Who am I

I'm a Research Scientist in Artificial Intelligence at DeepMind.
I investigate ways to compress and scale up neural networks, and I bring my scientific approach to Machine Learning research.
I was a postdoc at Facebook AI Research (FAIR) in Menlo Park, CA.
I earned my PhD in Physics from Yale University.
I worked in experimental High Energy Physics as a member of the ATLAS experiment at CERN.
I love sports, technology, fine dining, and design.

CV Publications Public Speaking GitHub
DeepMind

DeepMind

At DeepMind, our mission is to solve intelligence and to use it to solve everything else. My personal focus is to solve intelligence intelligently, thoughtfully, and sustainably, while scaling up its capabilities.

FAIR

Facebook AI Research (FAIR)

I spent two years as a Postdoctoral Researcher at Facebook Artificial Intelligence Research in Menlo Park, CA. My work focused on empirically characterizing neural network dynamics in the over-parametrized and under-parametrized regimes using pruning as a tool for model compression. I am broadly interested in the science of deep learning, with a focus on emergent behavior in constrained networks. Other topics on my mind include fairness, interpretability, and uncertainty estimation in machine learning.

LBNL view

Berkeley National Lab

For five years, I held a concurrent appointment at LBNL as a visiting Research Affiliate with NERSC (the National Energy Research Scientific Computing center). I collaborated on projects at the intersection of physics and computing. Joint work focused on training Generative Adversarial Networks (GANs) to speed up High Energy Physics simulation.

ATLAS Detector CERN

ATLAS Experiment at CERN

I spent five wonderful years on and off in Geneva, Switzerland where I conducted research for my PhD thesis as a member of the ATLAS collaboration at CERN (European Council for Nuclear Research). I developed software for the optimization of particle identification for the flavor tagging group, and I was involved in the LHC-wide Machine Learning working group to push for the adoption of modern ML in High Energy Physics and the improvement of libraries and tools. Find out more about my work on Machine Learning at CERN here. My physics analysis focused on the search of Higgs boson pairs decaying into $\gamma\gamma$bb.

Yale ATLAS Tipton Group

Yale University

I earned my PhD from Yale, where I worked with Prof. Tobias Golling and Prof. Paul Tipton on the design and application of Deep Learning algorithms to develop taggers for the identification of top quarks and gauge bosons in boosted topologies. During my ATLAS qualification task, I worked on optimizing b-tagging techniques, by including track-jets for Run II at the LHC (Large Hadron Collider). At Yale, I was a Teaching Fellow for two years, a McDougal Fellow and a member of the Graduate Student Assembly.

Berkeley Perlmutter

UC Berkeley

I graduated from Cal in 2013 with a double major in Astrophysics and Physics. During my undergraduate studies, I worked across different research fields, including theoretical, observational and computational galactic dynamics, search for extraterrestrial intelligence at the SETI Institute, space weather at the Space Sciences Lab, and antimatter production with the AEgIS experiment at CERN. At Berkeley, I discovered my passion for teaching while tutoring at the Athletic Study Center and designing a new course named Sense & Science & Sensibility with professors Saul Perlmutter, Rob MacCoun and John Campbell.

Upcoming Dates

  • Dec 5 - 12

    Vancouver, BC, Canada

    • all day - NeurIPS
      Remote

  • Dec 11 - 12

    Vancouver, BC, Canada

    • all day - Workshop on Machine Learning and the Physical Sciences
      Remote

  • Dec 11 - 12

    Vancouver, BC, Canada

    • all day - Workshop on Preregistration in Machine Learning
      Remote

  • Jan 11 - 16

    Milan, Italy

    • Outreach and Involvement

    When I'm not working on my research, I enjoy participating in outreach events
    and educating myself and others on topics in science, policy, and technology.

    • Women in Machine Learning (WiML)

      As the WiML Connection Chair, I led a team of organizers in running a successful WiML 2019 workshop with around one thousand attendees, invited and contributed speakers, mentorship roundtables, and sponsors.

    • Women in AI at Facebook

      I care about building inclusive and welcoming teams. At Facebook, I've spearheaded and assisted in the creation of diversity programs and events for the Women in AI community.

    • Congressional Liaison

      I acted as a US LUA delegate in the annual meeting with Congress in Washington, DC, to advocate for funding for high energy physics and present reports on recent achievements and discoveries. I trained at the CCC's Leadership in Science Policy Institute.

    • TEDxCERN Volunteer

      TEDxCERN is one of the many exciting events organized at CERN every year. As an assistant stage manager, I helped the organizers during the event, got the chance to interact with absolutely inspiring speakers in the backstage, and assisted them during rehersals.

    • CERN Guide

      As an official CERN guide, I led tours of tourists and students who visit the laboratory. I conducted visits in English and Italian. My goal was to leave visitors with a better understanding of the importance of fundamental research, and curiousity towards High Energy Physics.

    • Pop Science - Nuit des Chercheurs

      A scientifically educated public is an empowered and responsible public. During the Nuit des Chercheurs, CERN volunteers set up a day of events in a large mall with the objective of making physics more accessible and fun for shoppers and passersby.

    Contact me

    Feel free to contact me via email or follow me on social media using the links below. Reach out to me with teaching and speaking opportunities, deep learning projects, or opportunities at DeepMind.