Danielle Maddix Robinson

Danielle Maddix Robinson

Senior Applied Scientist, AWS AI Labs, PhD in Computational and Mathematical Engineering, Stanford University

Talks and presentations

Mitra: Tabular Foundation Model via Synthetic Pretraining

August 18, 2025

Talk, Machine Learning University (MLU), Amazon Web Services (AWS), Palo Alto, CA

I spoke at the Machine Learning University (MLU) on our custom tabular foundation model (TFM), Mitra, which is trained purely on synthetic data. We released Mitra in AutoGluon 1.4. See the tutorial on how to call Mitra with only 3 lines of code in AutoGluon 1.4. We have also released the weights on HuggingFace. Find the Mitra classifier here and regressor here.

Science in the Age of Foundation Models

August 01, 2025

Talk, Columbia Water Resource Research Seminar, New York, New York

I spoke at the Columbia Water Resource Research Seminar on recent time series foundation models including Chronos and their applications to scientific disciplines, highlighting the importance of uncertainty quantification and physical constraints. Please find my slides here.

LLMs for Time Series

June 25, 2025

Talk, Deep Learning for Science School, Lawrence Berkeley Laboratory, Berkeley, CA

I will be giving an invited talk and tutorial at the Deep Learning for Science Summer School at the Lawrence Berkeley Laboratory (LBL) on our work on foundation model for time series forecasting (Chronos) and AI4Science works. Please find the recording here.

ICME’s 20th Anniversary Celebration Event

November 22, 2024

Talk, Institute of Computational and Mathematical Engineering (ICME), Stanford University

In this panel, I discussed my PhD experiences at ICME and how that prepared me for a research career in industry. See the recording here and news article here.

UC Berkeley Math Career Panel

November 07, 2024

Talk, UC Berkeley Mathematics Department, University of California, Berkeley

In this panel, I discussed my journey and career in mathematics after graduating from UC Berkeley with my bachelors in Applied Mathematics and then my PhD in Computational and Mathematical Engineering from Stanford University. I advised aspiring young mathematics students on the vast career opportunities with a mathematics degree.

Physics-constrained Machine Learning for Scientific Computing

March 14, 2024

Talk, CS294 Graduate Course on Physics Inspired Deep Learning, University of California, Berkeley

I gave a guest lecture in Professor Krishnapriyan’s advanced Computer Science 294 graduate course on Physics Inspired Deep Learning at the University of California, Berkeley.

Physics-constrained Machine Learning for Earth and Sustainability Science

January 31, 2024

Talk, 2nd AI for Good Webinar Series: AI for Earth and Sustainability Science, Virtual

My talk on “Physics-constrained Machine Learning for Earth and Sustainability Science” at the 2nd AI for Good Webinar Series for AI for Earth and Sustainability Science covers our following four research works in various scientific disciplines from epidemiology to weather and climate:

Physics-constrained Machine Learning for Scientific Computing

April 13, 2023

Talk, Machine Learning and Dynamical Systems Seminar, Alan Turing Institute, London, England

My invited talk on “Physics-constrained Machine Learning for Scientific Computing” at the Machine Learning and Dynamical Systems Seminar at the Alan Turing Institute covers our following three research works:

ICME Xpo Research Symposium

May 20, 2016

Talk, Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA

I presented posters on my research at the ICME Xpo Research Symposium from 2015-2018 on the following topics: