Caleb has applied his skills to diverse fields beyond neuroscience, showcasing versatility in addressing various computational and analytical challenges. Caleb's interest in Explainable AI (xAI) demonstrates a commitment to enhancing the transparency and interpretability of complex AI/ML models.

With a strong foundation in both theoretical concepts and practical implementation, Caleb is well-equipped to contribute to a wide range of roles in software engineering, machine learning, data science, and interdisciplinary research combining neuroscience with cutting-edge computational methods.

• Allen Institute, Seattle, WA, Software Engineer 2 - March, 2024 - March, 2025
  • Leveraged transfer learning to enhance AUC of ROC curves for real-time mask generation in the SLAP2 microscope, eliminating manual neuron annotation in voltage imaging data. [Git].
  • Developed and improved a iGluSnFR simulation pipeline to model dendrites imaged using a Bergamo microscope in mice expressing the iGluSnFR indicator, varying parameters such as motion amplitude, brightness, and synapse count.[Git].
  • Designed a state-of-the-art iGluSnFR motion correction pipeline for dendrite imaging, surpassing existing methods like Suite2p, Caiman, and Patchwarp in performance. This pipeline incorporates an ETL (Extract, Transform, Load) framework to handle large datasets efficiently, ensuring robust data extraction, precise transformation for motion correction, and seamless integration into downstream analysis workflows.[Git].
  • Contributed to a super-resolution microscopy-based iGluSnFR source extraction pipeline to identify synapses within dendrites. [Git].
  • Co-authored an upcoming paper detailing novel image processing methods for iGluSnFR indicator data analysis.
• Biotronics, Ames, IA, Machine Learning Engineer - Jan, 2024 - Feb, 2024
  • Worked on cleaning up the image preprocessing codebase.
  • Focused on explainablility of ResNet model which predicted Intra-Muscular Fat (IMF) values from ultrasoud images from livestocks.
• University of Arizona, Tucson, AZ, Graduate Research Assistant - Aug, 2021 - Dec, 2023
  • Developed a network-based finger tapping and imaging rating application using PyGame [Git].
  • Acquired fNIRS, EEG and Gaze data from multiple subjects.
  • Developed a real-time physio visualization tool using PyQT5 [Git].
  • Developed a script for data conversion and labeling, improving the quality and usability of data [Git] [Git] [Git].
  • Conducted classification experiments using LSTM to classify valence and arousal from fNIRS and EEG signals, and authored a paper that was accepted for the 2023 NeurIPS conference [nips.cc].
• Sree Chitra Tirunal Institute for Medical Sciences & Technology, Kerala, India, Project Scientist - Jan, 2021 - Jul, 2021
  • Developed a neurofeedback game application using PyGame that performs real-time filtering of fNIRS signals. This application incorporates a deep learning model for real-time prediction of brain states. The predictions generated by the model are dynamically integrated into the PyGame interface, thereby facilitating an advanced interaction between the model output and user input.
  • Developed an explainable AI (xAI) model for fNIRS signal classification using DeepSHAP, which interprets model predictions in terms of channel names. For instance, if the model predicts activation of the left motor cortex, the xAI module will illustrate which channels have positively and negatively contributed to that prediction.
• St. Jude Children’s Research Hospital, Memphis, TN, Research Intern - Sep, 2020 - Jan, 2021
  • Developed a deep learning-based classification system aimed at differentiating between active and passive brain states. These states are associated with single-trial lower limb motor preparations in stroke patients. Even though the motor cortex controls the knee and hip regions closely, the model was successful in classifying the brain activations caused by knee and hip movements in both active and passive states.
• Sree Chitra Tirunal Institute for Medical Sciences & Technology, Kerala, India, Research Intern - Jun, 2019 - Sep, 2020
  • Developed handcrafted features for fNIRS signals using PCA and ICA. These features were then utilized to improve the classification accuracy of machine learning classifiers, such as SVM and KNN, in the classification of fNIRS signals as left-, right- brain activation or rest.
  • Created a sliding window-based CNN and LSTM Deep Learning model for fNIRS signals, increasing the classification accuracy from 55% to 97%.