Ronny Luss 

       email: ronnyluss@gmail.com


  • Research Staff Member, IBM Watson Research Center, Solutions and Mathematical Sciences Department, 2013-present.
  • Postdoctoral Fellow, University of California, Berkeley, EECS and INRIA – SIERRA project-team, 2011-2012.
  •  Postdoctoral Fellow, Tel Aviv University, Department of Statistics and Operations Research, School of Mathematical Sciences, 2009-2011.

  • Ph.D. in Operations Research & Financial Engineering (2009),  M.A. (2006), Princeton University. 
    • Thesis: “Mathematical Programming for Statistical Learning with Applications in Biology and Finance.”
    • Advisor: Alexandre d’Aspremont.
  • M.S. in Management Science & Engineering (2004), Stanford University.
  • B.S.E. in Computer Science & Engineering (2003), University of Pennsylvania. 

Research Interests
  • Convex optimization: numerical algorithms, robust optimization, implementation.
  • Machine learning and statistics: classification and regression, sparse statistics, and kernel learning.
  • Applications: finance, text mining, operations management, biology.

Research Papers

Conference Proceedings
  • "Interpretable Policies for Dynamic Product Recommendations", M. Petrik, R. Luss. Uncertainty in Artificial Intelligence (UAI), 2016.
  • "Orthogonal Matching Pursuit for Sparse Quantile Regression", A.Y. Aravkin, A. Kambadur, A. Lozano, R. Luss. International Conference on Data Mining (ICDM), 2014.
  • “Decomposing Isotonic Regression for Efficiently Solving Large Problems”, R. Luss, S. Rosset, M. Shahar. Advances in Neural Information Processing Systems (NIPS), 2010.
  • “Support Vector Machine Classification with Indefinite Kernels”, R. Luss, A. d’Aspremont. Advances in Neural Information Processing Systems (NIPS), 2007.

  • “Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint”
    • International Conference on Continuous Optimization (ICCOPT), Portugal, July 2013.
    • European Conference on Operational Research (EURO) Conference, Vilnius, Lithuania, July 2012.
    • INFORMS Annual Meeting, Charlotte, NC, Nov. 2011.
    • Operations Research Society of Israel (ORSIS) Conference, Akko, Israel, June 2011.
  • “Isotonic Recursive Partitioning”
    • Argonne National Lab, Jan. 2012.
    • Final Ltd, Herziliya, Israel, Aug. 2011.
    • Tel Aviv University, Industrial Engineering Seminar, Mar. 2011.
    • Hebrew University, Statistics Seminar, Jan. 2011. 
    • Technion, Operations Research Seminar, Jan. 2011.
    • ICSA International Conference, Guangzhou, China, Dec. 2010.
    • Statistics Seminar, School of Mathematical Sciences, Tel Aviv University, Nov. 2010.
    • IBM Research Lab, Haifa, Machine Learning and Data Mining group, Israel, Oct. 2010.
    • IBM T.J. Watson Research Center, Yorktown Heights, NY, Aug 2010.
    • Operations Research Society of Israel (ORSIS) Conference, Nir Etzion, Israel, June 2010.
  • “Predicting Abnormal Returns From News Using Text Classification”
    • Technion Operations Research Seminar, Israel, May 2010.
    • Bar Ilan University, Engineering Colloquium, Israel, Jan. 2010.
    • Tel Aviv University, School of Mathematical Sciences, Statistics Seminar, Nov. 2009.
    • Advances in Machine Learning for Computational Finance Workshop, London, U.K. Jul. 2009.
    • Carnegie Mellon University, Tepper School of Business, IS Seminar, Feb. 2009.
    • University of Rochester, Simon Graduate School of Business, OM and IS Seminar , Jan. 2009.
    • INFORMS Annual Meeting, Washington, D.C., Oct. 2008.
  • “Support Vector Machine Classification with Indefinite Kernels”
    • NIPS 2007, Vancouver, B.C., Dec. 2007.
    • INFORMS Annual Meeting, Seattle, WA, Nov. 2007.
  • “Matrix Exponential Computations for Semidefinite Programming”
    • INFORMS Annual Meeting, Pittsburgh, PA, Nov. 2006.
    • ISMP, Rio de Janeiro, Brazil, July 2006.

Teaching Assistantships
  • ORF 557, Organize Graduate Student Seminar on numerical methods in finance (Fall 2008).
  • ORF 245, Engineering Statistics (Fall 2005, 2006)
  • ORF 307, Optimization (Spring 2006, 2007)
  • ORF 405, Applied Regression and Time Series (Fall 2007)

  • IRP: Isotonic Recursive Partitioning for isotonic regression.
  • IndefiniteSVM: Support Vector Machines with indefinite kernels.
  • DSPCA: Sparse PCA using semidefinite programming.

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