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Contributed in the development of multiple features of IP Office telephony system wherein specifically worked on call-media management, administration, and system monitoring features for on-premise and subscription models.
Contributed significantly to the integration of a standalone telephony app with Microsoft Teams for softphones, and call-control capabilities.
Responsible for building a cross-platform mobile application using the flutter framework along with core telephony services to improve user engagement, accessibility, and administrative experience.
Managed streamlining of real-time diagnostics log-management feature for backup and restore activities within multiple clusters. These projects allowed me to cater to large-scale problems and apply my skills in designing architecture to establish communication channels within a product ecosystem.
Arizona State University, August 01, 2023 - May 06, 2024
Collaborated with MLCommons and NASA Harvest to design globally diverse benchmarking datasets for crop field boundaries using DataPerf standards.
Designed a pipeline for creating and matching stratified samples of crop parcels to standardized EO images, including download, conversion, and patchlet sampling.
Optimized cross-validation performance on boundaries with K-fold splits based on morphological characteristics.
Evaluated baseline models MaskRCNN, Segment Anything Model (SAM), and UNET to improve a benchmarking accuracy for sparse crop field annotations.
Built a web portal for the management and registration of various events and activities of the CSI student branch, which affected 1.1k+ participants.
For end users, the portal offers insights into all event updates and news feeds, and for administrators, it presents a graphical representation of the distributions of various entities and membership statistics.
Constructed a deep neural network-based model for language processing and regeneration that made AI-based voices indistinguishable from natural speech by capturing the distinct voice feature to perform TTS on constrained inputs.
Optimized language processing model with MelNet and WaveNet to retain linguistic information of multiple speakers.
Achieved accuracy of 86.2% using sequence-to-sequence recurrent network (Tacotron 2) along with modified vocoders.
Developed a voice-based payment platform that employs machine learning to facilitate payments through linguistics commands to focus on the booming digital payments market in India, catering to all user groups.
Reduced the amount of processing needed for the model while improving the response time by 20% and adaptability with 9 additional speech dialects.
Proposed and implemented a transfer learning-based approach to classify galaxies based on its morphology using existing models such as VGG16, ResNet50, Xception, DenseNet, and InceptionNet.
Significantly improved classification accuracy by 17% compared to the previous architecture.
Developed an InfoVis system using React.js and D3.js to visualize demographic data, networking information, and financial statistics, enabling efficient city planning.
Employed Python for data preprocessing, utilizing pandas for grouping and Excel for database merging and outlier detection.
Enhanced React.js skills to manage large-scale datasets, implementing React state and Redux stores and addressing node clustering issues in D3 visualizations with anti-colliding forces.
Pune Institute of Computer Technology, August 2016 - May 2020 , GPA 9.58/10
Coursework: Data Structures, Operating System, Machine Learning and Algorithm, Distributed Computing System, Computer Networks, Data Science and Big Data Analytics.
Arizona State University, August 2022 - May 2024 , GPA 4/4
Coursework : Data Mining, Data visualization, Knowledge Representation and Reasoning, Cloud Computing, Foundation of Algorithm, Software Requirements and Specifications.