The 2026 Applied Machine Learning Conference is a two-day, in-person event that brings together data scientists, AI engineers, computational researchers, and other technical leaders from around the world to share knowledge, learn from each other, and advance the fields of applied machine learning, AI, and scientific computing.

Suggested Topic Areas

The AMLC Program Committee welcomes proposals for 30-minute talks and 90-minute tutorials covering a wide range of topics related to data science, AI, machine learning, scientific computing, and related fields. Suggested topic areas include, but are not limited to:

- Applied Machine Learning: Applications of data science and ML methods in a variety of domains, such as healthcare, biotechnology, finance, climate, energy, social sciences, or others; case studies of ML in production; lessons learned from real-world deployments
- AI & Large Language Models: LLM application development, multimodal models, prompt engineering, retrieval-augmented generation (RAG), agentic systems, fine-tuning, evaluation and benchmarking, interpretability
- Machine Learning Methods: Computer vision, embeddings and vector search, recommendation systems, geospatial data analysis, time series forecasting, NLP beyond LLMs, reinforcement learning
- Tools, Infrastructure & Engineering: MLOps and model deployment, data pipelines, feature stores, experiment tracking, cloud and edge computing, open-source tools and frameworks
- Data Analysis & Visualization: Exploratory data analysis, tools and methods for data visualization, dashboards and analytics, communicating insights from data effectively
- Classical Modeling Approaches: Statistical methods, causal inference, probabilistic programming, simulation modeling, network science, reproducible research
- Organizational and Societal Context: Managing data science and AI projects in a company setting; the data science and AI job market; ethics, responsibility, and fairness in data science and AI initiatives; data security, privacy, and regulatory concerns; and so on