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Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University

Researchers working on graph algorithms use a broad range of different criteria for deciding what makes an algorithm efficient. While in theory the dominant benchmark is the asymptotic running time, in practice the story is more nuanced: an algorithm needs to be simple enough to be implementable, fast on graphs of bounded size, space efficient, cache-friendly, and easy to test. While many of these requirements motivate interesting algorithmic questions that are highly relevant in practice, they are often overlooked by the theory community. The goal of the workshop is to foster the exchange of ideas between researchers working on graph algorithms, which have high practical relevance. The workshop will include overview talks on the various perspectives, research talks, an open problem session, and structured time for collaboration.

ICERM Program Staff;     Tél.: [+1 (401) 863-5030];     Email.: info@icerm.brown.edu

graph algorithms, data science, networks

Algorithmes et structures de données,    Théorie des graphes et combinatoire

Simons Laufer Mathematical Sciences Institute (SLMath)

In a growing number of applications, one needs to analyze and interpret data coming from massive networks. The statistical problems arising from such applications lead to important mathematical challenges: building novel probabilistic models, understanding the possibilities and limitations for statistical detection and inference, designing efficient algorithms, and understanding the inherent limitations of fast algorithms. The workshop will bring together leading researchers in combinatorial statistics, machine learning, and random graphs in the hope of cross-fertilization of ideas.

combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation

Réseaux complexes,    Théorie des graphes et combinatoire

On its 10th anniversary, ODSC East will feature 250+ speakers, instructors, and practitioners, 2,500 of your fellow data and AI practitioners, and hands-on instruction for world-class experts in: AI Agents | Generative AI | Large Language Models | AI Engineering | Machine Learning | Deep Learning | MLOps and LLMOps | AI for Biotech, Pharma, and Life Sciences | Data Engineering | Data Visualization and Analysis | and much more. You’ll also be able to build meaningful connections with speakers and peers in a relaxed setting during our networking events like the ODSC Networking Reception, Dinner with Data Scientists, Meet the Speaker session, Book Signing sessions, and more.

Email.: iryna@odsc.com

Data Science, Machine Learning, AI, Artificial Intelligence

American Institute of Mathematics, Pasadena, California (AIM)

This workshop is designed for faculty, postdocs, and PhD students who are interested in teaching statistics and data science through an interdisciplinary approach that leverages the strengths of both applied mathematics and statistics. Participants will exchange pedagogical ideas, curricula, and research applications, fostering collaboration between those teaching data science. The workshop is especially relevant for statistics faculty looking to expand their data science toolbox and applied mathematicians aiming to equip their students with essential data science skills. The workshop is also open to individuals in engineering, epidemiology, computer science and other STEM or public health fields.

Mathématiques appliquées (en général),    Pédagogie de la science

Institute for Computational and Experimental Research in Mathematics, Brown University, Providence, RI (ICERM)

Summer@ICERM is a residential undergraduate research program hosted at the Institute for Computational and Experimental Research in Mathematics at Brown University. Led by experienced faculty and graduate student teaching assistants, participating undergrads will spend seven weeks learning how to collaborate, engage in data science, and conduct research as a team. The goal of Summer@ICERM 2025: Building Useful Insights from Local Data through Sustainable partnerships (BUILDS) is to introduce students with limited experience to the field of data science through projects related to issues such as global food systems, food production, and food security.

Cours et évènements pour étudiants en mathématiques,    Conférences publiques et sensibilisation

This workshop, sponsored by AIM and the NSF, will bring together researchers from across the mathematical sciences to apply a broad range of mathematical, statistical, and data science tools to study the mathematical sciences community. The workshop will include both researchers currently working in this area, as well as researchers new to the field interested in applying their expertise to this area.

Developing MetaMath as an example of using mathematical, statistical, and computational methods to investigate the mathematical sciences community itself. Catalyzing research collaborations aimed at using data to answer questions about the mathematical sciences community. Exploring the variety of mathematical, statistical, and data science techniques that can be applied to gain insight into the systems and structure of the mathematical sciences community.

Mathématiques générales et evénement multidisciplinaires,    Mathématiques appliquées (en général)