Distributed intelligence
Scalable pipelines and data architectures that support machine learning without sacrificing reproducibility or operational clarity.
RESEARCH / 04
My research explores how distributed and intelligent systems can remain reliable, interpretable and useful in high-stakes environments.
Scalable pipelines and data architectures that support machine learning without sacrificing reproducibility or operational clarity.
Drift, anomaly and fairness-aware methods that detect when data or models stop behaving as expected.
Privacy-conscious systems for clinical data integrity, medical imaging and decision support.
RESEARCH PREPRINT / 2025
Deepala, V.K., Buttul, S.F., & Pally, C.
IEEE CONFERENCE PAPER / 2020
Deepala, V.K., et al. · Contributed across research, system design, technical writing and peer mentoring.
CURRENT RESEARCH / MANUSCRIPTS
Developed the NG-IDL framework to combine structured trauma data with clinician-readable narratives through semantic mapping, cross-modal attention and interpretable prediction.
Built a lightweight four-branch EfficientNet-B3 model with attention fusion and MC Dropout to classify LGG versus HGG when MRI protocols are incomplete.
ACADEMIC WORK / SELECTED STUDIES
Exploratory analysis and supervised classification of diagnostic features, comparing Logistic Regression, Random Forest, Decision Tree and SVM with cross-validation and hyperparameter tuning.
Open research repositoryA grid-world study comparing the safer on-policy route learned by SARSA with the optimal but risk-exposed path learned by Q-learning under epsilon-greedy exploration.
Open research repositoryCNN–LSTM pipeline using VGG16 and transfer learning on Flickr8k, improving BLEU score by 15%.
Open research repositoryMulti-task face detection with ResNet-152 and MobileNet, reducing localization loss by 40% across ten epochs.
Open research repository