Tianyi Gu

Pic of Tianyi Gu, taken May 2015
Email of Tianyi Gu

My name is Tianyi Gu. I am a Ph.D. student at the University of New Hampshire in the Computer Science department. I am a member of the UNH Artificial Intelligence Group. I am currently working with Professor Wheeler Ruml in the area of Artificial Intelligence, Heuristic Search, Robotics and Motion Planning.

My full CV is available here.


Teaching Assistantship

I have been a teaching assistant at the University of New Hampshire for a variety of courses. Involved in creating assignments, exams and conducting recitation sessions for Intro to Computer Science (Java), From Problems to Algorithms to Programs (Python), Database Programming (C#, SQL), Scripting Languages (Shell).


I worked for the Shanghai International Port(Group) for 3 years (2012-2015) after I graduated from the Shanghai Maritime University with my Master Degree in Logistics Engineering. While I was at the SIPG I worked in the following projects:

  • Member of the team that designed, built, and deployed a new automated container terminal operations management system, including algorithm development for the crane allocation and scheduling module and the financial module.
  • Helped launch previous terminal operating management system.


During the summer of 2011 I worked as an intern at Alcatel-Lucent(Shanghai). While there, I developed a global electrical elements database, including web interface and database maintenance software.


  • Scott Kiesel, Tianyi Gu, and Wheeler Ruml, An Effort Bias for Sampling-based Motion Planning, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2017.

    [pdf] [publisher] [video] [talk] [slides]

  • Yi Ding, Shuai Jia, Tianyi Gu, and Chung-Lun Li, SGICT Builds an Optimization-based System for Daily Berth Planning , Interfaces 46(4) 281-296, 2016.

    [pdf] [publisher]

  • Chengji Liang, Tianyi Gu, Bo Lu, and Yi Ding, Genetic Mechanism-based Coupling Algorithm for Solving Coordinated Scheduling Problems of Yard Systems in Container Terminals , Computers & Industrial Engineering 89 34–42, 2015.

    [pdf] [publisher]

  • Chengji Liang, Miaomiao Li, Bo Lu, Tianyi Gu, Jungbok Jo, and Yi Ding, Dynamic Configuration of QC Allocating Problem Based on Multi-objective Genetic Algorithm , Journal of Intelligent Manufacturing 28(3) 847-855, 2015.

    [pdf] [publisher]

  • Yi Ding, Tianyi Gu, Guolong Lin, and Chengji Liang, The Establishment and Solution of Coupling Model on Coordinated Scheduling of Handling Facilities in Container Terminals , Applied Mathematics & Information Sciences 6(3) 915–924, 2012.

    [pdf] [publisher]

Research Visits and Invited Talks

  • 2018 Invited lecture at Shanghai Maritime University's Logistics Research Center.

    [talk] [slides]

  • 2017 Invited lecture at the University of New Hampshire's Robotics Seminar Series.

    [talk] [slides]


  • In CS980 Topics in Reinforcement Learning, my final project study the problem of dynamic obstacles avoidance for mobile robots. We studied two deterministic approaches which use heuristic search techniques, and four stochastic approaches which use reinforcement learning techniques. We proved these two type of approach are mathematically different. The experiment results show that deterministic approaches are not only faster but also robuster than stochastic approaches. But stochastic approaches still applicable for certain problem scenarios. All of the source codes can be found here.

  • In CS980 Planning for Robots, my final project design two control algorithm: sampling based model-predictive control (SBMPC) and bisection search based model-predictive control (BBMPC). The algorithms are implemented as the controller for a real-time planning system in ROS to enable a Pioneer robot to move quickly in environments with dynamic obstacles. The behaviors of both algorithms are demonstrated through straight and curve line following experiments from simulation and real world environments. We also discussed several issues of the real-time planning system. All of the source codes can be found HERE.

  • In CS830 Introduction to Artificial Intelligence, my final project present a new anytime motion planning approach called B-SST. B-SST first runs BEAST, an effort-aided planner, to find a first solution as quickly as possible, then switch to another motion tree growth process called SST-with-cost-pruning, which adopt both idea from SST and cost pruning algorithms. We first introduce several related work that we build upon. Then B-SST is described in detail. Results with a variety of vehicles and environments showed that B-SST is competitive compared to A-BEAST and other successful anytime planners. We also discussed a more sophisticated idea on how to create a better anytime motion planner in the end. All of the source codes can be found here.

  • In CS880 Introduction to Information Retrieval, my final project study the task of helping an automated player win a computer game by reading a strategic user's guide designed for human players. In complex computer games such as Star Craft, War Craft, and Civilization, finding a winning strategy is challenging even for humans. Therefore, human players typically rely on manuals and guides. Recently, researchers have tried to using such textual information to train an automated playe. Our goal, is to better understand the retrieval model used in their paper in terms of algorithms and metrics from the field of information retrieval. Our results provide some evidence that inverse document frequency out performs recurrent neural networks at assisting human players, and could be used as a baseline for evaluating retrieval models used in playing games like this. All of the source codes can be found here.

  • In CS880 Probabilistic AI and Machine Learning, my final project compared the accuracy and reliability of several different classifiers for recognizing handwritten digit, training on MNIST dataset. Classifiers such as K-Nearest Neighbors, Decision Tree, and Random Forest are applied to the problem, and it turns out they both have their pros and cons. All of the source codes can be found here.

  • In CS980 Topics in Multi-Agent and Multi-Robot Systems, my final project was an MDP based approach to slove the Quay Crane Scheduling Problem under Uncertainty in Container Terminals. The number of time-conflict tasks for yard cranes (YC) in yard operation is considered as one of the important measures for the evaluation of the level of fluency for quay crane scheduling by terminal experts. In this project, an MDP model for QCSP is developed. A UCT algorithm is designed to solve the model.

Contact Information

Email is likely the fastest, most reliable way of getting ahold of me.

Postal mail, express mail, couriers:
UNH Computer Science
Kingsbury Hall
33 Academic Way, Durham, NH 03824-2619 USA

UNH CS on Google Maps.


Some photographs I've taken are posted here.