Developing cross-platform applications (Android, iOS, and Web) using React Native for Leica Geosystems digital manuals, with integration of AI features to enhance user experience.
Designing and maintaining NestJS API server, managing S3 buckets, and implementing access policies to support AI training and secure data workflows.
Implementing the optimal ways to use reinforcement learning to identify high-risk patient conditions and suggest the best treatments in healthcare, while also improving clinical decision-making systems to provide better, personalized care for patients.
Implemented Wavelet Generative Adversarial Networks (WGANs) to augment limited EEG datasets for motor imagery classification, boosting BCI system accuracy to 72% and providing more signal(synthetic) data for training.
Applied Q-learning algorithms and reward functions to optimize traffic signal control, aiming to reduce congestion and improve traffic flow by developing a dynamic system that learns from real-time traffic patterns to enhance overall efficiency and minimize wait times at intersections.
Performed an in-depth analysis of 22 distinct web metrics on data from the Webby Awards (2017–2022) to improve overall web page quality and identified key factors influencing web performance and user experience, driving web page optimization.
Conducted exhaustive research and developed a framework called CJPR, designed to predict Supreme Court case outcomes and provide relevant case recommendations using NLP and transformer models to accelerate decision-making for faster judgments in complex legal scenarios.
Designed and implemented end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, validation, and deployment.
Collaborated with clients from various countries to understand project requirements, define objectives, and deliver tailored machine learning solutions.
Worked on Computer Vision, GANs and various Deep Learning Projects showcasing expertise in these domains.
Developed ISPSO, an optimized image segmentation method using meta-heuristic algorithms, achieving 80% segmentation accuracy. This approach leverages Particle Swarm Optimization (PSO) for enhanced precision in medical and industrial image analysis.
Built a football match prediction model incorporating Kelly Criterion to optimize betting strategies, achieving 75% accuracy. Analyzed historical match data, odds, and team performance using machine learning for data-driven betting decisions.
Designed and implemented a robust Computer Vision Algorithm, leveraging OCR Tools such as PaddleOCR & EasyOCR.
Spearheaded the design and implementation of a mathematical algorithm, resulting in a 20% improvement in system performance.
This achievement contributed significantly to project enhancements and played a pivotal role in boosting overall operational efficiency in our research.
Spearheaded a project team of 3 members, overseeing the successful completion of tasks and ensuring alignment with strategic goals.
Conducted an exhaustive analysis of the Electric Vehicle Market in India, utilizing advanced segmentation techniques and generating a predictive model.
Education
B.Tech CSE (Gold Medalist)
Indian Institute of Information Technology Sonepat
GPA: 9.57/10.0
Courses included:
Data Structures and Algorithms
Artificial Intelligence and Machine Learning
Software Engineering and Development
Operating Systems and Computer Networks
Advanced & Discrete Mathematics for Computer Science
Database Management Systems
Achievements:
Awarded the Gold Medal for Academic Excellence in CSE
Led the Cricket team in inter-collegiate tournaments
Completed a course focused on identifying and preventing common coding mistakes that can lead to insecure software. Covered topics like improper input validation, improper error handling, and other flaws that hackers often exploit. Gained practical knowledge of how to write more secure code by following industry best practices and secure coding guidelines.
Completed comprehensive training on secure coding principles, focusing on preventing vulnerabilities in modern software systems. Learned best practices for input validation, secure authentication, and mitigating common security flaws such as buffer overflows, injection attacks, and data exposure.
Elsevier — *Engineering Applications of Artificial Intelligence (EAAI)* ∙
December 2024
Recognized for reviewing research papers in deep learning and AI for EAAI. Provided detailed evaluations to ensure academic rigor, clarity, and innovation. This experience enhanced my analytical ability and deepened my understanding of cutting-edge AI methodologies and applications.
Covered foundational and advanced deep learning topics including CNNs, RNNs, Boltzmann Machines, and Autoencoders. Gained hands-on experience in building and optimizing deep neural networks and structuring large-scale machine learning projects.
Explored metaheuristic algorithms such as Particle Swarm Optimization and Genetic Algorithms. Learned to model and solve real-world optimization challenges by simulating natural processes like evolution and swarm intelligence.
Studied the Grey Wolf Optimization (GWO) algorithm, inspired by the hunting behavior of grey wolves. Implemented GWO in Python and analyzed its performance in solving complex non-linear optimization problems.
Completed an in-depth course on data structures and algorithms with C++. Covered complexity analysis, algorithm design techniques, recursion, trees, graphs, sorting, and searching — with emphasis on interview and competitive programming preparation.