Rohit Punnoose, Ph.D.
Bengaluru, Karnataka, India
8K followers
500+ connections
Articles by Rohit
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Setting up your offshore BI & Analytics super-team? 5 common mistakes and how to avoid them...
Setting up your offshore BI & Analytics super-team? 5 common mistakes and how to avoid them...
By Rohit Punnoose, Ph.D.
Activity
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Excited to share a great example of how #AI is enhancing our guests' shopping experience at America's favorite discovery destinations…
Excited to share a great example of how #AI is enhancing our guests' shopping experience at America's favorite discovery destinations…
Shared by Rohit Punnoose, Ph.D.
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What a weekend! At Target in India, we celebrated #TargetFam—our Family Day, where we all had the chance to bring our loved ones to the office. It…
What a weekend! At Target in India, we celebrated #TargetFam—our Family Day, where we all had the chance to bring our loved ones to the office. It…
Liked by Rohit Punnoose, Ph.D.
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Digital is all-pervasive in #retail guest (customer) interactions whether in a store or online, and retailers need to pay close attention to driving…
Digital is all-pervasive in #retail guest (customer) interactions whether in a store or online, and retailers need to pay close attention to driving…
Liked by Rohit Punnoose, Ph.D.
Licenses & Certifications
Volunteer Experience
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Volunteer
Christel House International
- Present 10 years 9 months
Education
Provided career coaching and counselling to students at Christel House, Bangalore, a preferred NGO partner for Target India
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Volunteer
Jeevodaya, Bangalore
- Present 10 years 1 month
Education
Provided career coaching and counselling to students at Jeevodaya, Bangalore, a preferred NGO partner for Target India
Publications
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Phase-wise migration of multiple legacy applications–A graph-theoretic approach
Information and Software Technology (ABDC - A journal)
Abstract:
Many organizations undertake large-scale projects of application migration due to availability of scalable and cost-efficient technologies. Such legacy application migration projects are very complex since the process involves in-depth profiling of the applications.
During the initial profiling phase, it is imperative to understand the underlying complexities of individual applications, as well as the interdependencies among applications in the organization. This…Abstract:
Many organizations undertake large-scale projects of application migration due to availability of scalable and cost-efficient technologies. Such legacy application migration projects are very complex since the process involves in-depth profiling of the applications.
During the initial profiling phase, it is imperative to understand the underlying complexities of individual applications, as well as the interdependencies among applications in the organization. This analysis phase can take considerable time and effort, depending on number and complexity of the applications. The main goal of this paper is to provide a framework that provides a cost-effective and quick approach to study the interdependencies between legacy applications with minimal prior knowledge of application usage.
In this paper, we propose a framework that uses community detection algorithms and other established techniques from graph theory, to discover interdependencies of legacy applications within an organization, group these highly interdependent legacy applications in clusters, and finally sequence the clusters for migration to a modern platform. We study the proposed framework through three case studies, using network datasets from a large US organization.
The experimental results from the proposed framework suggests that legacy applications can be grouped into clusters with high interdependencies between each other. Also, the framework shows how organizations can then appropriately sequence the clusters of legacy applications into a phase-wise migration project, thereby reducing migration costs.
The proposed framework provides a valuable design input to organizations on how to determine the interdependencies between the various legacy applications that are in scope for migration to a modern platform. Such large-scale migration projects can be simplified and broken down to use a systematic approach, thereby reducing migration costs and data integrity challenges.Other authorsSee publication -
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms : A case for Extreme Gradient Boosting
International Journal of Advanced Research in Artificial Intelligence (IJARAI)
Number of Citations: 160+
DOI: https://doi.org/10.14569/ijarai.2016.050904
Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem…Number of Citations: 160+
DOI: https://doi.org/10.14569/ijarai.2016.050904
Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover.Other authorsSee publication
Languages
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English
Native or bilingual proficiency
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Hindi
Native or bilingual proficiency
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Malayalam
Native or bilingual proficiency
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French
Professional working proficiency
Organizations
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Project Management Institute
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- Present
Recommendations received
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Join now to viewMore activity by Rohit
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Learning Reflection from the HR Analytics Program at IIM, Ahmedabad!!! As I near the end of the 5-day HR Analytics program at IIM Ahmedabad, I’m…
Learning Reflection from the HR Analytics Program at IIM, Ahmedabad!!! As I near the end of the 5-day HR Analytics program at IIM Ahmedabad, I’m…
Liked by Rohit Punnoose, Ph.D.
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Over 100 students and professors united in a silent candle march across the campus, followed by a minute of silence, to express their outrage against…
Over 100 students and professors united in a silent candle march across the campus, followed by a minute of silence, to express their outrage against…
Liked by Rohit Punnoose, Ph.D.
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Gold medal performance! Congratulations to the entire team on a great quarter. Big shout out to Digital and Target Plus teams - amazing results…
Gold medal performance! Congratulations to the entire team on a great quarter. Big shout out to Digital and Target Plus teams - amazing results…
Liked by Rohit Punnoose, Ph.D.
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As more details emerge of the heinous rape and murder of a promising young medic in Kolkata, it appears that the motive had to do with the doctor…
As more details emerge of the heinous rape and murder of a promising young medic in Kolkata, it appears that the motive had to do with the doctor…
Liked by Rohit Punnoose, Ph.D.
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🎓 I am thrilled to announce that I have successfully completed my #Masters in #ArtificialIntelligence from the Indian Institute of Science (#IISc)…
🎓 I am thrilled to announce that I have successfully completed my #Masters in #ArtificialIntelligence from the Indian Institute of Science (#IISc)…
Liked by Rohit Punnoose, Ph.D.
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Join me as we explore the potential and opportunities of AI for startups at #AIStartupSchoolbyGoogle on August 20th at 10.30 AM! Looking forward to…
Join me as we explore the potential and opportunities of AI for startups at #AIStartupSchoolbyGoogle on August 20th at 10.30 AM! Looking forward to…
Liked by Rohit Punnoose, Ph.D.
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It was very exciting to see tech and business teams collaborate to solve real problems, and equally to see the creative solutions presented that is a…
It was very exciting to see tech and business teams collaborate to solve real problems, and equally to see the creative solutions presented that is a…
Liked by Rohit Punnoose, Ph.D.
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Fabulous work from our Data Sciences team, helping make smarter business decisions
Fabulous work from our Data Sciences team, helping make smarter business decisions
Liked by Rohit Punnoose, Ph.D.
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Last week, our newest tech hires had a meaningful start to their journey with Target. Joining hands with Mathru Foundation, an organization dedicated…
Last week, our newest tech hires had a meaningful start to their journey with Target. Joining hands with Mathru Foundation, an organization dedicated…
Liked by Rohit Punnoose, Ph.D.
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