Transforming data into strategic insights through advanced analytics and artificial intelligence
View My ResumeGraduate student pursuing M.S. in Business Analytics at University of Massachusetts Amherst with a
perfect 4.0 GPA.
Specialized in Data Science, AI, and Machine Learning with hands-on experience in developing
predictive models.
Skilled in Python, SQL, Tableau, and various ML frameworks with proven track record in building
high-accuracy models.
Experience in real-world applications including cryptocurrency analysis, demand forecasting, and
cybersecurity threat detection.
Published researcher with expertise in semi-supervised machine learning and cybersecurity
applications.
Demonstrated leadership experience and commitment to social causes through community coordination
roles.
Cumulative GPA: 4.0
Comprehensive program focusing on data management for business leaders, business intelligence and analytics, statistical analysis for business decision-making, Python applications in business contexts, project management methodologies, and practical applications of artificial intelligence in business environments.
Cumulative GPA: 3.2
Developed a strong foundation in Machine Learning algorithms, programming, and computational thinking with focus on artificial intelligence applications. Awarded Silver Medal in NPTEL's "The Joy of Computing with Python" for exceptional performance, demonstrating excellence in programming fundamentals and problem-solving skills.
Built and deployed supervised ML models including House Price Prediction, Iris Classification, and Resume Parsing. Conducted EDA, tuned hyperparameters, and applied preprocessing to improve model accuracy and reliability. Researched latest advancements to enhance existing workflows with state-of-the-art ML techniques.
Gained comprehensive understanding of AI workloads, machine learning principles, computer vision concepts, natural language processing, and conversational AI on Microsoft Azure platform.
Mastered fundamentals of generative AI technologies, prompt engineering techniques, ethical AI considerations, and practical applications of large language models in business contexts.
Developed expertise in data collection, cleaning, and transformation techniques, statistical analysis methods, data visualization best practices, and storytelling with data for business insights.
Enhanced skills in supervised and unsupervised learning algorithms, feature engineering, model evaluation techniques, and end-to-end machine learning pipeline development for enterprise applications.
Research paper focusing on innovative semi-supervised ML approaches for cybersecurity threat detection with 94.8% accuracy in identifying DDoS attacks.
Led community outreach initiatives and coordinated social welfare programs, demonstrating organizational leadership and commitment to social impact.