Saad M. Khan is Director, AI and Machine Learning at ACTNext. With expertise in computer vision and machine learning, his interests span a spectrum of multidisciplinary research that includes NLP, multimodal analytics, psychometrics, educational games and simulations. At ACTNext, he leads research in the development of AI and machine learning based next generation learning and assessment systems. He is currently PI on programs funded by the US Army and IES to develop innovative solutions for assessment of collaborative problem solving and team skills. Prior to joining ACT, Saad was at ETS and SRI International where he spearheaded research on multimodal analytics in educational assessment and led design and development of intelligent training/learning systems that can adapt to both changing pedagogical objectives and trainee behavior. He led the development and transition of APELL: Automated Performance Evaluation and Lessons Learned, an immersive, interactive, mixed-reality training system that provides real-time sensing and automated analysis of trainee actions in Military Operations on Urban Terrain (MOUT) sites. He has served as PI on programs in immersive training, human performance assessment, and automated target recognition funded by DARPA, ONR, and AFRL among others. His work in automated image-based localization earned an Honorable Mention award at the International Conference of Computer Vision 2005. He has authored over 30 publications and holds four issued patents. Khan received a Ph.D. in computer science from the University of Central Florida in 2008. He is a senior member of IEEE and chairs the Signal Processing Chapter for the Princeton/Central Jersey Section.