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GGAM Annual Meeting

Special Events

Speaker: Dave Doty, Luis Rademacher, ...
Location: 1147 MSB
Start time: Thu, Dec 1 2016, 2:10PM

The GGAM 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.
This year we have two exciting 30-minute talks by new GGAM members, Professors Dave Doty and Luis Rademacher, as well as an opportunity to give 4-minute lightning presentations. Afterwards there will be a reception, followed by our GGAM student forum.
  • 2:10pm Welcome
  • 2:20pm Talk by GGAM member Prof. Dave Doty
    Title: 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.
  • 3:00pm Talk by GGAM member Prof. Luis Rademacher
    Title: 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.
  • 3:33pm Announcements and Lightning Talks
    Members of the GGAM community are invited to
    • share their research achievements in 4-minute lightning presentations (4 slides maximum)
    • advertise upcoming classes in the Winter and Spring quarters


  • ca. 3:45pm Reception


  • 4:20pm GGAM Student Forum
    Modeled on a successful event last year, in our Student Forum, all students are encouraged to share their thoughts about the program with a panel of GGAM faculty.