Adithya Narayan is a Graduate Research Assistant at Carnegie Mellon University’s Human Sensing Lab, advised by Prof. Fernando De la Torre, and a Master’s student in the Computer Vision program at CMU’s Robotics Institute. His research interests span computer vision, artificial intelligence, and computer graphics. Adithya has industry experience as a Machine Learning Engineer at Arintra, where he applied large language models to medical coding, and at Klothed, collaborating with Prof. James O’Brien on clothed human mesh recovery for virtual try-on applications.
Utkarsh Ojha is a postdoctoral researcher at Carnegie Mellon University, collaborating with Prof. Fernando De la Torre. He earned his Ph.D. from the University of Wisconsin–Madison under Prof. Yong Jae Lee. His research focuses on generative models in computer vision, aiming to extend capabilities beyond training distributions. Notable contributions include developing universal fake image detectors that generalize across generative models, methods for interactive batch image editing, and techniques for disentangling object shape and appearance across domains. His work has been presented at top conferences such as CVPR, NeurIPS, and ICLR.
Dr. Fernando De la Torre Frade is an Associate Research Professor at Carnegie Mellon University’s Robotics Institute, where he leads the Human Sensing Lab. His research encompasses machine learning, signal processing, and computer vision, with a particular focus on understanding human behavior through multimodal sensors such as video and body sensors. He is especially interested in component analysis methods, aiming to develop a unified theory for various techniques like kernel PCA and multidimensional scaling, to efficiently model high-dimensional data. Dr. De la Torre’s work also includes real-time face tracking, recognition, and expression analysis, with applications spanning human health, computer graphics, biometrics, and human-machine interaction.