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ORCIDSuchi Saria
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- affiliation: Johns Hopkins University, Baltimore, MD, USA
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2020 – today
- 2025
[j13]Philip R. O. Payne
, Kevin B. Johnson, Thomas M. Maddox, Peter J. Embí, Kenneth D. Mandl, Deven McGraw, Suchi Saria, Laura Adams:
Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community. J. Am. Medical Informatics Assoc. 32(2): 408-412 (2025)- 2024
- [j12]H. Echo Wang, Jonathan P. Weiner, Suchi Saria, Harold P. Lehmann, Hadi Kharrazi:
Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models. J. Biomed. Informatics 156: 104683 (2024) - [j11]Adarsh Subbaswamy, Berkman Sahiner, Nicholas Petrick, Vinay Pai, Roy Adams, Matthew C. Diamond, Suchi Saria:
A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform. npj Digit. Medicine 7(1) (2024) - [c32]Drew Prinster, Samuel Don Stanton, Anqi Liu, Suchi Saria:
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them). ICML 2024 - [c31]Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria:
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion. NeurIPS 2024 - [i29]Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria:
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion. CoRR abs/2402.03226 (2024) - [i28]Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria:
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them). CoRR abs/2405.06627 (2024) - [i27]Huy Nguyen, Xing Han, Carl Harris, Suchi Saria, Nhat Ho:
On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions. CoRR abs/2410.02935 (2024) - 2023
- [j10]Ryan T. Scott, Lauren M. Sanders, Erik L. Antonsen, Jaden J. A. Hastings, Seung-Min Park, Graham Mackintosh, Robert J. Reynolds, Adrienne L. Hoarfrost, Aenor Sawyer, Casey S. Greene, Benjamin S. Glicksberg, Corey A. Theriot, Daniel C. Berrios, Jack Miller, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Stuart J. Chalk, Guillermo M. Delgado-Aparicio, Melissa A. Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, John Kalantari, Kia Khezeli, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Héctor García Martín, Christopher E. Mason, Mona Matar, George I. Mias, Jerry G. Myers Jr., Charlotte A. Nelson, Jonathan Oribello, Patricia Parsons-Wingerter, R. K. Prabhu, Amina Ann Qutub, Jon Rask, Amanda Saravia-Butler, Suchi Saria, Nitin Kumar Singh, Michael Snyder, Frank Soboczenski, Karthik Soman, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Jason H. Yang, Marinka Zitnik, Sylvain V. Costes:
Biomonitoring and precision health in deep space supported by artificial intelligence. Nat. Mac. Intell. 5(3): 196-207 (2023) - [j9]Lauren M. Sanders, Ryan T. Scott, Jason H. Yang, Amina Ann Qutub, Héctor García Martín, Daniel C. Berrios, Jaden J. A. Hastings, Jon Rask, Graham Mackintosh, Adrienne L. Hoarfrost, Stuart J. Chalk, John Kalantari, Kia Khezeli, Erik L. Antonsen, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Guillermo M. Delgado-Aparicio, Benjamin S. Glicksberg, Casey S. Greene, Melissa A. Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Christopher E. Mason, Mona Matar, George I. Mias, Jack Miller, Jerry G. Myers Jr., Charlotte A. Nelson, Jonathan Oribello, Seung-Min Park, Patricia Parsons-Wingerter, R. K. Prabhu, Robert J. Reynolds, Amanda Saravia-Butler, Suchi Saria, Aenor Sawyer, Nitin Kumar Singh, Michael Snyder, Frank Soboczenski, Karthik Soman, Corey A. Theriot, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Marinka Zitnik, Sylvain V. Costes:
Biological research and self-driving labs in deep space supported by artificial intelligence. Nat. Mac. Intell. 5(3): 208-219 (2023) - [c30]Drew Prinster, Suchi Saria, Anqi Liu:
Efficient Approximate Predictive Inference Under Feedback Covariate Shift with Influence Functions. COPA 2023: 324-327 - [c29]Drew Prinster, Suchi Saria, Anqi Liu:
JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift. ICML 2023: 28167-28190 - [c28]Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei:
Causal-structure Driven Augmentations for Text OOD Generalization. NeurIPS 2023 - [c27]Yoav Wald, Suchi Saria:
Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection. UAI 2023: 2179-2191 - [i26]Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei:
Causal-structure Driven Augmentations for Text OOD Generalization. CoRR abs/2310.12803 (2023) - 2022
- [j8]H. Echo Wang, Matthew Landers, Roy Adams, Adarsh Subbaswamy, Hadi Kharrazi, Darrell J. Gaskin, Suchi Saria:
A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models. J. Am. Medical Informatics Assoc. 29(8): 1323-1333 (2022) - [j7]Katharine Henry, Rachel Kornfield, Anirudh Sridharan, Robert C. Linton, Catherine Groh, Tony Wang, Albert Wu, Bilge Mutlu, Suchi Saria:
Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system. npj Digit. Medicine 5 (2022) - [c26]Drew Prinster, Anqi Liu, Suchi Saria:
JAWS: Auditing Predictive Uncertainty Under Covariate Shift. NeurIPS 2022 - [i25]Drew Prinster, Anqi Liu, Suchi Saria:
JAWS: Predictive Inference Under Covariate Shift. CoRR abs/2207.10716 (2022) - 2021
- [c25]Adarsh Subbaswamy, Roy Adams, Suchi Saria:
Evaluating Model Robustness and Stability to Dataset Shift. AISTATS 2021: 2611-2619 - [c24]Suchi Saria, Marzyeh Ghassemi, Ziad Obermeyer, Karandeep Singh, Pei-Yun S. Hsueh, Eric J. Topol:
Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery. AMIA 2021 - [c23]Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser:
Partial Identifiability in Discrete Data with Measurement Error. UAI 2021: 1798-1808 - [i24]Ryan T. Scott, Erik L. Antonsen, Lauren M. Sanders, Jaden J. A. Hastings, Seung-Min Park, Graham Mackintosh, Robert J. Reynolds, Adrienne L. Hoarfrost, Aenor Sawyer, Casey S. Greene, Benjamin S. Glicksberg, Corey A. Theriot, Daniel C. Berrios, Jack Miller, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Stuart J. Chalk, Guillermo M. Delgado-Aparicio, Melissa A. Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, John Kalantari, Kia Khezeli, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Héctor García Martín, Christopher E. Mason, Mona Matar, George I. Mias, Jerry G. Myers Jr., Charlotte A. Nelson, Jonathan Oribello, Patricia Parsons-Wingerter, R. K. Prabhu, Amina Ann Qutub, Jon Rask, Amanda Saravia-Butler, Suchi Saria, Nitin Kumar Singh, Frank Soboczenski, Michael Snyder, Karthik Soman, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Jason H. Yang, Marinka Zitnik, Sylvain V. Costes:
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health. CoRR abs/2112.12554 (2021) - [i23]Lauren M. Sanders, Jason H. Yang, Ryan T. Scott, Amina Ann Qutub, Héctor García Martín, Daniel C. Berrios, Jaden J. A. Hastings, Jon Rask, Graham Mackintosh, Adrienne L. Hoarfrost, Stuart J. Chalk, John Kalantari, Kia Khezeli, Erik L. Antonsen, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Guillermo M. Delgado-Aparicio, Benjamin S. Glicksberg, Casey S. Greene, Melissa A. Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Christopher E. Mason, Mona Matar, George I. Mias, Jack Miller, Jerry G. Myers Jr., Charlotte A. Nelson, Jonathan Oribello, Seung-Min Park, Patricia Parsons-Wingerter, R. K. Prabhu, Robert J. Reynolds, Amanda Saravia-Butler, Suchi Saria, Aenor Sawyer, Nitin Kumar Singh, Frank Soboczenski, Michael Snyder, Karthik Soman, Corey A. Theriot, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Marinka Zitnik, Sylvain V. Costes:
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs. CoRR abs/2112.12582 (2021) - 2020
- [i22]Adarsh Subbaswamy, Suchi Saria:
I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models. CoRR abs/2002.08948 (2020) - [i21]Adarsh Subbaswamy, Roy Adams, Suchi Saria:
Evaluating Model Robustness to Dataset Shift. CoRR abs/2010.15100 (2020) - [i20]Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser:
Partial Identifiability in Discrete Data With Measurement Error. CoRR abs/2012.12449 (2020)
2010 – 2019
- 2019
- [c22]Peter Schulam, Suchi Saria:
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning. AISTATS 2019: 1022-1031 - [c21]Adarsh Subbaswamy, Peter Schulam, Suchi Saria:
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. AISTATS 2019: 3118-3127 - [c20]Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria:
Learning Models from Data with Measurement Error: Tackling Underreporting. ICML 2019: 61-70 - [c19]Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. ICML 2019: 6046-6055 - [i19]Peter Schulam, Suchi Saria:
Auditing Pointwise Reliability Subsequent to Training. CoRR abs/1901.00403 (2019) - [i18]Gregory D. Hager, Ann W. Drobnis, Fei Fang, Rayid Ghani, Amy Greenwald, Terah Lyons, David C. Parkes, Jason Schultz, Suchi Saria, Stephen F. Smith, Milind Tambe:
Artificial Intelligence for Social Good. CoRR abs/1901.05406 (2019) - [i17]Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria:
Learning Models from Data with Measurement Error: Tackling Underreporting. CoRR abs/1901.09060 (2019) - [i16]Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. CoRR abs/1904.05268 (2019) - [i15]Suchi Saria, Adarsh Subbaswamy:
Tutorial: Safe and Reliable Machine Learning. CoRR abs/1904.07204 (2019) - [i14]Adarsh Subbaswamy, Bryant Chen, Suchi Saria:
The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance. CoRR abs/1905.11374 (2019) - 2018
- [j6]Hossein Soleimani, James Hensman, Suchi Saria:
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. IEEE Trans. Pattern Anal. Mach. Intell. 40(8): 1948-1963 (2018) - [c18]Adarsh Subbaswamy, Suchi Saria:
Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms. UAI 2018: 947-957 - [i13]Adarsh Subbaswamy, Suchi Saria:
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms. CoRR abs/1808.03253 (2018) - [i12]Peter Schulam, Suchi Saria:
Discretizing Logged Interaction Data Biases Learning for Decision-Making. CoRR abs/1810.03025 (2018) - [i11]Adarsh Subbaswamy, Peter Schulam, Suchi Saria:
Learning Predictive Models That Transport. CoRR abs/1812.04597 (2018) - 2017
- [c17]Peter Schulam, Suchi Saria:
Reliable Decision Support using Counterfactual Models. NIPS 2017: 1696-1706 - [c16]Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria:
Learning Treatment-Response Models from Multivariate Longitudinal Data. UAI 2017 - [i10]Peter Schulam, Suchi Saria:
What-If Reasoning with Counterfactual Gaussian Processes. CoRR abs/1703.10651 (2017) - [i9]Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria:
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions. CoRR abs/1704.02038 (2017) - [i8]Hossein Soleimani, James Hensman, Suchi Saria:
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. CoRR abs/1708.04757 (2017) - 2016
- [j5]Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh:
AI's 10 to Watch. IEEE Intell. Syst. 31(1): 56-66 (2016) - [j4]Peter Schulam, Suchi Saria:
Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses. J. Mach. Learn. Res. 17: 234:1-234:35 (2016) - [j3]Kirill Dyagilev, Suchi Saria:
Learning (predictive) risk scores in the presence of censoring due to interventions. Mach. Learn. 102(3): 323-348 (2016) - [c15]Jimeng Sun, Jim Rehg, Suchi Saria, Hua Xu, Eric P. Xing:
Advanced Machine Learning for Healthcare. CRI 2016 - [c14]Daniel P. Robinson, Suchi Saria:
Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models. IJCAI 2016: 1974-1982 - [c13]Austin Reiter, Andy J. Ma, Nishi Rawat, Christine Shrock, Suchi Saria:
Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor. MICCAI (1) 2016: 482-490 - [c12]Yanbo Xu, Yanxun Xu, Suchi Saria:
A Non-parametric Bayesian Approach for Estimating Treatment-Response Curves from Sparse Time Series. MLHC 2016: 282-300 - [i7]Andong Zhan, Max A. Little, Denzil A. Harris, Solomon O. Abiola, Earl Ray Dorsey, Suchi Saria, Andreas Terzis:
High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection. CoRR abs/1601.00960 (2016) - [i6]Daniel P. Robinson, Suchi Saria:
Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models. CoRR abs/1604.05819 (2016) - [i5]Yanbo Xu, Yanxun Xu, Suchi Saria:
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves. CoRR abs/1608.05182 (2016) - 2015
- [j2]Suchi Saria, Anna Goldenberg:
Subtyping: What It is and Its Role in Precision Medicine. IEEE Intell. Syst. 30(4): 70-75 (2015) - [c11]Peter Schulam, Fredrick Wigley, Suchi Saria:
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery. AAAI 2015: 2956-2964 - [c10]Kirill Dyagilev, Suchi Saria:
Learning a Severity Score for Sepsis: A Novel Approach based on Clinical Comparisons. AMIA 2015 - [c9]Peter Schulam, Colin Ligon, Robert Wise, Laura K. Hummers, Fredrick Wigley, Suchi Saria:
A Probabilistic Graphical Model for Individualizing Prognosis in Chronic, Complex Diseases. AMIA 2015 - [c8]Peter Schulam, Suchi Saria:
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure. NIPS 2015: 748-756 - [i4]Kirill Dyagilev, Suchi Saria:
Learning (Predictive) Risk Scores in the Presence of Censoring due to Interventions. CoRR abs/1507.07295 (2015) - [i3]Andy Jinhua Ma, Pong C. Yuen, Suchi Saria:
Deformable Distributed Multiple Detector Fusion for Multi-Person Tracking. CoRR abs/1512.05990 (2015) - 2014
- [j1]Suchi Saria:
A $3 Trillion Challenge to Computational Scientists: Transforming Healthcare Delivery. IEEE Intell. Syst. 29(4): 82-87 (2014) - [c7]Suchi Saria, Gabriel J. Escobar, Paul C. Tang, Lucila Ohno-Machado, Alex Dummett:
Predictive Analytics in Healthcare (HPA): Considerations and Challenges. AMIA 2014 - 2013
- [c6]Chris Paxton, Suchi Saria, Alexandru Niculescu-Mizil:
Developing Predictive Models Using Electronic Medical Records: Challenges and Pitfalls. AMIA 2013 - 2012
- [i2]Suchi Saria, Uri Nodelman, Daphne Koller:
Reasoning at the Right Time Granularity. CoRR abs/1206.5260 (2012) - 2011
- [b1]Suchi Saria:
The digital patient: machine learning techniques for analyzing electronic health record data. Stanford University, USA, 2011 - [c5]Suchi Saria, Andrew Duchi, Daphne Koller:
Discovering Deformable Motifs in Continuous Time Series Data. IJCAI 2011: 1465-1471 - [c4]Vladimir Jojic, Suchi Saria, Daphne Koller:
Convex envelopes of complexity controlling penalties: the case against premature envelopment. AISTATS 2011: 399-406 - 2010
- [i1]Suchi Saria, Daphne Koller, Anna Penn:
Discovering shared and individual latent structure in multiple time series. CoRR abs/1008.2028 (2010)
2000 – 2009
- 2007
- [c3]Suchi Saria, Uri Nodelman, Daphne Koller:
Reasoning at the Right Time Granularity. UAI 2007: 326-334 - 2004
- [c2]Suchi Saria, Sridhar Mahadevan:
Probabilistic Plan Recognition in Multiagent Systems. ICAPS 2004: 287-296 - [c1]Hugo Zaragoza, Nick Craswell, Michael J. Taylor, Suchi Saria, Stephen E. Robertson:
Microsoft Cambridge at TREC 13: Web and Hard Tracks. TREC 2004
Coauthor Index
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