Our Annual Meeting is an opportunity to:

  • celebrate the achievements of the academic term ending this week;
  • meet new members of the GGAM community;
  • catch up with old friends; etc.
The Annual Meeting took place on December 1. This year we had two exciting 30-minute talks by new GGAM members, Professors Dave Doty (previously featured on the GGAM News) and Luis Rademacher. In addition, several faculty used the opportunity for "lightning talks" and short announcements about classes and research activity.

Talk by GGAM member Prof. Dave Doty: Computation by (not about) chemistry

Abstract: Chemical reactions among abstract species, such as X → 2Y and A+B → C+D, comprise one of the oldest formal mathematical models in science, dating to the formulation of the Law of Mass Action by Guldberg and Waage in 1864. Despite its traditional role as a modeling language, due to recent advances in DNA nanotechnology, enabling the synthesis of artificial chemicals that undergo designed reactions, the model has received attention as a programming language. This talk will
show examples of what it means to say that chemistry can "do computation" and describe at a high level recent theoretical advances in our understanding of the computational abilities and limitations of well-mixed chemistry.

Dr. Doty received his Ph.D. in Computer Science from Iowa State University in 2009. After spending his postdoctoral years mostly at Caltech, he joined the UC Davis Computer Science department as an Assistant Professor in 2015 and became a member of GGAM in early 2016.

Talk by GGAM member Prof. Luis Rademacher: Provably efficient high dimensional feature extraction

Abstract: The goal of inference is to extract information from data. A basic building block in high dimensional inference is feature
extraction, that is, to compute functionals of given data that represent it in a way that highlights some underlying structure. For
example, Principal Component Analysis is an algorithm that finds a basis to represent data that highlights the property of data being close to a low-dimensional subspace. A fundamental challenge in high dimensional inference is the design of algorithms that are provably efficient and accurate as the dimension grows. In this context, I will describe a well-established feature extraction technique, independent component analysis (ICA). I will also present work by my coauthors and myself on new applications of ICA and ICA for heavy-tailed distributions.

Dr. Rademacher received his Ph.D. in Mathematics from MIT in 2007. After appointments in Georgia Tech and the Ohio State University, he joined the UC Davis Mathematics department as an Assistant Professor in 2016 and recently became a member of GGAM.


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