Summer Schools 2026
Ethan Nowaski & Emerald Win
Advancing Morphogenetic Biosciences & Bioengineering
University of Notre Dame, June 22-26th
https://www.purdue.edu/research/embrio/research/jointsymposium.php
Jointly hosted on June 25th, 2026 by the NSF EMBRIO Biology Integration Institute, the NSF RECODE project at the University of Notre Dame, and the newly formed ND-MBI (Notre Dame Morphogenetic Bioengineering Institute), this symposium, and it's associated workshops, brings together visionary researchers working at the intersection of mechanobiology, morphogenesis, physiology and mechanics, and AI in biology. The symposium aims to foster collaboration and explore transformative ideas in understanding how multicellular systems coordinate signals across spatial and temporal scales to build, maintain, and repair complex tissues.
Ethan Nowaski
Deep Learning for Science (DL4SCI) Summer School 2026 hosted at Lawrence Berkeley National Lab July 20-24th https://dl4sci-school.lbl.gov/
Hosted by Computing Sciences at Berkeley Lab, the 2026 Deep Learning for Science (DL4SCI) Summer School is a five-day intensive program bringing together researchers and engineers to explore the latest advances in deep learning and generative AI (GenAI), with a special emphasis this year on foundation models, reasoning, and agentic AI for scientific discovery.
Khoi Vo
2026 Gene Golub SIAM Summer School: Fault-tolerant Algorithms in Quantum Computing, at Duke University. July 27th to Aug 7th, 2026
https://sites.duke.edu/siamss2026/
Brief description: In this summer school, we will introduce quantum algorithms and quantum computing from a numerical analysis and applied mathematics perspective. We will first go over fundamental principles and basics of quantum mechanics and quantum computing, and then delve into discussing quantum algorithms of various tasks for scientific computing purposes, including quantum dynamics simulation, numerical linear algebra tasks, numerical differential equations, and quantum learning tasks. During the first week, participants will systematically acquire the mathematical foundations of quantum algorithms and quantum computing. The program will introduce contemporary and essential techniques for constructing quantum algorithms, including quantum phase estimation, Trotterization, Linear Combination of Unitaries (LCU), block-encoding, quantum signal processing (QSP), and quantum singular value transformation (QSVT). In the second week, we will delve into more advanced topics, such as quantum dynamics simulation, quantum advantage, quantum learning theory.
Vladimir Lopez
ICTP-INdAM-SLMath Summer Graduate School for Machine Learning
Location: Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
06/10/2026 - 07/03/2026
Machine learning and Artificial Intelligence are now ubiquitous in every dimension of contemporary life, from high tech applications to precision medicine, from scientific research to entertainment. Despite this undeniable fact, the theoretical foundation of many popular algorithms and procedures are not yet fully understood, and this poses several questions to the mathematical community. It is to be expected that research motivated by machine learning and AI will play a key role in many areas of mathematics in the incoming years; for this reason, it seems especially important that the greatest number of PhD students and young researchers are made aware of the most recent developments and open issues in the Mathematics of Machine Learning.
In view of the previous considerations, the aim of this summer school is to provide an introduction to theoretical ideas that have been developed with the objective of understanding machine learning methods and their domain of applicability. The focus will be on proof technique and general mathematical tools. The lecturers are two worldwide experts in the area and the material is regularly taught in Mathematics and Statistics Departments of the top world Universities.