Published by
ICMLDE-2025 (Under Review)
Summary
Led an MSME-supported research project focused on classifying depression severity using single-channel EEG signals and advanced ML/DL models, with the paper submitted for peer review to ICMLDE-2025.
About
Highly motivated and results-driven Computer Engineering student with a strong foundation in Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing. Proven ability to develop and optimize high-accuracy predictive models and robust data analysis solutions, demonstrated through internships and impactful projects. Eager to leverage expertise in AI/ML to solve complex challenges and contribute to innovative teams in the technology sector.
Work
Ranchi, Jharkhand, India
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Summary
Coordinated community initiatives, organized tech talks and hackathons, and delivered lectures on machine learning algorithms and techniques.
Highlights
Organized and facilitated tech talks covering emerging technologies in AI and VR, fostering knowledge exchange among students.
Delivered lectures on machine learning algorithms and techniques, and co-organized multiple successful college hackathons.
Ranchi, Jharkhand, India
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Summary
Spearheaded AI/ML project development and fostered student engagement in competitive programming and workshops.
Highlights
Mentored and guided students in building multiple AI and ML projects, enhancing practical skill development.
Drove student engagement in Kaggle competitions and hackathons, and conducted workshops to cultivate advanced AI/ML capabilities.
Remote
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Summary
Designed and implemented advanced consumption forecasting models and computer vision solutions, achieving high predictive accuracy for diverse product applications.
Highlights
Analyzed consumption trends and seasonality across 70 products, developing ARIMA and SARIMAX models with exponential moving averages to achieve a 60% prediction accuracy.
Implemented region-based detection for a computer vision problem, utilizing YOLOv8, Detectron2, contrastive loss, and ResNet18, achieving a 92% model accuracy through fine-tuning.
Noida, UP, India
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Summary
Applied advanced statistical and NLP techniques to refine prediction models for real-world datasets, significantly enhancing prediction accuracy.
Highlights
Applied advanced statistical and NLP techniques to analyze diverse real-world datasets, refining predictions with expert guidance.
Developed and optimized machine learning models using TensorFlow and PyTorch, implementing advanced fine-tuning and experimental approaches to significantly enhance prediction accuracy.
Education
Awards
Flipkart Grid Robotics Challenge - Round 2 Qualifier
Awarded By
Flipkart
Qualified for Round 2 of the Flipkart Grid Robotics Challenge in both 2023 and 2024, placing among the top 1% of over 24,000 students nationally.
Amazon ML Challenge Hackathon
Awarded By
Amazon
Secured an impressive rank of 357 out of 74,000 participants in the Amazon ML Challenge hackathon, demonstrating advanced problem-solving skills.
LeetCode Badge (Top 4.2% Solver)
Awarded By
LeetCode
Earned a badge on LeetCode for being in the top 4.2% of solvers within 100 days, showcasing strong competitive programming and algorithmic expertise.
Publications
Skills
Programming Languages & Developer Tools
Python, Java, SQL, VS Code, IntelliJ IDEA, Linux, GitHub, Streamlit.
Concepts & Methodologies
DBMS, Operating Systems, Optimization, Linear Programming, Data Structures and Algorithms, OOP, Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Time Series Analysis.
AI/ML Technologies & Frameworks
TensorFlow, Scikit-learn, PyTorch, Plotly, OpenCV, YOLOv8, Detectron2, ARIMA, SARIMAX, XGBoost, Random Forest, GRU, VADER Sentiment Analyzer, Word2Vec, Pandas, Flask, JavaScript.