Vaisakh Shaj

Doctoral Student at KIT, Germany

avatar.JPG

Room 215

Adenauerring 4, Gebäude 50.21,

Karlsruhe, Germany 76139

Welcome to my personal website. I am an AI researcher currently pursuing my PhD in Probabilistic Machine Learning and Robotics under Prof Gerhard Neumann at the ALR Lab, Karlsruhe Institute Of Technology. Before that I was a Data Scientist at the cybersecurity firm McAfee (Intel Security). Previously I worked with Intel for 2 years. I hold a post graduate degree in Machine Learning and Computing from the Indian Institute of Space Science and Technology.

My doctoral thesis focussed on building “World models With Hierarchical Temporal Abstractions” based on probabilistic and Bayesian principles. My primary interests lie in both understanding human intelligence and replicating its capabilities within AI agents, using mathematical and computational tools. These agents are designed to continually learn within dynamic, non-stationary environments and tackle tasks that require long-term planning. I employ neural network architectures grounded in probabilistic principles to develop these models.

🌈Diversity and Inclusion Statement: I care deeply about making work places more diverse and the inclusion of women, LGBTQ+ and under-represented minorities in AI research. I believe innovation/creativity thrives under diversity in perspectives and life experiences.

news

Dec 9, 2023 Attending NeurIPS 2023 in New Orleans, US.
Sep 21, 2023 PhD work “Multi Time Scale World Models” accepted in NeurIPS 2023 as a Spotlight (Top 3% of all submitted papers).
Jan 21, 2022 PhD work “Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios” accepted in International Conference On Learning Representations(ICLR) 2022.
Oct 1, 2020 PhD work “Action Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning” accepted in Conference on Robot Learning(CoRL) 2020. Video
Apr 15, 2019 Our Paper “Zero Shot Knowledge Distillation in Deep Networks” with Gaurav and Konda Reddy got accepted in ICML 2019.

latest posts

selected publications

2023

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    Multi Time Scale World Models
    Vaisakh Shaj, Saleh Gholam Zadeh, Ozan Demir, and 2 more authors
    2023

2021

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    Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
    Vaisakh Shaj, Dieter Büchler, Rohit Sonker, and 2 more authors
    In International Conference on Learning Representations, 2021

2020

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    Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
    Vaisakh Shaj, Philipp Becker, Dieter Büchler, and 5 more authors
    In Conference on Robot Learning, 2020

2019

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    Zero-shot knowledge distillation in deep networks (ICML 2019)
    Vaisakh Shaj*, Gaurav Kumar Nayak*, Konda Reddy Mopuri*, and 2 more authors
    In International Conference on Machine Learning, 2019