Madhavun Candadai

Expertise in 2D and 3D Computer Vision, with experience leading projects involving classical and deep learning approaches. Led teams to develop products in this space from conception, research all the way to deployment and support. Current role also includes creating technical roadmaps and creating the vision for the CV/ML team. I received my Ph.D. in Cognitive Science, with a minor in Computer Science in May 2020 from Indiana University, Bloomington under the mentorship of Dr. Eduardo Izquierdo.

Areas of research interest: 2D/3D Computer Vision, Robotics, Reinforcement Learning, Computational Neuroscience, Evolutionary Robotics, Information Theory, Complex Systems, Dynamical Systems Theory, Artificial Life.

Education and Experience

Sr. Engineering Manager: Perception
Path Robotics

Jun 2023 - Present


Engineering Manager: Perception
Path Robotics

Feb 2022 - Jun 2023


Artificial Intelligence Research Scientist
Path Robotics

June 2020 - Feb 2022


Research Intern
Intel A.I. Labs

Summer 2018


Ph.D., Cognitive Science (minor in Computer Science)
Indiana University, Bloomington.

2015 - 2020

Student Reseacher
Cincinnati Children's Hospital Medical Center

2015

M.S. Electrical Engineering
University of Cincinnati

2015

Associate System Engineer
IBM

2011 - 2012

B.Tech. Electronics and Communication Engineering
Amrita School of Engineering

2011

Patents

"Machine learning logic-based adjustment techniques for robots." Lonsberry, Alexander, Andrew Lonsberry, Nima Ajam Gard, Madhavun Candadai Vasu, and Eric Schwenker. U.S. Patent Application 18/056,443.
"Autonomous Welding Robots." Lonsberry, Alexander, Andrew Lonsberry, Nima Ajam Gard, Colin Bunker, Fabian Benitez Quiroz, Madhavun Candadai Vasu. U.S. Patent Application US11648683B2.
[Patent pending] Title: Autonomous Assembly Robots. Type of application: US non-provisional.
[4 more patents filed]

Selected Publications

See Google Scholar or resume for an updated list of papers, talks and posters.

Computational Neuroscience / Cognitive Science

Candadai, Madhavun, and Eduardo J. Izquierdo. "Sources of predictive information in dynamical neural networks." Scientific reports 10.1 (2020): 1-12. [pdf]
Candadai, Madhavun "Information theoretic analysis of computational models as a tool to understand the neural basis of behaviors" submitted to arxiv [pdf]
Benson, Lauren V., Madhavun Candadai, and Eduardo J. Izquierdo. "Neural reuse in multifunctional neural networks for control tasks." Artificial Life Conference Proceedings. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2020. [pdf]
Candadai, Madhavun, Matthew Setzler, Eduardo J. Izquierdo, and Tom Froese. "Embodied dyadic interaction increases complexity of neural dynamics: A minimal agent-based simulation model." Frontiers in Psychology 10 (2019): 540. [pdf]
Vasu, Madhavun Candadai, and Eduardo J. Izquierdo. "Multifunctionality in embodied agents: Three levels of neural reuse." arXiv preprint arXiv:1802.03891 (2018). Proceedings of the 40th Cognitive Science Conference, 2018. [pdf]
Vasu, Madhavun Candadai , and Eduardo J. Izquierdo. "Information Bottleneck in Control Tasks with Recurrent Spiking Neural Networks". International Conference on Artificial Neural Networks. (ICANN) Springer, Cham, 2017. [pdf]
Vasu, Madhavun Candadai , and Eduardo J. Izquierdo. "Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks." In Proceedings of Genetic and Evolutionary Computing Conference, pp. 75-82. GECCO, 2017. [pdf]
Nominated for Best Student Paper, 2017, by International Society for Artificial Life: Student Chapter

Machine Learning / A.I.

Leite, Abe, Madhavun Candadai, and Eduardo J. Izquierdo. "Reinforcement learning beyond the Bellman equation: Exploring critic objectives using evolution." Artificial Life Conference Proceedings. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2020. [pdf]
Todd, Graham, Madhavun Candadai, and Eduardo J. Izquierdo. "Interaction between evolution and learning in nk fitness landscapes." Artificial Life Conference Proceedings. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2020. [pdf]
Dwiel, Zach*, Madhavun Candadai*, Mariano J. Phielipp. (2019, November). On Training Flexible Robots using Deep Reinforcement Learning. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. (Accepted) [pdf] (*equal contribution)
Dwiel, Zach, Madhavun Candadai, Mariano J. Phielipp, Arjun K. Bansal. "Hierarchical policy learning is sensitive to goal space design". In Task-Agnostic Reinforcement Learning Workshop (TARL), of International Conference on Learning Representations (ICLR), 2019. [pdf]
Candadai, Madhavun, Aashay Vanarase, Mei Mei, and Ali A. Minai. "ANSWER: An unsupervised attractor network method for detecting salient words in text corpora." In International Joint Conference on Neural Networks 2015, pp. 1-8. IEEE, 2015. [pdf]

Other

Candadai, M., & Izquierdo, E. J. (2019). infotheory: A C++/Python package for multivariate information theoretic analysis. arXiv preprint arXiv:1907.02339. [pdf]

Dissertation

Candadai, M. (2020). Bits from Behaviors: Understanding Function Using Information in Embedded, Embodied, and Dynamical Neural Networks (Doctoral dissertation, Indiana University). [pdf]
Candadai Vasu, M. (2015). ANSWER: A Cognitively-Inspired System for the Unsupervised Detection of Semantically Salient Words in Texts (Master's thesis, University of Cincinnati). [pdf]

Open Source Projects

Infotheory

A C++/Python package for information theoretic analysis.