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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Mixed Integer Optimization Formulation for Fair Regression
A. Deza, A. Gómez, A. Atamtürk. Under Preparation.
We revisit fair regression from a mixed integer optimization perspective. We present an algorithm to build fair models based on strong convex relaxations of the fair regression problem, and show competitive numerical results on real datasets.
</article> </div>talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
IEOR142: Introduction to Machine Learning and Data Analytics
Graduate Student Instructor, University of California, Berkeley, IEOR, Fall 2021
Graduate student instructor for an undergraduate-level course that introduces machine learning models and data analytics techniques. Key models studied include linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning. Examples and exercises are provided in Python and R.
IEOR262A: Mathematical Programming I
Graduate Student Instructor, University of California, Berkeley, IEOR, Fall 2022
Graduate student instructor for a graduate-level course covering fundamental concepts in optimization. Topics studied include convex analysis, linear programming, sensitivity analysis, Lagrangian duality, local optimality conditions for unconstrained and constrained nonlinear problems, introduction to discrete optimization. The course also studies mathematical modelling and various key optimization algorithms. Students complete theoretical and mathematical modeling exercises, as well as computational homework in AMPL.