- May 5, 2014 at Bloomberg L.P. Headquarters, 731 Lexington Avenue, New York, NY 10022
- What are the mathematical properties of these decompositions?
- Where are they found in theoretical physics?
- When can we interpret the mathematical variables and operations in terms of the underlying phenomena?
- Why doesn't (multi)linear and (sometimes) orthogonal mathematics imply linear and orthogonal phenomena?
- How can we compute these decompositions?

12:30–1:30pm at 7E MPR Room:

- Lecture: Discovery of Principles of Nature from Matrix and Tensor Modeling of Large-Scale (Molecular Biological) Data

- I will describe the use of matrix and tensor decompositions in the simultaneous modeling of different types of large-scale molecular biological data, from different studies of cell division and cancer and from different organisms, to computationally predict previously unknown physical, cellular and evolutionary mechanisms that govern the activity of DNA and RNA. I will present novel multi-matrix and multi-tensor generalizations of the singular value decomposition as well as experimental verification and validation of some of the computational predictions. These models bring physicians a step closer to one day being able to predict and control the progression of cell division and cancer as readily as NASA engineers plot the trajectories of spacecraft today.

3:00–4:00pm at LL2 Germany:

- Breakout Session: (Physics-Inspired) Mathematical Vocabulary for Discovery from Data

- It is a truth universally acknowledged that today's ever increasing number of large-scale datasets, in which different quantifiable aspects of all manner of interconnected phenomena are recorded, hold the key to fundamental understanding and control of these phenomena. We will discuss a mathematical "vocabulary" of multi-matrix and multi-tensor decompositions, which offers a systematic approach for the modeling of and discovery from multiple such datasets at a time: