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.
Talk, Computational Math in Industry and Beyond Seminar Series (CME 500), Stanford, CA
I will be giving an invited talk at the Computational Math in Industry and Beyond Seminar Series (CME 500) at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University.
Talk, Simon Fraser University (SFU) Statistics Seminar, Vancouver, Canada
I will be giving an invited talk at the (Simon Fraser University (SFU) Statistics graduate seminar)[https://www.sfu.ca/stat-actsci/research/seminars/24-25Seminars/danielle-maddix/].
Talk, Foundation Models for Science: Progress, Opportunities, and Challenges, NeurIPS 2024 Workshop, Vancouver, Canada
I spoke at the Foundation Models for Science: Progress, Opportunities, and Challenges at NeurIPS 2024 Workshop. Please find my talk here.
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.
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.
Talk, Ansys Monthly Seminar, Paris, France
I spoke at the Ansys Monthly Seminar on our physics-constrained machine learning works with applications to weather and climate forecasting and aerodyanmics.
Talk, ICERM Workshop on The Industrialization of SciML, Brown University
I gave an invited talk at the Institute for Computational and Experimental Research in Mathematics (ICERM)’s Workshop on the Industrialization of SciML at Brown University.
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.
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:
Talk, Amazon Science, Virtual
In this panel, we discuss challenges for women in STEM and how to persevere, internship mentoring opportunities and the future uses of machine learning in science.
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:
Talk, Daves Avenue Elementary School Science Fair, Los Gatos, CA
Please find my talk here.
Talk, Computational Math in Industry and Beyond Seminar Series (CME 500), Stanford, CA
Talk, International Conference on Machine Learning (ICML) Time Series Workshop, Long Beach, CA
ICML Time Series Workshop Talk with corresponding youtube link on GluonTS.
Talk, SIAM Conference on Mathematical and Computational Issues in the Geosciences 2017, Erlangen, Germany
PhD Thesis Research on finite-volume averaged-based methods for nonlinear porous media flow
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: