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Announcement
(Next Meeting): November. 19, 2021
This week, Pulak Agarwal will talk about the following papers:
Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing, Wang G, Gu Z, Li X, et al. Journal of Applied Statistics, 2021: 1-27.
Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection, Jombart T, Ghozzi S, Schumacher D, et al. Philosophical Transactions of the Royal Society B, 2021, 376(1829): 20200266.
Fall 2021 Schedule
| Date |
Speaker |
Description |
Slides |
| 11/19/2021 |
Pulak Agarwal |
Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing, Wang G, Gu Z, Li X, et al. Journal of Applied Statistics, 2021: 1-27.
Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection, Jombart T, Ghozzi S, Schumacher D, et al. Philosophical Transactions of the Royal Society B, 2021, 376(1829): 20200266.
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Slides
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| 11/12/2021 |
Gautham Gururajan |
A systematic survey on deep generative models for graph generation, Guo X, Zhao L. ArXiv 2020.
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Slides
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| 11/05/2021 |
Alexander Rodriguez |
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19, Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari, Anika Tabassum, Naren Ramakrishnan, B. Aditya Prakash. AAAI Conference on Artificial Intelligence (AAAI-21).
DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting, Alexander Rodríguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari, B. Aditya Prakash. AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-21).
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting, Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B Aditya Prakash. Conference on Neural Information Processing Systems (NeurIPS 2021)
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Slides
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| 10/29/2021 |
Alexander Rodriguez |
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression, Qian, Zhaozhi, William R. Zame, Mihaela van der Schaar, Lucas M. Fleuren, and Paul Elbers. NeurIPS 2021.
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Slides
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| 10/22/2021 |
Anika Tabassum |
Actionable Insights in Multivariate Time-series for Urban Analytics, Tabassum A, Chinthavali S, Tansakul V, et al. CIKM, 2021.
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Slides
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| 10/15/2021 |
Jiaming Cui |
Information Theoretic Model Selection for Accurately Estimating Unreported COVID-19 Infections, Cui J, Haddadan A, Haque A S M A U, et al. medRxiv, 2021.
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Slides
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| 10/08/2021 |
Harshavardhan Kamarthi |
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning, Zheng S, Trott A, Srinivasa S, et al. arXiv, 2021.
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist, Trott A, Srinivasa S, van der Wal D, et al. arXiv, 2021.
Covid-19 case study.
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Slides
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Summer 2021 Schedule [expand]
| Date |
Speaker |
Description |
Slides |
| 08/05/2021 |
Alexander Rodríguez |
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning, Zheng S, Trott A, Srinivasa S, et al. arXiv, 2021.
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist, Trott A, Srinivasa S, van der Wal D, et al. arXiv, 2021.
Covid-19 case study.
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Slides
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| 07/22/2021 |
Sungjun Cho |
Toward causal representation learning, Schölkopf B, Locatello F, Bauer S, et al. Proceedings of the IEEE, 2021, 109(5): 612-634.
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Slides
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| 07/15/2021 |
Harshavardhan Kamarthi |
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks, Yildiz C, Heinonen M, Lähdesmäki H. NIPS 2019.
Generative ODE modeling with known unknowns, Linial O, Ravid N, Eytan D, et al. CHIL 2021.
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling, Takeishi N, Kalousis A. arXiv 2021.
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Slides
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| 07/01/2021 |
Jiaming Cui |
Learning to simulate complex physics with graph networks, Sanchez-Gonzalez A, Godwin J, Pfaff T, et al. ICML 2020.
Connecting the dots: Multivariate time series forecasting with graph neural networks, Wu Z, Pan S, Long G, et al. SIGKDD 2020.
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Slides
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Spring 2021 Schedule [expand]
| Date |
Speaker |
Description |
Slides |
| 04/30/2021 |
Vahid Balazadeh |
Learning to Switch Between Machines and Humans, Meresht V B, De A, Singla A, et al. arXiv preprint arXiv:2002.04258, 2020.
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Slides
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| 04/23/2021 |
Alexander Rodriguez
Juan Carlos Barbaran |
A dual-stage attention-based recurrent neural network for time series prediction, Qin, Y, et al. IJCAI 2017.
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Slides
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| 04/16/2021 |
Jiajia Xie |
Using WiFi Mobility Data for Modeling Covid-19 on University Campuses.
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Slides
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| 04/09/2021 |
Anika Tabassum |
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure, John Sipple. Proceedings of the 37th ICML, 2020.
Deep reconstruction of strange attractors from timeseries, Wiliam Gilphin. Proceedings of the NeuRIPS 2020.
Benchmarking Deep Learning Interpretability in Time Series Predictions, ya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo, Soheil Feizi. Proceedings of the NeuRIPS 2020.
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Slides
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| 04/02/2021 |
Jiaming Cui |
DeepXDE: A Deep Learning Library for Solving Differential Equations, Lu L, Meng X, Mao Z, et al. SIAM Review 2021.
Solving high-dimensional partial differential equations using deep learning, Han J, Jentzen A, Weinan E. PNAS 2018.
Forecasting Sequential Data Using Consistent Koopman Autoencoders, Azencot O, Erichson N B, Lin V, et al. ICML 2020.
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Slides
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| 03/26/2021 |
Pulak Agarwal
Javen C Ho |
Anticipating future learning affects current control decisions: A comparison between passive and active adaptive management in an epidemiological setting, Atkins B D, Jewell C P, Runge M C, et al. Journal of Theoretical Biology, 2020, 506: 110380.
Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies, Probert W J M, Lakkur S, Fonnesbeck C J, et al. Philosophical Transactions of the Royal Society B, 2019, 374(1776): 20180277.
Bridging forecast verification and humanitarian decisions: A valuation approach for setting up action-oriented early warnings, Lopez A, de Perez E C, Bazo J, et al. Weather and climate extremes, 2020, 27: 100167.
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Slides
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| 03/19/2021 |
June Sungjun Cho |
Learning the probability of activation in the presence of latent spreaders, Makar M, Guttag J, Wiens J. Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).
A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers, Oh J, Makar M, Fusco C, et al. infection control & hospital epidemiology, 2018, 39(4): 425-433.
Using machine learning and the electronic health record to predict complicated Clostridium difficile infection, Li B Y, Oh J, Young V B, et al. Open forum infectious diseases. US: Oxford University Press, 2019, 6(5): ofz186.
A Data-driven Approach to Identifying Asymptomatic C. diff Cases, Jang H, Polgreen P M, Segre A M, et al. 2020.
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Slides
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| 03/12/2021 |
Harshavardhan Kamarthi |
Domain randomization for transferring deep neural networks from simulation to the real world, Tobin J, Fong R, Ray A, et al. 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2017: 23-30.
Using simulation and domain adaptation to improve efficiency of deep robotic grasping, Bousmalis K, Irpan A, Wohlhart P, et al. 2018 IEEE international conference on robotics and automation (ICRA). IEEE, 2018: 4243-4250.
Data Dreaming for Object Detection: Learning Object-Centric State Representations for Visual Imitation, Sieb M, Fragkiadaki K. 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). IEEE, 2018: 1-9.
Epopt: Learning robust neural network policies using model ensembles, Rajeswaran A, Ghotra S, Ravindran B, et al. arXiv preprint arXiv:1610.01283, 2016.
Driving policy transfer via modularity and abstraction, Müller M, Dosovitskiy A, Ghanem B, et al. arXiv preprint arXiv:1804.09364, 2018.
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Slides
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| 03/05/2021 |
Jiaming Cui |
Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK, Aanensen D M, Kraemer M U G, Rambaut A, et al. Science, 2021.
Whole-genome sequencing to track SARS-CoV-2 transmission in nosocomial outbreaks, Lucey M, Macori G, Mullane N, et al. Clinical Infectious Diseases: an Official Publication of the Infectious Diseases Society of America, 2020.
Genomic Diversity of SARS-CoV-2 During Early Introduction into the United States National Capital Region, Thielen P M, Wohl S, Mehoke T, et al. MedRxiv, 2020.
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Slides
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| 02/26/2021 |
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Predicting Emergence Of Virulent Entities By Novel Technologies (PREVENT)
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| 02/19/2021 |
Jiajia Xie |
Targeting Shutdowns by Repurposing WiFi Logs is More Effective than Moving Classes Online for Controlling Covid-19 on Campuses
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Slides
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| 02/12/2021 |
Alexander Rodríguez |
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19, Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari, Anika Tabassum, Naren Ramakrishnan and B. Aditya Prakash. in AAAI 2021 (virtual).
DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting, Alexander Rodríguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari and B. Aditya Prakash. in IAAI 2021 (virtual).
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