Bachelor of Applied Science (B.A.Sc.) @
University of Toronto
I studied statistical physics because I was fascinated by its ability to draw very broad, very correct conclusions about a collection of interacting entities using statistics and simple model of the entities' individual behavior. Much of my grad school was spent analyzing data in Matlab, trying to draw a connection between elegant theories with specific predictions, and
I studied statistical physics because I was fascinated by its ability to draw very broad, very correct conclusions about a collection of interacting entities using statistics and simple model of the entities' individual behavior. Much of my grad school was spent analyzing data in Matlab, trying to draw a connection between elegant theories with specific predictions, and real, noisy data collected by experimentalists.
I work now doing software engineering that enables researchers to efficiently stream through data and perform analyses. I write highly performant and flexible c++ code, and provide cross-language accessibility into python, allowing easy synergy with tools such as numpy and pandas.
I enjoy and wish to continue learning about, statistics and machine learning, from idea to industrial application. All the way from the mathematical proofs of certain aspects of the algorithms, to the practical considerations, to clean implementations in software, to lower-level performance considerations in programming, I have aptitude and desire, as well as experience.
Quantitative Developer @ I work on the Research Platform team at Tower. Primarily, I make contributions to a c++ library designed to accelerate quantitative trading research, enabling streaming through historical data and allowing statistical analyses and backtesting. This includes implementation of statistics and machine learning algorithms. I also help support access to these tools via python, as well as ensuring interoperability with third party tools such as numpy and pandas. From August 2013 to Present (2 years 5 months) Greater New York City AreaDoctoral Candidate @ I studied models in statistical physics and their ability to make successful predictions about collective phenomena in real-world systems.
I have two experimental collaborations: one with a neuroscience group at Indiana University, and one with a materials science group at CalTech. In both cases, I've performed extensive data analysis and compared results to theory.
In addition, I did Monte Carlo simulations in C++ on a computer cluster to find the effects of correlations on stochastic systems. From August 2007 to May 2013 (5 years 10 months)
Doctor of Philosophy (Ph.D.), Physics @ University of Illinois at Urbana-Champaign From 2007 to 2013 Bachelor of Applied Science (B.A.Sc.), Engineering Physics @ University of Toronto From 2003 to 2007 Nir Friedman is skilled in: Statistics, C++, Data Analysis, Matlab, Physics, LaTeX, Science, Monte Carlo Simulation, Research, C, Public Speaking, Programming, Teaching, Research Design, Python, Algorithms, Machine Learning, Mathematical Modeling
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