Development and Deployment of AI applications across Domains

Shailesh is Executive Director at Morgan Stanley, heading Wealth Management Technology’s AI, Analytics and Machine Learning teams based across the globe. He is considered thought leader in AI/ML practice area and guiding firm’s AI/ML strategy. In his role he is helping firm to improve sales and digital marketing strategy using big data and machine learning. Help protect firm by using state of art payment and internal fraud detection based on supervised learning and anomalous pattern detections. Shailesh has patent filed in the field of using anomaly detection to detect system outages. He is very passionate with his vision of democratizing AI/ML usage. He helped build Enterprise scale analytic platform that is used by over 500+ Data Analysts and citizen data scientists to find business insight and decision making. He helped build Self Service virtual assistant platform using AI/NLP that is used by over 20k FAs/CSAs of Morgan Stanley Wealth Management. Shailesh is a visionary leader with over 25 years of experience leading and managing global data and analytics organization. His areas of expertise include big data platforms, Business Intelligence, predictive analytics, AI and Machine Learning.

Vijay leads a team of researchers and engineers building knowledge extraction, representation and reasoning frameworks for the firm. He joined the firm as a vice president in 2017 and was named managing director in 2019. Over his 30 year research career, Vijay has been a member of the Research Staff at the Xerox Palo Alto Research Center; member of the Research Staff, district manager and technology consultant at AT&T Research, and distinguished member of the Research Staff as well as chief scientist at the IBM TJ Watson Research Center. Additionally, he has served as chief technology officer and vice president of engineering at two startups. Vijay has also served as a professor of Computer Science at The Pennsylvania State University and an adjunct professor at Columbia University. Vijay has published more than 100 papers and collaborated with more than 100 researchers. He received the 1989 Association for Computing Machinery (ACM) Doctoral Dissertation Award, and the 2005 Most Influential Object Oriented Programming Systems Languages and Applications (OOPSLA) Paper Award for his work. Vijay earned a BTech in Electrical Engineering from the Indian Institute of Technology Kanpur in 1982. He earned an MS and PhD in Computer Science from Carnegie Mellon University in 1985 and 1989, respectively.

Frida Polli is a venture-backed CEO, award-winning neuroscientist at Harvard and MIT, and holds an MBA from Harvard, a PhD from Suffolk and a BA from Dartmouth. She is the co-founder of pymetrics, a company pioneering the use of neuroscience and data science to accurately predict hiring outcomes and remove hiring bias through patented games and algorithms. Frida speaks four languages and lived in five foreign countries before settling in Manhattan with her husband and two daughters.

Shay is responsible for overall company growth and strategic direction. He has more than 18 years of entrepreneurial leadership, product development, and management experience with growth-stage, venture-backed companies, and high-growth software divisions within Fortune 50 companies. Previously, Shay was the global leader for Smarter Care at IBM. He successfully led and managed the acquisition of Curam Software to establish IBM’s leadership at the intersection of Social Programs and Healthcare. He has a BS & MS degree in Electrical & Computer Engineering from the University of Texas at Austin and an MBA from the Action School of Entrepreneurship. Shay currently serves on the board of the Austin Technology Council (ATC) and previously served as the Vice-Chair of Innovation for the Greater Austin Chamber of Commerce.

Dr. Sreekar Krishna is Principal/Partner in the Digital Technology practice in KPMG US. He specializes in innovation and creative business solution delivery using AI-enabled Digital Transformation, specifically, Big Data Machine Learning and Knowledge from Unstructured Data. Working with KPMG's customers, he has helped to evangelize the transition of various industry partners from a traditional data warehousing to a sophisticated Big Data Analytics driven culture. He focuses on developing successful operating models for Enterprise-scale AI and other associated technologies such as Natural Language Processing, Billion-scale Data Processing, Ranking, Information Retrieval and Simulation.