VAGVILASA SRIHARSHA Bolagani

Dedicated Grad student specializing in leading courses with a decisive and focused approach. Clear communication skills with advanced understanding of university-level standards and effective programming and teaching strategies.

• Certification by Exposys Data labs on “Software Development”
• Certification by Oracle on “Database Design and Programming With SQL”.
• Certification by Oracle on “Java Programming”.
• Certification by Data Visualization with PYTHON - IBM Coursera.

• Joint Secretary of Games & Sports Club
• Won two gold medals in college Badminton Doubles and one gold medal in Badminton Singles

Student Career Prediction using SVM Algorithm• Created a website through which students can put advertisements for college events and notify the participants easily. The website has been constructed by using HTML, CSS, PHP, MYSQL, JavaScript. 

GEOFENCE MONITORING• Developed an android application to create a Geofence that enables “Do Not Disturb Mode” when user enters the fenced area and disables on exiting.
Crime Analysis in the City Of Los Angeles

• Developed Python Script for identifying hidden patterns in the Los Angeles crime data obtained from US Government .
• The results were justified from visualizations that were built using broad range of Python Libraries.

Extracting insights and Prognosis Of Covid Disease

• Built a web application to seek user symptoms from the UI and hosted it on an AWS Ec2 instance.
• Random Forest machine learning algorithm was implemented in the backend and predicts the outcome based on the input

Movie Recommendation Using Data analysis

• Created a model which will recommend movies based on the user's history using KNN algorithm. Found the similarity between movies using cosine similarity.

• Finally based on the title entered by the user, we will predict set of movies to the user.

Product Review Using Sentiment analysis

• Took the raw data of amazon. Analyzed the data based on positive and Negative review using Support Vector machine and Logistic regression.
• Used Word Cloud to visualize Occurrences of word

Early Prediction of LifeTime Diseases
• Developed SSEPM, an innovative machine learning model utilizing irregularly sampled EHR data to outperform state-of-the- art algorithms in predicting Noncommunicable Diseases .
• Integrated a random connection process for data augmentation, showcasing the effectiveness of direct utilization of short sequential data.

 Privacy –Aware Deep learning:Honest-But-Curious classifiers

• Innovated an information-theoretical attack, showcasing how deepclassifiers can covertly encode sensitive attributes during inference,even under full white-box visibility.
• Developed efficient empirical methods for training honest-but-curiousclassifiers, exposing privacy risks in machine learning services, especially in scenarios like encrypted inferences and private knowledge distillation.