“I mentored Shireen for her advanced project of MS program. I enjoyed working with Shireen, she was thorough in her work --took responsibilities of her tasks, provided timely updates of the tasks, went beyond the task description. The work she did with me in the short span of a semester helped us profile applications which is essential for the project, and will likely result in a top tier conference paper. ”
About
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Activity
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In the light of Twisha Sharma's case I have a personal story to share. Ever since I was a little girl, I dreamed of finding my prince charming who…
In the light of Twisha Sharma's case I have a personal story to share. Ever since I was a little girl, I dreamed of finding my prince charming who…
Liked by Shireen Nagdive
Experience & Education
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Salesforce
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Licenses & Certifications
Volunteer Experience
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Student Mentor
Teach For India
Education
• Counseled 10th grade students to help them determine their career options post their higher-secondary education
• Helped 5th grade students speak and understand 'English' better by conducting guidance sessions
https://www.teachforindia.org/ -
Student Counselor
Barclays
- 4 months
Education
Mentored undergraduate students from a nearby university to help them prepare better for job interviews
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Student Mentor
Lila Poonawalla Foundation - India
- 1 month
Science and Technology
Informed high school students about Internet Technology, it's uses in current world, precautions to be taken while using internet banking etc
http://www.lilapoonawallafoundation.com/ -
Student Advisor
Global Talent Track
- 2 months
Education
Conducted mock interviews for undergraduate students pursing Bachelor of Engineering in Computer Science from VIIT College, Pune for better preparation of Campus Placements.
http://gttconnect.com/gtt2017/ -
Event Coordinator
WiN - Barclays Technology Center India
- 2 months
Human Rights
Co-ordinated the activities of Woman's Initiative Network's (WiN) Talking Success Event
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Courses
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Advanced Topics in Human Computer Interaction
CSE591
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Analysis of Algorithms
CSL313
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Artificial Intelligence
CSL412
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Computational Biology
CSE548
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Computer Graphics
CSL305
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Computer Networks
CSL317
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Data Mining and Data Warehousing
CSL407
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Data Science Fundamentals
CSE519
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Data Structures and Program Design
CSL214
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Database Management Systems
CSL315
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Discrete mathematics and Graph Theory
CSL202
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Information Retrieval
CSL436
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Introduction to Cloud Computing
CSL431
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Language Processors
CSL316
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Microprocessor Based Systems
CSL223
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Natural Language Processing
CSE538
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Operating Systems
CSL312
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Theory of Computation
CSL307
Projects
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Sharded Key/Value Service
Built a key/value storage system that "shards," or partitions, the keys over a set of replica groups.
This project's general architecture (a configuration service and a set of replica groups) follows the same general pattern as Flat Datacenter Storage, BigTable, Spanner, FAWN, Apache HBase, Rosebud, Spinnaker, and many others. These systems differ in many details from this project, though, and are also typically more sophisticated and capable.
The main challenge in this project was…Built a key/value storage system that "shards," or partitions, the keys over a set of replica groups.
This project's general architecture (a configuration service and a set of replica groups) follows the same general pattern as Flat Datacenter Storage, BigTable, Spanner, FAWN, Apache HBase, Rosebud, Spinnaker, and many others. These systems differ in many details from this project, though, and are also typically more sophisticated and capable.
The main challenge in this project was handling reconfiguration -- changes in the assignment of shards to groups. Within a single replica group, all group members must agree on when a reconfiguration occurs relative to client Put/Append/Get requests. For example, a Put may arrive at about the same time as a reconfiguration that causes the replica group to stop being responsible for the shard holding the Put's key. All replicas in the group must agree on whether the Put occurred before or after the reconfiguration. If before, the Put should take effect and the new owner of the shard will see its effect; if after, the Put won't take effect and client must re-try at the new owner. Ensured that at most one replica group is serving requests for each shard at any one time. -
Fault-tolerant Key/Value Service
Built a fault-tolerant key/value storage service in GoLang using my Raft library. It is replicated state machine, consisting of several key/value servers that use Raft to maintain replication. The service supports three operations: Put(key, value), Append(key, arg), and Get(key). It maintains a simple database of key/value pairs. Put() replaces the value for a particular key in the database, Append(key, arg) appends arg to key's value, and Get() fetches the current value for a key. Each client…
Built a fault-tolerant key/value storage service in GoLang using my Raft library. It is replicated state machine, consisting of several key/value servers that use Raft to maintain replication. The service supports three operations: Put(key, value), Append(key, arg), and Get(key). It maintains a simple database of key/value pairs. Put() replaces the value for a particular key in the database, Append(key, arg) appends arg to key's value, and Get() fetches the current value for a key. Each client talks to the service through a Clerk with Put/Append/Get methods. A Clerk manages RPC interactions with the servers.Provides strong consistency to applications calls to the Clerk Get/Put/Append methods
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MapReduce Library
Built a MapReduce Library in GoLang. Implemented a Master that hands out tasks to MapReduce workers, and handles failures of workers. The interface to the library and the approach to fault tolerance is similar to the one described in the original MapReduce paper - http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf
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Raft - Consensus Algorithm
Implement most of the Raft design described in the extended paper Raft paper in GoLang - https://raft.github.io/raft.pdf , including saving persistent state and reading it after a node fails and then restarts. Raft manages a service's state replicas, and in particular it helps the service sort out what the correct state is after failures. Raft implements a replicated state machine. It organizes client requests into a sequence, called the log, and ensures that all the replicas agree on the…
Implement most of the Raft design described in the extended paper Raft paper in GoLang - https://raft.github.io/raft.pdf , including saving persistent state and reading it after a node fails and then restarts. Raft manages a service's state replicas, and in particular it helps the service sort out what the correct state is after failures. Raft implements a replicated state machine. It organizes client requests into a sequence, called the log, and ensures that all the replicas agree on the contents of the log. Each replica executes the client requests in the log in the order they appear in the log, applying those requests to the replica's local copy of the service's state. Since all the live replicas see the same log contents, they all execute the same requests in the same order, and thus continue to have identical service state. If a server fails but later recovers, Raft takes care of bringing its log up to date. Raft will continue to operate as long as at least a majority of the servers are alive and can talk to each other. If there is no such majority, Raft will make no progress, but will pick up where it left off as soon as a majority can communicate again. Implemented it as a Go object type with associated methods, meant to be used as a module in a larger service
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Google Analytics Customer Revenue Prediction
See project• Performing data exploratory analysis and implemented a light GBM model to predict revenue per customer of Google merchandise store as part of a Kaggle contest using NumPy, Sci-kit, Pandas and Python.
• Performed permutation tests to see which features boost the model and applied logistic regression to find the top 10 most probable buyers in the future. Ranked in top 40% with RMSE of 1.43. -
Intelligent Search Techniques
Implemented A* search technique for solving 8 tile problem using Manhattan heuristic in Java.
Implemented genetic algorithm search technique for solving 8 queens problem in Java -
Automated Slot Allocation Using SAT Solver
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• Developed a C++ algorithm that helped the professors of Computer Science Department in assigning course and faculty to various slots of B.Tech. and M.Tech-- using various constraints regarding courses, instructors, and slot schedule as inputs
• The algorithm reduced the problem statement to a Boolean Satisfiability Problem, SAT, using 1000 variables thereby generating 4681 constraints. Later, the constraints were solved using a SAT Solver, MiniSATOther creators -
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Library Management System
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Managed the database of books with various data structures in C such as Arrays, Linked List, and B-Tree and implemented functions like search, sort, insert, delete, and update with efficient algorithms
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Do Popular Songs endure?
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Building a tool to predict whether popular old songs are popular or not in the future under the guidance of Professor Steven Skiena in Python.
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New York Taxi Fare Prediction
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See project• Performed data analysis and modeling to create a model predicting NYC Taxi Fare using random forest using NumPY, Pandas, Sci-Kit and Python
• Ranked in top 30% on Kaggle with RMSE of 3.55 -
Predict Parts of Speech Tags
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• Implemented Viterbi algorithm for finding the most likely sequence of hidden states that results in a sequence of observed events.
• Implemented features like stemming, lematization, metaphones in Python to help Conditional Random Field and Maximum Entropy Markov Model predict parts of speech tag. -
Word Prediction Model
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• Implemented skip-gram method for Word2Vec machine learning model using TensorFlow methods to learn word representations and predict context words based on an input word.
• Implemented Cross Entropy Model.
• Implemented Noise Contrastive Estimation Model by converting the problem of predicting the next word to predict whether a pair of words is good or bad (binary classification)
• Performed experimental analysis by exploring and varying hyper parameters like epochs, window size…• Implemented skip-gram method for Word2Vec machine learning model using TensorFlow methods to learn word representations and predict context words based on an input word.
• Implemented Cross Entropy Model.
• Implemented Noise Contrastive Estimation Model by converting the problem of predicting the next word to predict whether a pair of words is good or bad (binary classification)
• Performed experimental analysis by exploring and varying hyper parameters like epochs, window size, word vector dimension and learning rate.
Honors & Awards
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Certificate of Excellence
Barclays
Secured 100% score in the Coding Challenge season held at Barclays
Test Scores
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Test of English as a Foreign Language (TOEFL)
Score: 103
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Graduate Record Examination(GRE)
Score: 317
(Q163, V153)
Languages
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English
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Hindi
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Marathi
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Recommendations received
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