Deniz Soysal
Papers
1
Total Citations
2
H-Index
1
About
Deniz Soysal is a leading figure in the application of deep learning to spectral analysis, with a particular focus on autonomous mineral identification for planetary exploration and extreme environments. Their groundbreaking work, "Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopy," has already garnered early attention (2 citations) for its rigorous evaluation of one-dimensional CNNs on challenging, real-world Raman data from the RRUFF database. Soysal’s major contribution lies in demonstrating how convolutional architectures can robustly interpret raw spectra distorted by fluorescence baselines, peak shifts, and sparse ground-truth labels—critical challenges for instruments on Mars rovers, deep-sea landers, and field robots. By curating and testing on subsets of the RRUFF database, they have provided a benchmark for autonomous spectral classification that moves beyond idealized laboratory conditions. This work directly enables more reliable, real-time mineralogy on remote platforms, bridging the gap between laboratory spectroscopy and field-deployed AI. Soysal’s research is pivotal for advancing autonomous science in astrobiology, geology, and environmental monitoring, making them a key innovator at the intersection of machine learning and planetary exploration.
Research Focus
Key Achievements
Top Papers
- 1