Mayank Vyas
Objective
Aspiring Data Analyst with specialized training in Data Analytics from Arizona State University. Integrates academic studies with hands-on experience to create effective data solutions and drive impactful decisions in forward-thinking environments.
Education
Master's in Data Analytics (Aug 2024 - May 2026)
Arizona State University, Tempe, AZ
Bachelor's in Electrical Engineering (Nov 2020 - May 2024)
Institute of Infrastructure Technology Research and Management, Ahmedabad, India
Skills
- Programming: Python, SQL, R
- Frameworks: Numpy, pandas, scikit, tensorflow, keras, CNN, RNN, Image Processing, Apache Spark, Hadoop, DAG, MATLAB, Neural Networks, Deep Learning, OpenCV, PyTorch, Reinforcement Learning, Statistical Modelling, AI/ML, Cloud Computing, LLMs
- Tools: ower Bi, Git, VS code, overleaf, MS Excel, Tableau
- Databases: SQL, MongoDB, AWS, Azure
Experience
Research Intern – Data Analytics and IoT Applications (May 2022 - Aug 2022)
Indian Institute of Information Technology Design and Manufacturing, Chennai, India
- Improved irrigation efficiency to 98% by integrating IoT technology with conventional methods.
- Gathered real-life data using Arduino and LoRa, enhancing data transmission reliability.
- Authored an article recognized by two conferences and one journal.
Projects
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Intersection Control Using Reinforcement Learning and SUMO:
- Developed a traffic control system using reinforcement learning to optimize traffic flow in urban scenarios.
- Simulated real-world traffic using SUMO (Simulation of Urban MObility).
- Trained a deep Q-learning agent to dynamically adjust traffic light sequences.
- Achieved a 22% reduction in vehicle wait times and a 15% improvement in traffic throughput.
- Key Skills: Reinforcement Learning, Python, SUMO, Deep Q-Learning.
- View on GitHub
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Root Phenotyping Using Mask R-CNN:
- Built a machine learning pipeline for root detection and length computation with 96.5% accuracy.
- Utilized Mask R-CNN, TensorFlow, and OpenCV for root phenotyping across multi-species datasets.
- Applied transfer learning and advanced computer vision techniques to analyze non-annotated datasets.
- Optimized performance using IoU, Pixel Accuracy, and custom loss functions.
- Key Skills: Deep Learning, TensorFlow, Image Processing, OpenCV.
- View on GitHub
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ML Predictions Framework for Smart Farming:
- Designed a LoRa-enabled fog computing framework to optimize data transmission and enhance energy efficiency.
- Developed regression-based ML models for edge and fog layers, achieving 99.97% accuracy.
- Validated the framework using field data on temperature, humidity, and soil moisture.
- Key Skills: IoT, Fog Computing, Machine Learning, LoRa, Python.
- View on GitHub
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Re-imagined Businesses in Tucson:
- Conducted business-level analytics on Yelp datasets using Apache Spark to provide actionable insights for local businesses.
- Extracted and transformed JSON data to uncover key factors influencing business success.
- Presented findings using Power BI dashboards, highlighting trends and business strategies.
- Key Skills: Apache Spark, Power BI, ETL, Data Analytics, Python.
- View on GitHub
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MLP from Scratch:
- Implemented a Multi-Layer Perceptron (MLP) neural network from scratch to deepen understanding of neural networks.
- Trained the MLP on custom datasets and visualized its performance for learning analysis.
- Key Skills: Neural Networks, Python, NumPy, Deep Learning.
- View on GitHub
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DASA: Data Aggregation for Smart Agriculture:
- Designed an efficient data aggregation algorithm for LoRa-enabled fog layers to optimize data transmission.
- Reduced energy consumption and enhanced scalability, validated through real-world agricultural data.
- Published findings in Springer and IEEE conferences.
- Key Skills: IoT, Data Aggregation, Fog Computing, Research Writing.
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On Reducing Data Transmissions in Fog-Enabled LoRa Smart Agriculture:
- Implemented intelligent data-forwarding schemes to minimize data transmission while maintaining accuracy in smart agriculture systems.
- Demonstrated improvements in energy efficiency and data reliability in fog environments.
- Key Skills: IoT, LoRa, Data Transmission Optimization, Research.
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Intelligent Data Forwarding for Smart Agriculture:
- Developed a data-forwarding scheme to enhance system performance and sensor longevity in LoRa-enabled fog architectures.
- Optimized data pathways for reduced latency and improved reliability.
- Key Skills: IoT, LoRa, Fog Computing, Data Optimization.