PHI Lab Team
Gautam Reddy Nallamala
Scientist
Gautam Reddy Nallamala works at the intersection of physics, quantitative biology and machine learning. In particular, he is interested in developing algorithmic approaches to animal behavior, with the goal of understanding the strategies that animals employ to perform tasks necessary for survival. Ideas from sequential optimization and reinforcement learning form the backbone of his work. At PHI lab, he seeks to understand how neural representations of the world are leveraged to solve complex spatial and abstract tasks.
Awards
xxxFellow (2021)
xxxxInvestigator (2019)
xxxMurray Hopper Award (2015)
Presixxxdential Early Career Award for Scientists and Engineers (PECASE) (2011)
xxxFellowship (2011)
xxxxFaculty Fellowship (2011)
xxxxFellowship (2010)
Publications
- Epistasis and evolution: recent advances and an outlook for prediction
By Milo S Johnson, Gautam Reddy & Michael M Desai
BMC biology 2023
- Energy-positive soaring using transient turbulent fluctuations
By Danyun He, Gautam Reddy & Chris H Rycroft
arXiv preprint arXiv:2304.05983 2023
- Inferring Sparse Structure in Genotype-phenotype Maps
By Samantha Petti , Gautam Reddy & Michael M. Desai
bioRxiv 2022
- Alternation Emerges as a Multi-modal Strategy for Turbulent Odor Navigation
By Nicola Rigolli, Gautam Reddy, Agnese Seminara & Massimo Vergassola
Elife 2022
- A Reinforcement-based Mechanism for Discontinuous Learning
By Gautam Reddy
Proceedings of the National Academy of Sciences 2022
- Sticky Issues in Turbulent Transport
By Antonio Celani, Gautam Reddy & Massimo Vergassola
Annales Henri Poincaré 2022
- A Lexical Approach for Identifying Behavioural Action Sequences
By Gautam Reddy, Laura Desban, Hidenori Tanaka, Julian Roussel, Olivier Mirat & Claire Wyart
PLoS Computational Biology 2022
- Sector Search Strategies for Odor Trail Tracking
By Gautam Reddy, Boris I. Shraiman & Massimo Vergassola
Proceedings of the National Academy of Sciences 2022
- Reinforcement Waves as a Mechanism for Discontinuous Learning
By Gautam Reddy
bioRxiv 2022
- Olfactory Sensing and Navigation in Turbulent Environments
By Gautam Reddy, Venkatesh N. Murthy & Massimo Vergassola
Annual Reviews 2021
- Global Epistasis Emerges from a Generic Model of a Complex Trait
By Gautam Reddy & Michael M. Desai
Elife 2021
- Alternation Emerges as a Multi-modal Strategy for Turbulent Odor Navigation
By Nicola Rigolli, Gautam Reddy, Agnese Seminara & Massimo Vergassola
bioRxiv 2021
- Odorant Receptor Inhibition is Fundamental to Odor Encoding
By Patrick Pfister, Benjamin C. Smith, Barry J. Evans, Jessica H. Brann, Casey Trimmer, Mushhood Sheikh, Randy Arroyave, Gautam Reddy, Hyo-Young Jeong, Daniel A. Raps, Zita Peterlin, Massimo Vergassola & Matthew E Rogers
Current Biology 2020
- Antagonistic Odor Interactions in Olfactory Sensory Neurons are Widespread in Freely Breathing Mice
By Joseph D. Zak, Gautam Reddy, Massimo Vergassola & Venkatesh N. Murthy
Nature Communications 2020
- Receptor Crosstalk Improves Concentration Sensing of Multiple Ligands
By Martín Carballo-Pacheco, Jonathan Desponds, Tatyana Gavrilchenko, Andreas Mayer, Roshan Prizak, Gautam Reddy, Ilya Nemenman & Thierry Mora
Physical Review E 2019
- Glider Soaring via Reinforcement Learning in the Field
By Gautam Reddy, Jerome Wong-Ng, Antonio Celani, Terrence J. Sejnowski & Massimo Vergassola
Nature 2018
- Antagonism in Olfactory Receptor Neurons and its Implications for the Perception of Odor Mixtures
By Gautam Reddy, Joseph D Zak, Massimo Vergassola & Venkatesh N. Murthy
Elife 2018
- Demystifying Excessively Volatile Human Learning: A Bayesian Persistent Prior and a Neural Approximation
By Chaitanya Ryali, Gautam Reddy & Angela J. Yu
Advances in Neural Information Processing Systems 2018
- Learning to Soar in Turbulent Environments
By Gautam Reddy, Antonio Celani, Terrence J. Sejnowski & Massimo Vergassola
Proceedings of the National Academy of Sciences 2016
- Infomax Strategies for an Optimal Balance between Exploration and Exploitation
By Gautam Reddy, Antonio Celani & Massimo Vergassola
Journal of Statistical Physics 2016
