Daksh Patel

– Built an automated scenario modeling engine using Python and SQL to conduct high-fidelity ”what-if” simulations, reducing latency by 85% and enabling proactive threat/impact analysis. – Architected a migration of 20TB+ of financial workloads to ATLAS with 100% integrity, developing automated validation frameworks to resolve 99% of data discrepancies for security auditing. – Refactored PySpark ETL pipelines to process billions of records daily, optimizing compute efficiency by 30% and ensuring zero production downtime for mission-critical financial systems. – Collaborated with cross-functional teams to modernize legacy proprietary logic into scalable SQL/Spark architectures, improving end-to-end data delivery speeds by 25%

- Developed a viral content ranking algorithm, utilizing XClip embeddings for videos and ResNet-50 embeddings for images, enhancing the prediction of content popularity by analyzing feature correlations. - Automated metadata augmentation to enrich content tagging and classification, improving the precision of recommendation models and reducing manual intervention. - Optimized multimodal data pipelines for scalable content processing, ensuring seamless integration of structured metadata, embeddings, and classification outputs for downstream applications.

- Led large-scale metadata analysis on 9M+ images and 10M+ videos, developing automated data pipelines for content extraction, classification, and retrieval, enhancing searchability and recommendation systems. - Engineered and fine-tuned computer vision models for content analysis, leveraging deep learning techniques and metadata insights to optimize feature selection, classification accuracy, and processing efficiency. - Designed a data analysis pipeline to identify key content trends, improving feature selection and model robustness across two major processing modules, contributing to better prediction accuracy. - Integrated FAISS-based similarity search, enabling efficient image and video retrieval through optimized embedding-based indexing, reducing computational overhead and improving response times.

Course Producer – DSCI 552: Machine Learning for Data Science Under Prof. Mohammad Reza Rajati - Managed and responded to student queries on Piazza, ensuring clarity on course materials and assignments. - Provided dedicated support during help hours, assisting 350+ students in understanding complex machine-learning concepts. - Facilitated discussions and reinforced foundational ML principles, improving student engagement and comprehension.

Course Producer For DSCI 552 Machine Learning For Data Science under Prof. Mohammad Reza Rajati. - Proficiently grade assignments and provide constructive feedback to students. - Offer dedicated support during help hours, aiding students in understanding complex concepts to about 450+ students.

Course Producer For DSCI 552 Machine Learning For Data Science under Prof. Mohammad Reza Rajati. - Proficiently grade assignments and provide constructive feedback to students. - Offer dedicated support during help hours, aiding students in understanding complex concepts to about 250+ students.

Radiomics Lab, Department of Radiology - Led research to optimize medical diagnosis with PyTorch, enhancing accuracy through a pipeline integrating classification with Foundation models (SAM, SAM2, MEDSAM) for segmentation and comparing results with nnU-Net - Implemented cutting-edge deep learning techniques to optimize feature extraction and classification processes, leading to enhanced interpretability and efficiency of medical imaging systems. - Developed a novel data preprocessing technique aimed at mitigating imbalance and reducing confusion within the model during training, resulting in improved classification performance and enhanced model robustness.

- Developed and fine-tuned custom Large Language Models (LLMs) using PyTorch, designing a chatbot capable of understanding and responding in the style of anime characters while providing detailed story-related information. This enhanced user engagement by 25%, improving interactive query responses. - Integrated AWS services, including EC2, Lambda, and S3, to deploy and scale the chatbot efficiently, ensuring low-latency query processing and optimized server load. - Engineered an interactive text-to-speech (TTS) system, utilizing NVIDIA Tacotron 2 for voice generation, enabling anime-character-based speech synthesis for a more immersive user experience. - Implemented model optimization techniques, including quantization and distillation, reducing the TTS response time and overcoming latency challenges, ensuring smooth, real-time interaction between the chatbot and anime avatar. - Designed and deployed an inference pipeline, integrating LLM-based natural language understanding with TTS processing, facilitating seamless user interactions with voice-enabled AI.

- Optimized text-to-speech (TTS) conversion speed by implementing quantization, model distillation, and efficient batching techniques, significantly reducing inference time and ensuring low-latency responses. - Overcame latency challenges by fine-tuning NVIDIA Tacotron 2 and integrating parallel processing, enabling real-time voice generation with minimal delay. - Enhanced the interaction between the chatbot and anime avatar, ensuring a seamless and natural conversational experience through synchronized speech and text processing.

- Led core backend operations, managing the development, maintenance, and optimization of critical systems to ensure seamless functionality and scalability. - Engineered and automated catalog and inventory upload scripts, streamlining data processing workflows and reducing processing time by 80%, improving operational efficiency. - Optimized API performance, implementing caching strategies, query optimization, and load balancing, which resulted in a 66% reduction in response time and significantly enhanced the user experience. - Deployed and managed cloud-based infrastructure, leveraging AWS services (EC2, S3, RDS, Lambda) to ensure high availability, scalability, and security for backend operations. - Implemented CI/CD pipelines to automate deployment, reducing manual intervention and improving system reliability. - Developed and maintained database architectures using PostgreSQL, optimizing queries and indexing strategies for improved data retrieval efficiency.

- Evaluated and guided junior team members, directing them on projects and new technologies. - Uplifted the team's skills and knowledge, contributing to individual and collective growth.

- Managed and evaluated organizational activities, maintaining an excellent track record. - Organized Tekmux event for class 12 school students in August, showcasing effective leadership.

- Developed a Deep Learning model for Digit Recognition, deploying it over Heroku using Flask API. - Created a 3-Layered Neural Network from scratch using NumPy, showcasing strong technical skills. - Collaborated on a Healthcare Project, contributing to impactful research and development.

- Managed and Analyzed the workflow of the Python Developer Team on the Projects and led 3 different teams of new interns. - Implemented my very own Resume-Parser Module using NLP, Data-extracting, and many more.

- Worked on College Prediction Module, Scraped data for the same of more than 200+ colleges. - Created A Welcome Email Module for new users and a WhatsApp Automator for automating messages.

- Created a Script to handle streaming data of the stock market and reviewed it every minute with a script written in python. - Creating a script that allows storing a particular company's data in the database every minute from the API.

Coursework: • DSCI 552 Machine Learning for Data Science • DSCI 551 Foundation of Databases Management • DSCI 553 Foundations and Applications of Data Mining • ISE 529 Predictive Analytics • CSCI 566 Deep Learning and Its Applications • DSCI 517 Research Methods and Analysis for User Studies • DSCI 531 Fairness in AI • DSCI 599 Machine Learning in Security • ENGR 597 Internship in Engineering • ENGR 598 Internship in Engineering Credits: 32.5