Data Science course since its introduction in 2022, emphasizing reading data correctly over formula-based instruction — work recognized with... Read More →

Yoko Konishi

Professor, University of Tsukuba

Yoko Konishi is a professor at the University of Tsukuba, specializing in applied econometrics and statistics. She received her Ph.D. in Economics from Nagoya University. Her research uses official statistics, private-sector big data, and large-scale surveys to study consumer behavior... Read More →

Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #4

Invited Session

09:00 UTC

S401 - Health and Biostatistics 1

Tuesday October 6, 2026 09:00 - 10:00 UTC

Zoom Room #4

A Background Correction and Normalisation Framework for multiplex Immunofluorescence Spatial Proteomics | Menopause-related heterogeneity in lipoprotein(a) effect on atherosclerotic cardiovascular disease: a sex- and age-stratified Mendelian Randomization study in the UK Biobank | Income, Inequality, and Mortality: A Harmonized Comparative Framework Across Ecuador, Colombia, and Brazil|Absorbing Markov Chain Parameter Estimation Under Data Scarcity: A Comparative Study of Analytical and Monte Carlo Methods in Neonatal Care|Predicting the Right Treatment for the Right Patient: An AI-Powered Decision Support Framework Based on Predicted Individual Treatment Effects

Speakers

Malvika Kharbanda

SAiGENCI

Malvika is a PhD candidate in computational oncology at Adelaide University (Australia), where she develops statistical and computational methods to analyse complex biomedical data. Her research focuses on using large scale clinical and spatial imaging datasets to better understand... Read More →

Gabriela Sandoval

CISeAL - PUCE

PhD in Statistics, currently working as a postdoctoral researcher at the Center for Research on Health in Latin America (CISeAL), where she contributes to projects aimed at reducing health inequalities and mitigating the impact of economic crises in the region. Her expertise centers... Read More →

Daianna Gonzalez Padilla

University of Cambridge

Researcher in Genetic Epidemiology at the MRC Biostatistics Unit, University of Cambridge, specializing in Mendelian randomization, a causal inference framework that uses genetic variants to assess the effects of putative risk factors on disease from observational data. My research... Read More →

Leandra Braeuninger

University College London

Leandra Bräuninger (they/them; she/her) is a doctoral student at University College London, supervised by Dr Brieuc Lehmann and Prof. Ioanna Manolopoulou, using statistical and machine learning methods to define, quantify and mitigate genomic inequity. Drawing on algorithmic fairness... Read More →

Organizers

IDWSDS Program Committee

IDWSDS

S401 Abstracts pdf

Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #4

Contributed Session, Technical Statistical Session

Country Australia, United Kingdom
Session ID 401

10:00 UTC

S402 - Selected Topics in Statistical Methods and Applications

Tuesday October 6, 2026 10:00 - 12:00 UTC

Zoom Room #4

Beyond the Average: Random Slope Models for Revealing Territorial and Gender Inequalities | Randomization Inference on Policy Assignments | Becker’s models for mixture experiments: An A-optimal approach | Inference for GMANOVA model for Volatile Data

Speakers

Naomi Diz Rosales

PhD Student in Statistics, Universidade da Coruña

I hold a PhD in Statistics from the Universidade da Coruña (UDC), where my research focused on the development of mixed models with random slopes for Small Area Estimation. My work combines statistical methodology with applications to socioeconomic and public health challenges, including... Read More →

EunYi Chung

University of Illinois at Urbana-Champaign

She is an Associate Professor of Economics at the University of Illinois Urbana-Champaign, specializing in econometric theory. Her research focuses on developing improved methods of statistical inference for evaluating the causal effects of policy interventions using both experimental... Read More →

Bushra Husain

ALIGARH MUSLIM UNIVERSITY, ALIGARH, U.P., INDIA

Bushra Husain, Professor is the Chairperson of Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, U.P., India and has been working as Faculty of Statistics at Women's College since 2003. She has obtained B.Sc., M.Sc., M.Phil., and Ph.D. in Statistics... Read More →

Sudeepta Pal

PhD Scholar at IIT Hyderabad

I am currently pursuing my PhD in Statistics at IIT Hyderabad, where I also work as a Teaching Assistant. My research mainly focuses on statistical theory, multivariate analysis, and computational statistics. I cleared two of India's toughest competitive exams with high ranks, securing... Read More →

Organizers

IDWSDS Program Committee

IDWSDS

S402 Abstracts pdf

Tuesday October 6, 2026 10:00 - 12:00 UTC
Zoom Room #4

Contributed Session, Technical Statistical Session

Country Spain, United States, India
Session ID 402

12:00 UTC

S403 - Change point detection and its applications

Tuesday October 6, 2026 12:00 - 13:00 UTC

Zoom Room #4

Change point detection has long been an active and important area of statistical research because structural changes frequently arise in data collected over time or across different conditions. Detecting these changes helps researchers better understand complex processes and make more informed decisions in fields such as public health, finance, genomics, environmental science, manufacturing, and engineering, where the ability to detect shifts in trends, variability, or dependence structures can provide valuable insights into complex systems. As the volume and complexity of modern data continue to grow, the development of robust and efficient change point techniques remains a significant research challenge and opportunity. This invited session brings together researchers from three countries to present new methods and real-world applications of change point analysis. The session will highlight recent advances in the field, encourage international collaboration, and showcase the broad impact of change point research across diverse disciplines.

Speakers

Rebecca Killick

Clemenson University

Rebecca Killick is Associate Director of Research in Mathematical and Statistical Sciences at Clemson University. Dr. Killick received a PhD in Statistics from Lancaster University, where until 2026, held a Professor and Director of Research positions. In 2019 Dr. Killick was the... Read More →

Kun Liu

JPMorgan Chase & Company

Kun Liu is a Vice President and Quant Modeling Lead at JPMorgan Chase with expertise in AI, machine learning, credit risk, fraud detection, and causal inference. He holds a Ph.D. in Statistics from Georgia Tech and has over eight years of experience developing and deploying advanced... Read More →

Meenu Rani

Indian Institute of Technology Ropar

Meenu Rani is a Ph.D. student in the Department of Mathematics at the Indian Institute of Technology Ropar. Her doctoral research focuses on change point detection, including both offline and online settings. Her work has primarily involved applications to financial data.

Ayten Yiğiter

Hacettepe University

Dr. Yiğiter received the B.S., M.S., and Ph.D. degrees in statistics from Hacettepe University, Ankara, Türkiye, in 1995, 2000 and 2006, respectively. In 2007, she was a Visiting Scholar with the University of Missouri-Kansas City, Kansas City, MO, USA. She is currently working... Read More →

Chairs/Hosts

Jie Chen

Organizers

Jie Chen

S403 Abstracts pdf

Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #4

Invited Session, Technical Statistical Session

Country United States
Session ID 403

13:00 UTC

S404 - Coordinating Evidence for Better Research Design: Priors, Assumptions, and Scientific Judgment

Tuesday October 6, 2026 13:00 - 14:00 UTC

Zoom Room #4

This session examines how scientific judgment can be made explicit in research design and decision making with mixed methods evidence, causal knowledge, expert elicitation, and Bayesian priors. Noor Qaragholi opens by showing how qualitative and quantitative evidence can inform strong, untestable causal assumptions in study design, illustrated through CACE estimation in a randomized behavioral intervention with substantial noncompliance. Utkarshani Jaimini then turns to Causal Neuro-Symbolic AI, a framework for building systems that are explainable, interpretable, and causally aware rather than limited to correlational pattern matching. Her work spans knowledge representation, causal inference, and applied machine learning. Anna Heath discusses expert elicitation for Bayesian clinical trial design, focusing on remote, real time exercises that help experts translate experience into probabilistic statements for prior distributions. Lu (Maggie) Qian closes by examining how Bayesian trial design can make scientific judgment explicit without allowing prior choice to become opaque, drawing on FDA guidance and reverse-Bayes methods as an audit tool. Together, the talks show how making assumptions and priors explicit can strengthen research design and support credible, decision relevant conclusions.

Speakers

Noor Qaragholi

The Policy & Research Group

Noor Qaragholi is a public health researcher with 15 years of experience conducting qualitative, quantitative, and mixed methods research to improve health programs, services, and systems. As a Senior Research Analyst at The Policy & Research Group, Dr. Qaragholi leads large-scale... Read More →

Utkarshani Jaimini

University of Michigan Dearborn

Utkarshani Jaimini is an Assistant Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, where she joined as a faculty in Fall 2025. She holds her doctoral degree from the University of South Carolina. Her research lies at the intersection... Read More →

Lu Qian

Zetyra, LLC

Lu (Maggie) Qian is an independent biostatistician and founder of Evidence in the Wild and Zetyra. Her work focuses on Bayesian and adaptive clinical trial design, regulatory alignment, prior specification, prior-data conflict, and operating characteristics of composed adaptive pipelines... Read More →

Anna Heath

The Hospital for Sick Children

Dr. Heath is a Scientist at The Hospital for Sick Children (SickKids), Toronto, an Associate Professor at University of Toronto and Honorary Research Fellow at University College London, UK. She is the Canada Research Chair in Statistical Trial Design. She arrived at SickKids in 2018... Read More →

Chairs/Hosts

Elizabeth Eisenhauer

Organizers

Elizabeth Eisenhauer

S404 Abstracts pdf

Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #4

Invited Session, Technical Statistical Session

Session ID 404

15:00 UTC

S406 - Selected Topics in AI and Data Science

Tuesday October 6, 2026 15:00 - 16:00 UTC

Zoom Room #4

Predictive Modeling for Irregular and Unbalanced Longitudinal Data: Application to Bariatric Surgery Outcomes | Big Data Is Not Neutral: Hidden Biases in Electronic Health Record Research | Spatio-Temporal Air Quality Risk Modelling | Interpretable Machine Learning-Based Variable Selection under Missing Data with the Feasible Solution Algorithm

Speakers

Kristina Vatcheva

United States, University of Texas Rio Grande Valley

Dr. Kristina P. Vatcheva is an Associate Professor in the School of Mathematical and Statistical Sciences at the University of Texas Rio Grande Valley (UTRGV) College of Sciences. She has training and experience in mathematics, computer programming/application development, biostatistics... Read More →

Sima Sharghi

Akron Children's Hospital

Dr. Sima Sharghi is a Senior Statistician and researcher with expertise in causal inference, Bayesian statistics, real-world evidence, electronic health record analytics, and predictive modeling in healthcare. She holds a PhD in Statistics and has extensive experience collaborating... Read More →

María Bugallo

University of A Coruña

María Bugallo is a Juan de la Cierva postdoctoral researcher at the University of A Coruña working in methodological statistics, with a focus on small area estimation and robust statistical modelling. She completed her PhD in Statistics at the Miguel Hernández University of Elche... Read More →

Maliha Mehnaz Mitu

University of Kentucky

Maliha Mehnaz Mitu is a PhD Candidate in Biostatistics at the University of Kentucky whose research focuses on methodological challenges in interpretable machine learning and Artificial Intelligence for healthcare. Her work integrates statistical learning, missing data methodology... Read More →

Organizers

IDWSDS Program Committee

IDWSDS

S406 Abstracts pdf

Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #4

Contributed Session, Technical Statistical Session

Country United States, Spain
Session ID 406

17:00 UTC

S407 - Health and Biostatistics 2

Tuesday October 6, 2026 17:00 - 18:00 UTC

Zoom Room #4

Scalable Nearest-Neighbor Gaussian Graphical Models for Demographic Heterogeneity in GLP-1 Outcomes|Paired Portfolio Trading: A Statistical Approach|Methodological opportunities in genomic data analysis to advance health equity

Speakers

Gaoqianxue Liu

Drexel University

Gaoqianxue (Daphne) Liu is a PhD student in Biostatistics at the Dornsife School of Public Health, Drexel University. Her research includes scalable Bayesian methods for high-dimensional and graphical models in electronic health records, longitudinal and trajectory modeling, and machine... Read More →

Leandra Braeuninger

University College London

Sara Mezuri

Graduate Reasearch Assistant

I am a Ph.D. candidate in the Department of Mathematics and Statistics at Oakland University in Michigan, specializing in Applied Mathematics. Currently, I am in my fifth and final year, and I expect to graduate in December 2026. My research involves developing several statistical... Read More →

Organizers

IDWSDS Program Committee

IDWSDS

S407 Abstracts pdf

Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #4

Contributed Session, Technical Statistical Session

Country United States, United Kingdom
Session ID 407

18:00 UTC

S408 - Bridging the Gap: How Practicing Data Scientists Use LLMs in the Wild and What It Means for Data Science Education

Tuesday October 6, 2026 18:00 - 18:30 UTC

Zoom Room #4

Since the widespread availability of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), fundamental questions have emerged about the future of coding in data science. Some predict that data scientists will no longer need traditional coding skills, while others question whether LLMs might replace data scientists entirely. However, these discussions have largely proceeded without empirical evidence of how practicing data scientists actually use these tools.

This study addresses this gap by surveying trained, practicing data scientists to understand if and how they integrate LLMs into their workflows, particularly for writing and editing code and performing other data science tasks. Building on our recent investigation of data science educators' perspectives on LLMs, this research examines real-world usage patterns among practitioners to bridge the gap between current practice and educational preparation.

Our findings will contribute to the data science community in two critical ways. First, by documenting how data scientists are actually working with LLMs four years after their initial release, we provide actionable insights that allow practitioners to learn and adopt effective strategies for integrating these tools into their work. Second, we inform data science education by evaluating whether current pedagogies adequately prepare students for this evolving landscape.

Speakers

Tiffany Timbers

University of British Columbia

Dr. Tiffany Timbers is an Associate Professor of Teaching in the Department of Statistics and Instructor in the Master of Data Science program at the University of British Columbia. She holds a PhD in Neuroscience from UBC and completed postdoctoral research in behavioral and neural... Read More →

Organizers

Tiffany Timbers

University of British Columbia

Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #4

Plenary Talk, Career or Professional Development Session

Country Canada
Session ID 408

18:30 UTC

S409 - Every Step Counts: A journey through crossroads, turning points and learnings

Tuesday October 6, 2026 18:30 - 19:00 UTC

Zoom Room #4

A career is rarely a straight line. It is shaped by choices, unexpected opportunities, challenges, mentors, setbacks, and the willingness to keep learning along the way. In this talk, I reflect on my journey from studying statistics in India to pursuing a PhD in Biostatistics in the United States and building a career as a statistician in the biopharmaceutical industry. Along the way, my experiences have taken me across academic research, internships, clinical development, statistical methodology, multiple therapeutic areas, mentoring, professional service, and leadership within the statistical community.
Rather than focusing only on milestones, this talk explores the crossroads and turning points behind them—the decisions that changed direction, the opportunities that initially seemed small but became important, and the lessons learned from navigating uncertainty and growth. Drawing from experiences in research, clinical trials, interdisciplinary collaboration, mentoring, and professional engagement, I will share how curiosity, adaptability, relationships, and continuous learning have shaped my development as a statistician. The central message is simple: careers are built one step at a time, and even the steps that do not seem significant in the moment can ultimately help define where we go next.

Speakers

Arinjita Bhattacharyya

Arinjita Bhattacharyya, PhD, is a biostatistician and statistical scientist with nearly a decade of experience across the pharmaceutical industry and academia. Most recently an Associate Principal Scientist in Biostatistics at Merck, she has supported clinical development across oncology... Read More →

Tuesday October 6, 2026 18:30 - 19:00 UTC
Zoom Room #4

Plenary Talk, Career or Professional Development Session

Session ID 409

20:00 UTC

S410 - Statistical Inference with Incomplete Data

Tuesday October 6, 2026 20:00 - 21:00 UTC

Zoom Room #4

Incomplete data arise in many scientific fields and present major challenges for modern statistical methodology. Missing values, censoring, and selection bias may substantially affect estimation, hypothesis testing, prediction, and the generalizability of scientific findings. Developing statistical methods that appropriately account for these sources of incompleteness is therefore essential for obtaining reliable conclusions. This session brings together recent methodological advances addressing different aspects of statistical inference with incomplete data. Topics include assessing the generalizability of randomized controlled trials through a sensitivity analysis framework for selection bias, evaluating modern imputation methods for recovering the underlying data distribution, independence testing for high-dimensional incomplete data, and nonparametric testing for censored survival data. Together, these contributions provide complementary perspectives on the analysis of incomplete data and demonstrate how principled statistical methodology can improve inference across a broad range of applications.

Speakers

Rebecca Andridge

Professor, The Ohio State University

Rebecca Andridge is a Professor of Biostatistics at The Ohio State University College of Public Health. She conducts methodologic work on imputation methods for missing data, primarily when missingness is driven by the missing values themselves (missing not at random), and on measures... Read More →

Krystyna Grzesiak

University of Wrocław, Faculty of Mathematics and Computer Science

Krystyna Grzesiak is a PhD candidate at the Institute of Mathematics, University of Wrocław. She conducts research on developing machine learning methods for the analysis of proteomic and metabolomic data.

Bojana Milošević

Faculty of Mathematics, University of Belgrade

Bojana Milošević is an Associate Professor and Head of the Department of Probability and Statistics at the Faculty of Mathematics, University of Belgrade. Her research interests include nonparametric statistics, statistical hypothesis testing, survival analysis, dependence analysis... Read More →

Anke Steyn

North-West University

Anke Steyn is a lecturer at the North-West University in Potchefstroom, South Africa. Her research interests include survival analysis, goodness-of-fit testing and experimental design. She qualified as an actuary and recently completed her doctoral degree in Statistics. Her great... Read More →

Chairs/Hosts

Marija Cuparić

Organizers

Marija Cuparić

S410 Abstracts pdf

Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #4

Invited Session, Technical Statistical Session

Session ID 410

21:00 UTC

S411 - Fireside Chat with CWS Country Representatives: Navigating the Impact of AI in Teaching, Research, and Professional Practice

Tuesday October 6, 2026 21:00 - 22:00 UTC

Zoom Room #4

Artificial Intelligence (AI) is transforming higher education and professional practice at an unprecedented pace, reshaping teaching, research, and ethics while shifting the professional landscape for women and underrepresented groups in data science.
This invited fireside chat brings together CWS country representatives to share insights from diverse global contexts, highlighting how regional infrastructure gaps and regulatory frameworks shape AI adoption. Structured as an interactive, ask-me-anything-style conversation, the session will engage the audience to foster open dialogue.
Through dynamic exchanges, panellists will explore critical tensions in teaching and learning - such as adapting course design and student assessments to an AI-enabled landscape while combatting the threat of professional "deskilling". The discussion will also examine the dual-edged sword of AI in research, balancing efficiency gains in coding and data processing against concerns regarding algorithmic bias, automated plagiarism, and transparency. Moving beyond academia, the session will address how AI influences broader professional practice, focusing on the need for inclusive ethical frameworks and policies that promote equity.
Ultimately, this cross-regional dialogue aims to identify practical strategies for responsible AI use, fostering global awareness of the opportunities and systemic challenges faced by educators, researchers, and professionals in statistics and data science.

Speakers

Sofía Villar

MRC Biostatistics Unit, University of Cambridge, UK

Sofia S. Villar is a Research Professor and Group Leader within the Efficient Study Design theme at the MRC Biostatistics Unit, University of Cambridge, where she also serves as Academic Lead for Equality, Diversity and Inclusion.

Her research bridges optimization, machine lear... Read More →

Lígia Henriques-Rodrigues

Department of Mathematical Sciences and CEAUL, Faculty of Sciences, University of Lisbon

Lígia Henriques Rodrigues is an Associate Professor at the Faculty of Sciences of the University of Lisbon. She is a researcher at the Centre of Statistics and Its Applications of the University of Lisbon.

Her research focuses mainly on Extreme Value Statistics, semi-parametric estimation, bias reduction, risk modelling, Bayesian analysis, spatio-temporal models and survival analysis. Her work combines methodological developments in Statistics with applications to real data, particularly... Read More →

Gabriela Cybis

Department of Statistics, Federal University of Rio Grande do Sul, Brazil

Gabriela Bettella Cybis is an Associate Professor in the Department of Statistics at the Federal University of Rio Grande do Sul (UFRGS), Brazil, where she is also a faculty member of the Graduate Program in Statistics.

She holds a B.Sc. in Biology and an M.Sc. in Mathematics f... Read More →

Atinuke Adebanji

Purdue University

Atinuke Adebanji is an Associate Professor of Practice- Statistics at Purdue University, Indianapolis.

Her research and engagement activities focuses on applied multivariate methods, statistical modelling of public health outcomes, multidisciplinary statistical collaboration, and promoting statistical literacy. She has published articles on new distributions applied to health data... Read More →

Chairs/Hosts

Wendy Lou

Organizers

Vanda Lourenço

Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #4

Invited Panel, Other

Session ID 411

22:00 UTC

S412 - Building Trustworthy Financial Systems with Explainable AI: Applications in Credit Risk Modeling and Financial Inclusion

Tuesday October 6, 2026 22:00 - Wednesday October 7, 2026 18:30 UTC

Zoom Room #4

Artificial intelligence is transforming how financial institutions assess creditworthiness, detect fraud, and expand access to financial services. Yet many of today's most powerful machine learning models remain opaque, making consequential decisions that customers, regulators, and even financial institutions struggle to interpret. As AI adoption accelerates, ensuring transparency, accountability, and fairness has become essential to building public trust and achieving equitable financial outcomes.

Speakers

Angela Omogbeme

University of West Georgia

Angela Omogbeme is a financial technology researcher and data analytics professional specializing in artificial intelligence, explainable AI, fraud detection, credit risk modeling, and financial inclusion.

She holds an M.S. in Business Analytics (4.0 GPA) from the University of West Georgia and an MBA from Edinburgh Business School, Heriot-Watt University, UK. Her research has been presented internationally, including at conferences hosted at the University of Oxford ,UK and the University... Read More →

Organizers

Angela Omogbeme

University of West Georgia

Tuesday October 6, 2026 22:00 - Wednesday October 7, 2026 18:30 UTC
Zoom Room #4

Plenary Talk, Career or Professional Development Session

Country United States
Session ID 412

08:00 UTC

09:00 UTC

10:00 UTC

12:00 UTC

13:00 UTC

15:00 UTC

17:00 UTC

18:00 UTC

18:30 UTC

20:00 UTC

21:00 UTC

22:00 UTC

Get help with the event