Aditya is currently working as Data and Cloud Platform Engineer in West Pharmaceuticals in Bangalore, India. Being an ex-Microsoft employee, Aditya is an enthusiastic learner, who loves to explore new technologies and tries to grasp the in-depth knowledge of the concepts used in them. Aditya has roughly 4 years of experience in domains like Internet of Things (IoT), Machine Learning, Robotics and Cloud Computing. Currently, he is working on a research project to compare performance of Deep Learning Algorithms Variational Auto Encoder (VAE) and Deep Convolutional Generative Adversarial Networks (DCGAN) for generating pencil sketch images.
Joy Mustafi is Director and Principal Researcher at Salesforce, primarily responsible for leading the Salesforce Einstein and other intelligent cloud platform in India. Joy has overall seventeen years of experience in corporate, research and academic world. Had worked as Principal Applied Scientist at Microsoft - Artificial Intelligence and Research, Data Science and Machine Learning. Was with IBM for a decade as Data Scientist, and involved in the Business Analytics and Optimization, Watson Solutions, IT Operations Analytics etc. Got the Research Fellowship Award in Computer and Communication Sciences from Indian Statistical Institute. Collaborated with the ecosystem by visiting around twenty-five leading universities in India as visiting faculty, guest speaker, advisor, mentor, project supervisor, panelist, academic board member, curricula moderator, paper setter and evaluator, judge of events like hackathon etc. Supporting around fifteen start-ups and non-profit forums being in the board or as consultant for data sciences.
A leading Data Scientist and researcher with expertise in AI and Machine Learning. Also, a Startup Advisor and Industry Consultant having spent many years in USA and India in the high-tech industry. Co-Founder at CellStrat, India's leading Artificial Intelligence startup. Serial Entrepreneurial experience having Co-Founded or advised several startups in prior years including Healthiply.in (AI-driven online health startup), LocVille.com (online furniture and decor) and SalesGlobe (sales CRM). Long experience in telecom and digital industries in Strategy, Mobile Apps/Web, Data Analytics, Systems Integration and Enterprise Mobility, in leading MNCs like IBM, AT&T, Schlumberger, HCL-HP etc.
An AI Researcher doing research in experimental AI and theoretical AI and also an active entrepreneur with a mission of AI for social good. Also have an affinity towards AR/VR, Driverless cars, Cognitive sciences.
Building a Scorecard using Python
Speakers: Kavita D. Chiplunkar (Data Science Head, Infinite-Sum Modelling Inc.), Nirav Shah (Founder of OnPoint Insights)
This webinar will tell you the importance of Credit Scorecards in Banking /Financial Institutions, how they are used to measure the credit worthiness of a customer and how Machine Learning Algorithms are helping build better scorecards than traditional algorithms. We plan to briefly discuss the key data elements that would be required to build such scorecards. We will talk at a high level about various steps in building a scorecard.
Time Series Analysis in Python
Speakers: Ramanathan Ramakrishnamoorthy (Director & Co-Founder of Zentropy Technologies), Gurram Poorna Prudhvi (Machine Learning Engineer, mroads)
Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.
Integrating Digital Twin and AI for Smarter Engineering Decisions
Speaker: Amit Doshi (Sr. Application Engineer at MathWorks)
With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.