RESEARCH / 04

Questions that make
engineering more trustworthy.

My research explores how distributed and intelligent systems can remain reliable, interpretable and useful in high-stakes environments.

FOCUS 01

Distributed intelligence

Scalable pipelines and data architectures that support machine learning without sacrificing reproducibility or operational clarity.

FOCUS 02

Trustworthy monitoring

Drift, anomaly and fairness-aware methods that detect when data or models stop behaving as expected.

FOCUS 03

Healthcare analytics

Privacy-conscious systems for clinical data integrity, medical imaging and decision support.

RESEARCH PREPRINT / 2025

Generation of Automatic Image Captioning Using Natural Language Processing

Deepala, V.K., Buttul, S.F., & Pally, C.

IEEE CONFERENCE PAPER / 2020

Pandemic Stabilizer using Smartwatch

Deepala, V.K., et al. · Contributed across research, system design, technical writing and peer mentoring.

CURRENT RESEARCH / MANUSCRIPTS

SUBMITTEDSCIENTIFIC REPORTS

Narrative-Guided Interpretable Deep Learning for Early PTSD Prognosis

Developed the NG-IDL framework to combine structured trauma data with clinician-readable narratives through semantic mapping, cross-modal attention and interpretable prediction.

  • 20,000 samples
  • NG-IDL + SNSM
  • AUROC · AUPRC · F1
RESEARCH MANUSCRIPTMULTIMODAL MEDICAL AI

Uncertainty-Aware Multimodal Fusion for Robust Glioma Grade Classification

Built a lightweight four-branch EfficientNet-B3 model with attention fusion and MC Dropout to classify LGG versus HGG when MRI protocols are incomplete.

  • BraTS 2021 · 1,251 cases
  • AUC 0.8919 ± 0.0167
  • 15 missing-modality tests
  • 1.94× uncertainty signal

ACADEMIC WORK / SELECTED STUDIES

01MACHINE LEARNING · HEALTHCAREAcademic study

Breast Cancer Prediction

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 repository
02REINFORCEMENT LEARNINGAcademic study

Cliff Walking: SARSA vs Q-Learning

A 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 repository
03DEEP LEARNING · NLPResearch implementation

Automatic Image Captioning

CNN–LSTM pipeline using VGG16 and transfer learning on Flickr8k, improving BLEU score by 15%.

Open research repository
04COMPUTER VISIONResearch implementation

RetinaFace Localization

Multi-task face detection with ResNet-152 and MobileNet, reducing localization loss by 40% across ten epochs.

Open research repository