## Current Research and Working papers

Large-scale optimization algorithms and its applications in machine learning. More specifically,

- Distributed second-order methods in Deep Neural Network
- Stochastic Gradient Methods (SGD, SDCA, SVRG, etc.) for Large-scale Nonlinear Optimization

- Distributed Restarting NewtonCG Method for Large-Scale Empirical Risk Minimization with Chenxin Ma, Mudigere Dheevatsa, Alejandro Ribeiro, etc.
- An Inexact Regularized Stochastic Newton Method for Non-convex Optimization with Ioannis Akrotirianakis, Amit Chakraborty, Martin Takáč, etc.

## Talks

- An Inexact Regularized Newton-type Method for Non-convex Optimization, Phd Seminar, Lehigh University, 2017.
- Steps to Success in Training Neural Networks by Using Second-order Algorithms, INFORMS Annual Meeting, Houston, TX, 2017. slides
- Budgeted Category Offer Assignment Optimization, A Distributed Lagrangian Relaxation Approach, Precima, Chicago, IL, 2017. slides
- Distributed Hessian-Free Optimization for Deep Neural Network, AAAI, San Francisco, CA, 2017. slides
- Learning Deep Neural Networks by Inexact Newton-CG Optimization using Indefinite Stochastic Hessian, Phd Seminar, Lehigh University, 2016. slides
- Large Scale Distributed Hessian-Free Optimization for Deep Neural Network, MOPTA, Lehigh University, 2016. slides
- Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization, INFORMS Annual Meeting, Nashville, TE, 2016. slides
- Dual Free SDCA for Empirical Risk Minimization with Adaptive Probabilities, NIPS, Montréal, Canada, 2015. poster
- Estimating Portfolio Loss Probabilities with Optimal Risk Loading Coefficients and Fixed Dependency among Obligors, Siemens Corporation, Princeton, NJ, 2015. slides
- Random Coordinate Descent Method on Large-scale Optimization Problems, Coral Seminar, Lehigh University, 2015. slides

## Selected Course Projects

- Optimization Method in Machine Learning

Explored second-order methods to training fully connected deep neural network on handwritten digits classification problem (`Matlab`

). - Massive Data Mining

Designed competitive Q&A system to attain up to 39.5% accuracy by using Apache Lucene and Natural Language Toolkit, etc.. (`Python`

) - Computation Methods

Used l1-regularized lasso model to recovery pictures with missing pixels. Multiple algorithms (ISTA, FISTA, GRPS) are implemented in`C++`

and compared. - Pattern Recognition

Implemented a`Matlab`

software package to compare various of classifier technologies (SVM, Artificial Neural Network, Decision Tree, KKN) for character-image classification problem. - Integer Programming

Implemented a`Python`

software package to address mixed binary programming problem with branch and cut method. (Group project) - Nonlinear Programming

Developed a`Matlab`

software package for unconstrained nonlinear optimization using various of Nonlinear Optimization methods (GD, Newton, CG, BFGS/DFP, TR and Amijo/Wolfe). - Credit Portfolio Risk

Proposed model and developed a`R`

software package to estimate large-loss probability of a Portfolio with derived optimal loading coefficient.

## Teaching/Research Assistant

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA.

ISE410/Industrial Automation and Robotics (Spring 2018), with Prof. Derya Pamukcu

ISE324/Design of Experiments (Fall 2017), with Prof. Eugene Perevalov

ISE112/Applied Engineering Statistics (Spring 2015), with Prof. Alexander (Sasha) Stolyar

ISE112/Applied Engineering Statistics (Fall 2014), with Prof. Eugene PerevalovIntel Corporation, Parallel Computing Lab, Santa Clara, CA.

Adaptive sample-size sub-sampled Newton-CG algorithms, with Mudigere Dheevatsa

Distributed Hessian-free methods for deep neural network, with Mudigere DheevatsaBusiness Analytics and Monitoring, Siemens Corporation, Corporate Technology, Princeton, NJ.

Deep learning optimization, with Dr. Ioannis Akrotirianakis

Portfolio credit risk, with Dr. Amit ChakrabortyDepartment of Mathematics, Nankai University, Tianjin.

Theory of Optimization (Spring 2014), with Prof. Qingzhi Yang

Linear Algebra (Fall 2013), with Prof. Qingzhi Yang