Curriculum Vitae
Links
Education

University of Pittsburgh
06/2023  Ph.D. Physics
12/2018  M.S. Physics
Rensselaer Polytechnic Institute
05/2017  B.S. Physics
Postdoctoral Appointee
Cosmological Physics and Advanced Computing (CPAC) Group
Argonne National Laboratory
Contact me: alanpearl13@gmail.com
University of Pittsburgh
06/2023  Ph.D. Physics
12/2018  M.S. Physics
Rensselaer Polytechnic Institute
05/2017  B.S. Physics
galtab is a key Python package that enabled much of the work in my PhD thesis by pretabulating galaxy placeholders to improve prediction efficiency of my CountsinCylinders estimator.
JaxTabCorr is a Python package that integrates classes from TabCorr and halotools into a differentiable prediction framework made possible by JAX autodiff libraries.
mocksurvey is a Python package used for constructing mock galaxy catalogs and perform mock surveys seeded from the UniverseMachine empirical model.
I am a contributor to halotools, which is a Python package that provides a wide array of models of the galaxyhalo connection.
You can download my mock catalogs for PFS here (or here for the original May 2020 version).
I have led the development of the following data science projects during my postdoc at Argonne National Lab. Continue scrolling to learn about the class of astrophyiscal models they are being applied to.
kdescent
Fitting a population model with many parameters to multidimensional demographics is a notoriously difficult machine learning problem. kdescent presents a flexible stochastic gradient descent solution. kdescent draws a very small, random "minibatch" of the training data (n = 20 for this example) and constructs a kernel density estimation (KDE) of the distribution, using these 20 random kernel centers, as shown by the example below.
Figure 1: Each panel shows example training data, colored by a kernel weight. The kernel centers were chosen randomly from the training data. We adopt a Gaussian kernel with the Scott's rule bandwidth (stretched according to the inverse principle component transformation).
Figure 2: The left panel is colored by number density of the training data in small spatial bins. The right panel attempts to approximately reproduce the population distribution, while only knowing 20 points using KDE (basically just averaging the twenty kernels from above).
Now imagine we are modeling this population (for testing, we will simply populate samples from a 2D multivariate normal distribution, which means we are fitting 5parameters: 2 means, 2 variances, and 1 correlation coefficient). Thanks to the JAX implementation of kdescent, we can fit these parameters using a gradient descent algorithm, such as Adam, defining the loss as the meansquared error of the number density within each kernel. By redrawing our kernels at each iteration, we are able to (a) fairly probe the training data and (b) avoid getting stuck in local minima. The animation below shows this 5parameter fit in action.
Figure 3: Animation of 200 iterations of the Adam gradient descent algorithm, using the loss function described above. The left panel shows the parameters converging to their known true values. The right panel shows the model distribution moving, stretching, and rotating until it finally converges upon the correct distribution.
Parallel Gradients
The JAX Python library allows us to create extremely powerful, fast, differentiable models without requiring excessive development time. However, efficiently calculating a gradient in parallel is not trivial. This is a huge hurdle for bigdata problems, where our data must be distributed across several nodes, and prevents us from taking advantage of the full processing power of each node. Therefore, I built a framework to simplify this process, and implemented it in the multigrad package. In brief, it works by allowing users to define functions that compute linear statistics that are summed over the data on each node. Not only are the resulting statistics computed automatically, but the chain rule is preserved by exploiting the vectorJacobian product, allowing us to propagate the the derivatives to any desired loss function.
Figure 5: Subvolume division in which each partial gradient is computed before MPI combines everything with one simple addition reduction.
Particle Swarm Optimization
The greatest challenge posed to gradient descent is the risk of getting stuck in local minima. It is therefore necessary to start from a reasonable initial condition, either using analytic approximations or exploratory parameter scans. One of the most powerful methods to run an exploratory parameter scan is known as Particle Swarm Optimization (PSO), in which a set number of particles are initialized along a Latin Hypercube with randomized velocities and accelerate according to a cognitive weight (which pulls towards the best loss found by each particle) and a social weight (which pulls towards the best loss found by the entire swarm). I have built a PSO implementation, multiswarm, that hosts particles across various MPI tasks. Since PSO doesn't require gradient calculations, it is applicable to a wider range of models and has much lower memory requirements than differentiable methods, making it very fast and massively parallelizable.
Figure 4: A swarm of one hundred particles converging upon the global minimum of a 5dimensional Ackley loss function. Taking all 5 dimensions into account, there are roughly 10^{5} local minima, yet the swarm skillfully navigates closer and closer to the global minimum, reducing the search space as the particles slowly lose their momenta, until they arrive at the correct place.
Publications: See my papers on ADS
Postdoc at Argonne
I work closely with Andrew Hearin and others to develop models of the galaxyhalo connection, implemented with Python's JAX library to enable GPU acceleration and automatic differentiation. I am focusing on improving the scalability of our model to extremely large datasets by designing a framework that performs distributed parallel computation, while seamlessly preserving the advantages of JAX. I plan on utilizing this framework to make selfconsistent mock observations on cosmological simulations, thereby minimizing biases in the joint inference of cosmology and galaxy formation physics.
PhD Thesis
Illuminating and Tabulating the GalaxyHalo Connection
Part I: Illuminated the UniverseMachine to construct PFS mock catalogs
Using UniverseMachine as a model and UltraVISTA photometry as training data, I created a mock galaxy catalog specifically tailored to making predictions for the upcoming PFS survey. Using this mock, I published a paper which demonstrated that future extensions of the PFS survey should prioritize increasing the survey area to best improve scientific goals. This mock and the methods used to create it are publicly available.
Part II: Tabulated statistical estimators to be fast, precise, and differentiable
The galaxyhalo connection is typically analyzed via Markovchain Monte Carlo (MCMC) sampling of parameterspace in order to place constraints on models. However, this process is slowed down by the stochastic nature of halo occupation distribution (HOD) models. I have improved the efficiency of this process with two opensource projects: