New Spring 2026 Courses Announced
Boston University’s Faculty of Computing & Data Sciences is excited to announce three new courses for Spring 2026, offering students a diverse range of opportunities to explore data science—from large language models to scientific principles and biology.
Learn about the computing and data sciences courses and the faculty leading the charge.
CDS DS 593 - Theory and Applications of Large Language Models
Lauren Wheelock, CDS Clinical Assistant Professor
About the course: In this course, students will become savvy consumers and sophisticated developers of LLMs and related tooling. We will start by orienting ourselves to the history of natural language processing and the current state of AI tools, which students will learn to critically evaluate and use extensively throughout the course. Students will develop a deep intuition for LLM concepts, including attention and transformer architectures, sampling, and search, and will build small models from scratch. They will then apply pre-trained LLMs to solve real-world problems, working with advanced techniques including fine-tuning, prompt engineering, RAG, and AI agents. Throughout, the course emphasizes bias, safety, and responsible deployment. Through reflections, labs, and projects, students will demonstrate learning and develop a professional portfolio.
For more information, including prerequisites and class times, click here.
CDS DS 595 - AI for Science
Siddharth Mishra-Sharma, CDS Assistant Professor
About the course: The goal of the course is to equip students with the tools necessary to understand and carry out research at the forefront of AI and the natural sciences. Prerequisites: Multivariable calculus, linear algebra, probability theory; familiarity with neural networks and deep learning frameworks (PyTorch or JAX); proficiency in Python. Exemplary:
- Preliminaries: the AI4Science landscape, core ML concepts, automatic differentiation, Bayesian statistics, simulators, and common scientific data modalities
- Scientific computing infrastructure: data management, compute accelerators, benchmarking and evaluation, reproducibility
- Bayesian inference: MCMC and variational methods
- Generative modeling (e.g., diffusion models) and surrogate models
- Differentiable programming for scientific computing
- Neural network building blocks: encoding scientific inductive biases
- Neural ODEs and operator learning
- Uncertainty quantification
- Interpretability and symbolic regression
- Foundation models and LLMs for scientific applications
- Case studies from across the natural sciences
For more information, including prerequisites and class times, click here.
CDS DS 596 - Learning From Large-Scale Biological Data
Pawel Przytycki, CDS Assistant Professor
About the course: We are living in the age of large-scale biological data. Over the last two decades, the cost of genetic sequencing has decreased faster than Moore's law, meaning that the abundance of data is outpacing the improvement in computational power to analyze it. So while these data are incredibly exciting, extracting biological meaning from the sheer quantity of heterogeneous data being generated requires cutting-edge computational methods. In this course, we will study the modern algorithms and machine learning tools that have been developed to extract biological insights from these data, including deep learning approaches such as for protein structure prediction and learning from sequence data, Bayesian approaches for gene function prediction and network construction, and graph-based approaches. We will examine these through the lens of common pitfalls in learning from biological data and how to best avoid them. By the end of this course, students will understand both the contexts in which it is appropriate to use such tools as well as their limitations.
For more information, including prerequisites and class times, click here.