aakriti singla
Senior Software Engineer at Chime
engineering chime | fintech | ex-uber , ex-google
Chime
https://www.linkedin.com/company/chime-card
San Francisco Bay Area
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Senior Software Engineer
Google
https://www.linkedin.com/company/google
Worked in Google Payments and Risk Team
Mountain View, California, United States
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Software Engineer III
Uber
https://www.linkedin.com/company/uber-com
• Built payment APIs to support various new worldwide feature launches in UberEats.
• Initiated and led the proposal to build an e2e reporting tool giving insights into money services to understand unprocessed orders which enabled early detection and faster resolutions for improving customer experience.
• Bootstrapped first in-house end-to-end payment processing testing pipeline to improve service reliability, reduce losses in revenue and enable faster feature roll-outs.
• Organized Tech Talks for Uber Money Team, partnering with multiple companies and hosting 400+ attendees.
• Wrote the first eng blog on Uber Money's 5th Gen platform. 
• Hosted engineering boot-camps to help on-board 1200+ engineers to Uber’s internal systems, and onboarded 2 engineers on Uber Money team
San Francisco Bay Area
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Software Engineer II
Uber
https://www.linkedin.com/company/uber-com
• Developed a highly scalable and lossless distributed architecture for Uber’s payment disbursements/collections, accounting and secure data-storage systems, processing 4.5M+ weekly payouts worth > 800M.
• Led designing the validation and write-back service for Uber's payment system migration.
• Maintaining highly available (99.99%) systems processing 10K QPS end-to-end within < 3s.
San Francisco Bay Area
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Software Engineer
Amazon
https://www.linkedin.com/company/amazon
• Deliver customer-facing services like Prime Reading, Kindle Magazines having 3.5M+ customer-base in US.
• Analyzing customer friction points and launching experimental features on Digital Bookstore for increasing customer traction and revenue for the ecosystem.
Greater Seattle Area
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Software Development Engineer
Prof. Susan B. Davidson, Prof Zachary Ives - University of Pennsylvania
TA for CIS 450/550 - Databases & Information Systems with Prof. Susan Davidson and Prof. Zach Ives. Responsibilities include setting up infrastructure & problems for assignments, grading quizzes and project milestones, proctoring, and holding office hours to clarify doubts and help improve student understanding of class content, designing data-centric projects & mentoring teams to innovate ideas in various domains.
Philadelphia, Pennsylvania
Graduate Teaching Assistant
Dr. Ani Nenkova, Prof Susan B. Davidson, Prof Zachary Ives - University of Pennsylvania
Conducted open-ended research on techniques to improve medical search of structured and unstructured medical articles. Used a specialized medical multi-word tokenizer to index 60000+ PubMed citations and also summarize structured articles.
Philadelphia, PA
Graduate Research Assistant
Barclay's
https://www.linkedin.com/company/barclays-bank
Technologies Used : ASP.NET, MongoDB, SQL, Hadoop, RESTCalls, WebServices, Perforce
a. Worked with MarginGen Risk Analysis Team to create a tool to migrate operations from a legacy system to a more visually informative system
b. Implemented RESTCalls in existing WCF Services for migrating data in a standard format.
Greater New York City Area
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Technology Summer Analyst
Master of Science (MS)
Computer and Information Sciences
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University of Pennsylvania
https://www.linkedin.com/school/university-of-pennsylvania/
Bachelor of Technology (B.Tech.)
Computer Science Engineering
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Jaypee University of Information Technology
https://www.linkedin.com/school/jaypee-university-of-information-technology/
St Peter's Academy
Jaypee Youth Club - Hospitality Committee
Member
Jaypee Youth Club - Literary Committee
Member
Infosys Limited
Infosys SPOT Award
Analysis Of Algorithm
CIS 502
Computational Linguistics
CIS 530
Computer and Network Security
CIS 551
Databases & Information Systems
CIS 550
Internet & Web Systems
CIS 555
Introduction to Machine Learning
CIS 519
Software Engineering
CIS 573
Software Systems
CIS 505
Web Programming in Ruby on Rails
CIT 597
Implemented a fully ordered multicast chat server in C++ with  a central sequencer/leader (chosen from among the current group members). The leader will be responsible for sequencing messages as well as determining who the current active clients are, detecting eliminating any inactive/crashed users from the chat (and making sure that the remaining clients have an up to date list of those currently in the chat). A new client can add to the system by sending request either to the leader or any active client. Each client will communicate with the leader from time to time. The chat server also allows to detect if the sequencer/leader has crashed or left the server and let current users run elections to get a new leader. Along with this, various failure recovery mechanisms are implemented, like message failure recovery, add/delete users when the join/exit/crash from the system, start/elect a new sequencer when he starts/exit/crashes from the create chat server.
Distributed Chat Server
The focus of CodeScore was to give a quantifiable measure to a student’s coding ability which will allow programmers to get a simplified view of their coding quality in terms of adherence to coding standards based on Google Java Style Guide and Sun Code Conventions, and also measure the quality of a design in terms of its extensibility, reusability, and maintainability to manage package dependencies effectively. The open source tools CheckStyle and JDepend were used to achieve desired results.
CodeScore
https://docs.google.com/document/d/1EqzgMk60hzVWb74dKCWo99hb4iJqcO4UZp_mBcJyWGU/edit?usp=sharing
Designed a machine learning model to predict success of movies based on their box office collection and critic rating. Developed various models using Naive Bayes, SVM, ADA Boost and Logistic Regression to investigate the effectiveness of each model on the predictions. Data sets of approximately 5000 feature films was gathered from various sources like MovieLens, RottenTomatoes, TMDb and Wikipedia to train two distinct models. Overall classification results and performance analysis were reported.
Hit Or Flop
https://drive.google.com/file/d/0B1f6mQi8qhm1YkFDWjJOSUNNaVE/view?usp=sharing
Built a cloud based scalable search engine hosted on Amazon EC2 instances. The project comprised of the 4 main components - Distributed Mercator Style Web Crawler, Indexer and TF-IDF retriever, PageRanker and a Multi-threaded Search Engine with a UI. 
a. The Web Crawler crawled over 100,000 Web URLs and stored results in Berkley DB. The crawler was designed as a Master-Worker Model with coordination between amongst the 15 workers. It also followed Robots Exclusion Protocol.
b. The Indexer/TF-IDF Retriever and PageRanker was designed to run as Elastic MapReduce job and stored results on DynamoDB. The indexer in addition stored various important data related to the web pages like position of words, main tags, meta tags etc.
c. The Search Engine was a multi-threaded component with a user interface to retrieve pages ordered in a weighted manner based on results from Indexer and PageRanker.
FastAndFurious - Cloud Based Search Engine
https://docs.google.com/document/d/1Q32Jxf954uhawhTYLXsG8P-dOg7SR7vPmx9zOZwXb-A/edit?usp=sharing
The motivation behind the application comes from the clogged mailbox with pamphlets showing the deals on pizzas or any restaurants, custom websites for each business ( like eating joints, coffee shops, bars, salons etc). Yelp like applications help user to take a decision based on business information but there is nothing available for businesses to attract users on the fly, help users with a hangout planner with daily basis deals. Thus, Urban Beats lets the business in your area know about the potential customer and let them share more customized deals on the fly.
The application was hosted on Heroku Server and database stored on Amazon instances. The technology stack includes - NodeJS on the backend with MySQL and REDIS on the backend, HTML5, CSS, Bootstrap, Javascript, Jquerry, Facebook API, GoogleMaps and Image Search API.
Link : http://www.seas.upenn.edu/~cis330/g4.html
Urban Beats
https://docs.google.com/document/d/1n8uOq4heF-3wlSt7RxHd9qgCOCJJmsZYvs2If_ketkU/edit?usp=sharing
Designed a Master-Worker Hadoop style framework with single master node and multiple worker nodes. The master co-ordinates the workers, sends requests to different workers based on a hashing algorithm; the workers are in charge for running the map-reduce jobs and storing the data in the required format. The application has a status page in form of a User Interface which displays the list of currently active workers with details like workers IP address, status of worker (mapping/reducing/waiting etc). The status is updated periodically as the workers send information to the master node using REST form communication.
MapReduce Framework
https://github.com/aakritis/Map-Reduce-Framework.git
Designed a XPath evaluation engine using a recursive descent algorithm developed in Java based on a predefined CFG. Also crawled web pages and and aggregates them as specified in XPath expression. Berkeley DB was used for persistent storage.
XPath Engine and RSS Aggregator
https://github.com/aakritis/XPath-Engine-and-Web-Crawler.git
Developed a multi-threaded HTTP Web Server similar to Apache Tomcat which is capable of running Java Servlets to serve static as well as dynamic web content. It accepts GET, HEAD and POST requests from the web browser and invokes a worker thread from a thread pool to process each request. Appropriate exceptions are thrown for Bad Request, File Not Found, File Not Accessible etc. Stress Testing was performed on the server using apache workbench. The server was able to serve 10000 request with 1000 concurrent requests.
Multi-threaded Web Application Server
https://github.com/aakritis/MultiThreaded-Search-Engine.git
Python based supervised classifier to distinguish between information dense and non-information dense leads from new articles in various domains using a classifier trained on various lexical and syntactical feature sets. The application is focused on targeting unique distinction between the types of articles using various feature sets like MRC Databases, Production Rules, Bag of Words, TF-IDF etc. The project also exposed us to ML based validation techniques like 10-fold Cross Validation on the training data to decide on the best combination of features to achieve higher accuracy in results.
Detecting Information Dense Leads in Various Domains
https://github.com/aakritis/Computational-Linguistics-.git
Project built on client-server model for creating defense system for an AR Drone based on authentications and IP tables and thus forcing incoming traffic to go through an authentication process. The later half of the project hacked the same using various hacking techniques like Buffer Overflow, Man-In-Middle Attack etc.
AR-Drone Defense and Hacking Mechanism
An application to construct automatic summaries for given set of documents using two different techniques – Simple and Greedy Summarizer, and thus compares results of both the summarizers.
Document Summarizer
Python based application focused to compare documents using a trained classifier using various techniques like tf-idf weights, mutual information, and representing its semantic information using VSM models.
Document Similarity Using Vector Space Model
https://github.com/aakritis/Computational-Linguistics-.git
C# based application to automate build process for .Net Framework 2 and above applications, windows and web services using Team Foundation Server. After building the application, the health index is assigned to the application (based on various parameters like performance, coding style, security etc). It also allows user to release application from DEV environment to other environments like TEST, UAT, PROD etc based on certain criterias. 
The application also supported deployment of Sharepoint applications on servers.
FilerV2-TFS Automation of Build and Release
Automation of cancerous cells in an image by passing it as an input to a trained classifier. The classifier was trained based on the feature extraction done from a database of approximately 200 images after image enhancement and segmentation
Cancer Detection Using Image Processing
Education
Uber
https://www.linkedin.com/company/uber-com
Organizing Tech Talks 
Engineering Blogs
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Organizer
Environment
AIKYA
https://www.linkedin.com/company/aikya
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Team Member
Civil Rights and Social Action
The Art of Living
https://www.linkedin.com/company/the-art-of-living
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Volunteer
Microsoft
70-562
Microsoft .NET Framework 3.5, ASP.NET Application Development
Microsoft
70-433
Microsoft SQL Server 2008, Database Development
Microsoft
70-480
Programming in HTML5 with JavaScript and CSS3
https://www.linkedin.com/in/kevinolson
San Francisco, CA
Kevin Olson
Senior Engineering Manager at Chime
https://www.linkedin.com/in/sweetcircle
San Francisco, CA
Yuan Tian
Director Of Engineering at Chime
https://www.linkedin.com/in/jingweiyang
San Francisco, CA
Jingwei Yang
ML Platform at Chime
https://www.linkedin.com/in/shaun-benjamin-158280a6
Stanford, CA
Shaun Benjamin
Software Engineer at Chime
https://www.linkedin.com/in/kai-w-4311a767
Sunnyvale, CA
Kai W.
A SWE that thinks like a PM.
https://www.linkedin.com/in/jonathan-tappero
San Francisco Bay Area
Jonathan Tappero
Quality Assurance Engineer at Chime
https://www.linkedin.com/in/shwetamaurya
San Francisco, CA
Shweta Maurya
Lead Program Manager @ Chime
https://www.linkedin.com/in/charles-kong-0100142a
Mountain View, CA
Charles Kong
Senior Software Engineer Team Lead at Chime
https://www.linkedin.com/in/hoda-abdolrazek
San Francisco Bay Area
Hoda Abdolrazek Abunie
Experienced Technical Program Manager dedicated to delivering exceptional software solutions
https://www.linkedin.com/in/manasamoodalagiri
San Jose, CA
Manasa Moodalagiri
Web UI Developer
https://www.linkedin.com/in/basitalichaudhary
United States
Basit Ali
iOS Developer
https://www.linkedin.com/in/tobiaswooldridge
Seattle, WA
Tobias Wooldridge
Software Engineer at Facebook
https://www.linkedin.com/in/jamestsweetman
Brooklyn, NY
James Sweetman
Staff Software Engineer at Affirm
https://www.linkedin.com/in/mitchtrale
Oakland, CA
Mitch Trale
Partner at Startup Dev
https://www.linkedin.com/in/amlan-gupta
San Francisco Bay Area
Amlan Gupta
Software Engineer at Google | UBuffalo Alumnus
https://www.linkedin.com/in/sahil-goel-93b53787
Seattle, WA
Sahil Goel
Software Engineer at Google
https://www.linkedin.com/in/praajaktaa
San Francisco Bay Area
Prajakta Tathavadkar
Engineering @ Google
https://www.linkedin.com/in/songxiong
Mountain View, CA
Song Xiong
Senior Software Engineer at Google
https://www.linkedin.com/in/linghaozh
Mountain View, CA
Linghao Zhang
Software Engineer & Tech Lead at Google
Mayank Rawat
      
          



            
              
        
              
  

    
        Aakriti was instrumental in building partner payouts in our next-generation payments platform. Since the beginning of 2018, most of the heavy lifting for the project was done by Aakriti and 3 more engineers. The system pays out to millions of drivers weekly and also enables features such as instant cashout. In that regard, we have to design a payment system that has very high rps and has strict latency requirements. Aakriti also helped in the enablement of a partner arrear collection system that is used for arrears collection in cash-based markets where we have unbanked drivers. This was a novel collections project and helped improve the recoupment rates in the cash-based market remarkably. 
Besides sizeable impact in each of her projects, Aakriti was also an active participant in several ERGs, helped organize MoneyCon '19 meetup, and mentored several junior engineers. She is a great team player, self-starter, and always willing to lend a helping hand with customer issues/outages/on-call. 
I wish Aakriti the very best in her future endeavors and would love to work with her again. She will be missed at Uber!

 Nitin Nitin
      
          



            
              
        
              
  

    
        Smart and hard working is the phrase that comes to mind when I think about Aakriti. I have had the pleasure of working with Aakriti for 2 years at the Jaypee University of Information Technology guiding on several projects and have been her mentor ever since. I am particularly impressed by her simple and methodical approach to solve complex problems. She has always been very professional with her code and documentation for projects at school. That skill takes years to develop normally for under graduates but it comes very naturally to her. I have known her not only for academic excellence and enriched performance, but also for her sense of commitment, which is backed by her substantial achievements in academics world. As a team member or leader, she earns my highest recommendation and I am sure she will be a valuable asset to the modern day business ecosystem.
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One of the greatest mistakes of my investing career was passing on investing in Uber in the angel round twice.And part of the reason I invested in…
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AtoB is now YC's Top 100 Companies.  At just over a year old, AtoB has now reached the ranks of other fast growing, top YC companies!  Reach out…
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Very excited to be pivoting into the world of Fintech and starting a new position as Sr. Director of Data and Technology over at Chime!It’s an…
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Faridabad
Aakriti Singla
Product Application Developer - Data Scientist| Machine learning
https://in.linkedin.com/in/aakriti-singla-591a5b124
Mumbai
Aakriti Singla
ETL Developer | CRM Consultant | PL/SQL Developer
https://in.linkedin.com/in/aakriti-singla
Gurugram
Aakriti Singla
Business Analyst
https://in.linkedin.com/in/aakriti-singla-56b85b113
Gurgaon
Aakriti Singla
Senior Engineer at Nagarro
Salesforce Solutions & Tips
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Entry Level Engineers
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WhatsApp
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University of Pennsylvania Alumni
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DevOps, MLOps & Cloud (moderated)
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Jaypee Institute Of Information Technology University
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Product Managers Community
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YLD.com Open Source - All things JS, React.js, Node.js, Typescript, Kubernetes, Cloud Native
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