Vaisakh Shaj
Doctoral Student at KIT, Germany
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
2021
2020
- Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics LearningIn Conference on Robot Learning, 2020
2019
- Zero-shot knowledge distillation in deep networks (ICML 2019)In International Conference on Machine Learning, 2019