Postdoctoral Fellow Seminars: Fall 2017

Lecture: Detecting Planets in the Presence of Stellar Activity

September 13, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: David Jones, Duke University

Abstract

The radial velocity technique is one of the two main approaches for detecting planets outside our solar system, or exoplanets as they are known in astronomy. When a planet orbits a star it causes the star to move and this induces a Doppler shift (i.e. the star light appears redder or bluer than expected), and it is this effect that the radial velocity method attempts to detect. Unfortunately, these Doppler signals are typically contaminated by various “stellar activity” phenomena, such as dark spots on the star surface. A principled approach to recovering planet Doppler signals in the presence of stellar activity was proposed by Rajpaul et al. (2015), and involves the use of dependent Gaussian processes to jointly model the corrupted Doppler signal and multiple proxies for stellar activity.

We build on this work in two ways: (i) we propose using dimension reduction techniques to construct more informative stellar activity proxies; (ii) we extend the Rajpaul et al. (2015) model to a larger class of models and use a model comparison procedure to select the best model for the particular stellar activity proxies at hand. Our approach results in substantially improved statistical power for planet detection than using existing stellar activity models in the astronomy literature. Future work will move beyond our current class of models by making use of kernel-learning methods.

References

No references provided at this time


Lecture: Disjunctive Cuts for Mixed-Integer Conic Programs

September 20, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Sercan Yildiz, SAMSI Postdoctoral Fellow

Abstract

Mixed-integer linear programming (MILP) provides a powerful and versatile framework for optimization problems which require discrete decisions. However, many optimization problems of practical interest cannot be modeled with linear constraints alone. Mixed-integer conic programming (MICP) captures nonlinear relationships between the decision variables with conic constraints and enhances the representation power of MILP. Inspired by the practical success of cutting-planes in MILP, we consider in this talk inequalities derived from two-term disjunctions on regular cones. These inequalities can be used to strengthen generic problem formulations in MICP. In the cases where the cone under consideration is the second-order cone or the positive semidefinite cone, we show that the convex hull of the disjunction admits a simple tractable description in the original space under certain conditions. We also provide low-complexity convex relaxations that can be used as cuts when these conditions are not satisfied.

References

  • Fatma Kilinc-Karzan and Sercan Yildiz. Two-term disjunctions on the second-order cone. Mathematical Programming Ser B., 154(1):463–491, 2015.
  • Sercan Yildiz and Fatma Kilinc-Karzan. Low-complexity relaxations and convex hulls of disjunctions on the positive semidefinite cone and general regular cones. Optimization Online preprint (2016). http://www.optimization-online.org/DB_HTML/2016/04/5398.html.

Lecture: Two novel statistical methods from astro-statistics

September 27, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Hyungsuk Tak, SAMSI Postdoctoral Fellow

Abstract

I introduce two statistical methods motivated by two astrophysical problems. The first one is a new Markov chain Monte Carlo method for multi-modality, called the repelling-attracting Metropolis (RAM) algorithm, that maintains the simple-to-implement nature of the Metropolis algorithm, but is more likely to jump between modes. The RAM algorithm is a Metropolis-Hastings algorithm with a proposal that consists of a downhill move in density that aims to make local modes repelling, followed by an uphill move in density that aims to make local modes attracting. The downhill move is achieved via a reciprocal Metropolis ratio so that the algorithm prefers downward movement. The uphill move does the opposite using the standard Metropolis ratio which prefers upward movement.

The second one is a mixture of Gaussian and Student’s t measurement errors for robust and accurate inference. A Gaussian error assumption, i.e., an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student’s t-distribution helps reduce the bias, but it may be less efficient in estimating parameters if the heavy tailed assumption is uniformly applied to most of normally observed data. The proposed mixture error assumption selectively converts Gaussian errors into Student’s t errors according to latent outlier indicators, leveraging the best of the Gaussian and Student’s t errors; a parameter estimation becomes not only robust but also accurate.

References

  • H. Tak, X.-L. Meng, and D. A. van Dyk (2017+) “A Repelling-Attracting Metropolis Algorithm for Multimodality,” Journal of Computational and Graphical Statistics, to appear (arXiv preprint 1601.05633).
  • H. Tak, J. A. Ellis, and S. K. Ghosh (2017+) “Robust and Accurate Inference via a Mixture of Gaussian and Student’s $t$ Errors,” submitted (arXiv preprint 1707.03057).

Lecture: To Be Determined

October 4, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Peter Diao, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

October 11, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Maggie Johnson, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

October 18, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Yawen Guan, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

October 25, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Mikael Kuusela, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

November 8, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Whitney Huang, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

November 15, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Matthias Sachs, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

November 29, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Huang Huang, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

December 6, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Christian Sampson, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced


Lecture: To Be Determined

December 13, 2017, 1:15pm – 2:15pm
SAMSI Classroom
Speaker: Cheng Cheng, SAMSI Postdoctoral Fellow

Abstract

To Be Announced

References

To Be Announced