Schedule for Fall 2016
Seminars are on Wednesdays
Time: 12:00pm – 1:00pm
Location: Room 903, 1255 Amsterdam Avenue
Contacts: Susanna Makela, Robin Winstanley
Information for speakers: For information about schedule, direction, equipment, reimbursement and hotel, please click here.
Shawn Simpson, Senior Data Scientist, Tapad Inc.
Shawn Simpson is a 2011 PhD graduate of the statistics department. She is currently a data scientist at Tapad and was previously the head of data science at Dow Jones. Shawn will be talking about her experiences in industry and making the transition from PhD student to industry data scientist.
“Holland, P. 1986. Statistics and Causal Inference.”
|10/5/16|| Jonathan Auerbach and Florian Stebegg
“Akaike. 1973. Information theory and an extension of the maximum likelihood principle.”
“Rubin, D. B. 1976. Inference and Missing Data.”
|10/19/16||Student Town Hall|
“Peters, J., Bühlmann, P. and Meinshausen, N. 2016. Causal inference by using invariant prediction: identification and confidence intervals.”
Jerrod Ankenman (SIG)
Jerrod Ankenman will share how trading intersects with elections and how our strategies are affected by the decisions made on Election Day. After, we’ll work on an interactive guided exercise by brainstorming modeling and trading ideas about Brexit, the recently concluded 2016 UK-EU referendum.
Jerrod Ankenman is a Quantitative Researcher and Equity Options Trader at SIG. Before joining SIG, Jerrod was a professional poker player and co-authored The Mathematics of Poker along with fellow SIG quant, Bill Chen. Jerrod is a graduate of Pepperdine University, has a master’s degree from Columbia University, and received a PhD in Applied Mathematics from Yale University.
|11/16/16||Ian Wong, Co-founder and Head of Data Science, Opendoor Labs|
|11/30/16|| Wenda Zhou
“Dasgupta, S. and Gupta, A. 2003. An elementary proof of a theorem of Johnson and Lindenstrauss.”
Heng Liu (Google)
Heng Liu is a PhD graduate of the statistics department. He currently works at Google and will be talking about data science in industry.