Denis Gudovskiy is a Distinguished AI Engineer at Panasonic North America where he conducts R&D activities of various core AI methods, including multimodal and hardware-efficient agents, supervised and RL training pipelines, and robustness to out-of-distribution scenarios. He has over 10 years of algorithm development experience in machine learning and computer vision. Denis has an MS in Computer Engineering from the University of Texas at Austin.

In one of such projects he proposed and implemented a method of hardware-efficient neural network quantization and compression techniques for autonomous vehicles with stringent power and performance requirements. In the most recent work, Denis has demonstrated how data annotation costs could be significantly reduced with a use of advanced algorithms. His papers and demos are presented and published in top-tier machine learning and computer vision conferences such as NeurIPS, AAAI, CVPR, ECCV, ICLR and ICASSP.

Denis sees corporate research as an important layer between moonshot academia projects and clearly-defined product development roadmaps in business units. His goal is to find and promote viable academia-grade opportunities at Panasonic within the exponentially growing landscape of AI applications.