Talks

To create today is to create dangerously. Any publication is an act, and that act exposes one to the passions of an age that forgives nothing.

Albert Camus


2021
  • (upcoming) INFORMS Annual Meeting 2021, Institute for Operations Research and the Management Sciences Conference, Anaheim, CA.
    • Adaptive Algorithms for Nonlinear Equality Constrained Stochastic Optimization.
  • (upcoming) SIOPT 2021, SIAM Conference on Optimization, Spokane, WA.
    • Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization.
  • (upcoming) EURO 2021, 31st European Conference on Operations Research, Athens, Greece.
    • Analysis of Line Search and Trust Region Methods with Noise.
  • (upcoming) EUROPT 2021, 18th Workshop on Advances in Continuous Optimization, Toulouse, France.
    • Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization.
  • (virtual) Statistics Seminar, UW Madison, Department of Statistics.
    • Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. (slides)
  • (virtual) 3rd IMA and OR Society Conference on Mathematics of Operational Research.
    • Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. (slides)
  • (virtual) CSE 2021, SIAM Conference on Computational Science and Engineering.
    • A Symmetric Blockwise Truncated Optimization Algorithm for Machine Learning. (slides)

2020
  • (virtual) INFORMS Annual Meeting 2020, Institute for Operations Research and the Management Sciences Conference, National Harbor, MD.
    • Analysis Of A Generic Line Search Method With Noise: Algorithms, Convergence Rates And Gradient Approximation. (slides)
  • (postponed due to COVID-19) DataX Workshop (September 2020): Old and New Open Questions in Optimization, Princeton University, Princeton, NJ.
    • Limited-Memory BFGS with Displacement Aggregation.
  • (postponed due to COVID-19) SIOPT 2020, SIAM Conference on Optimization, Hong Kong.
    • Global Convergence Rate Analysis of a Line Search Algorithm with Noise.
  • (postponed due to COVID-19) MDS 2020: SIAM Conference on Mathematics of Data Science, Cincinnati, OH.
    • Sampled Quasi-Newton Methods For Deep Learning.
  • JMM 2020, Joint Mathematics Meetings, Denver, CO.
    • A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization: Algorithms, Convergence Analysis and Noise. (slides)

2019
  • Numerical Analysis and Scientific Computing Seminar, November 2019, Courant Institute of Mathematical Sciences, New York University, New York, NY. 
    • Limited-Memory BFGS with Displacement Aggregation. (slides)
  • INFORMS Annual Meeting 2019, Institute for Operations Research and the Management Sciences Conference, Seattle, WA.
    • Quasi-Newton Methods For Deep Learning: Forget The Past, Just Sample. (slides)
  • Young Researchers Workshop 2019, ORIE, Cornell University, Ithaca, NY.
    • Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise. (slides)
  • ICCOPT 2019, International Conference on Continuous Optimization, Berlin, Germany.
    • Aggregated Quasi-Newton Methods. (slides)
  • INFORMS PhD Seminar 2019, Lehigh University, Bethlehem, PA.
    • A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization. (slides)
  • CSE 2019, SIAM Conference on Computational Science and Engineering, Spokane, WA.
    • Scaling up and Randomizing Derivative-Free Optimization for Machine Learning. (slides)

2018
  • INFORMS Annual Meeting 2018, Institute for Operations Research and the Management Sciences Conference, Phoenix, AZ.
    • Stochastic Quasi-Newton Methods – Past, Present and Future. (slides)
  • 2nd NSF TRIPODS PI Workshop 2018,  University of California, Santa Cruz, CA.
    • Do we Need 2nd Order Methods in Machine Learning?  (poster)
  • MOPTA 2018, Modeling and Optimization: Theory and Applications, Bethlehem, PA.
    • Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods. (slides)
  • ISMP 2018, International Symposium on Mathematical Programming, Bordeaux, France.
    • Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods. (slides)
  • SPIE-MRSEC Student Seminar Series 2018, Northwestern University, Evanston, IL.
    • Methods for Large Scale Nonlinear and Stochastic Optimization. (slides)

2017
  • OptML Seminar 2017, Lehigh University, Bethlehem, PA.
    • Stochastic Newton and Stochastic Quasi-Newton Methods for Nonlinear Optimization. (slides)
  • INFORMS Annual Meeting 2017, Institute for Operations Research and the Management Sciences Conference, Houston, TX.
    • Derivative-Free Optimization via Quasi-Newton Methods. (slides)
  • IFORS 2017, International Federations on Operations Research Societies Conference, Quebec city, Canada.
    • Balancing Computation and Communication in Distributed Optimization. (slides)
  • MMLS 2017, Midwest Machine Learning Symposium, Chicago, IL.
    • Are Newton-Sketch and Subsampled Newton Methods Effective in Practice? (poster)
  • SIOPT 2017, SIAM Conference on Optimization, Vancouver, Canada.
    • A Numerical Investigation of Sketching and Subsampling. (slides)
  • ACNTW 2017, Northwestern University, Chicago, IL.
    • Are Newton-Sketch and Subsampled Newton Methods Effective in Practice? (poster)
  • CASSC 2017, SIAM Student Conference (Chicago area), Evanston, IL.
    • A Multi-Batch L-BFGS Method for Machine Learning. (slides)

2016
  • NeurIPS 2016, Neural Information Processing Systems Conference, Barcelona, Spain.
  • ECML 2016, European Conference on Machine Learning, Riva del Garda, Italy.
    • adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs. (slidesposter)
  • Fields Institute Workshop on Nonlinear Optimization Algorithms and Industrial Applications 2016, Toronto, Canada.
    • A Robust Multi-Batch L-BFGS Method for Machine Learning. (poster)
  • CASSC 2016, SIAM Student Conference (Chicago area), Chicago, IL.
    • An Adaptive Quasi-Newton Algorithm for Training RNNs. (slides)