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.