Sachin Panicker

• Digital leader with 20+ years of global experience across business (Business transformations, Operations Excellence, Strategy & Consulting) and core digital roles (Digital Build-Operate-Transfer, Product Engineering, Automation) for Fortune 500 companies across diverse verticals.
• Hands on experience of digitizing customer experiences and core enterprise processes, by building and transforming resilient, high volume B2B products & mission-critical enterprise class applications, on a stable enterprise core.
• Built and grown seasoned digital organizations, comprising of members from Product Management, Technology (Architecture & Engineering) & Data/Analytics, to execute on forward looking transformation strategy

Areas of Expertise/Interests:

• Hire, train and set up Product development and Research teams with capabilities on emerging technologies with team size of a hundred
• Develop products in the space of Mobile Apps, Augmented Reality, Internet of Things and Artificial intelligence, Analytics and Automation
• Research, prototype and develop Deep Learning models and build MLOps
• Strategy and Implementation of large-scale digital transformation programs
• Delivery modernization – Agile, DevOps, Automation
• Building enterprise applications, Product Engineering

Top 20 new Tech shows on iTunes - https://podcasts.apple.com/ie/podcast/data-science-et-al/id1490424424

• https://www.linkedin.com/pulse/inexplicably-explainable-ai-sachin-s-panicker
• https://www.linkedin.com/pulse/hyper-parameterization-holy-grail-ml-sachin-s-panicker
• https://www.linkedin.com/pulse/scaling-deep-networks-sachin-s-panicker
• https://www.linkedin.com/pulse/future-artificial-intelligence-sachin-panicker
• https://www.linkedin.com/pulse/india-needs-station-f-sachin-panicker

1. Applied an advanced machine learning technique, Deep Boltzmann machines as generative models, to find complex interaction patterns in medical data where it is hard to pool individual data due to data security and data protection concerns, and hence a synthetic data approach was used.

2. Demonstration of top data science tools, libraries and techniques from Uber, Netflix, Google, Lyft etc. for use in data science projects - Ludwig, GPipe, PyText, Zero Shot Learning, MLflow, Neptune, Vaex, Helix, AutoML, Michelangelo, Polynote, Manifold, Horovod, Flyte.

3. Developed a Review based Transformer Model for personalized product search to help customers base their decision on personal preferences besides product relevance.

4. Developed an Encoder Decoder based deep convolutional neural network architecture with Multi-scale-aware modules for crowd counting and generating high quality density maps.
5. Demonstrated the use of Big Data Analytics in Industrial Internet of Things with approaches such as Self Organizing Map algorithms for mass product customization towards lean manufacturing, Industrial Time Series Modeling, Intelligent shop floor monitoring, Industrial Micro grids, Monitoring machine health and intelligent predictive and preventive maintenance.

6. Used an automatic framework to minimize memory footprint on a deep learning network for video recognition. It helped reduce the overall memory requirement by 3× for the PyTorch model, with a 10% overhead in computation.