About me

I'm a Postdoctoral Fellow working with Nao Uchida, Venkatesh Murthy and Cengiz Pehlevan at the intersection of neuroscience and AI. I'm interested in how the structure of distributed representations affects learning and inference in biological and artificial networks. 

  • Distributed reinforcement learning: How can the functional heterogeneity in the basal ganglia can be understood through the lens of distributed reinforcement learning? I am exploring whether we can extend ideas fom distributional RL to other dimensions, such as learning over multiple timescales, and find evidence of these computations in the basal ganglia. 
  • Probabilistic representations in early sensory processing:  How can early sensory systems implement probablisitic computations at the speed of perception? With a focus on the olfactory system, I am investigating how representations in early sensory systems can leverage the geometry of the sensory world, particularly when as in olfaction there is no intuitive stimulus space, to perfom fast inference of complex sensory scenes (Masset*, Zavatone-Veth* et al., 2022). I am also interested in how to learn such representations and the relationship between single-neuron and population representations (Masset*, Qin*, Zavatone-Veth*, Biological Cybernetics, 2022).
  • Neural computations underlying complex cognitive computations: How can we study complex cognitive functions in rodents? I am interested in the design of experimental paradigms that allow us to probe such cognitive variables (such as decision confidence or sunk costs) in rodents models (Ott*, Masset* & Kepecs, 2019 ; Ott*, Masset* et al, Science Advances, 2022). In my PhD, I showed that the representations of single neurons in orbitofrontal cortex possess the properties of abstract representations of confidence (Masset*,Ott* et al., Cell, 2020). 
  • Computational tools for neural data analysis:  Along the way I also develop computational tools to study the functional role of neural populations. During my PhD with Adam Kepecs, I developed matrix and tensor based decomposition methods to indentify functional cell types (Hirokawa et al., Nature, 2019). I am currently collaborating witih Bahareh Tolooshams and Demba Ba to apply algorithm unrolling to single trial analysis of neural data. 

I did my undergrad at the University of Cambridge in Information and Computer Engineering and then did a Masters in Cognitive Science at the EHESS in Paris during which I worked with Sophie Denève at the Ecole Normale Superieure on Bayesian models of attentional modulation. I then moved to the School of Biological Sciences at Cold Spring Harbor Laboratory where i did my PhD thesis work with Adam Kepecs

You can find a full list of my publications here or on my Google Scholar profile