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Fall 2016 | MS|CS|GRE:322|TOEFL:112| UG:66.43% | Pune University|Work Ex:15 mnths

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Hi all,

I applied to 8 universities (UCLA, USC, UCI, UCB, Georgia Tech, UMCP, ASU, UMW) last year and I only got admit from ASU (that too a little late) and hence I dropped the plan then Brick wall. I did however got a mail from UC, Berkeley from someone in their admissions department if I would like to apply to their 1 year course as well but that too resulted in a reject Brick wall. I would like to add that almost all of the applications were submitted pretty close to the deadline.

My profile evaluation for last year can be tracked here: Profile evaluation 2016 .

I am reapplying again this year but I am not really sure yet what went wrong last year i.e. my SOP, GRE score, my LORs or my GPA. I am also considering retaking GRE. It would be really great if I could get some advice on what I should do and where to improve since I am really confused. Also please evaluate my profile as given below.
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B.Tech - CS MIT College Of Engg. ,Pune (Pune University)
Percentage-66.43% (First Class with Distinction) Toppers - 75 apprx
GRE:- Quant - 165 Verbal - 157 AWA - 3.5
TOEFL:112
Xth: 92% 12th:88.8%

Department Applying To - Computer Science
Major/Specialization - AI, Machine Learning

List of Universities:
1. UCLA
2. CMU
3. Georgia Tech (really want this)
4. USC
5. UC, Irvine
6. UIUC
7. University of Massachusetts, Amherst
8. UC, San Diego
9. SUNY, Buffalo
10. Virginia Tech
11. UC Davis

Would add more universities as I shortlist them.

Publications:
Analyzing Trends in Social Media Marketing in IJCSMS
Natural Language Processing using NLTK and Wordnet in IJCSIT

Projects:
1. Locating Targets through Mention in Twitter - 2nd prize in Poster and Presentation Competition. Highest grade in the Computer Science Dept.

Abstract: We formulate the problem of locating targets when posting promotion-oriented messages as a ranking based recommendation task, and present a context-aware recommendation framework as a solution. Specifically, we first extract four categories of features, namely content, social, location and time based features, to measure the relevance among publishers, targets and promotion messages. Then, we employ Ranking Support Vector Machine (SVM) model as the solution to our ranking based recommendation problem. By introducing two bias adjustment parameters, i.e., confidence contributions of publishers and the responsiveness of targets, our framework can effectively recommend top K proper users to mention. Finally, to validate the proposed approach, we conduct extensive experiments on a real world dataset collected from Twitter.

2. Private project - Android app named Aptitude++ - has over 30,000 downloads in the play store

3. Two more college level mini projects

4. Complete ready Backend with APIs for internal management of a store including catalogue, users and admin.

Work experience:
15 months
1. Working as Python Developer. I make python APIs and do research and work with Google APIs. Already completed 2 modules for a product name happierhr.
Earned the title of Google Cloud Platform Qualified Developer by clearing exams related to Google Cloud Products : Appengine, ComputeEngine, CloudSQL, Cloud Storage and BigQuery
Currently entering into reporting, analytics and smart insights using Google Bigquery and TensorFlow.

2. Co-Founder for Shozy (product at initial stages)

Extracurriculars:

1. 1st in college,state and 2nd nationally in Infosys Aspiration 2020 programming competition among 1,00,000 participants from 400+ colleges. (http://campusconnect.infosys.com/Aspirat...test.aspx)

2. Qualified and participated in ACM ICPC regional finals (https://icpc.baylor.edu)

3. Gave workshop on Android Programming in National Level FOSS (Free and Open-Source Software) Summit(Conference).

4. Took lectures for juniors in C/C++ as a TA in Data Structures and Algorithms.

5. Research work and seminar on IBM Watson.

6. Took part in various robotics competition.

7. Lead organizer in Tesla Talks (a national level Tech Talk)

Recently completed Machine Learning by Stanford University on Coursera and started my own tech blog.

Taking LORs from 3 professors who know me personally.


Any help/recommendation/advice/guidance is welcome.
Thanks for you help in advance! Cheers!

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