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Author Information
Aleksander Madry (MIT)
Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.
Ernest Mwebaze (Sunbird AI)
PhD in Machine learning from Groningen University in the Netherlands. 10 years in academia in Makerere University in Uganda. Co-founded the Makerere AI Lab. Worked with UN Pulse Lab Kampala and with Google AI in Accra, Ghana. Working with a not for profit Sunbird AI where I am a founding director.
Suchi Saria (Johns Hopkins University)
Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the ``AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science's ``Brilliant 10’’.
More from the Same Authors
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2022 : A Unified Framework for Comparing Learning Algorithms »
Harshay Shah · Sung Min Park · Andrew Ilyas · Aleksander Madry -
2022 : Invited Talk: Aleksander Mądry »
Aleksander Madry -
2022 Poster: JAWS: Auditing Predictive Uncertainty Under Covariate Shift »
Drew Prinster · Anqi Liu · Suchi Saria -
2022 Poster: 3DB: A Framework for Debugging Computer Vision Models »
Guillaume Leclerc · Hadi Salman · Andrew Ilyas · Sai Vemprala · Logan Engstrom · Vibhav Vineet · Kai Xiao · Pengchuan Zhang · Shibani Santurkar · Greg Yang · Ashish Kapoor · Aleksander Madry -
2021 : ML Model Debugging: A Data Perspective »
Aleksander Madry -
2021 : Dataset Shifts: 8 Years of Going from Practice to Theory to Policy and Future Directions »
Suchi Saria -
2021 : Distribution Shifts in AI for Social Good »
Ernest Mwebaze -
2021 Poster: Unadversarial Examples: Designing Objects for Robust Vision »
Hadi Salman · Andrew Ilyas · Logan Engstrom · Sai Vemprala · Aleksander Madry · Ashish Kapoor -
2021 Poster: Editing a classifier by rewriting its prediction rules »
Shibani Santurkar · Dimitris Tsipras · Mahalaxmi Elango · David Bau · Antonio Torralba · Aleksander Madry -
2020 : Discussion Panel with Amanda Coston »
Amanda Coston · Elaine Nsoesie · Catherine Nakalembe · Santiago Saavedra · Xiaoxiang Zhu · Ernest Mwebaze -
2020 : What Do Our Models Learn? »
Aleksander Madry -
2020 Poster: On Adaptive Attacks to Adversarial Example Defenses »
Florian Tramer · Nicholas Carlini · Wieland Brendel · Aleksander Madry -
2020 Poster: Do Adversarially Robust ImageNet Models Transfer Better? »
Hadi Salman · Andrew Ilyas · Logan Engstrom · Ashish Kapoor · Aleksander Madry -
2020 Oral: Do Adversarially Robust ImageNet Models Transfer Better? »
Hadi Salman · Andrew Ilyas · Logan Engstrom · Ashish Kapoor · Aleksander Madry -
2019 Workshop: Machine Learning with Guarantees »
Ben London · Gintare Karolina Dziugaite · Daniel Roy · Thorsten Joachims · Aleksander Madry · John Shawe-Taylor -
2019 Workshop: Learning Meaningful Representations of Life »
Elizabeth Wood · Yakir Reshef · Jonathan Bloom · Jasper Snoek · Barbara Engelhardt · Scott Linderman · Suchi Saria · Alexander Wiltschko · Casey Greene · Chang Liu · Kresten Lindorff-Larsen · Debora Marks -
2019 Poster: Image Synthesis with a Single (Robust) Classifier »
Shibani Santurkar · Andrew Ilyas · Dimitris Tsipras · Logan Engstrom · Brandon Tran · Aleksander Madry -
2019 Poster: Adversarial Examples Are Not Bugs, They Are Features »
Andrew Ilyas · Shibani Santurkar · Dimitris Tsipras · Logan Engstrom · Brandon Tran · Aleksander Madry -
2019 Spotlight: Adversarial Examples Are Not Bugs, They Are Features »
Andrew Ilyas · Shibani Santurkar · Dimitris Tsipras · Logan Engstrom · Brandon Tran · Aleksander Madry -
2018 : AI for agriculture, environmental protection and sustainability »
Ernest T Mwebaze -
2018 : Panel on research process »
Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth -
2018 : Adversarial Vision Challenge: Shooting ML Models in the Dark: The Landscape of Blackbox Attacks »
Aleksander Madry -
2018 Poster: Spectral Signatures in Backdoor Attacks »
Brandon Tran · Jerry Li · Aleksander Madry -
2018 Poster: How Does Batch Normalization Help Optimization? »
Shibani Santurkar · Dimitris Tsipras · Andrew Ilyas · Aleksander Madry -
2018 Poster: Adversarially Robust Generalization Requires More Data »
Ludwig Schmidt · Shibani Santurkar · Dimitris Tsipras · Kunal Talwar · Aleksander Madry -
2018 Oral: How Does Batch Normalization Help Optimization? »
Shibani Santurkar · Dimitris Tsipras · Andrew Ilyas · Aleksander Madry -
2018 Spotlight: Adversarially Robust Generalization Requires More Data »
Ludwig Schmidt · Shibani Santurkar · Dimitris Tsipras · Kunal Talwar · Aleksander Madry -
2018 Tutorial: Adversarial Robustness: Theory and Practice »
J. Zico Kolter · Aleksander Madry -
2017 : Ernest Mwebaze (UN Global Pulse): ML4D: what works and how it works - case studies from the developing world »
Ernest T Mwebaze -
2017 : Invited talk: Is interpretability and explainability enough for safe and reliable decision making? »
Suchi Saria -
2016 : Estimating What-if Outcomes for Targeting Interventions in a Clinical Setting »
Suchi Saria -
2016 Tutorial: ML Foundations and Methods for Precision Medicine and Healthcare »
Suchi Saria · Peter Schulam -
2015 Workshop: Machine Learning For Healthcare (MLHC) »
Theofanis Karaletsos · Rajesh Ranganath · Suchi Saria · David Sontag