Kathan Nilesh Mehta

Exploring the Frontiers of AI, Space, and Technology

I'm Kathan Mehta, a Machine Learning expert and proud alumnus of Carnegie Mellon University. By day, I serve as the AI Lead at Satelytics, architecting and deploying cutting-edge AI for geospatial data analysis. I'm passionate about leveraging technology to create practical, impactful solutions and am always eager to connect with fellow innovators.

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Kathan Nilesh Mehta

Professional Experience

Satelytics

AI Lead

Dec 2022 - Present

  • Leading end-to-end strategy for AI projects, overseeing planning, implementation, deployment, and continuous optimization of geospatial data processing workflows.
  • Driving a 30% improvement in model efficiency through advanced optimization techniques, enabling rapid data ingestion, processing, and delivery with minimal human intervention.
  • Mentoring a team of AI engineers, fostering a culture of innovation and technical excellence.

AI Engineer

Dec 2020 - Nov 2022

  • Developed and optimized preprocessing workflows for diverse satellite imagery, including monochromatic, multispectral, and hyperspectral data from platforms like Pleiades, WorldView, Sentinel, and PlanetScope.
  • Pioneered the development and automation of key geospatial algorithms for Methane Detection, Leak Detection, and identification of Invasive Grasses, improving detection accuracy by over 25%.
  • Engineered stereo DSM/DTM workflows for advanced tree height detection, health analysis, and comprehensive vegetation management solutions.

Carnegie Mellon University

Graduate Research Assistant - Biomedical Image Guidance Lab

Jan 2020 - Nov 2020

  • Led a research project on needle tip localization in Ultrasound images, applying both classical computer vision and novel Deep Learning approaches to track needle paths and speckle patterns.
  • Contributed to the paper "Ultrasound-Based Tracking Of Partially In-Plane, Curved Needles," published in the 2021 IEEE International Symposium on Biomedical Imaging (ISBI).
  • Developed a Deep Learning algorithm for detecting signs of Diabetic Retinopathy in retinal fundus images, contributing to early-stage disease identification research.

Education

CMU

Carnegie Mellon University

Master of Science - MS, Electrical & Computer Engineering

2019 - 2020

UC

University of the Cumberlands

Master of Business Administration - MBA

2023 - 2024

DJS

Dwarkadas J. Sanghvi College of Engineering

Bachelor of Engineering - BE, Electrical, Electronic and Communications Engineering

2014 - 2018

Featured Projects

Image Colorization using CNNs

Developed a CNN-based deep model combining low-level local features and high-level global features to colorize grayscale images.

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Technical Details

  • • U-Net encoder-decoder architecture
  • • Low-level + high-level feature fusion
  • • PyTorch implementation
  • • Custom loss functions
View on GitHub →

Speech to Text Translation

Listen-Attend-Spell model with pyramidal BiLSTM encoder and attention-based LSTM decoder.

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Technical Details

  • • Pyramidal BiLSTM encoder
  • • Attention-based LSTM decoder
  • • 11.57 Levenshtein distance on WSJ
  • • End-to-end speech recognition
View on GitHub →

Face Classification and Verification

CNN using MobileNetV2 for efficient face classification with transfer learning.

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Technical Details

  • • MobileNetV2 architecture
  • • Transfer learning approach
  • • 93.3% AUC on test dataset
  • • Efficient face verification
View on GitHub →

Blockchain-based Voting System

Secure voting system using hybrid Proof-of-Work and Proof-of-Authority consensus.

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Technical Details

  • • Hybrid PoW + PoA consensus
  • • AES and RSA encryption
  • • Identity authentication system
  • • Secure vote storage
View on GitHub →

Distributed File System

Global file system with REST APIs and hierarchical naming server architecture.

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Technical Details

  • • REST API architecture
  • • Hierarchical naming server
  • • Data replication for durability
  • • Global file system design
View on GitHub →

Braille Display for the Visually Impaired

Award-winning Braille display system using OCR technology.

🏆 1st Prize Winner

Technical Details

  • • OCR integration
  • • Real-time text processing
  • • Tactile feedback system
  • • Accessibility-focused design
View on GitHub →

Publications

Ultrasound-based Tracking of Partially In-plane, Curved Needles

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Refreshable Braille Displays

International Journal of Computer Applications

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Hand Gesture Controlled Robot using 8051 Microcontroller

Technofocus DJSCE

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Technical Skills

Core Technologies

Python & Machine Learning 95%
PyTorch & TensorFlow 90%
AWS & Cloud Technologies 85%
Docker & Kubernetes 80%
GIS & Remote Sensing 88%

Technologies

Python Deep Learning Machine Learning Computer Vision SQL PyTorch TensorFlow Scikit-learn Keras AWS Docker Kubernetes GIS Remote Sensing Git MLflow

Beyond the Code

When I'm not building AI models, here's what I'm passionate about.

Hiking

Photography

Cooking

Arsenal Fan

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