Events

Recent advances in computing hardware (FPGAs, distributed parallel computing) and numerical methods (machine learning algorithms, automatic differentiation) create new possibilities for Fusion Power Plant optimization and control. In this talk, I will discuss some of the recent accomplishments of the Plasma Control Group at Princeton that take advantage of these new capabilities.

The European Human Brain Project, a flagship project of the European Union, recently ended after 10 years of research and development. The goals of the HBP were to (1) explore the complexity of the human brain in space and time; (2) to transfer the knowledge broadly; (3) to provide research infrastructure for neuro-science; and (4) to create a community of researchers. One of the major challenges is to model neural activity, from micro- to macro-scale, in a way that enables simulation of the human brain. This leads to so-called in silico experiments, which will be used “to validate models, and to perform investigations that are not possible in the laboratory”. I will present examples of such experiments and discuss how they relate to, and can benefit from, statistical research on the design and analysis of computer experiments. My students and colleagues and I have been working on the potential advantages of replacing a slow/expensive simulator with a much faster and cheaper statistical emulator. Emulators are empirical replicas trained on data generated with the simulator. We have often used Gaussian process regression for this purpose, but in some applications other methods (random forests, polynomial regression) proved more effective. Emulators can be especially useful when the simulator runs are matched to data in the context of statistical inference. I will discuss the modeling options and present examples, including simulation of neural basket cells, calcium induced neural reactions, and stochastic simulators like the Hodgkin-Huxley model.

The Institute for Practical Ethics' working group on Data Governance and Accountability (aka IPE Data) is thrilled to announce our first talk, with UCSD's very own Prof. Berk Ustun next […]

Machine learning and high-dimensional inference are a wellspring of fundamental algorithmic challenges in data science. In this talk, I will discuss two strands of work in this line of research. (i) In the first part, I will talk about ""estimation of fit"" type problems -- the high level goal here is to understand how well the best model (from a class of models) fits the target data. My focus will be on ""sparse models"", a standard assumption in machine learning to deal with data deficiency. In this setting, we design algorithms for estimating fit where the complexity scales just in the sparsity parameter and is independent of the ambient dimension. (ii) In the second part, I will discuss ""noisy reconstruction problems"" where the algorithm gets access to the target object through noisy samples. This formulation captures many well studied problems in machine learning. We give a new general algorithmic technique for such problems based on Fourier analysis which we refer to as "Fourier stability" and use this to design new, state of the art algorithms for "population recovery" and "trace reconstruction" -- two basic problems in unsupervised learning which have received significant attention in theoretical computer science. Our algorithms leverage methods and techniques from Boolean function analysis -- a set of tools at the intersection of analysis and combinatorics -- which has been very influential in complexity theory, especially in areas like PCPs and hardness of approximation. Synergistically, the development of this new algorithmic toolkit for problems in learning and inference has also led to discovery of results of intrinsic interest to probability theory and analysis, some of which I will briefly survey.