Alexander W Blocker


Research interests
  • Statistical analysis of large datasets
  • Efficient computational and Monte Carlo methods
  • Dependent data (time series and networks)
  • Robust and nonparametric Bayesian methods
Selected publications
  • A Bayesian approach to the analysis of time symmetry in light curves: Reconsidering Scorpius X-1 occultations.
    Alexander Blocker, Pavlos Protopapas, & Charles Alcock.
    • arXiv:0904.0645v1[astro-ph.IM]

    Accepted for publication in ApJ
Selected talks
  • Doing Right By Massive Data: How To Bring Probability Modeling To The Analysis Of Huge Datasets Without Taking Over The Datacenter
    • The analysis of extremely large-scale complex datasets is becoming an increasingly important task in the analysis of scientific data. This trend is especially prevalent in astronomy, as large-scale surveys such as SDSS, Pan-STARRS, and the LSST deliver (or promise to deliver) unprecedented amounts of data. While both the statistics and machine-learning communities have offered approaches to these problems, neither has produced a satisfactory approach. Statistical solutions are typically rigorous and well-motivated but do not scale well to massive datasets, whereas machine learning solutions typically lack statistical rigor and fail to account for the nuances of the scientific problem at hand. I will discuss an approach for combining much of the power of probability modeling with the scalability of more ad-hoc machine learning approaches in the context of an event detection problem for massive collections of time series. I will also provide comments on the assessment of uncertainty in this context and some general remarks on "using all of your tools, but in the right order," as a much pithier writer once said.
  • Two Problems in X-ray Astronomy
    • Discussion of my work on two projects in x-ray astronomy: the development of a hierarchical Bayesian replacement for "stacking" and the analysis of events in x-ray light curves. For each problem, I outlined the development of an improved model for the data and the computational methods employed. I also discussd the unique challenges that each case has presented from a cultural perspective.
Software
  • bayesstack: Bayesian x-ray stacking analysis
  • Kalman tools for Matlab
    • Kalman filter & smoother
      • Allow for control inputs in state equation & affine term in measurement equation
    • Maximum likelihood estimation of linear state-space systems
      • Implementation of the expectation maximization algorithm
      • Can estimate input matrix and/or affine term in measurement equation
      • Optional diagonal restrictions on state & observation noise covariance matrices
    • 12/06/2007: Updated with moderate efficiency improvements for M-step routines & major change in EM convergence criterion (relative instead of absolute change)
    • 12/13/2007: Significant efficiency improvements and further tweaking of EM convergence criterion
    • Licensed under LGPL v3.0
  • A technical note on the EM algorithm for affine state-space systems & its usage
  • Some useful scripts for R
    • bagginglm.R: The beginning of a set of functions for bagging LMs and GLMs. Very preliminary. Licensed under GPL v2.0
    • AICc.R: A function to calculate corrected AIC (AIC with an adjustment term for small-sample bias). This is written in the same way as the base AIC function, and will work for any model with a logLik method.
    • split.data.R: A simple function to break apart a data frame or multivariate time series; it is particularly useful for dealing with the latter. Includes an option to omit missing values while splitting.
  • exif2kmz: a Python script to convert geotagged images to a KMZ file
    • Requires pyexiv2 and Python Imaging libraries.
    • Creates a KMZ file with a placemark for each image and the images themselves.
    • Licensed under GPL v2.0
Current Affiliations
  • Researcher with Harvard University Statistics Department
    • Currently working with astrostatistics group (CHASC) on X-ray stacking for ChaMP.
    • Also working with the Time Series Center (part of the IIC) on computationally-intensive time series analysis.
  • Teaching assistant with Harvard University Statistics Department
    • Head TF for Statistics 104 with Professor Stanley for Spring 2009
Background
  • Boston University Alumnus, Class of 2008
    • Bachelors in Mathematics & Economics
    • Masters in Economics
    • PhD-level coursework in statistics & econometrics
  • Formerly:
    • Teaching assistant for two sections of Stat 104 (Harvard, Fall 2008)
    • Intern with Weiss Asset Management (June 2008 - June 2009)
    • Research Assistant with Boston University Department of Economics
    • Intern with UBS Fixed Income Research
      • US Rates & Govt. Bonds Group
    • Senior Research & IT Advisor, Matté & Company
    • Research Assistant, Boston University School of Management

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