Satyamoy is a seasoned analytics professional and a hands-on leader. He has 18+ years of global experience with a deep focus on Banking and Financial Services industry. He has spent significant part of his career in variety of roles in companies such as Citigroup, and GE, enabling business impact through the application of analytics and data science.
In his current role, Satyamoy leads the Client Solutions and Product Strategy at Analyttica Datalab Inc., a fast growing analytics start-up headquartered in Delaware, US with offices in Wilmington, Bangalore, and Pune. He has been undertaking a leadership role in the journey of build through growth of the organization. His role spans across different industry verticals such as BFS, Healthcare, CPG/Retail, and Automotive.
Satyamoy has also been actively involved in driving the technology enabled solutions strategy for Analyttica and has been part of the core team in creation of a patented ML and AI platform called Analyttica TreasureHunt https://www.analyttica.com/precision. He holds a US patent in his name for invention in the analytics learning and knowledge immersion space and a filed patent extension in adaptive Machine Learning systems . He is passionate about creating business value, driven by analytical innovations through the right leverage of ‘Business Knowledge’, ‘Data Science’ and ‘Technology’.
Satyamoy has a Master’s in Industrial Engineering from Wichita State University, and an Executive General Management from IIM Bangalore.
Day 1 - Knowledge Talks 28 May
Interpretability of ML and explainable AI – Why and why now
There has always been a trade-off between “Accuracy” and “Interpretability” in the field of applied ML. The dichotomy presents significant challenges, when it comes to application of ML for creating sustainable and scalable impact for businesses. While the linear models are fairly explainable as those often depict the average behaviour based on data, the accuracies of those models are certainly sub-optimal. Non-linear models are significantly superior on accuracy, however are extremely difficult to explain. The lack of transparencies (read black box!) in complex ML models hit the trust factor amongst stake-holders hard.
In this talk Satyamoy will simplify the physical significance of the topic. He will bring in clarity around what it means for businesses and how one can leverage the progressive research in this field to drive explainability without sacrificing on accuracy. He will also walk the participants through Analyttica’s innovative IP solution addressing this field through one of the real business cases.