Check out the new tools and examples in BAND Framework v0.3!

See the BAND Manifesto in Journal of Physics G! (or on arXiv)

Nuclear physicists seek an accurate description of the properties of atomic nuclei, collisions between nuclei, and extreme environments such as the first few seconds of our universe or the interior of a neutron star. These situations involve many particles interacting through complex forces. They’re each described by a number of different models: these typically do a good job of explaining the results of experiments that have already occurred. The models don’t do as well predicting what will happen in future experiments or in environments that are inaccessible here on Earth.

The Bayesian Analysis of Nuclear Dynamics (BAND) Framework will use advanced statistical methods to produce forecasts for as-yet-unexplored situations that combine nuclear-physics models in an optimal way. These will be more reliable than the predictions of any individual model. BAND’s forefront computer codes will be widely available and will facilitate the design of nuclear-physics experiments that can deliver the largest gain in understanding. The adoption of BAND’s tools in other sciences dealing with “model uncertainty” could spur broad scientific innovation. Undergraduate and graduate students working on BAND will gain a broad range of technical skills in data science, machine learning, nuclear physics, and high-performance computing.

BAND Code of Conduct

BAND is committed to fostering a safe, diverse, inclusive, and equitable environment that values mutual respect and personal integrity. Therefore, all participants in BAND activities will conduct themselves in a welcoming and professional manner, treating one another respectfully and considerately. This is particularly important in interdisciplinary work that brings together collaborators from different scientific backgrounds and cultures. Creating such a culture–one that is collegial, inclusive, and professional–is the responsibility of all collaboration members and meeting participants.

Those participating in BAND activities shall not discriminate against, harass, or bully others. If you observe inappropriate comments or actions and personal intervention seems appropriate and safe, you are encouraged to intervene in ways that are, insofar as it is possible, considerate of all parties involved. BAND members and meeting participants will not discriminate against, act in an inappropriate way, or make inappropriate statements related to aspects of each other’s identity such as age, race, ethnicity, perceived or actual social class, sexual orientation, gender identity, gender expression, marital status, nationality, political affiliation, religion, ability status, and educational background. No kind of harassment (including sexual harassment) or bullying will be tolerated.

Bullying is unwelcome, aggressive behavior involving the use of influence, threat, intimidation, or coercion to dominate others. Harassment includes but is not limited to: inappropriate or intimidating behavior and language, unwelcome jokes or comments, unwanted touching or attention, offensive images, photography without permission, and stalking. Sexual harassment is unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature that creates an intimidating, hostile, or offensive environment.

Violations of this code of conduct policy should be reported to meeting organizers and/or the BAND “Community Leaders”: Daniel Phillips (, Dick Furnstahl (, Özge Sürer (, Frederi Viens (, and Stefan Wild ( Sanctions may range from verbal warning, to ejection from the meeting, to a suspension from future BAND activities, to the notification of appropriate local authorities. Retaliation for complaints of inappropriate conduct will not be tolerated and will result in an escalated sanction.

News ( see all News)

January 20, 2024

Three BAND PhD students received their doctorate in Fall Semester. Dan Liyanage has joined PayPal as a Machine Learning Scientist, after earning his Physics doctorate at Ohio State. Mookyong Son started a position as a Statistics Research Fellow at Unlearn.AI, having completed his PhD in Statistics and Probability at Michigan State. And Moses Chan, who's now working as an Assistant Professor of Instruction in the Engineering School at Northwestern, finished his PhD in Industrial Engineering and Management Sciences at Northwestern. Congratulations to Dr Chan, Dr Liyanage, and Dr Son!

January 10, 2024

Two BAND post-docs have recently moved into permanent positions! In December Pablo Giuliani transitioned to being a Specialist at FRIB/MSU, focused on Nuclear Science and Graduate Student Success, while also conducting research in Bayesian analysis and Machine Learning for nuclear physics. A few months earlier Daniel Odell joined Savannah River National Laboratory as a Data Scientist.

December 11, 2023

BAND members Pablo Giuliani and Alexandra Semposki, as well as BAND Fellow Jason Bub, presented their research at the recent joint meeting of the American Physical Society Division of Nuclear Physics and the Physical Society of Japan. All three talks were part of the Mini-Symposium on "Advanced Statistics and Machine Learning Methods in Nuclear Physics". Giuliani helped to organize the Mini-Symposium and gave an invited talk during it.

November 12, 2023

BAND member Witek Nazarewicz and two statistician collaborators, Vojta Kejzlar and Léo Neufcourt, co-authored a paper, published this week in Scientific Reports. In it, they propose a Bayesian statistical machine learning framework that uses the Dirichlet distribution to combine results of several imperfect models. They show that global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. See the BAND highlight for more details.

October 18, 2023

BAND members Moses Chan, Özge Sürer, Stefan Wild, and John Yannotty attended the INFORMS annual meeting in Phoenix, AZ, October 15 - 18. During the meeting, Wild received the 2023 Egon Balas Prize from the INFORMS Optimization Society and shared his insight in the designing of algorithms for derivative-free optimization problems. Chan, Sürer, and Yannotty each presented recent development in computational methods to facilitate uncertatinty quantification, in a two-part session "Computational Methods for Uncertainty Quantification", organized by Chan. Details can be found under the list of BAND methodology presentations.

October 10, 2023

BAND has released v0.3 of our software framework! This release includes a number of new tools, including: BMEX, a web application for exploring nuclear masses and related quantities; parMOO, a parallel multiobjective simulation optimization library; rose, a reduced-order scattering emulator; and Taweret, a package containing multiple Bayesian Model Mixing methods. It also includes updates to the surmise and SAMBA packages. Pull the repo to try these new capabilities and tell us about your experience!

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