Award Winners
Please honor the award winners by directing your attention to the Poster Prizes and Concluding Remarks.
Competition Details
The event will host a poster session from 11:45 am - 2:00 pm in Taylor Commons located at the Frick Building. We will be awarding a "Princeton Day of Optimization, Best Poster Award". The judges for this competition are Professor Nicolas Boumal, Professor Yuxin Chen, Professor Warren Powell, Professor Miklos Racz, Professor Peter Ramadge, Professor Robert Vanderbei, and Professor Mengdi Wang. Due to the large number of poster submissions we have received, winners will be chosen prior to the event based on a review of the electronic copies of the posters.
Participatory Requirements
- Poster should be no larger than 30x40 inches. Both landscape and portrait formats are fine.
- You are responsible for printing your poster and bringing it to the event at 8:00 am on September 28th.
- We will provide easels, poster boards, and pushpins.
List of Entrants
Poster # |
Last Name | Affiliation | Poster Title |
---|---|---|---|
1 | Erez | Princeton University | Estimation and optimization of mutual information used to improve cytometry |
2 | Singh | Princeton University |
The case for full-matrix adaptive regularization |
3 | Lu | Columbia University |
Managing customer churn via service mode control |
4 | Kunisky | NYU |
Equiangular tight frames and degree 4 sum-of-squares over the hypercube |
5 | Zhu | University of North Carolina | Sieve-SDP: a simple algorithm to preprocess semidefinite programs |
6 | Naghib | Princeton University | Fast Fourier linear programming with application to sphere packing |
7 | Pumir | Princeton University | Smoothed analysis of the low-rank approach for smooth semidefinite programs |
8 | Lu | MIT | Scalable huge-scale linear programming via first-order method |
9 | Johnson | Princeton University | General purpose adaptive Monte Carlo optimizer with acceptance ratio ... |
10 | Serra | MERL | Bounding and counting linear regions of deep neural networks |
11 | Weber | Princeton University | Riemannian Frank-Wolfe methods with applications to the geometric matrix ... |
12 | Agarwal | MIT | A marketplace for data: an algorithmic solution |
13 | Wangni | UPenn | Gradient sparsification for communication-efficient distributed optimization |
14 | Kenney | Pennsylvania State University | Efficient and effective L_0 feature selection |
15 | Liu | GA Tech | Towards acceleration tradeoff between momentum and asynchrony in ... |
16 | Dibek | Princeton University | A combinatorial approach for optimal coloring of perfect graphs |
17 | Sakr | Columbia University | A scheduling problem motivated by cybersecurity and adaptive ML |
18 | Chatterjee | RPI | From Dempster to Lyapunov: a dynamical systems interpretation of EM... |
19 | Lau | - | The multilinear minimax relaxation of games and comparison with Nash... |
20 | Bhandari | Columbia University | A finite time analysis of temporal difference (TD) learning... |
21 | Ma | Princeton University | Gradient descent with random initialization: fast global convergence ... |
22 | Zhang | Rutgers University | Robust vertex enumeration for convex hulls in high dimensions |
23 | Gaudio | MIT | On robustness and acceleration for linear dynamical systems |
24 | Bitar | Rutgers University | Codes for straggler mitigation in secure distributed linear regression |
25 | Flynn | Brookhaven National Lab | Finsler structures for neural network optimization |
26 | Tripp | Syracuse University | Structured sparsity promoting functions |
27 | Kobzar | NYU | Exact recovery in semidefinite relaxation of synchronization over the ... |
28 | Gatsis | UPenn | Optimization, control, and learning for the Internet-of-Things |
29 | Fallah | MIT | Robust accelerated gradient method |
30 | Sun | Boston University | ADMM for nonconvex semidefinite optimization |
31 | Cooper | IAS | The geometry of overparameterized neural networks |
32 | Pakniyat | University of Michigan | A semi-definite programming approach to finite-horizon stochastic optimal ... |
33 | Dahan | MIT | Strategic network inspection for resilience to correlated failures |
34 | Bandegi | NJIT | Convex relaxations for variational problems arising from self-assembly |
35 | Faal | WPI | Control function constriction via gradual refinement of time grid |
36 | Tasdighi Kalat | WPI | Space-time equivalence; a minimal computation approach to decentralized ... |
37 | Sun | Johns Hopkins University | SPSA method using diagonalized Hessian matrix |
38 | Oh | Columbia University | Directed exploration in PAC model-free reinforcement learning |
39 | Zhang | Columbia University | Legal assignments, the EADAM algorithm |
40 | Upadhyay | Johns Hopkins University | Differentially private robust PCA |
41 | Ullah | Johns Hopkins University | Streaming kernel PCA with $\tilde O(\sqrt{n})$ random features |
42 | Mianjy | Johns Hopkins University | On the implicit bias of dropout |
43 | Kelly | Princeton University | A comparison between cyclic coordinate descent and the parametric simplex ... |
44 | Qu | Harvard University | Achieving acceleration in distributed large scale optimization |
45 | Scope Crafts | Emory University | Multiresolution methods for convolutional neural networks |
46 | Zhang | Princeton University | On the complexity of testing attainment of the optimal value in nonlinear ... |
47 | Yagli | Princeton University | A technique for bounding the number of mass points for discrete optimal ... |
48 | Fazlyab | University of Pennsylvania | A control theoretic approach for the analysis and design of Douglas-Rachford ... |
49 | Franca | John Hopkins University | ADMM and accelerated ADMM as continuous dynamical systems |
50 | Shi | Lehigh University | A stochastic trust region algorithm based on careful step normalization |
51 | Grand-Clement | Columbia University | Robust data-driven decision making in healthcare |
52 | Wang | MIT | Optimal nonlinear trees for regression |
53 | El Khadir | Princeton University | Power and limitations of algebraic proofs of stability based on semidefinite ... |
54 | Shukla | Columbia University | Non-stationary streaming PCA |
55 | Zhong | Princeton University | Two near-optimal algorithms for phase synchronization |
56 | Yang | Princeton University | Near-optimal time and sample complexities for solving discounted Markov ... |
57 | Shi | Princeton University | The mirror relation in distributed optimization |