Page 43 - 2017 Brochure
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Massive Data Research Description
• Logics for Massive Data: Considerable amounts of information and knowledge Da-Wei Wang
are implicit in massive data. We intend to study the problems of knowledge
representation and reasoning in data science by using formal logics. With proper Research Fellow
representation frameworks and logical formalisms, knowledge discovered from
massive data can be used in data-intensive intelligent systems. Kai-Min Chung
• Efficient Data Intensive Algorithms: With the rapid development of computer and Associate Research Fellow
communication technology, it has become much easier to access and store
massive amounts of electronic data. We are interested in the research problems Tsan-sheng Hsu
concerning efficient computation of massive data, which include efficient
epidemic simulation, visualization and construction of disease networks, and Research Fellow
classical computer games.
Der-Tsai Lee
5. Graph Theory and Algorithms
Distinguished Research Fellow
Foundation: Graphs are used to model many important applications and are
the main tool for solving many theoretical problems. We often start by probing Churn-Jung Liau
fundamental theoretical problems, such as the structures of graphs with certain
properties. With these properties, we then design efficient solutions to the problems. Research Fellow
We are working on efficient graph algorithms for the streaming model.
Jing-Sin Liu
6. Computational Learning Theory
Associate Research Fellow
Many situations in daily life require us to make repeated decisions before knowing
the outcomes of those decisions. This motivates the study of the online decision Chi-Jen Lu
problem, in which one must iteratively choose an action and then receive some
corresponding loss for a number of rounds. For this problem, we identify natural Research Fellow
scenarios, for which we can improve performance of online algorithms. Moreover,
we discover new applications of this problem in different areas, such as machine Ming-Tat Ko
learning, game theory, and complexity theory.
Research Fellow
7. Robotics
Bo-Yin Yang
Path/trajectory planning and navigation of wheeled mobile robots with extension into
3D scenarios, and subject to environment constraints (such as obstacle avoidance) Research Fellow
and kinodynamic constraints (such as limits on curvature, velocity and acceleration)
are the focus of our work. 3D scenarios include unmanned aerial vehicles, or
mobile robots moving on curved terrain with elevation and curvature variations. We
developed an obstacle avoidance system based on a boundary value problem of
the Laplace equation, which is analagous to fluid flow. The real-time and anytime
characteristics of the obstacle avoidance system are verified via mobile robot
navigation experiments and numerical simulations using a finite difference method
for solving the Laplace equation.
41
• Logics for Massive Data: Considerable amounts of information and knowledge Da-Wei Wang
are implicit in massive data. We intend to study the problems of knowledge
representation and reasoning in data science by using formal logics. With proper Research Fellow
representation frameworks and logical formalisms, knowledge discovered from
massive data can be used in data-intensive intelligent systems. Kai-Min Chung
• Efficient Data Intensive Algorithms: With the rapid development of computer and Associate Research Fellow
communication technology, it has become much easier to access and store
massive amounts of electronic data. We are interested in the research problems Tsan-sheng Hsu
concerning efficient computation of massive data, which include efficient
epidemic simulation, visualization and construction of disease networks, and Research Fellow
classical computer games.
Der-Tsai Lee
5. Graph Theory and Algorithms
Distinguished Research Fellow
Foundation: Graphs are used to model many important applications and are
the main tool for solving many theoretical problems. We often start by probing Churn-Jung Liau
fundamental theoretical problems, such as the structures of graphs with certain
properties. With these properties, we then design efficient solutions to the problems. Research Fellow
We are working on efficient graph algorithms for the streaming model.
Jing-Sin Liu
6. Computational Learning Theory
Associate Research Fellow
Many situations in daily life require us to make repeated decisions before knowing
the outcomes of those decisions. This motivates the study of the online decision Chi-Jen Lu
problem, in which one must iteratively choose an action and then receive some
corresponding loss for a number of rounds. For this problem, we identify natural Research Fellow
scenarios, for which we can improve performance of online algorithms. Moreover,
we discover new applications of this problem in different areas, such as machine Ming-Tat Ko
learning, game theory, and complexity theory.
Research Fellow
7. Robotics
Bo-Yin Yang
Path/trajectory planning and navigation of wheeled mobile robots with extension into
3D scenarios, and subject to environment constraints (such as obstacle avoidance) Research Fellow
and kinodynamic constraints (such as limits on curvature, velocity and acceleration)
are the focus of our work. 3D scenarios include unmanned aerial vehicles, or
mobile robots moving on curved terrain with elevation and curvature variations. We
developed an obstacle avoidance system based on a boundary value problem of
the Laplace equation, which is analagous to fluid flow. The real-time and anytime
characteristics of the obstacle avoidance system are verified via mobile robot
navigation experiments and numerical simulations using a finite difference method
for solving the Laplace equation.
41
