Nishad Gothoskar

I am a research engineer at Vicarious AI where I build intelligence for robots. By combining insights from neuroscience and probabilistic generative modeling, we seek to design algorithms that can match humans' ability to learn and generalize.

From 2014-2017 I was an undergraduate at CMU SCS, where I studied Math and Computer Science. I did summer internships at zSpace, Google, and Uber ATG. I did research on mobile privacy, activity recognition, and search-based planning algorithms.

I was an engineer at Uber ATG, where I built prediction systems for autonomous vehicles, advised by Jeff Schneider and Ian Dewancker. My contributions were in predicting interactions between vehicles/bikers/pedestrains.

I was a visiting research associate at MIT CSAIL in the Learning and Intelligent Systems Group. I was advised by Leslie Pack Kaelbling and Tomas Lozano Perez. I implemented a Task and Motion Planning system that can learn primitives and plan.

Developed a method for learning to control a robot using visual feedback with no calibration or prior information about the setup.

Cognitive maps enable us to learn the layout of environments, encode and retrieve episodic memories, and navigate vicariously for mental evaluation of options.

CHMMs are a constrained version of HMMs with a simple sparsity structure that enforces many hidden states to map deterministically to the same emission state. We demonstrate the effectiveness of this model in various data domains.

Worked on road user trajectory prediction to enable planning. I built system for modeling interactions between road users.

Mobile applications prey on user's sensitive data. We developed an application that monitors the flow of private information and protects their data from being leaked.

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