As we experience the world, our brain learns internal models of the world so that we can solve new problems quickly.
How do neural circuits and algorithms learn these models, and how do we decide the next best action?
It has also been proposed that distortions to these internal models and learning algorithms contribute to psychiatric disorders.
Can we develop mathematical models to understand these phenomena?
Can we improve existing artificial systems and devise tools to alleivate disorders?
To explore these questions, I develop artificially intelligent agents, grounded to theory and experiments,
to understand learning computations for intelligence, and when it might fail.
Based on these insights, I hope to improve existing artificial systems, and develop technologies that can improve learning outcomes and alleviate learning disabilities.
Currently, I am a postdoctoral fellow in the Harvard Machine Learning Foundations Group,
developing reinforcement learning agents to understand representational learning in biological and artificial systems. I am advised by
Cengiz Pehlevan,
Demba Ba,
Lucas Janson and
Boaz Barak.
Previously, I was a research scientist at the Centre for Frontier AI Research (CFAR), A*STAR developing
vision-language reasoning datasets and models. I was advised by
Cheston Tan.
In 2022, I completed my Ph.D. in Computational Neuroscience at the National University of Singapore (NUS)
under the Integrative Sciences and Engineering Programme (ISEP).
My doctoral thesis was to develop a biologically plausible reinforcement learning agent that learned
new paired associations after a single example.
I was co-advised by Andrew Tan and
Shih-Cheng Yen and collaborated with
Camilo Libedinsky.
I completed my B.Sc in Life Sciences in 2017 at the National University of Singapore where
I worked on Brain-Computer Interfaces to control wheelchair using either human EEG or macaque intracortical spike data.
Besides research, I co-founded Nugen.ai that hopes to characterize
affective states during learning to aid parents and teachers in personalizing education.
I also love to tour either on my motorcycle or backpacking,
and be involved in the arts scene.
2024: Analytical Connectionism Summer School, Center for Computational Neuroscience, Flatiron Institute
2023 - 2025: Postdoctoral Fellow (Theory of representational learning), School of Engineering and Applied Sciences, Harvard University
2022 - 2023: Research Scientist (Vision-Language reasoning), Centre for Frontier AI Research, A*STAR
2018 - 2022: Ph.D. Candidature (Biologically plausible One-shot learning), National Unviersity of Singapore
2019: Center for Brains, Minds and Machines (CBMM) Summer School 2019, Massachusetts Institute of Technology
2017 - 2018: Research Engineer (Social roles & relationships recognition), A*STAR Artificial Intellignce Initiative (A*AI)
2013 - 2017: B.Sc. Life Sciences + University Scholars Programme (USP) + Special Programme in Science (SPS)
Cloos, N., Kumar, M. G., Manoogian, A. Cueva, C. J. Rhoads, S. A. (2024). Generating and Validating Agent and Environment Code for Simulating Realistic Personality Profiles with Large Language Models. NeurIPS workshops 2024 Short Paper
Lin, Z.*, Azaman, H.*, Kumar, M. G., Tan, C. (2024). Composing Word Groups using Visually Grounded Reinforcement Learning. Computer Vision and Pattern Recognition (CVPR) workshops 2024 https://arxiv.org/abs/2309.04504 [GitHub] [Poster]
Kumar, M. G., Ayyadhury, S., Murugan, E. (2024). Trends Innovations Challenges in Employing Interdisciplinary Approaches to Biomedical Sciences. In: Parthasarathy, K., Manikkam, R. (eds) Translational Research in Biomedical Sciences: Recent Progress and Future Prospects. Springer, Singapore. https://doi.org/10.1007/978-981-97-1777-4_20
Lee, C.*, Kumar, M. G.*, Tan, C. (2023). DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using Determiners. International Conference on Computer Vision (ICCV) 2023 https://arxiv.org/abs/2309.03483 [GitHub] [Poster]
Kumar, M. G., Tan, C., Libedinsky, C., Yen, S. C., & Tan, A. Y. Y. (2023). One-shot learning of paired association navigation with biologically plausible schemas. arXiv preprint arXiv:2106.03580. https://arxiv.org/abs/2106.03580 [GitHub] [Poster]
Kumar, M. G., Tan, C., Libedinsky, C., Yen, S. C., & Tan, A. Y. Y. (2022). A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation. Cerebral Cortex. https://doi.org/10.1093/cercor/bhab456 [GitHub]
Kumar M. G., Kai Keng Ang, Rosa Q. So. (2017). Reject Option to reduce False Detection Rates for EEG-Motor Imagery based BCI. In Engineering in Medicine and Biology Society, EMBC 2017. 39th Annual International Conference of the IEEE. https://doi.org/10.1109/EMBC.2017.8037479
Kumar M. G. (2023). Biologically plausible computations underlying one-shot learning of paired associations . Scholarbank@NUS. https://scholarbank.nus.edu.sg/handle/10635/238485 [Thesis]
Look forward to connecting with you!