Senior Applied ML Scientist at Cerebras Systems
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- I have been working as a data scientist in the field of deep learning since 2011 - Currently I work for Cerebras Systems as part of the ML applications team. My work focuses on developing and implementing diverse model architectures including computer vision models, GANS, autoencoders, and attention-based models. - I received my PhD in physics from the University of California, San Diego in 2007, from there I transitioned into computational neuroscience and then into the field of deep learning.
UC San Diego
Ph.D., Physics
January 1, 2000 – January 1, 2007
University of California, Berkeley
B.A., Physics and Economics
January 1, 1994 – January 1, 1999
Cerebras Systems
Senior Applied ML Scientist
September 1, 2022 – Present
Cerebras Systems
Data Scientist
February 1, 2020 – Present
Intel Nervana
Senior Data Scientist / Deep Learning Eng,
July 1, 2017 – February 1, 2020
San Diego
UC Berkeley
Post-doctoral Scholar-UC Berkeley EECS dept.
February 1, 2011 – June 1, 2012
Qualcomm
Staff Engineer
January 1, 2011 – July 1, 2017
Greater San Diego Area
Humboldt University Berlin
Post Doctoral Associate-Bernstein Center for Computational Neuroscience
August 1, 2007 – March 1, 2011
University of California, San Diego
Graduate Student Researcher UCSD Physics Dept.
January 1, 2002 – January 1, 2007
University of California at Berkeley
Undergraduate Researcher - UC Berkeley Physics Dept.
September 1, 1996 – June 1, 2000
Cultural Fit Analysis
The candidate's career trajectory shows a strong inclination towards advanced research and development, moving from academic neuroscience to applied machine learning in industry. This aligns well with a culture that values innovation, deep technical expertise, and scientific rigor. The long tenure at Cerebras Systems suggests stability and commitment. However, the lack of explicit project details or diverse industry exposure outside of ML/AI hardware could indicate a more specialized focus, which may or may not fit broader cultural needs depending on the specific team dynamics.
Soft Skills & Operational Fit
The candidate's extensive research background suggests strong problem-solving, critical thinking, and independent work capabilities. The progression from Data Scientist to Senior Applied ML Scientist indicates a capacity for growth and leadership within technical roles. Collaboration skills are implied through co-authored publications and team-based research projects.