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.
- 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
- International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
- International Conference on Machine Learning (ICML), 2018, 2017
- Neural Information Processing Systems (NIPS), 2018, 2017, 2016
- Neurocomputing, 2017
- Optimization Method in Machine Learning
Explored second-order methods to training fully connected deep neural network on handwritten digits classification problem (
- Massive Data Mining
Designed competitive Q&A system to attain up to 39.5% accuracy by using Apache Lucene and Natural Language Toolkit, etc.. (
- Computation Methods
Used l1-regularized lasso model to recovery pictures with missing pixels. Multiple algorithms (ISTA, FISTA, GRPS) are implemented in
- Pattern Recognition
Matlabsoftware package to compare various of classifier technologies (SVM, Artificial Neural Network, Decision Tree, KKN) for character-image classification problem.
- Integer Programming
Pythonsoftware package to address mixed binary programming problem with branch and cut method. (Group project)
- Nonlinear Programming
Matlabsoftware 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
Rsoftware package to estimate large-loss probability of a Portfolio with derived optimal loading coefficient.
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 Perevalov
Intel 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 Dheevatsa
Business Analytics and Monitoring, Siemens Corporation, Corporate Technology, Princeton, NJ.
- Deep learning optimization, with Dr. Ioannis Akrotirianakis
- Portfolio credit risk, with Dr. Amit Chakraborty
Department of Mathematics, Nankai University, Tianjin.
- Theory of Optimization (Spring 2014), with Prof. Qingzhi Yang
- Linear Algebra (Fall 2013), with Prof. Qingzhi Yang