Steve J Rennie
Director, Research, Fusemachines

Steven Rennie is the director of research at Fusemachines, an AI solutions and services company whose mission is to make AI accessible to everyone through education, software, and services. Previously, Steve worked at the IBM TJ Watson Research Center, where he led the Multimodal Group in the Watson Division. He has published over 50 peer-reviewed papers on machine learning and AI applications, including source separation, robust automatic speech recognition (ASR), multitalker speech recognition, LVCSR, graphical models, data-driven computational auditory scene analysis, machine translation, probabilistic array processing, reinforcement learning, and image captioning. He has served as a committee member for a number of leading conferences, including ICLR, AI-STATS, ACL, COLING, SIGGRAPH, INTERSPEECH, ICASSP, and ASRU, TASL, ICML, and NIPS. Steve was recently elected to the IEEE’s prestigious Speech and Language Technology Committee (SLTC) and has advanced the state-of-the-art in performance on several AI challenges, including the Pascal Speech Separation and Recognition Challenge, the Aurora 4 Noise Robust ASR Database, the Switchboard LVCSR Evaluation Benchmark, and most recently, the MSCOCO Image Captioning Challenge. He holds a PhD in electrical and computer engineering from the University of Toronto, with a dissertation titled Graphical Models for Speech Recognition in Adverse Environments. His primary research interest is in developing novel, practical algorithms for information processing that leverage graphical modeling and deep, reinforcement, and adversarial learning techniques.