### Related Publications

### Nikolay Atanasov

- E. Zobeidi, A. Koppel and N. Atanasov, "Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression",
*IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, October 2020. - S. Bowman, N. Atanasov, K. Daniilidis and G. Pappas, "Probabilistic Data Association for Semantic SLAM",
*IEEE International Conference on Robotics and Automation (ICRA)*, May 2017. (Link) - M. Shan, Q. Feng and N. Atanasov, "OrcVIO: Object residual constrained Visual-Inertial Odometry",
*IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, October 2020. - B. Schlotfeldt, D. Thakur, N. Atanasov , V. Kumar and G. Pappas, "Anytime Planning for Decentralized Multi-Robot Active Information Gathering",
*IEEE Robotics and Automation Letters (RA-L)*, January 2018. (Link) - P. Paritosh, N. Atanasov and S. Martinez, "Marginal Density Averaging for Distributed Node Localization from Local Edge Measurements",
*IEEE Conference on Decision and Control (CDC)*, December 2020.

### Esmaeil Atashpaz-Gargari

- E. Atashpaz-Gargari, "Smooth Optimal Control for a Class of Switched Systems Based on Fuzzy Theory and PSO",
*International Conference on Artificial Intelligence Applications and Technologies*, 2017. - E. Atashpaz-Gargari, M. S. Reis, U. M. Braga-Neto, J. Barrera and E. R. Dougherty, "A fast Branch-and-Bound algorithm for U- curve feature selection",
*Pattern Recognition*, 2018, pp. 172-188. - E. Atashpaz-Gargari, U. M. Braga-Neto and E. R. Dougherty, "Improved branch-and-bound algorithm for U-curve optimization",
*IEEE International Workshop on Genomic Signal Processing and Statistics*, 2013. - E. Atashpaz-Gargari, R. Rajabioun, F. Hashemzadeh and F. R. Salmasi, "A decentralized PID controller based on optimal shrinkage of Gershgorin bands and PID tuning using colonial competitive algorithm",
*International Journal of Innovative Computing, Information and Control*, 2009, pp. 3227-3240. - E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition",
*IEEE congress on evolutionary computation*, 2007.

### Mikhail Belkin

- M. Belkin, D. Hsu, S.Ma and S. Mandal, "Reconciling modern machine learning practice and the bias-variance trade-off",
*PNAS*, 2019. - C. Liu, L. Zhu and M. Belkin, "Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning",
*arxiv*, 2020. - C. Liu, L. Zhu and M. Belkin, "On the linearity of large non-linear models: when and why the tangent kernel is constant",
*NeurIPS*, 2020. - J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: spectral analysis beyond Davis-Kahan",
*ALT*, 2018. - J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!",
*NIPS*, 2016.

### Henrik Christensen

- H. I. Christensen, A. Khan, S. Pokutta and P. Tetali, "Approximation and online algorithms for multidimensional bin packing: A survey",
*Computer Science Review*, 2017. - P. Parashar, A. Goel, B. Sheneman and H. I. Christensen, "Towards lifelong adaptive agents: Using meta- reasoning for combining task planning and situated learning",
*The Knowledge Engineering Review*, October 2018. - J. Folkesson and H. I. Christensen, "Graphical SLAM for Outdoor Applications",
*Journal of Field Robotics*, February 2007, pp. 51-70. - M. Dogar, R. A. Knepper, A. Spielberg, C. Choi, H. I. Christensen and D. Rus, "Multi-scale assembly with robot teams",
*International Journal of Robotics Research*, July 2015, pp. 1645-1659. - T. Kunz, A. Thomaz and H. I. Christensen, "Hierarchical rejection sampling for informed kinodynamic planning in high-dimensional spaces",
*IEEE International Conference on Robotics and Automation (ICRA)*, 2016, pp. 89-96. (Link)

### Fan Chung Graham

- F. C. Graham, "Regularity Lemmas for Clustering Graphs",
*Advances in Applied Math*, 2019. - F. C. Graham, R. L. Graham and S. Spiro, "Slow Fibonacci walks",
*Journal of Number Theory*, 2020, pp. 142-170. - F. C. Graham and J. Tobin, "The spectral gap of graphs arising from substring reversals",
*Elec. J. Combinatorics*, 2017. - S. Aksoy, F. C. Graham and X. Peng, "Extreme values of the stationary distribution of random walks on directed graphs",
*Advances in Applied Math.*, 2016, pp. 128-155. - F. C. Graham, "A brief survey of PageRank algorithms",
*IEEE Transaction on Network Sciences and Engineering*, 2015, pp. 449-471.

### Sicun Gao

- Y. Chang, N. Roohi and S. Gao, "Neural Lyapunov Control",
*Conference on Neural Information Processing Systems*, December 2019. - S. Gao, J. Avigad, E. Clarke, "Delta-Decidability over the Reals",
*Logic in Computer Science*, 2012. - S. Gao, J. Avigad and E. Clarke, "Delta-Complete Decision Procedures for Satisfiability over the Reals",
*International Joint Conference on Automated Reasoning*, 2012. - S. Kong, A. Solar-Lezama and S. Gao, "Delta-Decision Procedures for Exists-Forall Problems over the Reals",
*International Conference on Computer Aided Verification*, 2018. - S. Gao, S. Kong and E. Clarke, "dReal: An SMT Solver for Nonlinear Theories of Reals",
*International Conference on Automated Deduction*, 2013.

### Hamed Hassani

- A. Fazeli, H. Hassani, M. Mondelli and A. Vardy, "Binary Linear Codes with Optimal Scaling: Polar Codes with Large Kernels",
*IEEE Transactions on Information Theory*, 2020. - H. Hassani, S. Kudekar, O. Ordentlich, Y. Polyanskiy and R. Urbanke, "Almost Optimal Scaling of Reed-Muller Codes on BEC and BSC Channels",
*International Symposium on Information Theory*, 2018. - M. Mondelli, S. H. Hassani and R. Urbanke, "Unified Scaling of Polar Codes: Error Exponent, Scaling Exponent, Moderate Deviations, and Error Floors",
*IEEE Trans. on Information Theory*, 2016. - M. Mondelli, S. H. Hassani, I. Sason and R. Urbanke, "Achieving Marton's Region for Broadcast Channels Using Polar Codes",
*IEEE Trans. on Information Theory*, 2015. - S. H. Hassani, K. Alishahi and R. Urbanke, "Finite-length Scaling of Polar Codes",
*IEEE Trans. on Information Theory*, 2014.

### Shatha Jawad

- S. Jawad, R. Uhlig, B. R. Sinha, M. Amin and P. P. Dey, "Multithread Affinity Scheduling Using a Decision Maker",
*Asian Journal of Computer and Information Systems*, August 2018. - S. Jawad, "Design and Evaluation of a Neurofuzzy CPU Scheduling Algorithm",
*IEEE International Conference on Networking, Sensing and Control*, April 2014. - S. J. Kadhim and K. M. Al-Aubidy, "Design and Evaluation of a Fuzzy-Based CPU Scheduling Algorithm",
*Information Processing and Management*, 2010. - S. K. Jawad, R. Rzouq, S. Hiary, S. Issa and A. Garageer, "A Design of Facial Recognition System Using Neural Network Based Geometrics 3d Facial",
*International Conference on Signal Processing, Pattern Recognition, and Applications*, 2009. - S. K. Jawad, S. M. Khamaiseh and M. F. Obaidat, "Data Encryption Using Artificial Neural Networks",
*International Multi-Conference on Systems, Signals & Devices*, March 2009.

### Tara Javidi

- M. J. Khojasteh, A. Khina, M. Franceschetti and T. Javidi, "Learning-based Attacks in Cyber-Physical Systems",
*IEEE Transactions on Control of Network Systems*, Forthcoming. (Link) - A. Lalitha, N. Ronquillo and T. Javidi, "Improved Target Acquisition Rates with Feedback Codes",
*IEEE Journal of Selected Topics in Signal Processing*, October 2018. - B. Rouhani, M. Samragh, T. Javidi and F. Koushanfar, "Safe Machine Learning and Defeating Adversarial Attacks",
*IEEE Security and Privacy (S&P) Magazine*, April 2019. - T. Javidi, Y. Kaspi and H. Tyagi, "Gaussian Estimation under Attack Uncertainty",
*Information Theory Workshop*, April 2015. - M. Rao, A. Kipnis, T. Javidi, Y. Eldar and A. Goldsmith, "System Identification from Partial Samples: Non-Asymptotic Analysis",
*IEEE Conference on Decision and Control*, December 2016.

### Stefanie Jegelka

- V. K. Garg, S. Jegelka and T. Jaakkola, "Generalization and representational limits of graph neural networks",
*In Int. Conference on Machine Learning*, 2020. - R. K. Iyer, S. Jegelka and J. Bilmes, "Fast semidifferential-based submodular function optimization",
*In Int. Conference on Machine Learning*, 2013. (Best Paper Award) - K. Xu, W. Hu, J. Leskovec and S. Jegelka, "How powerful are graph neural networks?",
*In Int. Conference on Learning Representations*, 2019. (Oral Presentation) - K. Xu, J. Li, M. Zhang, S. Du, K. Kawarabayashi and S. Jegelka, "What can neural networks reason about?",
*In Int. Conference on Learning Representations*, 2020. - M. Staib and S. Jegelka, "Robust budget allocation via continuous submodular functions",
*Applied Mathematics and Optimization, Special issue on Optimization for Data Sciences*, 2019.

### Andrew B. Kahng

- A. B. Kahng, "Machine Learning Applications in Physical Design: Recent Results and Directions",
*Proc. ACM/IEEE Intl. Symp. on Physical Design*, 2018, pp. 68-73. (Invited Paper) - K. D. Boese, A. B. Kahng and S. Muddu, "A New Adaptive Multistart Technique for Combinatorial Global Optimizations",
*Operations Research Letters*, 1994, pp. 101-113. - C. J. Alpert, T. Chan, A. B. Kahng, I. Markov and P. Mulet, "Faster Minimization of Linear Wirelength for Global Placement",
*IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems*, 1998, pp. 3-13. - L. Hagen and A. B. Kahng, "New Spectral Methods for Ratio Cut Partitioning and Clustering",
*IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems*, September 1992, pp. 1074-1085. - W.-T. J. Chan, P.-H. Ho, A. B. Kahng and P. Saxena, "Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning",
*Proc. ACM/IEEE Intl. Symp. on Physical Design*, 2017, pp. 15-21.

### Amin Karbasi

- H. Hassani, A. Karbasi, A. Mokhtari and Z. Shen, "Stochastic Conditional Gradient ++: (Non-)Convex Minimization and Continuous Submodular Maximization",
*SIAM Journal on Optimization*, November 2020. - A. Mokhtari, H. Hassani and A. Karbasi, "Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization",
*Journal of Machine Learning Research*, 2020. - B. Mirzasoleiman, A. Karbasi, R. Sarkar and A. Krause, "Distributed Submodular Maximization",
*Journal of Machine Learning Research*, 2016. - E. Tohidi, R. Amiri, M. Coutino, D. Gesbert, G. Leus and A. Karbasi, "Submodularity in Action: From Machine Learning to Signal Processing Applications",
*IEEE Signal Processing Magazine*, 2020. - L. Chen, Q. Yu, H. Lawrence and A. Karbasi, "Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition", 2020.

### Farinaz Koushanfar

- M. Javaheripi, M. Samragh, T. Javidi and F. Koushanfar, "AdaNS: Adaptive Non- Uniform Sampling for Automated Design of Compact DNNs",
*IEEE J. Sel. Top. Signal Process*, 2020, pp. 750-764. (Link) - P. Neekhara, S. Hussain, P. Pandey, S. Dubnov, J. J. McAuley and F. Koushanfar, "Universal Adversarial Perturbations for Speech Recognition Systems",
*In:Gernot Kubin, Zdravko Kacic, editors. Annual Conference of the International Speech Communication Association (Interspeech)*, 2019. Available. (Link) - H. Chen, C. Fu, B. Rouhani, J. Zhao and F. Koushanfar, "DeepAttest: an end-to-end attestation framework for deep neural networks",
*Proceedings of the 46th International Symposium on Computer Architecture*, 2019. (Link) - B. Rouhani, A. Mirhoseini, E. Songhori and F. Koushanfar, "Automated Real-Time Analysis of Streaming Big and Dense Data on Reconfigurable Platforms",
*ACM Transactions on Reconfigurable Technology and Systems*, December 2016. (Link) - A. Mirhoseini, E. Dyer, E. Songhori, R. Baraniuk and F. Koushanfar, "RankMap: A Framework for Distributed Learning From Dense Data Sets",
*IEEE Transactions on Neural Networks and Learning Systems*, 2018. (Link)

### Vijay Kumar

- X. Liu, S. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das and V. Kumar, "Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion",
*IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, October 2018. - S. Chen, S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor and V. Kumar, "Counting Apples and Oranges with Deep Learning: A Data-driven Approach",
*IEEE Robotics and Automation Letters*, April 2017, pp. 781-788. - E. Tolstaya, F. Gama, J. Paulos, G. Pappas, A. Ribeiro and V. Kumar, "Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks",
*Conference on Robot Learning*, 2019. - K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y. Mulgaonkar, C. J. Taylor and V. Kumar, "Robust stereo visual inertial odometry for fast autonomous flight",
*IEEE Robotics and Automation Letters*, 2018. - J. Paulos, S. W. Chen, D. Shishika and V. Kumar, "Decentralization of Multiagent Policies by Learning What to Communicate",
*International Conference on Robotics and Automation*, 2019, pp. 7990-7996.

### Melvin Leok

- M. Leok, "Variational Discretizations of Gauge Field Theories using Group-equivariant Interpolation",
*Foundations of Computational Mathematics*, pp. 965-989, 2019. (Link) - X. Shen and M. Leok, "Geometric exponential integrators",
*Journal of Computational Physics*, April 2019. (Link) - H. Parks and M. Leok, "Variational integrators for interconnected Lagrange-Dirac systems",
*Journal of Nonlinear Science*, 2017. (Link) - J. Hall and M. Leok, "Lie group spectral variational integrators",
*Foundations of Computational Mathematics*, 2015. (Link) - J. Vankerschaver, C. Liao and M. Leok, "Generating functionals and Lagrangian partial differential equations",
*Journal of Mathematical Physics*2013. (Link)

### Yian Ma

- Y.-A. Ma, Y. Chen, C. Jin, N. Flammarion and M. I. Jordan, "Sampling can be faster than optimization",
*Proceedings of the National Academy of Sciences (PNAS)*, 2019. - N. S. Chatterji, N. Flammarion, Y.-A. Ma, P. L. Bartlett and M. I. Jordan, "On the theory of variance reduction for stochastic gradient Monte Carlo",
*Proceedings of International Conference on Machine Learning*, 2018. - Y.-A Ma, N. S. Chatterji, X. Cheng, N. Flammarion, P. L. Bartlett and M. I. Jordan, "Is There an Analog of Nesterov Acceleration for MCMC?", 2019. (Link)
- W. Mou, Y.-A. Ma, P. L. Bartlett, M. I. Jordan and M. J. Wainwright, "High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm", 2019. (Link)
- E. Mazumdar, A. Pacchiano, Y.-A. Ma, P. L. Bartlett and M. I. Jordan, "On Thompson Sampling with Langevin Algorithms",
*Proceedings of International Conference on Machine Learning*, 2020.

### Arya Mazumdar

- A. Ghosh, R. K. Maity and A. Mazumdar, "Distributed Newton Can Communicate Less and Resist Byzantine Workers",
*Proceedings of Advances in Neural Information Processing Systems (NeurIPS)*, 2020. - V. Gandikota, A. Mazumdar and S. Pal, "Recovery of Sparse Linear Classifiers from Mixture of Responses",
*Proceedings of Advances in Neural Information Processing Systems (NeurIPS)*, 2020. - R. McKenna, R. K. Maity, A. Mazumdar and G. Miklau, "A Workload-Adaptive Mechanism for Linear Queries Under Local Differential Privacy",
*Proceedings of the VLDB Endowment (VLDB)*, 2020. - A. Mazumdar and S. Pal, "Recovery of Sparse Signals from a Mixture of Linear Samples",
*Proceedings of International Conference on Machine Learning (ICML)*, 2020. - A. Agarwal, L. Flodin and A. Mazumdar, "Linear Programming Approximations for Index Coding",
*IEEE Transactions on Information Theory*, 2019.

### David Pan

- S. Dhar, W. Li, H. Ren, B. Khailany and D.Z. Pan, "DREAMPlace: Deep Learning Toolkit Enabled GPU Acceleration for Modern VLSI Placement",
*DAC*2019. (Link) - H. Chen, M. Liu, B. Xu, K. Zhu, X. Tang, S. Li, Y. Lin, N. Sun and D.Z. Pan, "MAGICAL: An Open-Source Fully Automated Analog IC Layout System from Netlist to GDSII",
*IEEE Design & Test*, 2020. (Link) - M. B. Alawieh, Y. Lin, Z. Zhang, M. Li, Q. Huang and D.Z. Pan, "GAN-SRAF: Sub-Resolution Assist Feature Generation using Generative Adversarial Networks",
*IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)*, 2020. (Link) - W. Ye, M. B. Alawieh, Y. Lin and D.Z. Pan, "LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks",
*Design Automation Conference (DAC)*, 2019. (Link) - K. Zhu, M. Liu, Y. Lin, B. Xu, S. Li, X. Tang, N. Sun and D.Z. Pan, "GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance",
*IEEE/ACM International Conference on Computer-Aided Design (ICCAD)*, November 2019. (Link)

### Jodi Reeves

- A. W. Lo, J. Reeves, P. Jenkins and R. Parkman, "Retention Initiative for Working Adult Students in Accelerated Programs",
*Journal of Research in Innovative Teaching*, 2016, pp. 2-17. - B. Radhakrishnan, J. Ninteman, C. Hahm and J. Reeves, "Sustainability Intelligence: Emergence and Use of Big Data For Sustainable Urban Transit Planning",
*American Society for Engineering Education conference proceedings*, June 2016. - B. Arnold and J. Reeves, "Translating Best Practices for Student Engagement to Online STEAM Courses",
*American Society for Engineering Education PSW conference proceedings*, April 2014.

### Alejandro Ribeiro

- A. G. Marques, S. Segarra, G. Leus and A. Ribeiro, "Sampling of Graph Signals with Successive Local Aggregations",
*IEEE Trans. Signal Process.*, April 2016, pp. 1832-1843. - S. Segarra, G. Mateos, A. G. Marques and A. Ribeiro, "Blind Identification of Graph Filters",
*IEEE Trans. Signal Process.*, April 2016. - A. G. Marques, S. Segarra, G. Leus and A. Ribeiro, "Stationary Graph Processes and Spectral Estimation",
*IEEE Trans. Signal Process.*, March 2016. - S. Segarra, A. G. Marques, G. Leus and A. Ribeiro, "Reconstruction of Graph Signals through Percolation from Seeding Nodes",
*IEEE Trans. Signal Process.*, March 2016. - S. Segarra, A. G. Marques and A. Ribeiro, "Distributed Linear Network Operators using Graph Filters",
*IEEE Trans. Signal Process.*, January 2016.

### Saeedi Bidokhti

- X. Chen, X. Liao and S. Saeedi Bidokhti, "Real-time sampling and estimation in random access channels",
*submitted to Infocom*, 2020. - X. Chen, K. Gatsis, H. Hassani and S. Saeedi Bidokhti, "Age of information in random access channels", submitted to
*IEEE Trans. Inf. Theory*, 2020, short version appeared in*ISIT*2020. - S. Saeedi Bidokhti, M. Wigger and R. Timo, "Noisy broadcast networks with receiver caching",
*IEEE Trans. Inf. Theory*, November 2018, pp. 6996-7016. - R. Timo, S. Saeedi Bidokhti, M. Wigger and B. Geiger, "A rate-distortion approach to caching",
*IEEE Trans. Inf. Theory*, March 2018, pp. 1957-1976. - S. Saeedi Bidokhti and G. Kramer, "Capacity bounds for diamond networks with an orthogonal broadcast channel",
*IEEE Trans. Inf. Theory*, December 2016, pp. 7103-7122.

### Daniel Spielman

- D. Spielman and N. Srivastava, "Graph Sparsification by Effective Resistances",
*SIAM Journal on Computing*, January 2011. (Link) - D. Spielman and S. Teng, "Nearly Linear Time Algorithms for Preconditioning and Solving Symmetric, Diagonally Dominant Linear Systems",
*SIAM Journal on Matrix Analysis and Applications*January 2014. (Link) - D. Spielman and S. Teng, "A Local Clustering Algorithm for Massive Graphs and Its Application to Nearly Linear Time Graph Partitioning",
*SIAM Journal on Computing*, January 2013. (Link) - P. Christiano, J. Kelner, A. Madry, D. Spielman and S. Teng, "Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs",
*Proceedings of the 43rd annual ACM symposium on Theory of computing (STOC)*, 2011. (Link) - R. Kyng, Y. Lee, R. Peng, S. Sachdeva and D. Spielman, "Sparsified Cholesky and multigrid solvers for connection laplacians",
*Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing (STOC)*2016. (Link)

### Suvrit Sra

- S. Sra and R. Hosseini, "Conic geometric optimisation on the manifold of positive definite matrices",
*SIAM J. Optimization (SIOPT)*, 2015. - H. Zhang and S. Sra, "First-order methods for geodesically convex optimization",
*In Conference on Learning Theory*, 2016, pp. 1617-1638. - H. Zhang, S. J. Reddi and S. Sra, "Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds",
*In Advances in Neural Information Processing Systems*, 2016, pp. 4592-4600. - S. J. Reddi, A. Hefny, S. Sra, B. Poczos and Alex Smola, "Stochastic variance reduction for nonconvex optimization",
*In International conference on machine learning*, 2016, pp. 314-323. - K. Ahn and S. Sra, "From Nesterov's Estimate Sequence to Riemannian Acceleration",
*Conference on Learning Theory (COLT)*, 2020.

### Hao Su

- H. Tang, Z. Huang, J. Gu, B. Lu and H. Su, "Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs",
*Advances in Neural Information Processing Systems (NeurIPS)*, 2020. - Z. Jia and H. Su, "Information-Theoretic Local Minima Characterization and Regularization",
*International Conference of Machine Learning (ICML)*, 2020. - T. Mu and J. Gu, Z. Jia, H. Tang and H. Su, "Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals",
*Advances in Neural Information Processing Systems (NeurIPS)*, 2020 - Z. Huang, F. Liu and H. Su, "Mapping state space using landmarks for universal goal reaching",
*Advances in Neural Information Processing Systems (NeurIPS)*, 2019. - C. R. Qi, H. Su, K. Mo and L. J. Guibas, "Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation",
*IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2017.

### Camillo J. Taylor

- I. D. Miller, F. Cladera, A. Cowley, S. S. Shivakumar, E. S. Lee, L. Lipschitz, A. Bhat, N. Rodrigues, A. Zhou, A. Cohen, A. Kulkarni, J. Laney, C. J. Taylor and V. Kumar, "Mine tunnel exploration using multiple quadrupedal robots",
*ICRA*, 2020. - T. Nguyen, K. Mohta, C. J. Taylor and V. Kumar, "Vision-based multi-mav localization with anonymous relative measurements using coupled probablistic data association filter",
*ICRA*, 2020. - C. Qu, T. Nguyen and C. J. Taylor, "Depth completion via deep basis fitting",
*IEEE Workshop on the Applications of Computer Vision (WACV)*, 2020. - M. Quigley, K. Mohta, S. S. Shivakumar, M. Watterson, Y. Mulgaonkar, M. Arguedas, K. Sun, S. Liu, B. Pfrommer, V. Kumar and C. J. Taylor, "The open vision computer: An integrated sensing and compute system for mobile robots",
*ICRA*, 2019. - K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y. Mulgaonkar, C. J. Taylor and V. Kumar, "Robust stereo visual inertial odometry for fast autonomous flight",
*IEEE Robotics and Automation Letters*, April 2018, pp. 965-972.

### Nisheeth Vishnoi

- O. Mangoubi and N. Vishnoi, "Nonconvex sampling with the Metropolis-adjusted Langevin algorithm",
*Conference on Learning Theory*, June 2019. (Link) - H. Lee, O. Mangoubi and N. Vishnoi, "Online sampling from log-concave distributions",
*Advances in Neural Information Processing Systems*, December 2019. - O. Mangoubi and N. Vishnoi, "Faster Polytope Rounding, Sampling, and Volume Computation via a Sub-Linear Ball Walk",
*IEEE Computer Society*, November 2019. (Link) - O. Mangoubi and N. Vishnoi, "Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo",
*Advances in Neural Information Processing Systems*, December 2018. (Link) - J. Leake and N. Vishnoi, "On the computability of continuous maximum entropy distributions with applications",
*Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing*, June 2020.

### Xiaolong Wang

- X. Wang, R. Girshick, A. Gupta and K. He, "Non-local Neural Networks",
*Conference on Computer Vision and Pattern Recognition*, 2018. - W. Yang, X. Wang, A. Farhadi, A. Gupta and R. Mottaghi, "Visual Semantic Navigation using Scene Priors",
*International Conference on Learning Representations (ICLR)*, 2019. - X. Wang and A. Gupta, "Videos as Space-Time Region Graphs",
*European Conference on Computer Vision (ECCV)*, 2018. - R. Yang, H. Xu, Y. Wu and X. Wang, "Multi-Task Reinforcement Learning with Soft Modularization",
*Conference on Neural Information Processing Systems (NeurIPS)*, 2020. - Q. Long, Z. Zhou, A. Gupta, F. Fang, Y. Wu and X. Wang, "Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning",
*International Conference on Learning Representations (ICLR)*, 2020.

### Yusu Wang

- S. Banerjee, L. Magee, D. Wang, X. Li, B. Huo, J. Jayakumar, K. Matho, M. Lin, K. Ram, M. Sivaprakasam, J. Huang, Y. Wang and P. Mitra, "Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks",
*Nature Machine Intelligence*, 2020, pp. 585-594. - Q. Zhao and Y. Wang, "Learning metrics for persistence-based summaries and applications for graph classification",
*Conf. Neural Information Processing Systems (NeuRIPS)*, 2019, pp. 9855-9866. - J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!",
*NIPS*, 2016. - T. Dey, J. Wang and Y. Wang, "Graph reconstruction by discrete Morse theory",
*34th Sympos. Comput. Geom (SoCG)*, 2018. - A. Sidiropoulos, D. Wang and Y. Wang, "Metric embeddings with outliers",
*ACM-SIAM Sympos. Discrete Alg. (SoDA)*, 2017, pp. 670-689.