Alpan Raval
About Alpan Raval
Alpan Raval serves as the Chief Scientist for AI and Machine Learning at Wadhwani AI in Mumbai, India, a position he has held since 2021. With a strong academic background in Physics and extensive experience in various roles related to data science and machine learning, he has contributed to several organizations, including LinkedIn and Amazon.
Work at Wadhwani AI
Alpan Raval serves as the Chief Scientist for AI and Machine Learning at Wadhwani AI, a position he has held since 2021. In this role, he focuses on advancing AI technologies to address various challenges. Prior to this, he was the Head of Data Science at the same organization from 2019 to 2021. His work at Wadhwani AI is centered around creating AI solutions that enhance service delivery while minimizing disruption to existing public systems.
Education and Expertise
Alpan Raval earned his PhD in Physics from the University of Maryland, where he studied from 1990 to 1996. He also holds an M.Sc. in Physics from the Indian Institute of Technology, Kanpur, completed in 1990. His educational background provides a strong foundation for his expertise in AI and machine learning, which he has applied throughout his career in various roles.
Professional Background
Alpan Raval has a diverse professional background in AI and machine learning. He has held significant positions at several organizations, including LinkedIn, where he worked as a Senior Manager in Machine Learning and previously as Manager of Content Quality & Multimedia Machine Learning. His experience also includes roles at Amazon as a Senior Machine Learning Scientist and at D. E. Shaw Research as a Research Scientist. Additionally, he has an academic background as an Assistant and Associate Professor at Keck Graduate Institute & Claremont Graduate University.
Research and Views on AI Challenges
Raval has expressed insights regarding the complexities involved in developing AI models, particularly when dealing with unstable, incomplete, and erroneous data sources. He emphasizes the importance of designing AI solutions that effectively reach end users while ensuring that existing public systems remain largely unaffected. His views contribute to ongoing discussions in the field of AI regarding data integrity and user-centric design.