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Click here or scroll down to respond to this candidateCandidate's Name
Tempe, AZ PHONE NUMBER AVAILABLE EMAIL AVAILABLE LINKEDIN LINK AVAILABLEEDUCATIONM.S. Data Science, Analytics and Engineering, Arizona State University, Tempe, AZ Aug Street Address - May 2025 Analyzing Big Data, Data Mining, Statistical Machine Learning, Data Processing at Scale, Data Visualization 3.89/4 GPA B.Tech. Electronics and Communication Engineering, Nirma University, Gujarat, India Jun 2017 - May 2021(First Class with Distinction) 8.Street Address /10 CGPATECHNICAL SKILLS Languages: Python, C, C++, SQL Tools & Technologies: PostgreSQL, MS SQL Server, MySQL, Power BI, SQL Server Integration Services (SSIS), ETL, DWH, Jupyter Notebooks, MS Suites- Excel, PowerPoint, Word Data Analysis & Libraries: NumPy, Pandas, Matplotlib, Seaborn, Statsmodels, Scikit Learn Certifications: Microsoft Certified: Azure Fundamentals (AZ-900), Microsoft Certified: Azure Data Fundamentals (DP-900), Complete Data Science Bootcamp (Udemy)PROFESSIONAL EXPERIENCEArizona State University, Tempe, AZ: Graduate Services Assistant, Computer Science Department Aug 2023 - Present Assisted 300 freshman engineering students with team projects and labs. Designed lab and test questions for 5 cohorts.Cognizant, Bengaluru, India: Programmer Analyst Jun 2021 Jun 2023 Designed and implemented robust databases, optimizing schemas, constraints, and views for better query performance, resulting in a 20% reduction in query execution time. Created SQL Server Integration Services (SSIS) packages with complex transformations and mappings, increasing data integration efficiency by 30% and reducing processing time by 25%. Collaborated with Engineering team and conducted data migration process by implementing automated data validation checks, resulting in a 50% reduction in data discrepancies and ensuring a smooth and error-free migration. PROJECTSEmpirical Evaluation of Image-to-Image Translation using various GANs, Class Project Aug 2023 - Dec 2023 Orchestrated an empirical evaluation project on Generative Adversarial Networks (GANs) for image-to-image translation, leveraging UC Berkeley Lab's Maps dataset; achieved a significant 20% enhancement in image quality and accuracy, driving innovation in computer vision research. Spearheaded the exploration of Pix2Pix GAN, CGAN, CycleGAN, StarGAN, and BicycleGAN, showcasing expertise in diverse GAN architectures. Achieved superior results with Pix2Pix GAN, emphasizing its U-Net architecture and adversarial training for 92% accuracy in image translation tasks, with a notable low RMSE of 0.047. Demonstrated a strategic blend of theoretical depth and practical relevance, contributing to the evolving landscape of computer vision and GAN research.Deepfake Detection Using Convolutional Neural Networks, Class Project Aug 2023 - Dec 2023 Led a team project that developed an advanced deepfake detection system utilizing Convolutional Neural Networks (CNNs), reducing the risk of fraudulent content by 75% and safeguarding brand reputation. Conducted extensive evaluations on the Celeb-DF dataset to enhance the model's performance across various deepfake manipulation techniques. Explored CNN architectures, including VGG16, for effective feature extraction, achieving an outstanding accuracy of 94% on the test set. Oversaw fine-tuning strategies, optimizing the model for unique dataset features, resulting in a consistent 15% increase in real-world detection accuracy. Actively championed ethical technology development, emphasizing a nuanced balance between security, privacy, and individual rights in deepfake detection, aligning with a principled approach Improved Automatic Speaker Verification, Class Project Aug 2021 - Dec 2021 Spearheaded the design and implementation of an accurate (95%) and robust automatic speaker verification (ASV) system. Developed a customized Deep Learning architecture, combining Convolutional Neural Networks (CNN) and a modified version of the ResNet architecture, achieving a 95% accuracy rate. Contributed to academic advancement by publishing a research paper detailing the project's methodologies and outcomes through Springer. |
