Me !!

Willing to learn from and explore, new and interesting opportunities.

Hello, I’m Anol! A hardworking and enthusiastic Computer Science student at the University of Florida. I believe in my problem solving capabilities and am a great team player. I am confident that I can make a lasting impact in computer engineering roles with my creative ideas and ability to share them with others.

Do check out my LinkedIn profile and let me know how I can help you out.

I look forward to connecting and working together to create something and seeing a lot of people benefit from it!

Work

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About

I have a Bachelors degree in Computer Engineering from the University of Mumbai, post which I worked in the industry for close to 2 years. I worked at GEP as a Software Engineer and at Apisero as a Senior Software Engineer. After working in the industry I identified areas in which I needed some polishing and that leads to my current position wherein I am pursuing my Masters In Computer Science at the University of Florida.

To strengthen my foundation in computer science, I've taken courses in Analysis of Algorithms, Distributed Operating Systems, and Computer Network Security during my first semester. During my second semester, I took courses in Mathematics for Intelligent Systems, Trustworthy Machine Learning, and Engineering Leadership. The Engineering Leadership course, in particular, will help me hone my soft skills through projects and interactions with industry veterans. I am scheduled to take Machine Learning, Trustworthy Machine Learning and Advanced Topics in Data Science for the next semester.

Contact

Personal Email Id : anolkurian@gmail.com
University Email Id : anolkuri.vadakke@ufl.edu
Contact Number : +1 (352)-709-0564

Feel free to connect with me at your convenience on the above contact options. Additionally, find my LinkedIn and Github links below.

• LinkedIn
• GitHub

Mitigating attacks
on CNN models

• Conducted research on defense mechanisms against adversarial examples, assessing 8 different methods including smoothing techniques, noise addition, denoising techniques, color space reduction, and autoencoders.
• Designed a final defense using a combination of Autoencoder and Local Median Smoothing technique that maximally reduces the effectiveness of adversarial and privacy attacks, while minimally affecting benign accuracy.
• All approaches evaluated through 4 adversarial attacks and 3 privacy attacks; wherein final defense achieved an accuracy in range of 65% to 85% depending on attack which is an improvement of approximately 138%.

Algorithms Project
(Exploratory)

Repackaging in
Third-Party Marketplaces

• Investigated repackaging in 4 third-party android marketplaces, through an exploratory study and identified repackaged apps by comparing signatures of their dex-codes to those of reference apks from the Google Playstore.
• Examined 741 apps(excluding reference: 300 apps), finding that 3% had heavy repackaging and 7% had minor signature discrepancies.

Interview Prep
(AI Powered)

Check out related : Publication | Github

• Designed and implemented a mock interview platform that employs ML techniques to evaluate candidate confidence and suitability for specific roles.
• Utilized image processing to provide an assessment of expressions (63% accuracy), ASR to convert speech to text, and applied NLP to evaluate fluency in language and relevance of content.
• Deployed ahead of schedule, using Python Flask framework,TensorFlow, Multithreading, OpenCV, MongoDB, and Watson Speech services, resulting in refined interview training for candidates.

Elements

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    i++;
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