Abhishek Naik

AI Researcher

Ottawa, Ontario, Canada1 yr 6 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Ph.D. in Artificial Intelligence with a focus on RL.
Developed algorithms for long-lived decision-making systems.
Passionate about applying AI in space exploration.

Stackforce AI infers this person is a Reinforcement Learning specialist with a focus on AI applications in the tech and space industries.

Contact

abhisheknaik22296@gmail.com LinkedIn

Skills

Core Skills

Reinforcement LearningDeep LearningMachine Learning

Other Skills

AIAlgorithmsAutonomous DrivingCC++Classifier DevelopmentComputer VisionData AnalysisDeep Reinforcement LearningJavaLaTeXLarge-scale systemsLinuxMatlabProgramming

About

I just finished my Ph.D. at the University of Alberta with Richard Sutton, where I developed simple and practical algorithms from first principles for long-lived artificial decision-making systems. In particular, I developed the algorithms within the reinforcement-learning framework for continuing (non-episodic) problems—in which the agent-environment interaction goes on ad infinitum—with the goal of maximizing the average reward obtained per step. Empirically, my algorithms are easy to implement and use. I love space! I want to use my AI expertise in space sciences and technology. I envision a future where artificial systems will have human-like intelligence and adaptability, making space exploration significantly easier and safer for our species.

Experience

National research council canada / conseil national de recherches canada

Postdoc Fellow

Sep 2024 – Present · 1 yr 6 mos · Ottawa, Ontario, Canada

RL/AI research with a particular focus on applications in the space industry.

Reinforcement LearningAI

Google

Research Intern

Jun 2022 – Sep 2022 · 3 mos

Using reinforcement learning to improve large-scale recommender systems at Google Research's Brain team.

Reinforcement LearningLarge-scale systems

Huawei

Research Intern

Jun 2019 – Sep 2019 · 3 mos · Edmonton, Alberta, Canada

Exploring the continuing (non-episodic) setting of reinforcement learning for real-world applications

Reinforcement LearningReal-world applications

Intel labs

Research Intern

May 2017 – Jul 2017 · 2 mos · Bengaluru Area, India

Deep Reinforcement Learning for Autonomous Driving.
Started work on a risk-averse imitation learning approach that achieved up to 89% improvement over the then state-of-the-art on standard robotic control tasks.

Deep Reinforcement LearningAutonomous DrivingDeep Learning

Purdue university

Research Intern

May 2016 – Jul 2016 · 2 mos · Lafayette, Indiana Area

Machine Learning on Social Networks.
Analyzed the expected activity-lifespan of social-media users based on their early profile activity. Curated and released a rich social-media dataset for public use.

Machine LearningSocial Networks

Amazon

Software Engineer Intern

May 2015 – Jul 2015 · 2 mos · Chennai Area, India

Machine Learning for Kindle.
Helped build a classifier to determine the start-reading-location of books.  Now in production, this feature helps Kindle users start reading a book quicker after downloading it, without having to flip through pages like acknowledgements or copyright notices.

Machine LearningClassifier Development