NYC DEP

• Developed and re-engineered enterprise-level applications supporting NYC DEP’s contracts, procurements, and payment systems, valued at over $3 billion in transactions annually, using .
• Tools: MS SQL Server, Python, Power BI, DAX Studio, and Report Viewer, Web Services, jQuery, ASP.NET, and VB.NET

New York University

• Managed Opensource course development and assignment guidelines in Python and C++ with Prof. Kamen Yotov.
• Reviewed code for integrating an AI conversational bot with mail clients as a reusable package on GitHub, graded HW for over 50 students, and enforced best practices through continuous integration (CI/CD) and automated testing.
• Tools: Git, C/C++, python

IDFC First Bank

• Contributed to core product development of tablet and web apps impacting 20M customers using Agile and React.js.
• Implemented UPI, VKYC, and other features in the front end across pre- and post-account opening journeys.
• Conducted unit tests using jest, resulting in a substantial increase in overall test coverage to 97%.
• Collaborated with cross-functional teams to design and document API contracts for RESTful services using Java Micronaut and Kafka, resulting in a 10% increase in online zero-balance account openings.
• Optimized product quality by increasing unit test coverage to 97% with Jest, conducting browser testing, and boosting Lighthouse performance score from 70% to 85%.
• Configured Grafana and Prometheus dashboards to monitor performance metrics, reducing issue detection time by 30% and providing detailed impact reports to the business.
• Tools: React Js, React Native, GoCD, Micronaut, Jest, Grafana, Prometheus, kubernetes, Docker

Center of Advanced Data Science

• Spearheaded research on advanced machine learning models for water quality assessment, achieving 90% accuracy, and utilized LIME and SHAP for model interpretability.
• Tools: Python, Keras,Scikit-learn, Flask, JavaScript

Premware Services India LLP

• Led the development of Surati Taxi’s scalable backend with REST API, cutting customer request response time by 50%
• Implemented web sockets, notifications, and real-time location tracking, enhancing user engagement by 40% and reducing response latency by 20%.
• Tools: Node JS, Angular Js, Mongo DB, Firebase

Water is known as a "universal solvent" as it is extraordinarily frail against contamination. Water quality standards are developed based on logical evidence of the effects of hazardous compounds on a certain quantity of water used. Classification techniques of machine learning can be employed to understand the water quality status. In this work, supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset. An artificial neural network model is built using the features such as Oxygen, pH, temperature, total suspended sediment, turbidity, nitrogen, and phosphorus as inputs and water quality check as the target variable. This target variable is created using Canadian Council of Ministers of the Environment Water Quality Index, and the model works with an accuracy of 87%. The classification is done on XGBoost model as well and it performs with an accuracy of 90%. The explanations for predictions of these models for a data instance were performed using explainable artificial intelligence tools such as LIME and SHAP. The results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient. Through our research, we can benefit our readers by providing them clarity about exactly what features are having more influence on water quality than others from different machine learning algorithms. This will help the developers to gain insights into the significant factors of poor water quality and how to overcome that.

The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.