Achin Bhowmik

Achin Bhowmik

Starkey, CTO & Executive Vice President of Engineering

Achin Bhowmik, PhD

Chief Technology Officer at Starkey Hearing & Adjunct Professor at Stanford University

Dr. Achin Bhowmik is the Chief Technology Officer and Executive Vice President of Engineering at Starkey, a global leader in hearing technology, where he leads the transformation of hearing aids into multifunctional, AI-powered communication and health devices. Previously, he served as Vice President and General Manager of Perceptual Computing at Intel, where he founded RealSense and led pioneering work in three-dimensional sensing, computer vision, interactive devices, and autonomous systems.

Dr. Bhowmik is an adjunct professor at Stanford University and an affiliate faculty member of the Stanford Institute for Human-Centered Artificial Intelligence and the Wu Tsai Neurosciences Institute. He has authored more than 200 publications, including three books and over 80 patents worldwide.

He is a Fellow of the Society for Information Display (SID), the Institute for Electrical and Electronics Engineers (IEEE), the International Academy of Artificial Intelligence Sciences (AAIS), and the International Artificial Intelligence Industry Alliance (AIIA). He has served as the President of SID and is on the boards of RealSense, Mojo Vision, AstraNu, and the National Captioning Institute. His work has received numerous honors, including TIME’s Best Inventions, the Red Dot Design Award, and the Artificial Intelligence Excellence Award.

Fundamentals of Artificial Intelligence: Vision to Language Models

Abstract：

Artificial Intelligence (AI) is transforming the way we interact with technology, reshaping industries, and unlocking possibilities once thought to be science fiction. This short course provides a comprehensive introduction to AI technologies, designed to engage both newcomers and seasoned professionals.

We will begin with the fundamentals of machine learning and explore how algorithms learn patterns from data. From there, we examine convolutional neural networks for image recognition and vision tasks. The course then advances to transformer architectures that underpin today’s large language models (e.g., GPT, Gemini, Grok, Llama, DeepSeek, etc.), explaining how attention mechanisms enable multimodal reasoning and perception. Through drawing parallels with human perception and cognitive neuroscience, exploring the computational implementations, and engaging with interactive demonstrations, participants will gain an appreciation of how AI systems process, perceive, and generate information.

Whether you're curious about AI’s capabilities or looking to enhance your understanding of recent advancements, this course will equip you with the knowledge to grasp the current technology landscape and inspire you to envision its future applications. No prior technical expertise is required, just a basic understanding of linear algebra from high school or college.