8am-5pm, August 24, 2020

Prescriptive Analytics for the Physical World (PAPW 2020)

Workshop held in conjunction with KDD 2020

Prescriptive analytics focuses on analyzing data in order to find the best policy to prevent a disaster (e.g., disease pandemic) or to mitigate a problem (e.g., traffic congestion), and then on prescribing the best actions to implement such a policy in the physical world and/or to study the impact of the implementation of such a policy on the physical world. Encompassing both descriptive analytics and predictive analytics, prescriptive analytics goes beyond by providing actionable insights.

In the data mining research today, however, we see that problems related to prescriptive analytics, especially those problems in the physical world, remain largely unexplored. Take the application in transportation for example. We saw enormous research studies related to problems such as traffic prediction and traffic outlier detection. However, we see little work in terms of how to actually make traffic less congested by taking certain actions (e.g., better traffic signal control strategies or traffic restriction rules). For another example, in epidemic data analysis, we often see research studies to forecast how epidemics spread, but we seldom see data-driven solutions about how to implement policies (e.g., quarantine, public transportation) so that we can minimize the spread of epidemics, and how to study the impact of the implemented policies on the environment as a whole.

In this workshop, we would like to ask this critically important question: how could we learn from the data in order to take better strategic actions in the real physical world?

Topics of interest include but not limited to:

  •     - Prescriptive analytics
  •     - Reinforcement learning for real-world applications
  •     - Operations research and machine learning
  •     - Management science and machine learning
  •     - Prescriptive analytics in applications
    1.     - Epidemics
    2.     - Transportation
    3.     - Environment and Sustainability
    4.     - Smart city
    5.     - Economics
    6.     - Social goods
    7.     - Energy
    8.     - Public Health

Our workshop will have an exciting program with world-wide researchers and practitioners to share their research and experience about how to turn data into actions in the physical world. We will invite keynote talks, paper presentations, and panel discussions. We will not have workshop paper submission.

Challenge on Mobility Intervention for Epidemics

Can you come up with the most effective mobility intervention strategy?

In response to the COVID-19 pandemic, we are hosting a challenge to design mobility intervention strategies to contain an epidemic. 

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).

The winners can win up to $2000 cash prize and present and publish at our KDD’20 workshop!


May 11, 2020: Competition practice round starts

Practice round provides one scenario of 10K-people for 60 days.

JUNE 26, 2020: Official competition starts

Official competition has five different scenarios, each simulates 10K-people for 60 days.

JULY 17, 2020: Official competition ends

JULY 20, 2020: Winners announced

AUGUST 24, 2020: KDD Workshop

Invited Speakers


Alexandre Bayen

Professor of UC Berkeley

Alexandre Bayen is the Liao-Cho Professor of Engineering at UC Berkeley. He is a Professor of Electrical Engineering and Computer Science, and Civil and Environmental Engineering. He is currently the Director of the Institute of Transportation Studies (ITS). He is also a Faculty Scientist in Mechanical Engineering, at the Lawrence Berkeley National Laboratory (LBNL).


Stephen Eubank

Professor of University of Virginia

Stephen Eubank is deputy director in the Network Systems Science and Advanced Computing division and a tenured professor, Department of Public Health Sciences. Eubank has previously researched fluid turbulence, nonlinear dynamics and chaos, time series analysis of markets (as a founder of Prediction Company), natural language processing (as Visiting Scientist at ATR in Kyoto, Japan), and simulations of large interaction-based systems. As a staff member at Los Alamos National Laboratory, Eubank played a leading role in the development of the traffic microsimulation component of the Transportation Analysis and Simulation System (TRANSIMS), developed the Epidemiological Simulation System(EpiSims) project, and served as team leader for the Urban Infrastructure Suite (UIS), of which both TRANSIMS and EpiSims are parts.


Jim Gao

CEO & Co-founder of Phaidra, previous Team Lead of DeepMind Energy

Jim Gao is an entrepreneur with a background in AI and mechanical engineering. He created and led interdisciplinary teams at DeepMind and Google to apply and commercialize artificial intelligence (AI) technologies. This includes: 40% energy savings in cooling Google's data centers, 20% revenue increase in Google's wind farms, the world's first autonomous industrial plant, and future announcements.


Marta Gonzalez

Professor of UC Berkeley

Marta C. Gonzalez is Associate Professor of City and Regional Planning at the University of California, Berkeley, and a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). Her research team develops computer models to analyze digital traces of information mediated by devices. They process this information to manage the demand in urban infrastructures in relation to energy and mobility.


Jieping Ye

Head of DiDi AI Labs, Associate Professor of University of Michigan, Ann Arbor.

Jieping Ye is Head of DiDi AI Labs, a VP of Didi Chuxing and a DiDi Fellow. He is also a Professor at the University of Michigan, Ann Arbor. His research interests include data mining and machine learning with applications in transportation and biomedicine. He has served as a Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, AAAI, ICDM, and SDM. He serves as an Associate Editor of Data Mining and Knowledge Discovery and IEEE Transactions on Knowledge and Data Engineering. He won the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.


Yang Yu

Professor of Nanjing University

Yang Yu is a Professor of Artificial Intelligence School, Nanjing University. Yang Yu is an associate professor of computer science in Nanjing University, China. He joined the LAMDA Group as a faculty since he got his Ph.D. degree in 2011. His research area is in machine learning and reinforcement learning, particularly focusing on real-world reinforcement learning. He was recognized as an AI’s 10 to Watch by IEEE Intelligent Systems in 2018, invited to have an Early Career Spotlight talk in IJCAI’18, and received the Early Career Award of PAKDD in 2018.


More speakers to be confirmed



Zhenhui (Jessie) Li

Pennsylvania State University


Sanjay Chawla

Qatar Computing Research Institute


Yong Li

Tsinghua University


Naren Ramakrishnan

Virginia Tech


Cyrus Shahabi



Weinan Zhang

Shanghai Jiao Tong University


Dimitrios Gunopulos

University of Athens