Michael A. Gelbart: Curriculum Vitae

Transcription

Michael A. Gelbart: Curriculum Vitae 2988 Sasamat St. Vancouver, BC V6R 4R6 Canada Phone: 6043679445 Email: [email protected] Web: http://people.seas.harvard.edu/~mgelbart Citizenship: Canadian EDUCATION Ph.D. in Biophysics, Harvard University. Graduating May 2015. Dissertation: “Constrained Bayesian Optimization and Applications” (
PDF
) Advisor: Professor Ryan P. Adams. A.B.
summa cum laude
in Physics, Princeton University. 2010. Certificates (minors) in Applications of Computing, Biophysics, and Engineering Physics. Thesis: “Foraging Strategies for
Dictyostelium discoideum
” (
PDF
) HONOURS AND AWARDS 2013: 2013: 20102012: Certificate of Distinction in Teaching, Harvard University Derek Bok Center Teaching Certificate, Harvard University Herchel Smith Graduate Fellowship, Harvard University 2010: Inducted to Phi Beta Kappa honor society, Princeton University 2010: Jeffrey O. Kephart ’80 Engineering Physics Award, Princeton University Awarded to the top graduate of the Engineering Physics program each year 2010: Allen G. Shenstone Prize in Physics, Princeton University 2009: Kusaka Memorial Prize in Physics, Princeton University 2007, 2008: Shapiro Prize for Academic Excellence, Princeton University Awarded to the top ~2% of the 1st and 2nd year classes 2007: Manfred Pyka Memorial Physics Prize, Princeton University TEACHING EXPERIENCE 2014: Guest Lecturer, UBC CPSC 540: Machine Learning Delivered four guest lectures. Instructor: Mark Schmidt. 2013: Teaching Fellow, Harvard CS365: SEAS Teaching Practicum Coled the graduate seminar on teaching for the Harvard School of Engineering and Applied Sciences (SEAS). Instructor: John Girash. 20122013: Departmental Teaching Fellow, Harvard SEAS Trained all (~100) new TAs in the school of engineering. Ran seminars on teaching, and mentoring existing TAs. Supervisor: John Girash. 2012: Head Teaching Fellow,

Harvard CS20:

Discrete Math

for

Computer

Science Codeveloped the course curriculum and problem sets. Taught in the flipped classroom. Managed course staff (four TAs). Instructor: Harry Lewis. 2011: Teaching Fellow, Harvard AM205: Advanced Scientific Computing 20072010: Volunteer Teaching Assistant, Princeton Integrated Science Program Note: “Teaching Fellow” is Harvard’s name for Teaching Assistant. INDUSTRY EXPERIENCE 2011:
2005:
Summer Intern, Ikomed Technologies, Inc. Vancouver, BC, Canada. Researched and tested computer vision systems for a new Xray technology. Created an interface for physicians to demarcate regions of interest to surgeons in recorded surgical Xray videos, prototyped a computer vision algorithm, and wrote automated tests for the computer vision system. Summer Intern, Sierra Wireless, Inc. Richmond, BC, Canada. Integrated version control software with the company’s bug tracking database. Wrote scripts such that references to a bug in a version control commit message cause the commit log to be copied to referenced bug’s profile. INTERNATIONAL EXPERIENCE 2009:
Princeton in Beijing Summer Program. Beijing, China. Participated in an intensive Mandarin Chinese program for two months at nd
Beijing Normal University. Won 2
prize in the Chinese speech competition. Speech link:
http://youtu.be/iQYibv7SFHw PEERREVIEWED PUBLICATIONS José Miguel HernándezLobato*, Michael A. Gelbart*, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani. Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. To appear in
Proceedings of the 32nd International Conference on Machine Learning,
Lille, France, 2015. Acceptance rate: 26%. *equal contributions. Michael A. Gelbart, Jasper Snoek, Ryan P. Adams. Bayesian Optimization with Unknown Constraints.
Uncertainty in Artificial Intelligence, Proceedings of the Thirtieth Conference
, 250259. Quebec City, Canada, 2014. Acceptance rate for oral presentations: 8%. Oren Rippel, Michael A. Gelbart, Ryan P. Adams. Learning Ordered Representations with Nested Dropout. In Proceedings of
The 31st

International Conference on Machine Learning
. Beijing, China, 2014. Acceptance rate: 25%. José Miguel HernandezLobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani. Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. In the NIPS Workshop on Bayesian Optimization. Montreal, Canada, 2014. Michael A. Gelbart, Bing He, Adam C. Martin, Stephan Thiberge, Eric F. Wieschaus, Matthias Kaschube. Volume conservation principle involved in cell lengthening and nucleus movement during tissue morphogenesis.
Proceedings of the National Academy of Sciences
,
109(47): 19298303,

2012. Edward J. Banigan, Michael A. Gelbart, Zemer Gitai, Ned S. Wingreen, Andrea J. Liu. Filament Depolymerization Can Explain Chromosome Pulling during Bacterial Mitosis.
PLoS Computational Biology
,
7(9): e1002145
,
2011. Amelio VázquezReina, Michael Gelbart, Daniel Huang, Jeff Lichtman, Eric Miller, Hanspeter Pfister. Segmentation Fusion for Connectomics. In Proceedings of
13th

International Conference on Computer Vision
,
177184. Barcelona, Spain,
2011. Acceptance rate: 24%. Adam C. Martin, Michael Gelbart, Rodrigo FernandezGonzalez, Matthias Kaschube, Eric F. Wieschaus. Integration of contractile forces during tissue invagination.
Journal of Cell Biology
, 188(5):73549,
2010. Citations: 147. INVITED TALKS 07/2014:
07/2013:
11/2011:
08/2011:
06/2010:
Uncertainty in Artificial Intelligence Conference Main Track, Quebec QC. Summer Institute of Mathematics at the University of Washington, Seattle WA. CS281 Advanced Machine Learning, Harvard University, Cambridge MA. Deepmind Technologies, London UK. Molecular Biosciences, Northwestern University, Evanston IL. REVIEWING 2015:
2014:
2014:
International Conference on Machine Learning (ICML). Neural Information Processing Systems (NIPS). BayesOpt 2014 (NIPS Workshop on Bayesian Optimization). SOFTWARE Spearmint http://github.com/HIPS/Spearmint/ Spearmint optimizes expensive blackbox functions using Bayesian optimization. The unknown function is modeled using a Gaussian Process and the Expected Improvement acquisition function is used to select the next evaluation location. I was one of the main developers for Spearmint, which is written in Python and contains about 30,000 lines of code. Embryo Development Geometry Explorer (EDGE) https://github.com/mgelbart/embryodevelopmentgeometryexplorer EDGE analyzes microscopy images of developing embryos by extracting cell boundaries from these images and then building 3D models of cell geometries as they evolve over time. It also provides a GUI for cell visualization and manual image segmentation. I wrote EDGE in a combination of Matlab and Java; it contains about 10,000 lines of code.