Programs, Workshop & Conferences
Below I list some of the programs, workshops, and conferences I have been engaged in.
2026 BIRS Workshop
I am co-organizing (w./R. White (Sandia), L. L. R. Ramirez (CIMAT), & T. Bui-Thanh (UT Austin)) a workshop on Integrating Data- and Physics-Driven Methods for Decision Making under Uncertainty. The workshop will run at the Casa Matemática Oaxaca (CMO) May 31 — June 5, 2026. T
In an era marked by significant advances across science, technology, engineering, and mathematics, a critical frontier lies in addressing the intricate challenges posed by uncertainties in computational science. This workshop is set to convene leading experts, researchers, and practitioners at the forefront of data-enabled science and data-intensive, large-scale inverse problems. The workshop aims to explore innovative methodologies, data- and compute-scalable algorithms, and real-world applications to bridge the gap between models and data, ultimately paving the way for informed real-time decision-making in critical application domains.
From intricate parameter-to-observation maps in complex dynamical systems to high-dimensional unknowns, the challenges encompass multi-scale and coupled multiphysics behaviors. With a surge in data availability, the imperative for algorithms seamlessly integrating model-based and data-driven frameworks becomes paramount. This workshop presents a unique opportunity for researchers to showcase advancements in theory, computation, and scalability, bringing together the realms of mathematics, computational sciences, and engineering to tackle real-world challenges head-on. By fostering interdisciplinary collaboration and promoting concrete outcomes, the workshop seeks to catalyze mathematical innovation.
With a focus on uncertainty characterization and propagation to decision-making, we aim to lay the groundwork for transformative advancements for real-world problems.
he webpage for the workshop is https://www.birs.ca/events/2026/5-day-workshops/26w5632.
In an era marked by significant advances across science, technology, engineering, and mathematics, a critical frontier lies in addressing the intricate challenges posed by uncertainties in computational science. This workshop is set to convene leading experts, researchers, and practitioners at the forefront of data-enabled science and data-intensive, large-scale inverse problems. The workshop aims to explore innovative methodologies, data- and compute-scalable algorithms, and real-world applications to bridge the gap between models and data, ultimately paving the way for informed real-time decision-making in critical application domains.
From intricate parameter-to-observation maps in complex dynamical systems to high-dimensional unknowns, the challenges encompass multi-scale and coupled multiphysics behaviors. With a surge in data availability, the imperative for algorithms seamlessly integrating model-based and data-driven frameworks becomes paramount. This workshop presents a unique opportunity for researchers to showcase advancements in theory, computation, and scalability, bringing together the realms of mathematics, computational sciences, and engineering to tackle real-world challenges head-on. By fostering interdisciplinary collaboration and promoting concrete outcomes, the workshop seeks to catalyze mathematical innovation.
With a focus on uncertainty characterization and propagation to decision-making, we aim to lay the groundwork for transformative advancements for real-world problems.
he webpage for the workshop is https://www.birs.ca/events/2026/5-day-workshops/26w5632.
2025 CBMS AMML Conference
The conference will expose early career researchers to cutting-edge research at the interface of applied mathematics and machine learning. It will also help identify new research directions and will foster the building of new collaborations between research groups in the Texas-Louisiana area and other regions. The conference will include graduate students, postdoctoral fellows, and established researchers from academia and industry, and provide a platform for early career researchers to learn and discuss recent advances in mathematical methods for machine learning and data science.
In more detail, the conference will feature ten lectures delivered by Dr. Lars Ruthotto from Emory University. The lectures will be divided into three modules. Module 1 consists of three introduction lectures on machine learning (e.g. deep neural networks, learning problems). The second module, also of three lectures, will introduce important components of applied mathematics in machine learning (e.g. optimization, regularization). The last module will focus on the use of machine learning in critical problems in computational and applied mathematics (e.g. inverse problems, high dimensional partial differential equations). These lectures will be supplemented by a dozen contributed talks from participants, a poster session, a mentoring academic panel and a second panel that will feature researchers from industry (e.g. oil and gas, medical center).
The webpage for the conference is https://www.math.uh.edu/cbms-amml.
This summer school is financially supported by NSF under the award DMS-2430460.
2025 ChAMELEON Summer School
This first installment introduced participants to mathematical techniques at the intersection of machine learning, inverse problems, and statistical inference, with an emphasis on numerical aspects. Mornings included lectures that provide a foundational understanding of the field. Students learned state-of-the-art approaches for solving both deterministic and statistical inverse problems of varying complexity. In the afternoons, participants engaged in hands-on assignments to gain practical experience. They learned how to execute code on a modern high-performance computing architecture.
The conference featured research talks by leading scientist in the field. Students were able to present their research results during a poster session.
More information about the first edition of this summer school can be found here: https://andreasmang.github.io/chameleonline.
This summer school is financially supported by NSF under the award DMS-2145845 and the Research Computing Data Core at the University of Houston.
2023 Dagstuhl Seminar
The Dagstuhl Seminar Inverse Biophysical Modeling and Machine Learning in Personalized Oncology brought together leading experts in computational and applied mathematics, computer science, biomedical imaging, and medical imaging sciences with research interests in data science, machine learning, modeling, optimization, and statistical and deterministic inversion with applications in medical imaging, and - in particular - oncology. The seminar had four main thrusts: (i) machine learning in the context of data analytics and data-driven model prediction, (ii) predictive computational modeling through statistical and deterministic inversion, (iii) integration of machine learning with model-based priors, and (iv) use of these methods to aid decision making. We discussed these topics through the lens of foundational algorithmic complications and mathematical and computational challenges. We also explored how advances in the applied sciences (e.g., data analytics, medical imaging, radiomics, genomics, or experimental design) can aid us to tackle challenges associated with the design of computational and mathematical methods.
The webpage of the seminar is https://www.dagstuhl.de/23022.
The webpage of the seminar is https://www.dagstuhl.de/23022.