Jay Bandlamudi

Jay Bandlamudi

Research Software Engineer

Work Experience

IBM Research Labs [ MAR 2018 - Present]

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Tool usage: Python, docker , OpenShift , pytorch, FastAPI

At present Jay is working as Advisory Research Engineer , developing AI solutions for enterprise automation using Machine Learning, Deep Learning, Natural Language Processing, Process Mining, and cloud-native tools. He created significant business impact for IBM by developing several AI solutions for products/platforms and lead several proof-of-concepts(POC) on AI use-cases with clients. Along with creating business impact his day to day work involves contributing to reserach papers and patents. Below are few example projects and POCs

  • Developed knowledge curation from documents solution, curated knowledge was used for bootstrapping chat-bots, enhancing document search, and constructing knowledge graphs
    • Used by 300+ accounts and A-level research accomplishment with over 25M USD per year revenue
    • 2 OTAA awards, from IBM and client for project contributions
  • Developed automated ticket dispatching system for assigning correct resolver group to tickets. Used a combination of automated rules,SVM and deep learning methods to achieve human-level accuracy
    • Work published in AI magazine in deployment practices and best deployment paper award at IAAI
    • Used by 12 different clients and served over 2 Million tickets till date
  • Developing AI for business automation platform for busines/IT process discovery, performance monitoring, workforce insights and process automation on the event logs
    • A-level research accomplishment with revenue of 3M USD
    • 2 product related patents, Application paper at ICPM
    • OTAA award for leading and developing the product features
  • Client-POCs:
    • Bootstrapping chat-bots for retail and manufacturing domain documents and evaluating chat-bot knowledge gap with SME in the loop
    • Process discovery from banking application click-stream data, to identify the process behind application interactions and cognitive load estimation, automation recommendation for applications

      More details on projects can be found in ‘projects’ section

      Apart from his daily work , he likes to mentor student/professionals willing for a switch to Data science/ML related roles.

Mayo Clinic [ MAY 2017 - AUG 2017]

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In the summer of 2017, Jay worked at Mayo clinic as a Intern.There he was responsible to develop tools for data pipelines and enhance existing tools.

Tool usage: Python, Unix, Perl, Selenium, beautiful soup, pptx

At Mayo clinic, Jay worked on multiple projects and his major project was ‘Automated Gene Report Generation’.Goal of this project is to automate the patient gene report (.pptx format) generation. Creating these reports is cumbersome as gene data comes from various sources such as files, web sites etc., and genetic counselors required these reports for analyzing genetic variations.
As obtaining gene data from multiple sources, creating a power point gene report is tedious and often gene counselors spend hours in preparing these reports. So through this project Jay automated pptx document generation using python to save time (saved ~ 3 hours per report) such that gene counselors can spend more time with patients than to worry about making reports manually.
Other than his major project,Jay developed enhancements for a “Metabolic Modelling tool”.Also,Jay was part of Machine learning club at Mayo where he presented,discussed research work with peers.

Git links of projects:
​flash_ppt
cobra_babel

University of Illinois [ MAY 2016 - DEC 2017]

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Tools usage : R,Python,Markdown,Shiny,Spark,APIs

Jay worked at University of Illinois as a Research Assistent while pursuing his Master’s degree. His work was about finding answers to several media research questions by analyzing natural language/text on issues such as Privacy, Secrecy ,Immigration etc.During the course of research,he worked on multiple projects using text mining techniques such as topic modelling ,text classification,Named entity recognition,POS tagging,sentiment analysis and advanced ML techniques such as SVM,Xgboost,clustering methods etc.,
Below are the projects
1)”Privacy VS Secrecy analysis” using data from twitter feeds
2)”Immigration Analysis” with data from the New York times articles. 

Git links of projects
​Twitter Privacy VS Secrecy Analysis
NYtimes Immigration Analysis

Infosys Limited [ AUG 2013 - DEC 2015]

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Tool usage: R, SQL, CARET, NLTK
At Infosys, Jay worked for a European banking clients on projects related to Data Analysis and Machine Learning. He has experience working on different phases of end to end data science pipeline

  • Requirements: Communicated with client and Subject Matter Experts to identify and document business needs and problem statements that require data driven solutions
  • Data Extraction: Used SQL,PL/SQL, Hive query language to obtain data required for analysis from multiple sources such as Oracle DB, Hive tables
  • Data Preparation: Used R packages such as dplyr, tidyr, lubridate for data preparation activities such as data cleaning,dealing with missing values,parsing the dates,creating new variables ,regular expressions to process text etc.
  • Data Exploration: Used R packages ggplot2 , pca, alr4,dplyr to check distributional assumptions using statistical tests,for obtaining summary statistics and aggregated tables, checking associations among variables using correlation analysis/plots and removing high correlations in data, principal component analysis for dimensionality reduction etc.,
  • Model Building: Used CARET,h20 packages in R to train different supervised learning models on data such as Regression, Logistic regression, Decision Tree, Random forests and methods such as cross validation,regularization to deal with over fitting
  • Reporting: Presented and discussed the data driven model results with non technical audience (client) to identify the factors that can help in business decision making.


    During his stint with Infosys,Jay got the opportunity to work on multiple projects and learned many things like text mining methods,HIVE and core banking domain.Apart from work routine, Jay delivered training sessions to peers on technology such as SQL,Big Data Basics and for the best efforts as a team player he was honored with “INSTA AWARD”. ​ ​