Large spectroscopic surveys are trailblazing endeavours in the study of stellar archaeology and near eld cosmology. Access to homogeneous databases of thousands of stellar spectra allow for a detailed and statistically satisfying look into the chemical abundance distribution of our Galaxy and its surrounding satellites, ultimately working towards a better understanding of galactic chemical evolution. This thesis presents the work of three new studies at the current frontier of stellar archaeology. Through the rst look at carbon-enhanced metal-poor (CEMP) stars using H-band spectra, six new CEMP stars and another seven likely candidates were found within the APOGEE database following Data Release 12. These stars have chemical compositions typical of metal-poor halo stars, however the alpha-abundances of two stars indicate possible origins in an accreted dwarf galaxy. A lack of heavy element spectral lines impedes further sub-classi cation of these CEMP stars, however, based on radial velocity scatter, we predict most are not CEMP-s stars which are typically found in binary systems. This preliminary investigation warrants optical observations to con rm the stellar parameters and low metallicities of these stars, to determine the heavy-element abundance ratios and improve the precision in the derived abundances, and to examine their CEMP sub-classi cations. Additionally, the rst results for the spectroscopic follow up to the Pristine survey are presented. Using a sample of 149 stars, a success rate of 70% for finding stars with [Fe/H]<-2.5 and 22% for finding stars with [Fe/H]<-3.0 is reported, significantly higher than other surveys that typically report success rates of 3-4% for recovering stars with [Fe/H]<-3.0. Finally, the new spectral analysis tool StarNet is introduced. A deep neural network architecture is used to examine both synthetic stellar spectra and SDSS-III APOGEE spectral data and can produce the stellar parameters of temperature, gravity, and metallicity with similar or better precision as the APOGEE pipeline values when trained directly with the APOGEE spectra. StarNet is capable of being trained on synthetic data as well, and is able to reproduce the stellar parameters for both synthetic and APOGEE spectra, including low signal-to-noise spectra, with similar precision to training on the APOGEE spectra itself. The residuals between StarNet predictions and APOGEE DR13 parameters are similar to or better than the di erences between the APOGEE DR13 results and optical high resolution spectral analyses for a subset of benchmark stars. While developed using the APOGEE spectral database (real spectra and corresponding ASSET synthetic data with similar normalization functions), StarNet should be applicable to other large spectroscopic surveys like Pristine. / Graduate