Ruoyan Kong

Home Education Research Interests


New Experiment Platform bulk-comm.com!

This platform is for bulk email senders. It will enable you to test the per-message cost of bulk emails. It is going to be in a live experiment in 2022 fall. Please register if you'd like to receive test emails (you'll receive an email if your registration is approved). Email Ruoyan at kong0135@umn.edu for any issues with this platform.
bulk-comm.com


Publication

Ruoyan Kong, Chuankai Zhang, Ruixuan Sun, Joseph A. Konstan. Multi-stakeholder Personalization: A Field Experiment on Incentivizing Employees Reading Important Organizational Bulk Messages. To be appeared in CSCW22.

Ruoyan Kong, Joseph A. Konstan. The Challenge of Organizational Bulk Email Systems: Models and Empirical Studies. The Elgar companion to information economics (under review). 2022.

Charles Chuankai Zhang, Mo Houtti, C. Estelle Smith, Ruoyan Kong, and Loren Terveen. 2022. Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' Motivations. Proc. ACM Hum.-Comput. Interact. 6, CSCW1, Article 135 (April 2022), 21 pages. https://doi.org/10.1145/3512982

Ruoyan Kong, Zhanlong Qiu, Yang Liu, and Qi Zhao. "NimbleLearn: A Scalable and Fast Batch-mode Active Learning Approach." In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 350-359. IEEE, 2021. doi: 10.1109/ICDMW53433.2021.00050.

Ruoyan Kong, Haiyi Zhu, and Joseph A. Konstan. 2021. Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 80 (April 2021), 23 pages. DOI:https://doi.org/10.1145/3449154

Ruoyan Kong, Ruobing Wang and Zitao Shen, "Virtual Reality System for Invasive Therapy," 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2021, pp. 689-690, doi: 10.1109/VRW52623.2021.00227.


Ruoyan Kong, Haiyi Zhu, and Joseph Konstan. 2020. Organizational Bulk Email Systems: Their Role and Performance in Remote Work. NFW 2020.

Hongke Zhao, Qi Liu, Yong Ge, Ruoyan Kong, Enhong Chen, "Group Preference Aggregation: A Nash Equilibrium Approach," 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016, pp. 679-688, doi: 10.1109/ICDM.2016.0079



Projects

Towards an Effective Organization-Wide Email System

Virtual Reality System for Invasive Therapy

Group Recommender systems

Crowdsourcing systems

Prediction of Markets and Economies, Markov Chain Monte Carlo (MCMC)

Risk Management

Virtual Reality

Web Development

Animation & Planning in Games

09/2018 -- (Ongoing), Grouplens Lab, University of Minnesota, supervised by Prof. Joseph Konstan.

1. Towards an Effective Organization -Wide Email System

1)In organizations, Ineffective communication or email overload could result in substantial wasted employee time and lack of awareness or compliance.
2)We study the reading behavior of employees and interviewed representative organizational senders to understand current practice, the effectiveness of the communication channels, and factors that lead to ineffective email communication.
3)We found significant disorder and ineffectiveness resulting in low read rates and wasted employee time. A major factor underlying these results is the disparate views of different stakeholders on the use of email channels.
4) I built a bulk email personalization/delivering system which runs for 2 months in a field experiment (python + javascript + html), and a bulk email testing platform which enables senders to evaluate the time / money cost of each piece of information within bulk emails (vue + html + firebase) (https://bulk-comm.com/).

Ruoyan Kong, Chuankai Zhang, Ruixuan Sun, Joseph A. Konstan. Multi-stakeholder Personalization: A Field Experiment on Incentivizing Employees Reading Important Organizational Bulk Messages. To be appeared in CSCW22.

Ruoyan Kong, Joseph A. Konstan. The Challenge of Organizational Bulk Email Systems: Models and Empirical Studies. The Elgar companion to information economics (under review). 2022.

Ruoyan Kong, Haiyi Zhu, and Joseph A. Konstan. 2021. Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 80 (April 2021), 23 pages. DOI:https://doi.org/10.1145/3449154
Ruoyan Kong, Haiyi Zhu, and Joseph Konstan. 2020. Organizational Bulk Email Systems: Their Role and Performance in Remote Work. NFW 2020

email_poster

 

09/2020 - 12/2020 Virtual Reality System for Invasive Therapy

In invasive therapies such as invasive ventilation and deep brain stimulation, doctors face the challenge of planning, performing, and learning complex surgical procedures. VR systems were built to help doctors plan surgeries. However, the previous VR designs focused on navigation and visualization but not risk estimation. In this paper, we introduced a novel VR system for invasive treatment.  Our approach supports 1) 3D navigation of anatomical models by a bi-manual miniature-world design; 2) simulation of probe trajectory and calculation of the corresponding risks; 3) visualization of different layers; 4) visualization of cross-sectional cutting by a plane. These functions allow doctors to easily manipulate the anatomical model and plan probe trajectory based on the estimation of risks. https://www-users.cselabs.umn.edu/~kong0135/deepbrainvr/

Ruoyan Kong, Ruobing Wang and Zitao Shen, "Virtual Reality System for Invasive Therapy," 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2021, pp. 689-690, doi: 10.1109/VRW52623.2021.00227.



09/2014-05/2016, Department of Data Mining, National Engineering Laboratory for Speech and Language Information Processing, supervised by Prof. Qi Liu.
1. Design recommendation systems for group users.

1)Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem.

2)In traditional preference aggregation methods, such as preference aggregation and score aggregation, the interactions and fairness of group members are still largely ignored. Therefore, these aggregation approaches, which are unable to figure out the optimal selections that can be accepted by all members of a group, may lead to unsatisfying services.

3) Inspired by game theory, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. The game process could capture the group members’ interactions and the Nash equilibrium solution considers the fairness as much as possible.

4) Along with this line, we first calculate the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided.

1

5) We examine our method on Yelp dataset who has 22333 ratings. The results show that the nash method has an outstanding performance on 4 major metrics over other methods.


6)
Outstanding Student Research, USTC, top 3% out of undergraduates in USTC

Hongke Zhao, Qi Liu, Yong Ge, Ruoyan Kong, Enhong Chen, Group Preference Aggregation: A Nash Equilibrium Approach, In Proceedings of the 16th IEEE International Conference on Data Mining (ICDM'16), Barcelona, Spain, 2016, 679-688

2. Model of Incentives in Repeated Crowdsourcing System

1) Repeated crowdsourcing system refers to the crowdsourcing systems with tasks which need to be conducted several times. A problem needs to be solved in repeated crowdsourcing system is how to set appropriate incentives for requesters to maximize the profits of requesters and workers.

2)Modeled the effects of performance-contingent financial rewards in crowdsourcing systems and provided answers to the question: how does the anchoring effect influence the cumulative profits of requesters and workers?

3)– Proved that when the anchoring effect coefficient r of requesters is smaller than 1, the cumulative profits of requesters will converge to a certain value increasingly, and the value is negatively correlated with r. Tthe optimal strategy for requesters is to increase the wage slowly.

– Proved that when the anchoring effect coefficient P of requesters is smaller than 1 and r is smaller than P, the cumulative profits of workers will converge to a certain value increasingly, and the value is negatively correlated with P.The optimal strategy for workers is to increase the effort slowly but keep it being larger than the reaction of requester. Otherwise, the workers should leave the game.

4) The crowdsourcing platforms can use this model to design a principal scheme to incentivize the participation of both requesters and workers.

5) A-level undergraduate thesis, top 10% out of undergraduates in USTC.

 

12/2016-08/2017 Department of Investment Management, Derivatives-China, supervised by Mr. You Zhang and Dr. Ling Long.

1. A Half-supervised Hidden Markov Model to Forecast Index Futures.

1) In order to predict the trend of markets precisely to make profits, we use the hidden Markov model (HMM) to divide the market into N different status.

2) The Baum-Welch algorithm that is currently used to estimate the HMM only converges to the local solution of HMM and can't be applied in the real trading because its result will change every time we optimize it. We design a parallel-serial estimation algorithm to solve this problem and got an approximate stable solution. This is a creative work which has not been done before.

Fig 1. The status of 399905.SZ predicted by HMM (which gives a green warning before the great market crash in 2015)

We can find out that HMM has a good performance in describing the market.

3) The estimation of HMM is unsupervised and may not towards the direction which we need to make profits from. Also, the estimation process is largely influenced by the big trend of the economy, for example, the big trend of the market from 2006 to 2015 is increasing, then HMM will tend to give an increasing status instead of taking the local situation of the market into consideration. We need to build a market timing strategy which can be applied to different market environments.

Because Baum-Welch algorithm and HMM include dependent time series, we can't solve this problem by simply enlarge the weights of negative samples.

We use an AdaBoost-subsection estimation method to solve this problem. To our knowledge, this work has not been done before.

Our algorithm has an outstanding performance in predicting the trend of the market.

 

It can also bring a consistent return.

2. Application of Markov Chain Monte Carlo (MCMC) and HMM in GDP prediction.

1) If we want to apply HMM in GDP prediction, we will find out that the large scaling of features (E.g. the overall GDP of last year) will keep us from a good estimation of HMM, especially the means matrix and covariance matrix in HMM. We need to find a method to avoid this drawback of HMM estimation algorithm.

2) We use Markov Chain Monte Carlo (MCMC), which we can input our prior information about the distribution of parameters in, and Sobel Sequence can generate highly independent random sequences. MCMC has a Markov process which will converge to the best estimation based on the posterior distribution and the prior distribution of parameters.

3) We can see that MCMC has a lower error rate in the estimation of parameters who have high dimensions and large scalings.


Thanks to the beneficial suggestions from Prof. Thomas J. Sargent.

 

09/2017- 06/2018 School of Economics and Management, Tsinghua University, supervised by Prof. Michael R. Powers.

1.A Risk Finance Strategy Paradigm with Copulas

1) When dealing with risks, companies can choose to pool, hedge or avoid them. However, the traditional risk finance paradigm can't give a quantitative method to describe the condition of the application of these strategies.

2) In our prior work (Risk Finance for Catastrophe Losses with Pareto-Calibrated Levy-Stable Severities, Michael R. Powers), we used stable distributions to model this paradigm. In this paradigm, we assume that losses are independent, which is consistent with our real experience.

If we assume losses are correlated, the paradigm would be different. The reason for taking correlation into consideration is that correlation between losses will affect people’s decisions about whether to hedge or pool the loss portfolio L. For example, a group of marine traders wants to limit their ship sinking risks. If their routes are different, their ship sinking risks are uncorrelated and they can divide their merchandise into portions and distribute across all ships (pooling), then no trader will be devastated by a sinking of one ship. However if their routes are the same, their ship sinking risks are correlated (their ships may be attacked by hurricanes or tidal waves together), then pooling will be useless in this case because they will be devastated by the sinking of ships altogether and they should choose to hedge this portfolio like to buy insurance from an insurer (who can afford this because he can pool the risks from different marine trader groups).

3) We also designed a parallel-serial numerical algorithm to get Fourier-analytic risks for levy-stable variables.

4) Heavy-tailed case: As p increases, the firm will be more sensitive to risks, and the lower boundary will decreases more rapidly as the expected frequency increases. It can be observed that when p < 2, a firm will still choose to pool when the dependence between risks is as small as the expected frequency increases. However, when p > 2, as the expected frequency is large enough, the firm will always choose to hedge no matter the dependence is small or not.


5) Light-tailed case:

When the expected frequency become a little larger, the firm will be able to accept higher dependence(TypeII). It may be attributed to that a little larger frequency offers more choice for the firm to distribute the risks.

When the expected frequency achieves some point, the firm can only accept lower dependence as the expected frequency increases(TypeI). It may be attributed to that higher frequency will increase a firms’ overall risks to a large extent. And an interesting inverse lower part appears(TypeIII). In this part, the firm will be exposed to high frequency and low dependence risks, and it will choose to hedge to diminish the risks brought by the high frequency.

As p increases, the firm will be more sensitive to risks, and the lower part of the TypeI boundary will decreases more rapidly as the expected frequency increases. When p > 4/3, the hedging districts in the right-upper corner and in the right-lower corner will merge when the expected frequency is large enough, which means that the firm will always choose to hedge when the expected frequency is large enough. When p < 4/3, there will always be a room left for pooling no matter how larger the expected frequency will be.