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<!DOCTYPE html>
<html>
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<meta charset="utf-8">
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<title>PAPW 2020 - CFP</title>
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<div class="col-md-12">
<div class="section-title">
<h2>KDD'20 WORKSHOP</h2>
<h1>CALL FOR PARTICIPATION:</h1><h1>Challenge on Mobility Intervention for Epidemics</h1>
<h3><br></h3>
<h3>Can you design effective mobility intervention strategies to contain epidemics?</h3>
<h3>In response to the COVID-19 pandemic, we are hosting a challenge to design mobility
intervention strategies to contain an epidemic. </h3>
<h3>In this challenge, participants can choose different mobility intervention actions
for each individual on any given day (e.g., confine in a neighborhood, quarantine
at home, isolation from everyone else).</h3>
</div>
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<div class="col-sm-2 col-md-2"> </div>
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<figcaption class="caption text-center">
<h4>Mobility Intervention Strategies </h4>
</figcaption>
<img src="img/intervention_up.png" class="img-responsive" alt="Image">
<img src="img/intervention_down.png" class="img-responsive" alt="Image">
</figure>
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<div class="col-sm-2 col-md-2"> </div>
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<figure class="thumbnail">
<figcaption class="caption text-center">
<h4>The microscopic epidemic simulator will run the individual-level epidemic simulation according to the intervention strategy. </h4>
</figcaption>
<img src="img/simulator_up.png" class="img-responsive" alt="Image">
<img src="img/simulation.gif" class="img-responsive" alt="Image">
</figure>
</div>
<div class="col-sm-2 col-md-2"> </div>
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<div class="col-sm-2 col-md-2"> </div>
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<h4>The goal is to minimize the number of people who get infected and also to minimize the number of interventions.</h4>
</figcaption>
<img src="img/score_up.png" class="img-responsive" alt="Image">
<img src="img/score_down.gif" class="img-responsive" alt="Image">
</figure>
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<div class="col-sm-2 col-md-2"> </div>
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<div class="section-wrapper text-center">
<h3>Can you come up with the most effective mobility intervention strategy?</h3>
<h3><b>
Come and participate in the challenge!</b></h3>
<h3>The winners can win up to $2000 cash prize and present and publish at our <a href="https://prescriptive-analytics.github.io" target="_blank">KDD’20 workshop!</a></h3>
<a href="https://hzw77-demo.readthedocs.io/en/round2/introduction.html"><button type="button" class="btn btn-default btn-lg" >Challenge ended, view the challenge info</button></a>
<a href="https://prescriptive-analytics.github.io/#section-winners"><button type="button" class="btn btn-default btn-lg" >Challenge winners</button></a>
</div>
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<div class="section-wrapper text-center">
<h2>Timeline</h2>
<h4><del>May 11, 2020: Practice starts </del></h4>
<p>Practice round provides one scenario of 10K-people for 60 days.</p>
<h4><del>JUNE 26, 2020: Official competition starts</del> </h4>
<p>Official competition has five different scenarios, each simulates 10K-people for 60 days.</p>
<h4><del>JULY 17, 2020: Official competition ends </del>></h4>
<h4>JULY 20, 2020: <a href="https://prescriptive-analytics.github.io/#section-winners">Winners announced</a> </h4>
<h4>
AUGUST 24, 2020: <a href="http://prescriptive-analytics.github.io">KDD Workshop</a>
</h4>
<h4>All deadlines expire on 23:59 PST (Pacific Time)</h4>
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<h4>The challenge is co-hosted by Penn State University, Tsinghua University,
University of Southern California, Qatar Computing Research Institute,
Shanghai Jiao Tong University, National and Kapodistrian University of Athens,
and Virginia Tech,
with the technical support from CSPIRE Technologies, and Tianrang Intelligence
and media support from Synced and Datawhale.</h4>
</figcaption>
<img src="img/sponsors.png" class="img-responsive" alt="Image">
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<h1>Invited talks</h1>
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<figure class="thumbnail">
<a href="https://www.linkedin.com/in/dragos-margineantu-352a3211" target="view_window"><img src="img/dragos.jpg" class="img-responsive" alt="Image"></a>
<figcaption class="caption text-center">
<h3>Dr. Dragos Margineantu</h3>
<h3><small>Boeing Research & Technology</small>
</h3>
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<h2>Robust Machine Learning for Tomorrow’s Transportation Needs</h2>
<h3>Speaker: Dragos Margineantu</h3>
<h4>TBD</h4>
<h5>Bio: Dragos Margineantu is the AI Chief Technologist and a Technical Fellow of Boeing Research & Technology. His research interests include robust machine learning, anomaly detection, inverse reinforcement learning, decision systems, human-in-the-loop learning, validation and testing of decision systems, cost-sensitive, active, and ensemble learning.</h5>
<h5>Dragos was one of the research pioneers in ensemble learning and cost-sensitive learning. At Boeing, he designed and developed machine learning and AI based solutions for airplane maintenance, autonomous systems, surveillance, and design. Dragos is the Boeing AI lead for the DARPA “Assured Autonomy” program, focusing on robust machine learning techniques for autonomous systems. He also served as PI of DARPA's "Learning Applied to Ground Robots" and “Bootstrapped Learning” programs.</h5>
<h5>Dragos serves as the Editor of the Springer book series on “Applied Machine Learning” and as the Action Editor for Special Issues for the Machine Learning Journal (MLj). He serves on the editorial board of both major machine learning journals (MLj and JMLR), and served as senior program committee member of all major machine learning and AI research conferences. He was the chair of the KDD 2015 Industry and Government Track.</h5>
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<a href="https://scholar.google.com/citations?user=ihrsE0sAAAAJ&hl=en" target="view_window"><img src="img/tony-qin.jpeg" class="img-responsive" alt="Image"></a>
<figcaption class="caption text-center">
<h3>Dr. Zhiwei (Tony) Qin</h3>
<h3><small>Didi Chuxing</small>
</h3>
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<h2>TBD</h2>
<h3>Speaker: Zhiwei (Tony) Qin</h3>
<h4>With the rising prevalence of smart mobile phones in our daily life, online ride-hailing platforms have emerged as a viable solution to provide more timely and personalized transportation service, led by such companies as DiDi, Uber, and Lyft. These platforms also allow idle vehicle vacancy to be more effectively utilized to meet the growing need of on-demand transportation, by connecting potential mobility requests to eligible drivers. In this talk, we will discuss our train of research on ride-hailing marketplace optimization at DiDi, in particular, order dispatching and driver repositioning. We will show single-agent and multi-agent RL formulations and how value functions can be designed to leverage different amount of information.</h4>
<h5>Bio: Dr. Zhiwei (Tony) Qin leads the reinforcement learning research at DiDi AI Labs, working on core problems in ride-sharing marketplace optimization. He received his Ph.D. in Operations Research from Columbia University and B.Sc. in Computer Science and Statistics from the University of British Columbia, Vancouver. Tony is broadly interested in research topics at the intersection of optimization and machine learning, and most recently in reinforcement learning and its applications in operational optimization, digital marketing, traffic signals control, and education. He has published in top-tier conferences and journals in machine learning and optimization, including ICML, KDD, IEEE ICDM, WWW, JMLR, and MPC. He has served as Senior PC/PC of NeurIPS, AAAI, IJCAI, KDD, JMLR, TPAMI, and select operations research journals.
</h5>
</div>
</div>
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<div class="col-sm-6 col-md-3">
<figure class="thumbnail">
<a href="http://www.pitt.edu/~kpele" target="view_window"><img src="img/kostas.jpg" class="img-responsive" alt="Image"></a>
<figcaption class="caption text-center">
<h3>Dr. Konstantinos Pelechrinis</h3>
<h3><small>University of Pittsburgh</small>
</h3>
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<div class="col-sm-0 col-md-9">
<div class="section-title">
<h2>PittSmartLiving: Rethinking Public Transportation through Incentives</h2>
<h3>Speaker: Konstantinos Pelechrinis</h3>
<h4> "An advanced nation is one where the rich people use public transit, and not one, where the poor have cars" said Enrique Penalosa, the celebrated mayor of Bogota. Inspired by this idea, in this project we rethink the ways to improve the public transit experience and convert more auto commuters to public transit. We a holistic view of all the stakeholders involved, namely, transit operator, commuter and local businesses (the latter rely on transportation systems to bring in customers) and we look among other things into what types of information will help commuters make the transition to public transit, how local businesses can help improve the public transit experience and how we can make this without increasing inequalities in the population.</h4>
<h5>Bio: Kostas is an associate professor at the School of Computing and Information at the University of Pittsburgh. He received his diploma from the Department of Electrical and Computer Engineering of the National Technical University of Athens, while he holds a PhD degree from the Department of Computer Science of the University of California, at Riverside. His research interests include applied machine learning and data science, with an emphasis on applications in urban science and sports. He has received the prestigious Young Investigator Award from the Army Research Office for his research on multidimensional networks, while he has also consulted for professional sports teams.</h5>
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<a href="https://faculty.ist.psu.edu/jessieli" target="view_window"><img src="img/jessie-li.png" class="img-responsive" alt="Image"></a>
<figcaption class="caption text-center">
<h3>Dr. Zhenhui (Jessie) Li</h3>
<h3><small>Pennsylvania State University</small>
</h3>
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<div class="col-sm-0 col-md-9">
<div class="section-title">
<h2>TBD</h2>
<h3>Speaker: Zhenhui (Jessie) Li</h3>
<h4>TBD.</h4>
<h5>Bio: Dr. Zhenhui (Jessie) Li is a tenured associate professor of Information Sciences and Technology at the Pennsylvania State University. She is Haile family early career endowed professor. Prior to joining Penn State, she received her PhD degree in Computer Science from University of Illinois Urbana-Champaign in 2012, where she was a member of data mining research group. Her research has been focused on mining spatial-temporal data with applications in transportation, ecology, environment, social science, and urban computing. She is a passionate interdisciplinary researcher and has been actively collaborating with cross-domain researchers. She has served as organizing committee or senior program committee of many conferences including KDD, ICDM, SDM, CIKM, and SIGSPATIAL. She has received NSF CAREER award, junior faculty excellence in research, and George J. McMurtry junior faculty excellence in teaching and learning award. </h5>
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<h1>Lightening Talks and Posters</h1>
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<div class="col-md-11">
<div class="session">
<h3>- Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data</h3><h4>Sobhan Moosavi (The Ohio State University); Mohammad Hossein Samavatian (The Ohio State University); Arnab Nandi (The Ohio State University); Srinivasan Parthasarathy (The Ohio State University); Rajiv Ramnath (The Ohio State University)</h4>
<h3>- Origin-Destination Matrix Prediction via Graph Convolution: A New Perspective of Passenger Demand Modeling</h3><h4>Yuandong Wang (Beihang University);Hongzhi Yin (The University of Queensland);Hongxu Chen (The University of Queensland);Tianyu Wo (Beihang University);Jie Xu (University of Leeds);Kai Zheng (University of Electronic Science and Technology);</h4>
<h3>- A Deep Value-network Based Approach for Multi-Driver Order Dispatching</h3><h4>Xiaocheng Tang (Didi Chuxing), Zhiwei Qin (Didi Chuxing), Fan Zhang, Zhaodong Wang, Zhe Xu (Didi Chuxing), Yintai Ma, Hongtu Zhu and Jieping Ye (Didi Chuxing)</h4>
<h3>- Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network</h3><h4>Junchen Ye (SKLSDE Lab and BDBC Beihang University);Leilei Sun (SKLSDE Lab and BDBC Beihang University);Bowen Du (SKLSDE Lab and BDBC Beihang University);Yanjie Fu (Missouri University of Science and Technology);Xinran Tong (SKLSDE Lab and BDBC Beihang University);Hui Xiong (Rutgers Business School Rutgers University);</h4>
<h3>- Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation</h3><h4>Jingyuan Wang (Beihang University);Ning Wu (Beihang University);Xin Zhao (Renmin University of China);Fanzhang Peng (Beihang University);Xin Lin (Beihang University);</h4>
<h3>- Time Critic Policy Gradient Methods for Traffic Signal Control in Complex and Congested Scenarios</h3><h4>Stefano Giovanni Rizzo (Qatar Computing Research Institute);Giovanna Vantini (Qatar Computing Research Institute);Sanjay Chawla (Qatar Computing Research Institute);</h4>
<h3>- PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network</h3><h4>Hua Wei (Pennsylvania State University); Chacha Chen (Shanghai Jiao Tong Univerisity), Guanjie Zheng (Pennsylvania State University); Kan Wu (Pennsylvania State University); Vikash Gayah (Pennsylvania State University); Kai Xu (Shanghai Tianrang Intelligent Technology Co., Ltd), Zhenhui Li (Pennsylvania State University)</h4>
<h3>- Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning</h3><h4>Yunchao Zhang (Missouri University of Science and Technology);Pengyang Wang (Missouri University of Science and Technology);Xiaolin Li (Nanjing University);Yu Zheng (JD);Yanjie Fu (Missouri University of Science and Technology);</h4>
<h3>- Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning</h3><h4>Zheyi Pan (Shanghai Jiao Tong University);Yuxuan Liang (National University of Singapore);Weifeng Wang (Shanghai Jiao Tong University);Yong Yu (Shanghai Jiao Tong University);Yu Zheng (JD);Junbo Zhang (JD);</h4>
<h3>- UrbanFM: Inferring Fine-Grained Urban Flows</h3><h4>Yuxuan Liang (National University of Singapore); Kun Ouyang (National University of Singapore); Lin Jing (Xidian University); Sijie Ruan (Xidian University); Ye Liu (National University of Singapore); Junbo Zhang (JD); David Rosenblum (National University of Singapore); Yu Zheng (JD)</h4>
<h3>- Travel Time Estimation using Sparse Data (Poster)</h3>
<h4>Nikolas Zygouras, Nikos Panagiotou, Yang Li, Leonidas Guibas, Dimitrios Gunopulos (University of Athens). </h4>
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