Hello, I'm Mayank Vyas
ML Engineer | IoT Innovator | Data Analyst | Grad Student | Focused
A data scientist & ML enthusiast passionate about AI, deep learning, and big data. I transform raw data into actionable insights to drive innovation. Letโs connect and innovate together! ๐
About Me
๐ Hi, I'm Mayank Vyas! A data scientist & ML enthusiast, passionate about deep learning, AI, and big data analytics. Pursuing my MS in Data Analytics at ASU, I specialize in LLMs, neural networks, and reinforcement learning.
๐ My path has taken me from optimizing agricultural irrigation with IoT to developing AI-driven solutions for root phenotyping, traffic control, and business analytics. Recently, I contributed to Intelโs self-checkout AI project, enhancing real-time system monitoring with Grafana, MQTT, and Docker.
๐ก Always exploring new frontiers in AI, ML, and data scienceโletโs build something innovative together!
Technical Stack
Professional Experience
Indian Institute of Information Technology Design & Manufacturing Kancheepuram
May 2023 - January 2024
Improved database performance and data integrity for banking systems.
- ๐ IoT Infrastructure Development: Engineered a LoRa-based fog computing framework for smart agriculture, reducing sensor energy consumption by 40% and optimizing data transmission using regression models.
- ๐ Data Efficiency: Deployed APAEs (Analytical Prediction Algorithm) across edge-fog-cloud layers, cutting data transmissions by 93.6% while maintaining <10% MAE.
- ๐ System Integration: Streamlined sensor data collection (temperature, humidity, soil moisture) using Arduino and LoRa, achieving 98% irrigation efficiency.
Publications:
Optimizing Kalman Filters for Data IntegrityIndian Institute of Information Technology Design & Manufacturing Kancheepuram
April 2023 - January 2023
Developed core modules for a Chargeback Automation system.
- ๐ Designed a Regressive Prediction Data Forwarding Model (RPDM) using TensorFlow Lite, reducing bandwidth usage by 85% in IoT networks. modules using MongoDB, Node.js, and RabbitMQ, eliminating manual email-based validation.
- ๐ Achieved 99.97% prediction accuracy with Decision Trees, enabling real-time actuation on edge devices during internet outages.uced dispute resolution time by 25%, processing 6,000+ monthly chargeback transactions.
- ๐ Implemented lightweight model compression for deployment on Raspberry Pi/Arduino, reducing power consumption by 82.89%.
Indian Institute of Information Technology Design & Manufacturing Kancheepuram
May 2022 - August 2022
Optimized crowd detection models for real-time scenarios.
- ๐ Designed a Wardโs method clustering algorithm to compress IoT sensor data by 57.39%, deployed on fog nodes to reduce cloud transmission costs by 38%.
- ๐ Integrated with The Things Network, achieving 1.1s latency for real-time field monitoring, improving response time by 35% over traditional cellular networks.
- ๐Published in IEEE AINA 2023 and tested on a 20-acre testbed, cutting energy consumption by 82.89% at tolerance thresholds (ฮต=1.0)
Featured Projects
๐ Enterprise Sales Analytics Dashboard
Developed an enterprise-grade Power BI dashboard implementing DAX measures and advanced data modeling techniques to transform raw sales data into actionable business intelligence. The solution features multi-dimensional analysis capabilities with drill-through functionality for granular insights.
๐ Intel Automated Checkout System (OSS Contribution)
Engineered a microservices-based observability solution for Intel's retail edge computing platform that processes real-time computer vision data. Implemented comprehensive telemetry capturing CPU/GPU utilization, inference latency, and throughput metrics critical for retail deployment reliability.
๐ฑ MaskRoot: Computer Vision for Agricultural Phenomics
Engineered an instance segmentation pipeline utilizing Mask R-CNN architecture to automate root phenotyping at scale. The system overcomes occlusion challenges through a custom-designed loss function and transfer learning from MS COCO weights to compensate for limited agricultural training data.
๐ก DASA: Distributed Agricultural Sensing Architecture
Designed a hierarchical IoT architecture leveraging LoRaWAN's low-power wide-area network capabilities for agricultural monitoring in remote areas. Implemented a novel fog computing layer using edge devices to perform data preprocessing, anomaly detection, and compression before cloud transmission.
๐ฆ Deep Reinforcement Learning for Urban Traffic Control
Developed an adaptive traffic signal control system using Deep Q-Networks (DQN) in the SUMO traffic simulation environment. The system leverages vehicle-to-infrastructure (V2I) communication to optimize traffic flow based on real-time density and waiting time metrics.
๐ง Multi-Layer Perceptron Implementation from First Principles
Built a neural network framework from mathematical foundations without reliance on deep learning libraries. Implemented forward propagation, backpropagation, gradient descent optimization, and regularization techniques to demonstrate core principles of neural computation.
๐ถ RPDM: Resource-efficient Predictive Decision Model for IoT
Designed an ultra-lightweight machine learning inference system for resource-constrained IoT devices that optimizes when to transmit sensor data based on predictive value. The framework uses model quantization and pruning techniques to enable ML on microcontrollers with severe memory constraints.
๐ ๏ธ Scalable Data Processing Pipeline for Time-Series Analytics
Architected a distributed ETL pipeline for processing high-frequency sensor data from industrial equipment. The system handles data ingestion, cleansing, transformation, and aggregation while maintaining data lineage for regulatory compliance and audit purposes.
๐ Geospatial Market Intelligence Platform for Tucson Businesses
Developed a comprehensive market intelligence platform integrating geospatial, demographic, and economic data sources to identify growth patterns and market opportunities in Arizona's urban centers. Utilized advanced spatiotemporal analysis to reveal hidden business patterns.
Hackathon Adventures
From concept to creation in hoursโshowcasing innovation under pressure
DevHacks x Stratergy Hackathon
DevHacks and Stratergy
Hire-Smart: AI-Powered Technical Recruitment Platform
Designed an end-to-end NLP candidate search engine using BERT (Hugging Face Transformers) and FAISS to convert natural-language queries into embeddings, enabling real-time semantic matching across 10,000+ profiles with <100ms latency.
Engineered a scalable data pipeline using BeautifulSoup, to scrape, clean, and structure 10,000+ GitHub profiles, extracting features like project complexity, commit frequency, and tech stack relevance, which improved candidate-match accuracy by 40% for hiring teams.
Developed a holistic applicant evaluation portal (React frontend + FastAPI backend) where candidates showcase GitHub activity (stars, forks, PRs) alongside resumes. Integrated a Popularity Index algorithm to auto-rank talent, cutting recruiter screening time by 60% while boosting candidate visibility for niche roles.
Zoom App Hackathon
Zoom
Gamify: Interactive Learning through Automated Quiz Generation
Developed an innovative Zoom application that leverages real-time transcription of lecture content to automatically generate interactive quizzes for students.
Integrated Zoom's Real-Time Messaging System (RTMS) to capture and process lecture transcripts as they happen, ensuring immediate content relevance.
Implemented Gemini AI to analyze transcriptions and intelligently generate contextually appropriate quiz questions based on the lecture material.
Built an intuitive user interface using React, TypeScript, and Tailwind CSS that seamlessly integrates with the Zoom platform as pop-up quizzes.
Created a backend infrastructure with Supabase for user authentication, quiz storage, and performance analytics.
Devils Invent Hackathon
Honeywell & Arizona State University
TwinGenius: AI-Powered Digital Twin Generator
Revolutionized industrial digital twin creation by developing a system that generates complete digital twin environments from natural language prompts in under 60 seconds.
Integrated Gemini AI to interpret complex prompts like "Build a 3D model of a 10-assembly-line factory" and translate them into actionable outputs.
Engineered automatic Boto3 script generation that dynamically builds assets, hierarchies, and 3D scenes within AWS IoT TwinMaker and SiteWise.
Implemented real-time data monitoring through AWS SiteWise telemetry integration with LLM interaction capabilities for instant insights.
Reduced digital twin setup time from hours to seconds through complete end-to-end automation.