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Lead Data Scientist with keen knowledge in data science and related technical skills…
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Ravi Sankar Pediredla shared thisThis week, I reached 1000 citations for the research work done in Machine Learning area. However this figure is not so high but it matters a lot and which helps me like a catalyst for my quest towards learning new things all the time. I believe these citations are tangible proof that my work has simulated further thought of research, potentially leading to new breakthroughs. On this occasion, I want to thank my Professor Ravi Vadlamani for all the guidance which helped me to achieve these papers in my early career. My publications contributed to collective knowledge of ML, enabling other researchers to build on previous findings to advance further. Thanks to all my colleagues, collaborators and mentors #selfmotivation #gratitude #happy #1000citations #datascience #publications #learning
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Ravi Sankar Pediredla posted thisCongratulations team on presenting your papers at IEEE conference on churn prediction & trade discount optimization !! Preetam Bendapudi, HEERA LAL, Athira Surendran, Manoj Reddy L, Senthil Nathan M and Akansha Kumar, Ph.D.
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisI moved recently to Germany , my colleagues are so helpful want to post their appreciation, nice office nice home
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisI'm heading to MRO Asia-Pacific next week. If you are attending, let's connect! At Cyient, we are helping our customers unlock MRO capacity that already exists in their set up, with our smart MRO platform. I'm looking forward to meeting MRO providers and understand more about the practical challenges they face and how we can solve it together. See you there on the 22 - 24 September! #MRO #IntelligentEngineering Aviation Week Cyient
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisThe C. R. Rao Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS), University of Hyderabad Campus, Gachibowli, celebrated the 106th Birth Anniversary of Prof. C. R. Rao, the legendary statistician, Padma Vibhushan Awardee and recipient of the prestigious 2023 International Prize in Statistics, widely regarded as the highest international recognition in the field of Statistics.
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Ravi Sankar Pediredla liked thisOur recent mining roundtable tackled the industry's unstructured data challenge. Discussion focused on using AI to unlock critical operational intelligence locked inside sources like CAD, legacy PDFs, BOMs, images, voice, videos, engineering drawings, manuals, SOPs, logs...Ravi Sankar Pediredla liked thisEvery asset, machine, and operation generates data. ⛏️🚜 Yet data alone doesn't improve uptime, productivity, or performance. So, how do you turn asset data into measurable business value? That question brought mining leaders, technology experts, and industry analysts together at our 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗠𝗶𝗻𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱𝘁𝗮𝗯𝗹𝗲, hosted at the Microsoft Perth Office. The conversation moved quickly from the scale of the opportunity to what it will take to realize it. Leaders shared practical perspectives on where digital and AI are delivering the greatest impact across the mining value chain. Thank you to our speakers, participants, Microsoft, Prospecta Software, ISG (Information Services Group), and everyone who made the morning so engaging.🤝 Here are a few moments from the conversation.📸 #IntelligentEngineering
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisExcited to share that our research article has been published in the International Journal of Advanced Operations Management (IJAOM)! Our work focuses on Rake Scheduling and Wagon Allocation Optimization for Petroleum Product Transportation, addressing a practical planning problem in the petroleum supply chain. The study develops a Mixed-Integer Linear Programming (MILP) model to generate optimized rake schedules and wagon allocation plans while accounting for operational and business constraints. What makes this work particularly meaningful to me is the opportunity to apply Operations Research and Mathematical Optimization to a real-world industrial problem. A big thank you to my co-authors Pravin Suryawanshi (Ph.D.), Akansha Kumar, Ph.D., and Sai Krishna J for the collaboration and contributions throughout this work. 📄 Read the paper: https://lnkd.in/dysgBk7G Published in: International Journal of Advanced Operations Management, Vol. 17, Issue 3 (2026). DOI: 10.1504/IJAOM.2026.155520 #OperationsResearch #MathematicalOptimization #SupplyChainOptimization #RailLogistics #MILP #OperationsManagement #SupplyChainAnalytics #TransportationPlanning #OilAndGas #RefineryOperations #LogisticsOptimization #ResearchPublicationA novel approach to allocation and scheduling of rail rakes and wagon management for transportation of petroleum products | International Journal of Advanced Operations ManagementA novel approach to allocation and scheduling of rail rakes and wagon management for transportation of petroleum products | International Journal of Advanced Operations Management
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisA Chapter Comes to an End — Thank You, Carelon Global Solutions India ! 💙 August 6,2026, marked my last working day at Carelon Global Solutions India . As I close this chapter, I want to take a moment to express my sincere gratitude to everyone who has been part of my journey over the years. I’m truly thankful for the support, trust, opportunities, and encouragement I received throughout my time at Carelon. The experiences here have helped me grow not only as a professional but also as a leader. From working on AI/ML, Data Science, MLOps, Cloud, and Generative AI initiatives to collaborating with amazing teams and solving challenging problems, every experience has been a valuable learning opportunity. A special thank you to my leaders, managers, colleagues, mentors, and friends who believed in me, challenged me, supported me, and made this journey memorable. 🙏 I’m taking with me many great memories, meaningful relationships, and countless learnings that will continue to shape the next chapter of my career. Thank you, Carelon Global Solutions India , for the trust and the journey. ❤️ On to the next chapter, new challenges, and new opportunities! 🚀 Carelon Global Solutions India #CareerJourney #Gratitude #AI #MachineLearning #GenerativeAI #DataScience #MLOps #Leadership #NewBeginnings
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisThe AI Boomerang: Tech is Quietly Rehiring the People it Just Let Go Years in data science taught me one thing: getting AI to produce something is easy. Knowing if it's any good is the hard part. Earlier this year, around 120,000 tech jobs were cut globally. The pitch sounded great, let AI do the work, save the money. A few months later, the story is changing. Many big companies are quietly reversing their AI cuts. 55% of leaders who did AI layoffs now admit it was a mistake. And companies are rehiring, sometimes at higher salaries, the same people they let go, because the ones left behind can't fill the gap. The lesson is simple. AI isn't here to replace people. It works best when it helps good people do more. Someone still has to check the work, ask the right questions, and make the tough calls. Experience matters. You usually don't notice how much, until it walks out the door. But this also means we have to adapt. It's worth asking honestly, is my role one that could get waved off, or am I building skills that make me harder to replace? The people who grow with AI, not around it, are the ones who'll stay ahead. The companies that win won't be the ones replacing their teams. They'll be the ones making their teams stronger with it. I'd love to hear your side, are you seeing this shift where you work? Let's learn from each other. LineEquation #AI #DataScience #FutureOfWork #Leadership #IT #LineEquation
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Ravi Sankar Pediredla liked thisRavi Sankar Pediredla liked thisGraphify – An open-source project to create queryable knowledge graph from codebases. Came across it over the weekend and tried it with Claude Code. ✳️ It helps map code, docs, PDFs, images, and videos into a knowledge graph. ✳️ Code is parsed using the Tree-sitter AST deterministic parser. ✳️ Other types, such as PDFs, images, videos, etc., are parsed using LLMs. ✳️ It comes up with god nodes and communities. ✳️ It outputs an HTML file to interactively visualize the graphs, along with a report. ✳️ Supports almost all major agentic coding tools, such as Claude Code, Codex, etc. The picture shows the knowledge graph generated from the Graphify repo with the identified communities. Github repo: https://lnkd.in/giVdwztd
Experience & Education
Licenses & Certifications
Publications
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Failure Prediction of Banks Using Threshold Accepting Trained Kernel Principal Component Neural Network
World Congress on Nature and Biologically Inspired Computing (NaBIC'09)
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Detection of Financial Statement Fraud and Feature Selection Using Data Mining Techniques
Decision Support Systems
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Support vector machine and wavelet neural network hybrid: application to bankruptcy prediction in banks
International Journal of Data Mining, Modelling and Management
Courses
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Business
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Projects
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Driver Telematics Analysis
For automobile insurers, telematics represents a growing and valuable way to quantify driver risk.
Instead of pricing decisions on vehicle and driver characteristics, telematics gives the opportunity to measure the quantity and quality of a driver's behavior. This can lead to savings for safe or infrequent drivers, and transition the burden to policies that represent increased liability.
AXA has provided a dataset of ~547,200 anonymized driver trips, with only centered location…For automobile insurers, telematics represents a growing and valuable way to quantify driver risk.
Instead of pricing decisions on vehicle and driver characteristics, telematics gives the opportunity to measure the quantity and quality of a driver's behavior. This can lead to savings for safe or infrequent drivers, and transition the burden to policies that represent increased liability.
AXA has provided a dataset of ~547,200 anonymized driver trips, with only centered location data, based on which a "telematic fingerprint" capable of distinguishing between drivers is to be developed.
Does a driver drive long trips? Short trips? Highway trips? Back roads? Do they accelerate hard from stops? Do they take turns at high speed? The key to the project is based on ability to extract information on such questions from the minimal location co-ordinates (centered) provided.
Other creatorsSee project -
Failure prediction of banks and dotcom companies via intelligent techniques
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Cleansing of the available data, data pre-processing, model the data using several datamining techniques and evaluating the best model out of it.
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