Interview with Shalini Ghosh, Director of AI Research at Samsung Research

You have a really exciting career what with your work at Google Research, SRI International and of course now at Samsung Research, but before we get stuck in, how did you start your work in AI?
You’re now at Samsung Research America, can you tell me a bit more about your current work there
At the Deep Learning Summit you’re going to be presenting on DL for Incremental Object Detection and Visual Dialog - can you briefly explain this and give us a summary of your talk?
What are some of the recent advancements in situated AI and multi-modal learning that have helped your work?
What are some of the real world applications that you’re currently working on?
There’s been a lot of discussion around the ethics of AI and how systems can be built to be fair or unbiased - how do you think this can be done, and are we close to this?
How can we ensure that AI doesn’t inherit some of the intrinsic faults of humans?
What industries do you think are going to be most positively transformed by AI/DL in the coming years?
What’s next for you in your work?
Where can we keep up to date with you - do you have Twitter or a website?

Shalini Ghosh, Principal Scientist (Global) and ML Research Leader, Visual Display Intelligence Lab (Smart TV Division) at Samsung Research America

Dr. Shalini Ghosh is the Director of AI Research at the Artificial Intelligence Center of Samsung Research America, where she leads a group works on Situated AI and Multi-modal Learning (i.e., learning from computer vision, language, and speech). She has extensive experience and expertise in Machine Learning (ML), especially Deep Learning, and has worked on applications to multiple domains. Before joining Samsung Research, Dr. Ghosh was a Principal Computer Scientist in the Computer Science Laboratory at SRI International, where she has been the Principal Investigator/Tech Lead of several impactful DARPA and NSF projects. She was a Visiting Scientist at Google Research in 2014-2015, where she worked on applying deep learning (Google Brain) models to dialog systems and natural language applications. Dr. Ghosh has a Ph.D. in Computer Engineering from the University of Texas at Austin. She has won several grants and awards for her research, including a Best Paper award and a Best Student Paper Runner-up award for applications of ML to dependable computing. Dr. Ghosh is also on the program committee of multiple impactful conferences and journals in ML and AI (e.g., NIPS, ICML, KDD, AAAI), has served as invited panelist in multiple panels, and was invited to be a guest lecturer at UC Berkeley.

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