Computer Vision Data Analyst/ Scientist

Shail Rajesh Shah

+1-716-***-****, New York, United States

********@*******.*** • linkedin.com/in/shail-shah-94b850250 • github.com/shail-git AI ENGINEER SUMMARY

Computer Vision Engineer with 3+ years of experience building exciting CV projects using OpenCV, Py- Torch, and TensorFlow. Led computer vision research improving action recognition in soccer videos. Excited to take on new computer vision opportunities.

SKILLS

• Libraries:PyTorch, TensorFlow, Pandas, NumPy,

SciKit-Learn, FastAI, HuggingFace, ONNXjs

• AI/ML: CNNs, Object Detection, Image Segmenta-

tion, Image Classification, Computer Vision, Trans- formers, Vision Transformers (ViT), GANs, VAEs.

• Programming:Python, R, C/C++,

JavaScript

• Software & Tools: Git, Linux, AWS,

GCP, Azure, Kafka, MatLab, Docker,

React, Next, Flutter

EXPERIENCE

Contracted Freelancer — BluePen (India) Jan 2021 – Jul 2022

• Mentored 2 junior developers & Delivered 50+ web and ML projects over 1.5 years.

• Developed web scraper and OCR pipeline in Python to extract insights from 10K+ documents monthly.

• Created drowsiness detection CV model with OpenCV and TensorFlow, for client’s education app.

• Built semantic scene analysis model in PyTorch to auto-tag client’s photos with 96% accuracy, improving image search.

Web Development Intern — WhitePocket (India) Aug 2020 – Jan 2021

• Designed multiple React components enhancing UX for web app serving 10K+ users.

• Integrated TensorFlow models into NodeJS apps, enabling AI features. Cut down load time by 20%.

• Collaborated with cross-functional agile team to ship 3 customer-facing features on deadline. EDUCATION

M.S. in Artificial Intelligence — University at Buffalo, SUNY Aug 2022 – Dec 2023 GPA: 3.625/4.0

AI Coursework completed: Fundamentals of AI, Pattern Recognition, ML, DL, CVIP, RL, Numerical Math for Data Science, Robotics Algorithms.

B.E. in Computer Engineering — Shah & Anchor Engineering College Aug 2018 – Jul 2022 GPA: 8.34/10.0

Publication: ”A Comparative Study on Performance Improvement for Camouflaged Object Detection,” 2022 ICSCDS. Researched strategies to improve accuracy of detecting camouflaged objects in challenging settings. PROJECTS

• Soccernet Challenge Research:

– Leading innovative research in soccer video action spotting under the AI & DS lab at UB.

– Collaborating with a team of three PhD students, working on diverse tasks to develop and test proof-of-concept solutions using advanced multimodal techniques and the ActionFormer architec- ture.

• IEEE Paper on Camouflaged Object Detection:

– Published IEEE paper on optimizing camouflaged object detection in ICSCDS.

– Provided valuable insights for hyper-parameters and architecture selection from 280 tests through rigorous experimentation.

• Computer Vision GameBot Course:

– Mentored 40+ students in building a GameBot with UB AI Club.

– Taught computer vision techniques including template matching, edge detection, and object clas- sification. Used Python for automation tasks.

HOBBIES

Anime & sci-fi comics, Beatboxing and Music, Video Games, Hackathons, and Tinkering with Computers.

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