Join our one-day online workshop on AI-powered image analysis for PhD students, professors, and professional researchers, led by Nikolay Oskolkov, Group Leader/PI at LIOS. This intensive hands-on seminar bridges rigorous theory and practical implementation, showing how to turn microscopy, high-throughput imaging, and clinical image datasets into reproducible, publication-ready measurements. Participants will work with ChatGPT-assisted coding, Jupyter notebooks on Linux, and Python-based tools to build end-to-end image analysis workflows covering CNNs, transfer learning, UNet, Mask R-CNN, object detection, OpenCV, OTSU thresholding, Bayesian uncertainty quantification, GRAD-CAM interpretability, evaluation metrics, experiment tracking, and reproducible reporting. By the end of the workshop, attendees will be able to design and implement segmentation, detection, and classification pipelines for biological image data, critically evaluate model performance, and prepare robust analyses for theses, papers, and grant applications. The seminar is delivered live via Zoom, with recordings, slides, code, data, and a monitored Q&A forum available for 30 days after the event, plus an official Instats certificate of completion. #ComputationalBiology #DataAnalytics #DataScience #MachineLearning #BiomedicalEngineering #Bioinformatics #ComputerScience #ArtificialIntelligence #ChatGPT #Jupyter #Linux #Python #R #Research #ResearchTraining #Instats

Join us for a three-day online seminar on Demand Prediction in Supply Chains, taught by Isik Bicer (Schulich School of Business, York University), designed specifically for PhD students, faculty, and professional researchers. This hands-on workshop bridges statistics, machine learning, and operations research, combining rigorous theory with practical Python implementation to help participants build reproducible, research-ready forecasting workflows. Across live lectures and labs, attendees will explore regression and tailored regularization, SARIMA time-series forecasting, classification models for product selection, Monte Carlo simulation and evolutionary modeling, model validation, backtesting, calibration, and cost-sensitive evaluation aligned with supply-chain objectives. Participants will work with annotated Jupyter notebooks, realistic datasets, and tools such as scikit-learn and statsmodels, leaving with concrete skills to support dissertation research, scholarly publications, and real-world supply-chain decision-making. The seminar is offered via Zoom, with recordings, materials, and a monitored Q&A forum available for 30 days after the seminar, plus an official Instats certificate of completion. #MachineLearning #Statistics #SupplyChainManagement #Management #ComputerScience #DataScience #OperationsResearch #Economics #Python #Research #ResearchTraining #Instats