Federated Learning | TensorFlow Quantum | Automated Data Visualization and ML

Details
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Speaker Profiles:

Speaker 1 : Gunjan Narulkar

Topic : Federated Learning

Designation : Principal Data Scientist at Fidelity Investments

Bio : Gunjan started his career as a mainframe developer but got an opportunity to work on analytics tools like SAS and SQL in telecom domain early in his career which introduced him to the world of analytics. Inspired, he finished his M. Mgt in Business Analytics from Indian Institute of Science (IISc) and joined an analytics startup where he established their Big Data and Analytics Consulting division. Right now, is a Principal Data Science @ Fidelity Investments. His current research interests are TinyML, Graph Neural networks.

Speaker 2 : Rishit Dagli

Topic : [Advanced]: TensorFlow Quantum to build hybrid quantum-
classical models

Designation : 10 STD,TED-X,Ted-Ed speaker|Google certified mobile site
dev|Intel AI Scholar|2XGCP Champ|TFUG Mumbai-
Mentor|Kotlin MUM

Bio : Rishit Dagli is a 10 grade student and is a past TED-X and Ted-Ed speaker. He is an AI and Cloud enthusiast and is also a Google Certified Mobile site developer. He has often represented India in international level Hackathons and competitions and also won a few. He most recently represented the country and won the International Hackathon on Blockchain and IoT by IET. While free, he loves to conduct research and has written 7 research papers in the field of AI and Robotics and Mathematics. He also was among the top 10 winners for Palo Alto Networking Challenge with GCP.

Abstract : With the new launch of TensorFlow Quantum building hybrid quantum-classical models has become a lot easier, I plan to give the audience a brief introduction about Quantum computers and how they differ from an elementary computer. With this I would also introduce them to circuits and qubits. Having done so I would then move on to building circuits with Cirq and TF Quantum and also show how these can be simulated. Thus, I plan to build on top of building simple models with Keras and how they can be extended to build hybrid models. I also plan to speak about how using Quantum computing with classical computing could give wonderful results.

Speaker 3 : Ram Seshadri

Topic : Faster Time to Insights using Automated Data Visualization and
Machine Learning.

Designation : Google Machine Learning Program Manager

Bio :
Ram Seshadri is a Data Scientist with deep experience in financial services and tech/media/telecom. Ram currently works as a Machine Learning Program Manager at Google and was formerly a Data Scientist at Morgan Stanley and an instructor at General Assembly & New York Institute of Finance.

Abstract : Data Scientists today grapple with two problems: 1. Big Data 2. Bewildering Choice. Big Data enables data scientists to analyze more of what's available, but they can't visualize it or explain it so easily. In addition, there is a bewildering array of tools available which means substantial work with steep learning curves to master each one of them. The solution is automation: automate what's mundane so we can focus on the most important. Here I will describe what's available in terms of Open Source and Proprietary tools for automating Data Science tasks and introduce 2 new tools: one to visualize any sized data set with one click, another: to try multiple ML models and techniques with a single call.

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