AI, Machine Learning and Data

 

My Experience ranges from Data collection to building end-to-end ETL Pipelines to Innovations in AI, Machine Learning, Natural Language Processing, Computer Vision, Information Retrieval, Robotics to doing analytics on large scale data to integrating apis to building Scaling Machine Learning systems to productionizing Machine Learning!

Work Featured on: 

Awards in
this domain

1. Google Moonshot Award, HackPrinceton 2016, Project FiB

2. Techcrunch Disrupt Qualifiers, Won free tickets to Techcrunch Disrupt 2017, Lawbot

3. 1st Place, Microsoft Hack for Good 2018, Disrupting Human Trafficking in Seattle

4.  Grinspoon Entrepreneurial Concept Award 2017, Project FiB

5. MIT Media Lab Best Up & Coming Hacker Award & Most Refined Mobile VR Experience Award, VR Story Tellers and invitation to Harvard Innovation Labs.

6. Winners, Hack for Healthcare 2016, University of Washington and Microsoft - Won 125000$ Azure Credits, Poddlebop

7. Winners, IEEE Student Paper Contest 2014

8. Winners, Capital One DevExchange Hackathon 2018, Microsoft and AngelHack Seattle 2018

9. Winners, Paper Presentation, BITS Pilani 2013

10. Third place, Microsoft Oneweek Hackathon 2017 (Worldwide) - Hack Advisors - Helping people build Machine Learning projects.

11. Women in Machine Learning Travel Scholarship, Poster accepted to Women in Machine Learning 2015, coorganized with NiPS 2015.

12. Facebook F8 Scholarship 2017

13. UMass Amherst 2015-2016 International Student Waiver Award for Masters in Computer Science with Specialization: AI and Machine Learning.

14. Redhat Women in Open Source Academic Award 2017 Finalist

15. 2 of my undergrad projects selected as top student research projects in the country at UGSRC ( Undergrad Research Competition in 2013 and 2 different projects in 2014. (https://www.adu.ac.ae/urc)

16. "Music Recommender using User's Mood" selected as Top Research Projects at Microsoft Research funding.

Tools

Jupyter Notebooks

Azure Machine Learning

Microsoft Cognitive Services

TLC

Scikit-Learn

Tensorflow

Pandas

Matlab

Weka

CNTK

Microsoft Bot Framework

Microsoft Azure

AWS

Visual Studio

Mission Control

XFlow

Data Pipelines

SSRS

SSMS

Relevant
Work​
experience​
Software Engineer,
Microsoft

(July 2017 - Dec 2019​)

Data Science and ML

1. Worked closely with the Dev team to identify important features and conducted several ML experimentations. Implemented and integrated Natural Language Processing and Machine Learning end-to-end ML models for various projects and identified significant features contributing to ML predictions to provide insights on product behavior, forecast, customer uptake and cause of errors.

2. Conducted Data Studies and used statistical measures to devise effective ways to sample yet correctly represent terrabytes of data for effective data analysis, reducing processing time by 90%

3. Intern Mentor for Machine Learning and Data Science Projects

Tools: Azure Machine Learning, Jupyter Notebooks, Python, C#, Augur, TLC, Scikit-Lean, Pyspark, Cosmos, ScopeML, Scope, Datalake, R

Data Engineering & Analysis

1. Collaborated with cross-functional Development and Program Management teams to garner requirements and feature brainstorming and worked on data migration (on-premise to Cloud), automation of big data pipelines, collection, aggregation, processing, GDPR tagging, maintaining, analyzing and data visualization of terrabytes of active telemetry data streams to provide platform insights for making data driven product decisions and debugging product error causes for 500 Million+ users for Windows.

2. Enabled Data team to intuitively measure Windows product health, progress, success and insights through identifying, creating, onboarding and monitoring meaningful and impactful 250+ measures, dashboards and automated reports, sent to Senior Leadership daily and brought down metadata authoring issues by 21% 

Tools: Spark, Jupyter Notebooks, Cosmos, Scope, SQL, Excel, Databricks, SQL Server, SSMS, Xflow, PowerBi, SSRS, Visual Studio, Mission Control, C#, Xml, Python, R, Kusto, Xflow, Scala

Team Outreach Machine Learning Efforts

1. Wrote ML Conference paper which was accepted to Machine Learning and Data Science Conference.

2. Presented Machine learning Posters at Windows Devices Group AI Presentation Day and AI Immersion Day 2017 and 2018.

 

 

Founder, 
FiB

​(November 2016 - present)

  1. Led a team to build a Fake news detecting Chrome extension "Project Fib", Google Moonshot Prize winner at HackPrinceton 2016

  2. Facebook F8 Scholarship 2017

  3. Featured on Business Insider, Washington Post, The Next Web, Mashable, Huffington Post, Microsoft Blog, Wired, Hacker News, Fortune, CNN, BBC, CBS, CBC News, Bloomberg TV, Boston Globe etc -

  4. Invited to "Forbes 30 Under 30 Conference" 2017 for the work 50K User Requests per min, 650 stars on Github and 20K Dev views on Github page on 1st week

  5. Won Grinspoon Entrepreneurial Concept Award 2017

  6. Link - http://projectfib.azurewebsites.net/

Microsoft Student Partner,
Microsoft (Dubai & Massachusetts)

​(June 2016 - September 2016)

1. Held various workshops, teaching students cloud services like Azure and AI tools like Azure Machine Learning, how to build chatbots etc & volunteered for Microsoft Annual Roadshow

2. Organised Annual Umass Data Science Hackathon Hack20

Software Engineering Intern,
Microsoft

​(June 2016 - September 2016)

1. Drove automation of data collection & transformation pipeline from various on-premise telemetry streams to Cloud, on the Windows Servicing & Delivery Data Analytics team, for future iterations of Windows Updates predictive models

2. Collaborated with the Windows Devices Group Datascience team and Implemented Windows updates predictive models for various updates revisions(including the anniversary update) with 88% Accuracy after extensive feature selection experimentations on Azure Machine Learning & Azure HDInsight (Apache SparkMLlib on Jupyter Notebooks), utilizing the built telemetry dataset

Research Software Engineering Intern,
Microsoft Research

​(July 2014 - August 2014)

Implemented an US state level Location Inferring Classifier for Twitter Users with overall 21% accuracy boost from Baseline for Project Trupil ( http://research.microsoft.com/en-us/projects/trupil/ ), lead by the Social Analytics Team. This SVM Classifier, built with 5.1 million user tweets, uses the text content of the User's tweets (indicator of dialect of the region), time-of-tweeting as the main features and reverse geocoded latitude-longitude info of the location tagged tweets as class label

Software Engineering Intern
Paramount 

(February 2014 - July 2014​)

 

1. Implemented Spam-Ham Filter Classifier on Azure Machine Learning with 92% accuracy. Spam-ham dataset consisted of retrieved spam/ham emails from my Gmail & Yahoo accounts with focus on features like time/day, sender info, attachment info as well as mail content

2. Retrieved and processed data from over 1500 companies details from Dubai Financial Center website using Java & HTML Parser

3. Worked cross-functionally with Finance Department on generating LPOs & reports, with pre-sales team on designing product bundles recommender systems, with post-sales team on Customer Engagement Research - driving Business Decisions, with HR Department on generating & monitoring Employees Database, 

Junior Data Analyst

Palm Square International

(September 2014 - October 2014)

Worked on the project on an online shopping website. Suggested & automated data correction through Programming in Java.

IT Trainee and Consultancy Intern

CEM Business Solutions

(June 2013 - August 2013)

Was trained on Office 365 and Microsoft Dynamics CRM. Built Customer Database, analyzed leads and opportunities.

ML Volunteering Experience & Outreach

1. Mentor and Judge, HackMIT 2016, Judged and helped students build Machine Learning projects.

2. Keynote Speaker and Mentor, HackNEU 2017, mentored Machine Learning projects

3. Organizer, Hack20 - Hack20 is UMass Amherst's Annual Data Science Hackathon [ http://gridclub.io/Hack2O/ ]. I co-organized it with UMass Graduate Researchers in Data as the chair of the Microsoft Student Partner Community at UMass. The technical part involved, hosting various sessions before and during the Hackathon on topics like Azure Machine Learning and Hackathons 101 and mentoring Data Science projects throughout the Hackathon.

4. Speaker, Nerd Summit, UMass Amhersthttps://nerdsummit.org/
Gave a session on the topic "Building predictive models without writing any code".

5. Speaker, UMass ICT Summithttps://www.umass.edu/itprogram/content/ict-summit-2017 on detecting Fake News on Social Media

6. Judge, Special Olympics Robotics Competition 2017 

7. Volunteer, Technology Showcase Booth, Grace Hopper 2018, for Microsoft for Hololens and AI.

Languages

Java

Python

R

SQL

Javascript

Scala

C#

C

Scope

HTML

CSS

Hive

Kusto

Relevant Research and Projects
1. ID-FREE - Combines Biometric Virtual ID Auth for users and marketplace for Business - https://medium.com/@tnabanitade/future-of-idsin-washington-a9275081592b - Empowers Businesses to use Biometrics like Face Recognition and Fingerprint Recognition to make validate age for people as 18+ to be used as a Virtual ID for Business Interactions in Food and Restaurant Industry.
 
2. Masters Thesis : Detection of Fake News from Social Media.
Built Fake news detector Chrome Extension "FiB", which was featured in Washington Post, Business Insider, Mashable, Financial Times, TheNextWeb, Hacker news, The Huffington Post, the Boston Globe, Microsoft Blog, CBC News, CNBC News, CNN News, BBC News, CNET etc. - https://projectfib.azurewebsites.net 
 
 
Social Media Analytics, Natural Language Processing & Machine Learning Space
3. Text to Virtual Reality (Open-sourced)
This project "VR Storytellers"  was built at the World's largest VR-AR Hackathon - Virtually Reality Hackathon at the MIT Media Lab. The interface convert text (stories) in real time to Virtual Reality Experience (mapping keywords and its descriptions to objects in unity) coupled with a Background score based on the mood of the story.
Links:
a. https://www.youtube.com/watch?v=0TdY-7lcVP8&feature=emb_title
b. http://devpost.com/software/vr-story-tellers
c. GitHub Link - https://github.com/ababilinski/VR_Storytellers
4. Poddlebop - Connecting Mental Health Patients to Providers  
At University of Washington - Hack for Healthcare, built a Mental Health Monitoring App, which leverages natural language processing and Computer Vision to track a patient's recovery through Quantatitive and Qualitative measures.
User is asked self-assessment questions for Journal entry which is analyzed for keywords, word count, tone and writing style and data is also collected from a quick yes/no survey based on the PHQ-9 and HCL-32 peer reviewed assessment questionnaires.
This data is combined to create a metric on a scale from 0 (depressive) to 10 (manic) in order to help manage bipolar symptoms and determine treatment directives.
Won 1,25,000$ Azure Credits.
Link - https://docs.google.com/presentation/d/1Cg6ZYTuYWXdpLyLvZ5_hiP0b1eUvjZ9UxaVX2koSUC0/edit#slide=id.g35f391192_00
5. Building Inclusive Job Descriptions
This project was built at Microsoft OneWeek Hackathon to intuitively do automatic detection of non-inclusive text in Job Descriptions to create a much more inclusive hiring. It utilizes Natural Language Processing Techniques to tokenize and tag words and language on the fly.
 
6. Analyzing Twitter User Profiles - It involves analyzing User's personality based on user's Twitter profile given his/her twitter id, by looking at the texts & image content of User tweets to do Sentiment Analysis, Tweets Topic extraction, Named Entity recognition, political views extraction and language detection
 
7. Detection of Cyberbullying from Social Media using Machine Learning - Given a set of tweets, the classifier model can detect with 78% accuracy if its an instance of cyberbullying or not
 
8. Music Recommendor based on User's Mood from Social Networking Sites - Implemented a Music Recommendation system (Accuracy : 86%), which, given an input of User’s Twitter UserID, predicts the User’s mood as one of 20 mood classes from the User’s recent tweets using a Naïve Bayes Classifier & plays/recommends relevant mood song tracks mapped to Gaana.com. The training set of the classifier consists of 85% automatically annotated labels using mood hashtags tweets retrieval using Twitter4J & 15% manually annotated tweets and the classifier is built using 32 kinds of emoticons as well as alphabetical characters as features through Weka JAVA API.
 Compared the performance of various Classification algorithms on a smaller training dataset.
 This project’s paper won the IEEE Student Paper Contest 2015 for BPDC & is further submitted to compete in IEEE Student Paper Contest for R8 region.
 This project was also selected as top Graduation Sponsorship Program 2014 projects by Microsoft Research, ATL Cairo.
9. Text Analysis From Social Networking Sites - Implemented a system that generates trending topics from current tweets through Twitter Streaming API. Performed Sentiment Analysis on tweets, retrieved by Twitter Rest API into positive, neutral and negative classes & also rating them on a scale of 1-5. Tools used : Stanford NLP, Alchemy API
Chatbots
10. Disrupting Human Trafficking Seattle
  • Worked for the NGO "Seattle Against Slavery" for their project disrupting Human Trafficking in Seattle.
  • Built a Chatbot using Cortana Skills for information from the project website and further helped building their PowerBi Dashboards.
  • Winners, Microsoft Hack for Good Hackathon 2018, Microsoft Garage Team
11. Cortana Money Transfer
  • Built a Cortana skill to facilitate money transfer between Family using the Capital one money transfer API.
  • Winners, Capital One DevExchange Hackathon 2018 and AngelHack Seattle 2018
12. LawBot 
Our team won free tickets to Techcrunch disrupt NY conference for building a chatbot called Lawbot, which using Language Modelling, Natural Language processing and Machine Learning to understand if any case has any legal implications and pertains to any law & further connects to Lawyers and Law makers like police stations, who support service on this Law.
The Techcrunch featured link is https://techcrunch.com/video/lawbot/5918915392fdde0c0d8113b1/
https://devpost.com/software/lawbot
13. MoodMusicBot - A Facebook Music Chatbot which maps Mood to Music
Given an Image, the chatbot can figure out the mood of the user using Microsoft Cognitive Services Emotion API and retrieves the appropriate mood based music from our Database and recommends songs.
https://www.facebook.com/getoverandletgo/?fref=ts
 
Computer Vision, Bio-informatics & Machine Learning
 
14. Spoiled Food Or No.AI
Detects if a food is spoiled or not from pictures of the food. Was built as a feature to integrate with the Seeing AI App to aid the build. Built at Microsoft Oneweek Hackathon 2017.
Can be used in agriculture AI scenarios.
15. Fashion.AI
Detects and identifies the type of clothing, details like Ruffles and Stripes and the color of the clothing from a picture to be captioned to integrate with the Seeing AI app to aid the Blind. The Dataset was built through results from search engines on every categories. Built at Microsoft Oneweek Hackathon 2017.
16. Safe Pedestrian Go
A mobile app to alert users to look up while crossing the street. Utilizes the rear camera to generate Computer Vision and object detection models to identify crosswalks vs streetwalks while the user is texting and utilizes the rear camera to identify if a car is approaching, and how far the car is, while the user is on a call. Built at Oneweek Hackathon 2016.
17. Phoenix.AI
Built real time detection of Forest Fires and Wild Fires from Satellite Images by training an Image Classifier. Built at Microsoft Oneweek Hackathon 2017.
18. Road Scene Understanding for Pedestrians & Self Driving Cars through Computer Vision
Detection of Crosswalks, Pedestrians, Traffic signals, distance from other vehicles, street intersections etc from a road scene image, to use as features for the speed prediction for self-driving cars on the road.
19.  Object Relation based Image Translation using Machine Learning - Implementation of an images descriptor in Natural Language, based on the location relationships of the Objects in the Image
 
20. Object Recognition using Machine Learning Techniques - Implemented Supervised ML, unsupervised clustering Techniques and pattern matching through Matlab Computer Vision Lib for Object Recognition. Object classes, obtained from google, were manually labelled. Preprocessed by resizing images & extracting Speeded-Up robust features. Further Simulated by normalizing images with or without resizing
 
21. Implementation of Face Recognition using OpenCV - Implementation of a Real Time (WebCam video based) Multi-Face Recognition system using Eigen Vectors Method & EmuguCV (Wrapper class of OpenCV)
 
22. Implementation of Clustering techniques on Diabetic Retinopathy - Implemented and Compared performance (using cluster sum of square errors) of Clustering Algorithms – K Means, Fuzzy C Means and Hierarchical Clustering in clustering Diabetic Retinopathy dataset, using Matlab
 
23. Simulation Studies on Face Recognition using BackPropagation Neural Network - Implemented an efficient Face Recognition system by extracting features from the Datasets (through Euclidean distance formula & normalization to form preprocessed data) and then using Back propagation Neural Network technique to train the multilayer perceptron & then conducted simulation studies to minimize recognition error. Learnt Matlab & Neural Networks in the process. Used Abrosoft FaceMixer & Neuroph studio to recognize faces through neural networks & derived relation between various factors like learning rate, momentum & others with respect to Training & Testing errors by simulating values(trial & error Method.
 Won 1st Position in the interuniversity Paper Presentation Enguinity, 2014, Computer Science Category.
 Its Research paper – “Face Recognition using Backpropagation Neural Networks is accepted as one of the top 100 undergraduate research projects in UAE at UGSRC’14 - The Second United Arab Emirates Undergraduate Student Research Competition(http://www.adu.ac.ae/step-up-shabab.html).
 Research Paper accepted for Publication as well as presentation at IIENG Conference, Malaysia, 2014.

 
24. Deep Reinforcement Learning - Training Atari Games - to play against each other - Using Image Frames to train R-CNN and Q-Learning Deep Learning Algorithms to train Models to learn playing various Atari games to learn playing against a computer. 
 
25. Robotics : IFOR(Intelligent Flying object for Reconaissance), IFOR is UAE's first unmanned automated quadroter. I served as a Technical team member on the team - Worked on the GUI for automating functions of the quadrotor using Python, Pyside, Qt4 Designer and ROS & also on optimal Searching patterns(Exploration) for the Autonomous Quadrotor to pick up an object from a completely Unknown location within the least amount of time. 
(https://www.youtube.com/watch?v=HLdTPXp9keM
Relevant Education
University of Massachusetts Amherst
Masters of Science (MS), Computer Science
Specialization: Artificial Intelligence

​2015-2017

 

Relevant Courses taken:

1. Computer Vision

2. Data Analytics & Visualization in R

3. Deep Learning

4. Information Retrieval

5. Introduction to Natural Language Processing

6. Introduction to Numerical Computing in Python

7. Machine Learning

8. Systems for Data Science

9. Algorithms for Data Science

 

 

BITS Pilani Dubai Campus, 
Bachelor of Engineering (Hons) Computer Science

​2011 - 2015

 

Relevant Courses taken :​

1. Artificial Intelligence

2. Data Mining

3. Graphs & networks

4. Probability and Statistics

5. Database Systems

6. Introductory Psychology

7. Gestured Controlled Robotics Workshop (IEM Kolkata)

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© 2023 Nabanita De
 

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T: 413-404-1900

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