Modeling of Exoplanet Atmospheres

Spectrally characterizing exoplanet atmospheres will be one of the fastest moving astronomical disciplines in the years to come. Especially the upcoming James Webb Space Telescope (JWST) will provide spectral measurements from the near- to mid-infrared of unprecedented precision. With other next generation instruments on the horizon, it is crucial to possess the tools necessary for interpretating observations. To this end I wrote the petitCODE, which solves for the self-consistent atmospheric structures of exoplanets, assuming chemical and radiative-convective equilibrium. The code includes scattering, and models clouds. The code outputs the planet’s observable emission and transmission spectra. In addition, I constructed a spectral retrieval code, which derives the full posterior probability distribution of atmospheric parameters from observations. I used petitCODE to systematically study the atmospheres of hot jupiters and found, e.g., that their structures depend strongly on the type of their host stars. Moreover, I found that C/O ratios around unity can lead to atmospheric inversions. Next, I produced synthetic observations of prime exoplanet targets for JWST, and studied how well we will be able to distinguish various atmospheric scenarios. Finally, I verified the implementation of my retrieval code using mock JWST observations

Sequential and parallel algorithms for sequence analysis problems in bioinformatics

The study of biological and genetic information, mostly DNA data, is an extremely important subject which can provide critical information in many areas, such as understanding human diseases or discovering new drugs. A huge number of computing algorithms are developed and available now to help with the study of these, and in order to solve these problems more efficiently and accurately, much attention has been paid in recent decades to developing new and better algorithms. [continues] </p

Visualization 1.mp4

The evolution of phase over time (ground truth, wrapped, 2D unwrapped and 3D unwrapped) is demonstrated in the video (Visualization 1)

Visualization 2.avi

Doppler signal (color) overlaid with amplitude image (gray scale) of cells detached from the bottom of the petri dish and nanoparticles, when the magnetic nanoparticles were mechanically excited to move the cells by an external magnetic field

Visualization 1.avi

Doppler signal (color) overlaid with amplitude image (gray scale) of cells and nanoparticles, when the magnetic nanoparticles were mechanically excited by an external magnetic field

visualization 2.mp4

The changes of cell morphology over time during the detachment process

Overview of the SubCDR.

(1) Extraction of drug subcomponents. The SMILES string is decomposed into a set of fragments using the BRICS algorithm, to obtain substructures (as subcomponents) for the drug, and the GRU layer is adopted to capture the latent features of substructures. (2) Extraction of cell line subcomponents. The transcriptome profile is converted into a set of gene subsets (as subcomponents) according to the CGC classification, and the latent features of gene subsets are learned by the CNN layer. (3) Construction of subcomponent interactions. An interaction map measuring interaction intensity among subcomponents is generated by Eq 5, which is further established as a network. Later, we leverage the GCN layer to learn the representations hidden in the network. (4) Extraction of side information. The side information of drugs and cell lines is acquired from the known CDRs through a singular value decomposition (SVD) algorithm. (5) Predicting CDRs. The side information combined with the learned representations is fed into a decoder, a multi-layer perceptron, to output final response values.</p

Top 10 cell lines with the lowest predicted response values of two approved drugs.

Top 10 cell lines with the lowest predicted response values of two approved drugs.</p