Scholarly Works

Publications (Peer Reviewed)

  1. Alexander AS, Carstensen LC, Hinman JR, Raudies F, Chapman GW, Hasselmo ME (2020) Egocentric boundary vector tuning of the retrosplenial cortex. Science Advances, 6: eaaz2322. DOI: DOI: 10.1126/sciadv.aaz2322.
  2. Hasselmo ME, Alexander AS, Hoyland A, Robinson JC, Bezaire MJ, Chapman GW, Saudargiene A, Carstensen LC, Dannenberg H. (2020). The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation. Neuroscience, doi: 10.1016/j.neuroscience.2020.03.048.
  3. Hausler S, Chen Z, Hasselmo ME, Milford M. (2020). Bio-inspired multi-scale fusion.  Biol. Cybern. 114(2):209-229, doi: 10.1007/s00422-020-00831-z.
  4. A.V.Savkin and H.Huang, Navigation of a Network of Aerial Drones for Monitoring a Frontier of a Moving Environmental Disaster Area, IEEE Systems Journal, accepted. DOI: 10.1109/JSYST.2020.2966779.
  5. X. Li, H. Huang and A.V. Savkin, A Novel Method for Protecting Swimmers and Surfers from Shark Attacks using Communicating Autonomous Drones, IEEE Internet of Things Journal, accepted. DOI: 10.1109/JIOT.2020.2987997.
  6. A.V.Savkin, H.Huang and W.Ni, Securing UAV Communication in the Presence of Stationary or Mobile Eavesdroppers via Online 3D Trajectory Planning, IEEE Wireless Communications Letters, accepted. DOI: 10.1109/LWC.2020.2986291.
  7. H.Huang, A.V. Savkin and W.Ni, Energy-Efficient 3D Navigation of a Solar-Powered UAV for Secure Communication in the Presence of Eavesdroppers and No-Fly Zones, Energies, 13(6): 1445, 2020. doi:10.3390/en13061445.
  8. Tingting Xu, Henghui Zhu, and I. C. Paschalidis, “Learning Parametric Policies and Transition Probability Models of Markov Decision Processes from Data”, European Journal of Control, doi:10.1016/j.ejcon.2020.04.003.
  9. Shi Pu, Alex Olshevsky, and I. C. Paschalidis, “Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning”, IEEE Signal Processing Magazine, Vol. 37, No. 3, pages 114-122, 2020, doi:10.1109/MSP.2020.2975212.
  10. Artin Spiridonoff, Alex Olshevsky, and I. C. Paschalidis, “Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions” Journal on Machine Learning Research, Vol. 21, No. 58, pages 1–47, 2020, doi: http://jmlr.org/papers/v21/18-813.html.
  11. Henghui Zhu, I. C. Paschalidis, Allen Chang, Chantal E. Stern, Michael E. Hasselmo, “A Neural Circuit Model for a Contextual Association Task Inspired by Recommender Systems”, Hippocampus, Special Issue: Computational models of hippocampus and related structures–Part I, Vol. 30, Issue 4 (April), pages 384-395, 2020, doi: 10.1002/hipo.23194.
  12. Henghui Zhu, Hao Liu, Armin Ataei, Yonatan Munk, Thomas Daniel, and I. C. Paschalidis, “Learning from Animals: How to Navigate Complex Terrains”, PLOS Computational Biology, 16(1): e1007452, 2020, doi:10.1371/journal.pcbi.1007452.
  13. Manjesh K. Hanawal, Hao Liu, Henghui Zhu, and I. C. Paschalidis, “Learning Policies for Markov Decision Processes from Data”, IEEE Transactions on Automatic Control, Vol. 64, Issue 6, June 2019, pages 2298–2309, doi:10.1109/TAC.2018.2866455.
  14. A. Pavlov, I. Shames, and C. Manzie. Minimax strategy in approximate model predictive control. Automatica, 111:108649, 2020.

Conference Papers (Peer Reviewed)

  1. Yiwen Gu, Shreya Pandit, Elham Saraee, Timothy Nordahl, TerryEllis, and Margrit Betke. Home-based Physical Therapy with an Interactive Computer Vision System. Assistive Computer Vision and Robotics Workshop at the International Conference on Computer Vision Workshop, Seoul, South Korea, October 2019.
  2. J. Baillieul, “Perceptual Control with Large Feature and Actuator Networks,”  2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, December 11-13, 2019, pp. 3819-3826, doi: 10.1109/CDC40024.2019.9029615.
  3. J. Baillieul and F. Kang, “Visual Navigation with a 2-pixel Camera – Possibilities and Limitations,” to appear in the Late Breaking Results Sections of the Proceedings of the 21st IFAC World Congress, 2020.
  4. D. Fourie, A. Teran Espinoza, M. Kaess, and J. Leonard. Characterizing Marginalization and Incremental Operations on the Bayes Tree. In Proceedings of the Workshop on Algorithmic Foundations of Robotics (WAFR), June, 2020.
  5. X. Li, H. Huang and A.V. Savkin, Autonomous Drone Shark Shield: A Novel Shark Repelling System for Protecting Swimmers and Surfers, IEEE International Conference on Control, Automation and Robotics, 2020.
  6. H.Huang, A.V.Savkin and W.Ni, A Method for Covert Video Surveillance of a Car or a Pedestrian by an Autonomous Aerial Drone via Trajectory Planning, IEEE International Conference on Control, Automation and Robotics, 2020.
  7. M.Gnanasekera, A.V.Savkin and J.Katupitiya, Range Measurements Based UAV Navigation for Intercepting Ground Targets, IEEE International Conference on Control, Automation and Robotics, 2020.
  8. Ruidi Chen and I. C. Paschalidis, “A Distributionally Robust Optimization Approach for Multivariate Linear Regression under the Wasserstein Metric”, Proceedings of the 58th IEEE Conference on Decision and Control}, pages 3655–3660, December 11-13, 2019, Nice, France,  doi: 10.1109/CDC40024.2019.9029832.
  9. Taiyao Wang and I. C. Paschalidis, “Convergence of Parameter Estimates for  Regularized Mixed Linear Regression Models”, Proceedings of the 58th IEEE Conference on Decision and Control, pages 3664-3669, December 11-13, 2019, Nice, France, doi: 10.1109/CDC.2018.8619435.
  10. Ruidi Chen and I. C. Paschalidis, “Selecting Optimal Decisions via Distributionally  Robust Nearest-Neighbor Regression”, Proceedings of Advances in Neural Information Processing Systems 32 (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada, https://papers.nips.cc/paper/8363-selecting-optimal-decisions-via-distributionally-robust-nearest-neighbor-regression.
  11. Renganathan, Venkatraman and Shames, Iman and Summers, Tyler H, Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints, IFAC World Congress, 2020.
  12. Pavlov, Andrei and Muller, Matthias an Manzie, Chris and Shames, Iman , Complexity minimisation of suboptimal MPC without terminal constraints, IFAC World Congress, 2020.
  13. G. Colabufo, P. Dower, and I. Shames. Newton’s method: sufficient conditions for practical and input-to-state stability. IFAC World Congress, 2020.

Theses

  1. Tingting Xu, “Machine Learning for Effective Predictions and Prescriptions in Health Care”, Ph.D. in Systems Engineering, Boston University, May 2020. Advisor: Paschalidis.
  2. Henghui Zhu, “Making Decisions Based on Context: Models and Applications in Cognitive Sciences and Natural Language Processing”, Ph.D. in Systems Engineering, Boston University, December 2019. Advisor: Paschalidis.
  3. Ruidi Chen, “Distributionally Robust Learning under the Wasserstein Metric”, Ph.D. in Systems Engineering, Boston University, September 2019. Advisor: Paschalidis.