News and Update
- July 2017, Our paper entitled Metacognitive Approach for Online Tool Condition Monitoring has been accepted for publication by Journal of Intelligent Manufacturing (IF:3.01)
- March 2017, Our special issue, entitled Advanced Soft Computing for Prognostics Health Management, has been accepted by Applied Soft Computing (IF:2.8)
- March 2017, Dr. Pratama will join the School of Computer Science and Engineering NTU as a faculty member in June 2017
- March 2017, Dr. Pratama has been appointed as an associate editor of International Journal of Intelligent Autonomous Systems
- February 2017, Dr. Pratama will organize a special session in Data Stream Mining at IJCNN 2017 (Core Ranking: A)
- January 2017, Dr. Pratama will publish an editod book in Nova Science Publisher
I currently look for PhD students and Research Fellow (RF) to work with me at the School of Computer Science and Engineering, Nanyang Technological University in the advanced data stream analytics for various real-life applications. The RF position is for two years but initial contract will be for one year and will be extendible for another one year subject to the performance of candidate. The PhD position is a fully funded position for four years. The PhD scholarship position follows terms and conditions of NTU graduate scholarship.
Your main contribution in this project involves algorithmic development of novel algorithms for data stream analytics (clustering, prediction and classification). Your algorithm will be tested in real-world streaming data and will cope with heterogeneous and uncertain data (image, text, video, etc). Memory efficiency and predictive accuracy under non-stationary conditions will be the underlying focus of investigation. Despite the scope of this project, I welcome applicants with strong expertise in other aspects of data science and computational intelligence.
For RF position, the candidate is supposed to hold a PhD degree in computer science or related discipline from reputable universities. The candidate must have excellent publication track record in the top venues: IEEE TFS, IEEE TNNLS, IEEE TCB, IEEE TEC etc as well as research experience in the data science related fields. Fluency in programming is necessary for this position (JAVA, C/C++, MATLAB, R). Good writing and speaking skill in English is required to this position.PhD
For PhD position, the candidate is supposed to hold a master degree or bachelor degree with honours in computer science, or related fields with good CGPA (first class honours). The candidate should have experience with data mining, computational intelligence and machine learning as well as good programming skills. The Publication track record in good journals or conferences will be a plus. Good writing and speaking skill in English is required to this positionApplicant From Indonesia
I am always happy to take candidates supported by Indonesia government (DIKTI, LPDP, etc). I encourage for applicants wishing to apply scholarship from me to apply Indonesia government scholarship. My requirements for candidates sponsored by Indonesia government are not as exhaustive as aforementioned but you must satisfy NTU entry requirements. Please refer to PhD admission part of the NTU website.
Interested applicants please attach your full CV, with the names and contacts (including email addresses) of 3 character referees, and all relevant academic certificates to Asst Prof Mahardhika Pratama (firstname.lastname@example.org). We regret that only shortlisted candidates will be notified.
Applications close when position is filled.
General Areas of Interest
- Neural and Fuzzy Systems
- Intelligent Control Systems
- Maching Learning
- Data Stream Mining
- Big Data Analytics
- Real World Applications of Computational Intelligence
Topic 1: Advanced Evolving Intelligent System
Data stream mining is today one of the most challenging research topic, because we enter the data-rich era. This condition requires a computationally light learning algorithm, which is scalable to process large data streams. Furthermore, data streams are often dynamic and do not follow a specific and predictable data distribution. A flexible machine learning algorithm with a self-organizing property is desired to overcome this situation, because it can adapt itself to any variation of data streams. Evolving intelligent system (EIS) is a recent initiative of the computational intelligent society (CIS) for data stream mining tasks. It features an open structure, where it can start either from scratch with an empty rule base or initially trained rule base. Its fuzzy rules are then automatically generated referring to contribution and novelty of data stream. In this research project, you will work on extension of existing EISs to enhance its online learning performance, thus improving its predictive accuracy and speeding up its training process. Research directions to be pursued in this project is to address the issue of uncertainty in data streams.
Topic 2: Machine Learning Algorithm for Online Big Data Analytics
The era of big data refers to a scale of dataset, which goes beyond capabilities of existing database management tools to collect, store, manage and analyze. Although the big data is often associated with the issue of volume, researchers in the field have found that it is inherent to other 4Vs: Variety, Velocity, Veracity, Velocity, etc. Various data analytic tools have been proposed. The so-called MapReduce from Google is among the most widely used approach. Nevertheless, vast majority of existing works are offline in nature, because it assumes full access of complete dataset and allows a machine learning algorithm to perform multiple passes over all data. In this project, you are supposed to develop an online parallelization technique to be integrated with evolving intelligent system (EIS). Moreover, you will develop a data fusion technique, which will combine results of EIS from different data partitions.
Topic 3: Metacognitive Scaffolding Learning Machine
Existing machine learning algorithm is always cognitive in nature, where they just consider the issue of how-to-learn. One may agree the learning process of human being always is always meta-cognitive in nature, because it involves two other issues: what-to-learn, when-to-learn. Recently, the notion of the metacognitive learning machine has been developed and exploits the theory of the meta-memory from psychology. The concept of scaffolding theory, a prominent tutoring theory for a student to learn a complex task, has been implemented in the metacognitive learning machine as a design principle of the how-to-learn part. This project will be devoted to enhance our past works of the metacognitive scaffolding learning machine. It will study some refinements of learning modules to achieve better learning performances.
Topic 4: Advanced Evolving Intelligent System in a Complex Manufacturing Industry
Undetected or premature tool failure may lead to costly scrap or rework arising from impaired surface finishing, loss of dimensional accuracy or possible damage to the work-piece or machine. The issue requires the advancement of conventional TCMSs using online adaptive learning techniques to predict tool wear on the fly. The nonlinear and uncertain nature of machining processes presents very complex issues to be resolved by both academia and industry, because of the use of multi-point cutting tools at high speed, varying machining parameters, and inconsistency and variability of cutter geometry/dimensions. The cutting-edge learning methodologies developed in this project will pioneer frontier tool-condition monitoring technologies in manufacturing industries
Topic 5: Online Text Classification Using Advanced Evolving Intelligent System
Today, we confront social media text data explosion. From these massive data amounts, various data analytic tasks can be done such as sentiment analysis, recommendation task, web news mining, etc. Because social media data constitute text data, they usually involve high dimensionality problem. For example, two popular text classification problems, namely 20 Newsgroup and Reuters21578 top-10 have more than 15,000 input features. Furthermore, information in the social media platform is continuously growing and rapidly changing, this definitely requires highly scalable and adaptive data mining tools, which searches for information much more than the existing ones used to do (evolving intelligent system). The research outcome will be useful in the large-scale applications, which go beyond capabilities of existing data mining technologies. This project will not only cope with the exponential growth of data streams in the social media, but also will develop flexible machine learning solution, which adapts to the time-varying nature of the social media data.