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Workshop
AI for Humanitarian Assistance and Disaster Response
Ritwik Gupta · Robin Murphy · Trevor Darrell · Eric Heim · Zhangyang Wang · Bryce Goodman · Piotr Biliński

Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ West 217 - 219
Event URL: https://www.hadr.ai »

Natural disasters are one of the oldest threats to not just individuals but to the societies they co-exist in. As a result, humanity has ceaselessly sought way to provide assistance to people in need after disasters have struck. Further, natural disasters are but a single, extreme example of the many possible humanitarian crises. Disease outbreak, famine, and oppression against disadvantaged groups can pose even greater dangers to people that have less obvious solutions.
In this proposed workshop, we seek to bring together the Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Response (HADR) communities in order to bring AI to bear on real-world humanitarian crises.
Through this workshop, we intend to establish meaningful dialogue between the communities.

By the end of the workshop, the NeurIPS research community can come to understand the practical challenges of in aiding those in crisis, while the HADR can understand the landscape that is the state of art and practice in AI.
Through this, we seek to begin establishing a pipeline of transitioning the research created by the NeurIPS community to real-world humanitarian issues.

Fri 8:00 a.m. - 8:15 a.m. [iCal]
Introduction and Welcome (Programmatic)
Ritwik Gupta, Sandra Sajeev
Fri 8:15 a.m. - 10:15 a.m. [iCal]
  • Tracy Adole (WorldPop)
  • Yossi Matias (Google)
  • Col Jason Brown (US Air Force)
  • Alex Jaimes (Dataminr)
 Yossi Matias, Tracy Adole, Jason M Brown, Alex Jaimes Jaimes
Fri 10:15 a.m. - 10:30 a.m. [iCal]
Break
Fri 10:30 a.m. - 12:00 p.m. [iCal]

Paper IDs * 8 - "Two Case Studies of Building Modeling using Machine Learning" * 9 - "Feature Engineering for Entity Resolution with Arabic Names: Improving Estimates of Observed Casualties in the Syrian Civil War" * 12 - "Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector" * 13 - "FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video" * 14 - "Deep Crowd-Flow Prediction in Built Environments" * 15 - "Few-shot Tweet Detection in Emerging Disaster Events"

 Chaofeng Wang, Nic Dalmasso, Jigar Doshi, Junghoon Seo, Mubbasir Kapadia, Anna Kruspe
Fri 12:00 p.m. - 1:30 p.m. [iCal]
Lunch (Food)
Fri 1:30 p.m. - 2:30 p.m. [iCal]
  • Maj Megan Stromberg (US Air Force)
  • Eric Rasmussen (Infinitum Humanitarian Systems)
 Eric Rasmussen, Megan Stromberg
Fri 2:30 p.m. - 3:30 p.m. [iCal]

Paper IDs * 18 - "Flood Detection On Low Cost Orbital Hardware" * 19 - "Machine Learning for Generalizable Prediction of Flood Susceptibility" * 20 - "Inundation Modeling in Data Scarce Regions" * 24 - "Explainable Semantic Mapping for First Responders"

 Josh Veitch-Michaelis, Chelsea Sidrane, Sella Nevo, Jean Oh
Fri 3:30 p.m. - 3:45 p.m. [iCal]
Break
Fri 3:45 p.m. - 4:30 p.m. [iCal]

Paper IDs * 7 - "Language Transfer for Early Warning of Epidemics from Social Media" * 26 - "Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures" * 27 - "Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks"

 Patrick Schrempf, Rohit Dubey, Wenhan Lu
Fri 4:30 p.m. - 5:15 p.m. [iCal]

Speakers from Berkeley, Oak Ridge National Lab, Red Cross, and more.

 Rachel Dzombak, Lexie Yang,
Fri 5:15 p.m. - 6:00 p.m. [iCal]
Poster Session (Posters)

Author Information

Ritwik Gupta (Carnegie Mellon University - Software Engineering Institute)

**Ritwik Gupta** is a Machine Learning Research Scientist at Carnegie Mellon University’s Software Engineering Institute. His research focus lies on the hand-off between humans and autonomous systems. Specifically, his current projects include robotic anomaly detection in constrained environments, humanitarian assistance and disaster response, and efficient machine learning with limited compute resources. Prior to CMU, Ritwik’s back- ground was in the world of biomedical informatics, focusing on causal driver-passenger mutation identification.

Robin Murphy (Texas A&M University)
Trevor Darrell (UC Berkeley)
Eric Heim (Carnegie Mellon University, Software Engineering Institute)
Zhangyang Wang (TAMU)
Bryce Goodman (Defense Innovation Unit)
Piotr Biliński (University of Warsaw / University of Oxford)

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