Resume for research opportunities in deep learning

1.
Kshitij Patil Senior Undergraduate,
Dept. of Computer Engineering,
Pune Institute of Computer Technology
kshitijpatil98@gmail.com
+919423706080
www.linkedin.com/in/kshitijpatil98
www.github.com/Kshitij09
https://kshitij09.github.io/dev-blog/
EDUCATION
B.E. Computer Engineering
Pune Institute of Computer Technology
Aug 2017- Present
CGPA: 8.3
Diploma in Information Technology
Government Polytechnic, Kolhapur
Aug 2014 - May 2017
Percentage: 90.88%
EXPERIENCE
Periwinkle Technologies, ​Pune - ​Project
Intern (Medical Imaging)
Sept 2019 - June 2020
- Develop a visual evaluation algorithm for
Screening Cervical Precancer/Cancer using deep
learning-based multimodal systems.
PICT ACM Student Chapter, ​Pune ​—
Technical core committee member &
Domain Director of Android
Sep 2018 - Sept 2019
- Guiding fellow PASC members by conducting
seminars and mentoring projects. Keeping them
abreast of recent trends in software
development.
CoReCo technologies, ​Pune — ​Intern
May 2016 - June 2016
- ​Explored Docker technology with the
deliberate study of containerization. Ascertained
how the effective usage of Docker containers
could reinforce application deployment.
PROJECTS
Handwritten text recognition system
(June’20 - present)
- Working on a cursive handwritten text recognition
system. Currently evaluating the feasibility of several
APIs and have attained the initial results of fine-tuning
tesseract OCR on the IAM dataset.
- ​Technologies Used: tesseract-ocr, PyTorch
Cervical Cancer Screening
(Sept’19 - present)
- A visual evaluation system for cervical cancer
screening based on the VIA image and demographic
data. Given the limited amount of data and overlapping
classes, we explored several pre-training techniques to
learn discriminative features. The best model was
further evaluated using Explainability algorithms to
support its decision.
- ​Technologies Used: Pytorch, fastai2
CNN based Forest Fire Detection
(Nov 2019 - Mar’20)
- A CNN based Forest Fire Detection algorithm intended
to be deployed on camera-enabled edge devices. Dataset
was created by extracting frames from YouTube videos
and by aggregating several resources. ​[​source code​]
- ​Technologies Used: Tensorflow, Pytorch, fastai, OpenCV
Crowd Counting for disaster management
(Aug 2018 - Sep 2018)
- Developed a system based on the novel deep learning
architecture - CSRNet (Y.Li et.al. CVPR '18) to output a
crowd density map corresponding to an input image,
and hence deduce the count from the density map.
- ​The model was then deployed on an Android platform
using a quantized version of it (TFLite).
- ​Technologies Used: Tensorflow, Keras, Android, Firebase

2. SKILLS
Deep Learning: Computer Vision, a thorough
understanding of standard deep learning
architectures, activations, optimizers, and loss
functions.
Deep Learning frameworks: ​TensorFlow, Keras,
PyTorch, fastai
Programming: ​Python, Java, Kotlin, C++, Swift
Front end development: ​Angular
Backend development: ​Spring-boot, Flask
Database: ​MongoDB, Mysql
Miscellaneous: ​Android, Firebase, Docker
- Able to deploy machine learning models on
Android, web, and edge devices using the
TensorFlow ecosystem.
- Design and deploy Microservices using Docker
RELEVANT COURSES AND CERTIFICATIONS
CS20 -​Tensorflow for Deep Learning
Research
Stanford University
Machine Learning - Stanford
University
Coursera
Grade achieved: 95.7%
Convolutional Neural Networks -
deeplearning.ai
Coursera
Grade achieved: 98.4%
Google Cloud Training​ -
Qwiklabs
1. GCP Essentials
2. Baseline: Data, ML, AI
3. OK Google: Build Interactive Apps with
Google Assistant
CS231 - Convolutional Neural Networks
for Visual Recognition
Stanford university
Assignment solutions (2019) - [​source​]
Practical Deep Learning for Coders​:
part-1 & 2
Fast.ai
ACHIEVEMENTS
Winner: MindSpark Hackathon 3.0:
College of Engineering, Pune
- Part of a 4 member team who stood first
amongst 52 participant teams at Mindspark
Hackathon, the biggest technical event in Pune.
Winner: Software Development - Credenz’18:
PICT IEEE Student Branch, Pune
- ​Winner of senior category in software
development at Credenz, organized by PICT IEEE
Student Branch(R10) for the headcount
monitoring system.
Software Development - Credenz’17:
PICT IEEE Student Branch, Pune
- ​Winner of junior category in software
development at Credenz, organized by PICT IEEE
Student Branch(R10) for the project ‘FrameIT’.
CodeTrix - Avishkar 2016:
Government College of Engineering, Karad
- Winner of the coding competition organized by
the Government College of Engineering, Karad.
Best Outgoing Student: (2017 batch)
Government Polytechnic, Kolhapur
- Recognized as ​‘Best Outgoing Student’​ of IT
Department from Government Polytechnic,
Kolhapur
EXTRA-CURRICULAR ACTIVITIES
Speaker at Punecommunity.AnitaB.org
- Delivered a session on ​‘Exploring the World of
Android Apps’​.
ORGANIZATIONS
fastai
- Active member on fastai forums, answers deep
learning and fastai2 related questions. ​[​profile​]