I am a fifth-year PhD student in the Machine Learning Department at Carnegie Mellon University, advised by Prof. Zico Kolter working on semidefinite solvers and differentiable optimization layers. Before that, I was an undergrad in the CSIE Department at National Taiwan University, working with Prof. Chih-Jen Lin on convergence properties of SVMs.
Differentiable satisfiability solver as a layer 
- Logical reasoning within deep learning using a differentiable SAT solver.
- Approximates MAXSAT with Goemans-Williamson SDP, and differentiates through the low-rank SDP.
- Wrap-level GPU optimization with CUDA C.
- The first proof on the convergence to global optimum for a low-rank SDP method using the stable-manifold theorem.
- Converges linearly in neighborhoods, scales to millions of vertices.
- Orders of magnitude faster than other existing methods in experiments.
- Reuse gradient information to reduce communication time in distributed optimization.
- Converges linearly in optimal rate and enjoys local quadratic convergence.
- Outperforms the state-of-the-art first- and second-order methods in experiments.
Global linear convergence for non-strongly convex problems 
- The first global linear convergence rate for first-order methods on non-strongly convex problems.
- Applied to cyclic coordinate descent methods for dual SVC and SVR, published in JMLR.
- Solves the open problem of convergence rate of the Gauss-Seidel method on PSD matrices.
Differentiable learning of numerical rules in knowledge graphs
Po-Wei Wang, Daria Stepanova, Csaba Domokos, J. Zico Kolter. ICLR, 2020
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
[code] [slide] [poster] [Sudoku dataset, parity dataset]
Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter. ICML, 2019 (Best paper Honorable mention)
Low-rank semidefinite programming for the MAX2SAT problem
Po-Wei Wang, J. Zico Kolter. AAAI, 2019
The Common-directions Method for Regularized Empirical Risk Minimization
[supp and exp code] [slide]
Po-Wei Wang, Ching-pei Lee, and Chih-Jen Lin. JMLR, 2019.
Realtime query completion via deep language models
Po-Wei Wang, Huan Zhang, Vijai Mohan, Inderjit S. Dhillon, and J. Zico Kolter. SIGIR Workshop on eCommerce, 2018
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
Po-Wei Wang, Wei-Cheng Chang, and J. Zico Kolter. arXiv preprint, 2017.
Polynomial optimization methods for matrix factorization
Po-Wei Wang, Chun-Liang Li, and J. Zico Kolter. AAAI, 2017.
Limited-memory common-directions method for distributed optimization and its application on empirical risk minimization
[supp and exp code]
[Implemented in Distributed LIBLINEAR]
Ching-pei Lee, Po-Wei Wang, Weizhu Chen, and Chih-Jen Lin. SDM, 2017.
The Mixing method for Maxcut-SDP problem
Po-Wei Wang and J. Zico Kolter. NIPS LHDS Workshop, 2016.
Epigraph projections for fast general convex programming
Po-Wei Wang, Matt Wytock, and J. Zico Kolter. ICML, 2016.
Convex programming with fast proximal and linear operators
Matt Wytock, Po-Wei Wang and J. Zico Kolter. NIPS Optimization Workshop, 2015.
Iteration Complexity of Feasible Descent Methods
for Convex Optimization
Po-Wei Wang and Chih-Jen Lin. JMLR, 15(2014), 1523-1548.
Support Vector Machines
Data Classification: Algorithms and Applications.
Po-Wei Wang and Chih-Jen Lin. CRC Press, 2014.
- Best paper honorable mention from ICML 2019.
- Scholarship from Amazon A9.com for works on deep query completion.
- Our team NTU RobotPal earned the 3rd place in Robocup Standard Platform League.
- Our team are Champions for both tracks in the Yahoo KDD Cup 2011 on Music Rating Prediction.
- NTU Presidential Awards (top 5%).
- Undergraduate scholarship from Macronix Education Foundation.
- Grant and prize from the National Science Council for Creative Undergraduate Research.