Tamara Ann Broderick is an American computer scientist at the Massachusetts Institute of Technology. She works on machine learning and Bayesian inference.[1]
Tamara Broderick | |
---|---|
Born | Tamara Ann Broderick |
Alma mater | Princeton University (BS) University of Cambridge (MAS) University of California, Berkeley (PhD) |
Awards | National Science Foundation CAREER Award |
Scientific career | |
Fields | Machine Learning Statistics Bayesian Inference[1] |
Institutions | Massachusetts Institute of Technology |
Thesis | Clusters and features from combinatorial stochastic processes (2014) |
Doctoral advisor | Michael I. Jordan[2] |
Website | tamarabroderick |
Broderick is from Parma Heights, Ohio.[3] She attended Laurel School and graduated in 2003.[4] Whilst at high school she took part in the inaugural Massachusetts Institute of Technology Women's Technology Program.[5] She studied mathematics at Princeton University, earning a bachelor's degree in 2007.[3] She was a Marshall scholar, allowing her to pursue graduate research at the University of Cambridge.[3] She was a runner-up in the Association for Women in Mathematics Alice T. Shafer Prize for Excellence in Mathematics.[3][6] She was co-president of the Princeton Math Club and organised a competition for high school maths teams.[3] She won the Phi Beta Kappa Prize for the highest academic average at Princeton University.[7] During her undergraduate degree, Broderick worked on dark matter haloes with Rachel Mandelbaum.[8] Broderick moved to the United Kingdom for her graduate studies, earning a Master of Advanced Studies for completing Part III of the Mathematical Tripos at the University of Cambridge in 2009.[9][10] Her Master's thesis looked at the Nomon selection method, improving the efficiency of communications.[11][12] She returned to America in 2009, joining University of California, Berkeley for her Master's and PhD.[10] Her graduate research was supported by the Berkeley Fellowship and a National Science Foundation Fellowship.[7] Her PhD thesis Clusters and features from combinatorial stochastic processes looked at clustering and speeding up the analysis of large, streaming data sets.[13][2] In 2013 she was selected for the Berkeley EECS Rising Stars conference.[14]
Broderick joined Massachusetts Institute of Technology as an Assistant Professor in 2015.[14] She is interested in Bayesian statistics and Graphical models.[15] She was the recipient of a Google Faculty Research Grant and International Society for Bayesian Analysis Lifetime Members Junior Researcher Award.[16] She was awarded an Army Research Office young investigator program award to investigate machine-learning to quantify uncertainty in data analysis.[17] Broderick is also Alfred P. Sloan Foundation scholar.[18][19][20][21]
In 2018, Broderick spoke at the Harvard University Institute for Applied Computational Science Women in Data Science conference.[22] She spoke about Bayesian inference at the 2018 International Conference on Machine Learning.[23] She led a three-day Masterclass on machine learning at University College London in June 2018.[24][25] Broderick is a scientific advisor for AI.Reverie and WiML (Women in Machine Learning).[26][27] She has developed a high-school level introduction to machine learning with the Women's Technology Program (WTP).[28] Software she has developed is available on her website.[29]
Broderick was awarded the Evelyn Fix Memorial Medal and Citation and the International Society for Bayesian Analysis Savage Award for her doctoral thesis.[30][31] She was awarded a National Science Foundation CAREER Award to scale her machine learning techniques.[32][28] She was a 2021 Leadership Academy winner of the Committee of Presidents of Statistical Societies.[33]
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