School of Computer Science and Engineering (SCSE)

​​​​​​Name of NTU Supervisor

Research Title

Assoc Prof Kwoh Chee Keong​ Software tool for rendering DNA nano-structures​The research on the structural stability of 3 Dimensional Origami is attracting many to consider the possibility to make useful tools for simulating thermodynamically stable DNA structures. Most of the major architectural monuments around the world are a conglomeration of basic geometric structures such as cylinders, spheres, tetrahedrons, cuboids. For example, the Big Ben Tower of London, is roughly a collection of four cuboids, one truncated pyramid and one pyramid in a fixed structural ratio. Combining these basic structures to form such complex structures is still a difficult task in 3 Dimensional Origami.

The ultimate key is to create such architectural monuments or other such structures using Origami. The primal aim is to formulate a software that can render such basic geometric structures of any input length from the existing data available about them. The software must learn from the statistical data available about a particular structure and approximate the result from it. The output will be the possible structure and its corresponding data such as the total base pairs required, total crossovers, edge/ radius length, error in the length, base pairs/turn etc.

After a successful rendering algorithm for these basic structures is deduced, the subsequent step is to input complex 3 Dimensional Structures such as architectural buildings. This software should be able to take into account of the structural ratio (in terms of size) of these basic structures and output the required stable complex structure by customising the sizes of the basic structures.

Prof​ Dusit Niyato Deep Reinforcement Learning for Resource Allocation in 5G Networks​Reinforcement learning (RL) is used extensively in artificial intelligence domain to facilitate model free learning of the underlying environment. Deep learning on the other hand is used extensively by machine learning community for performing supervised or unsupervised learning via artificial neural networks. Deep reinforcement learning (DRL) is an enhanced version of traditional RL that uses deep learning to control practical systems. This project aims to propose efficient resource allocation algorithms based on DRL for 5G enabled wireless networks.
Prof. Thambipillai SrikanthanStereo Region-of-Interest Generation for Pedestrian Protection Advanced driver assistance systems (ADAS), and particularly pedestrian protection systems (PPS), have become an active research area due to the increasing need for improving traffic safety. Robust and real-time pedestrian detection is a challenging task as there is a need to take into account cluttered background, non-rigid appearance of pedestrians, etc. Region of Interest (ROI) generation is an essential component in vision based PPS. In this project, the candidate will evaluate stereo vision based ROI generation techniques for robust and real-time pedestrian detection in ADAS.
​Assoc Prof Anwitaman DattaImplementing and experimenting with novel erasure codes for distributed storage systems​This project will involve understanding and implementing several families of recently proposed erasure codes, followed by thorough experimentations in computer clusters to benchmark these codes under varied workloads. The student needs to have good understanding of number theory, particularly finite fields (in order to understand the code structures), and good programming skills in general, as well as with ​socket/network programming. Knowledge of distributed systems is desirable, but not necessary.
Assoc Prof Anwitaman DattaAdapting proof of data possession techniques for efficient RAID monitoring​​Proof of data possession (PDP) techniques are usually used to detect data integrity when data is stored/outsourced with an untrusted server (e.g., on the cloud). It is a probabilistic technique and thus highly efficient, and scales well with volume of data. The purpose of the project will be to adapt the ideas from PDP in the context of RAID (redundant array of independent disks) systems. The student should have very good linux kernel + C programming skills. Knowledge of RAID systems will be an additional advantage to have.​
​Asst Prof Anupam ChattopadhyayEvolvable Video COMPression(EVoComp) system design for online video compression   ​With increase in the resolution of videos, higher bandwidth is required for transmission, thereby necessitating the need for compression. The project aims at developement of an FPGA based system that is – adaptive, so that it adapts according to changes in video characteristics and – on-line so that it can compress/decompress a video stream as it is being transmitted/received. The EvoComp system will allow evolutionary algorithms to change the hardware configuration  in real time to change compression methodolgy depending on video characteristics. We aim to benchmark the developed system with the existing video compression technqiues.
Asst Prof Anupam Chattopadhyay​Collaborative  energy-aware downloading for locally connected Android Mobile Devices​Energy conservation is a critical aspect of mobile devices. Services like downloading content are energy hungry. The project aims at developemnt of energy efficient techniques to manage downloading large files by collaborative downloading across locally connected (over Bluetooth or Wifi) Android devices to maximize throughput and at the same time taking into account the characteristics of the peers such as network usage, current cpu utilization, battery level, etc for partioning the download.
​Assoc Prof Erik Cambria​​Concept-Level Sentiment Analysis with SenticNet

This project is in the context of sentiment analysis or opinion mining. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial prediction. 


The candidate will focus on improving the current version of the Sentic API (, a publicly available resource for concept-level sentiment analysis. Possible improvements are listed below.


Enriching SenticNet with new concepts.

Possible ways to do this are:

a) write some code (preferably in python but java also can) to import concepts from other resources, e.g., Bing Liu's Opinion Lexicon, MPQA Subjectivity Lexicon, SentiWordNet, Harvard General Inquirer, LIWC, etc.

b) crowdsourcing, e.g., through surveys, quizzes or games

c) an ensemble of the above


Finding errors in SenticNet, e.g., in terms of polarity or semantics. 

Possible ways to do this are:

a) do it manually

b) use the same code/resources mentioned before to find clashes (e.g., positive polarity in SenticNet but negative in SentiWordNet)

c) an ensemble of the above


Enriching SenticNet w/ new add-ons.

Possible ways to do this are:

a) add POS tags

b) add mood tags

c) add category tags

Possible extra activities for the candidate include working on any of the modules of our model for concept-level sentiment analysis (​

​Asst Prof Lam Siew KeiReliability-Aware High-Level Synthesis for Embedded Computing Platforms
​​ ​Single Event Upsets (SEU) induced faults poses a serious reliability problem in safety-critical embedded systems. Existing SEU mitigation techniques often incur unacceptable performance-power-cost overhead. The main objective of this project is to devise techniques that automatically synthesize reliable embedded systems from high-level design descriptions guided by constraints and target hardware architecture specifics. The candidate will investigate lightweight SEU mitigation techniques for fault tolerant custom hardware and realize the proposed techniques on state-of-the-art FPGA platforms. The candidate is expected to be experience in digital design and Verilog HDL, good foundation in data structures and algorithms, and good programming skills (C/C++).​
Asst Prof Lam Siew Kei​​​Accelerating Feature Detectors for Real-time Vision-based Applications
​​ ​​Feature detection is a fundamental step in many real time applications such as video tracking, visual SLAM and robotic navigation. However, existing implementations for feature detection is highly compute intensive and becomes a bottleneck for real time vision tasks. This project aims to develop hardware-efficient feature detectors on FPGA.​
Asst Prof Arijit KhanTowards Querying and Mining of Big-Graphs​With the advent of the Internet, sources of data have increased dramatically, including the World-Wide Web, social networks, genome databases, knowledge graphs, medical and government records. Such data are often represented as graphs, where nodes are labelled entities and edges represent relations among these entities. Knowledge is hidden in the complex structure and attributes inside these networks. While querying and mining these linked datasets are essential for various applications, traditional graph algorithms may not be able to capture the rich semantics in these networks. In this project, we shall design novel techniques and systems for emerging graph workloads including graph pattern matching, approximate subgraph mining, similarity search, ranking and expert finding, aggregation and OLAP, uncertainty, and streaming.


*Project Duration: Minimum project duration is 6 months. Hence, this is suitable only for final year Bachelors or dual degree (integrated Bachelors and Masters) students for their final-year research project. The aim is to publish a paper in a top-tier database or data mining conference such as SIGMOD, KDD, VLDB, and ICDE.

*Please do not apply for this project if unable to commit for a period of  6 months

​Assoc Prof Chng Eng Siong​​Information extraction from Text​In this work, we wish to examine the extraction of information from text.

E.g, its topic, its keywords, word cloud, its relationship to other paragraphs within a corpus.
An example of what can be achieved is seen in here:

Thomson Reuters Open Calais (*)

Detect entities, topic codes, events, relations and socialTags.

IBM Entity Extraction API (*)

Detect entities of people, places, companies, topics, facts, relationships, authors, and languages.

As well as here:

A potential project may be to link text transcription of a MOCC course such as DSP by
to his book or other DSP books, allowing cross-referencing of text that is discussing similar topics.

​​​Assoc Prof Chng Eng SiongMachine Learning by TensorFlow​Google has released its tensor flow codebase for machine learning

This represents the state of the art system for machine learning. In this work we wish to examine its
use for the following applications
(choose one)

   a) signal enhancement (cleaning up noise e.g,
   b) classification of audio signals (similar in problem to classifying digits MNIST)
   c) classification of topic from a given text (topic classification)

Assoc Prof Vun Chan Hua, NicholasRNS based embedded signal conversion and processing techniques enables highly efficient hardware based signal acquisition and processing techniques. This project is to investigate the optimization of the novel Residue Number System (RNS) based signal processing techniques that were invented in SCSE as listed below.

a) RNS encoding based folding ADC. ISCAS 2012: 814-817
A New RNS based DA Approach for Inner Product Computation. IEEE Trans. on Circuits and Systems 60-I(8): 2139-2152 (2013)

The project involves finding the most suitable moduli set to implement the most efficient system based on the above techniques, in term of balanced moduli,  efficient reverse conversion, as well as further novel features such as error detection and correction in the acquisition and processing stages.

​Assoc Prof Anwitaman DattaWeeding the fake out from social media media plays a important role in modern lives - in how information is shared and consumed. Yet, a lot of information on such platforms are not authentic, and furthermore, irrespective of the inherent nature of the information itself, oftentimes, the extent to which it has actually penetrated the network (say in terms of likes) on the platform is incorrect. The latter (fake likes) is used for a myriad of reasons – e.g., to project a better value to a social media asset than it actually has, or to give it a critical momentum first in hope of gaining organic traction afterwards, and so on. This project will aim to study the propagation of fake metrics on social media services such as fake likes on Instagram. The purpose of the project will be to develop automated and robust solutions to detect inorganic and fake likes on Instagram using Social Network Analysis techniques, machine learning and anomaly detection on a large dataset of social media (Instagram) users. The student should have good programming skills and a good understanding of statistics and machine learning. Prior knowledge of Web 2.0 APIs is desirable, but not necessary. The project has many steps – from data gathering, cleaning to analysis, and is aimed to be for 5–6 months. 
Prof Jagath C RajapakseIdentification of cancerous skin lesions


Skin cancers are the most common human malignancies in fair skin populations. The aim of this project is use deep convolution neural networks to identify skin cancer from skin lesion images. The student will develop algorithms to implement convolution networks and classify skin lesion images into cancer and non-cancer types.


The aims of this project are

  1. to extract potential features from skin images for cancer classification
  2. to develop deep a deep convolutional neural network to identify cancerous skin lesions images from cancer images.


The student will develop deep neural networks by using Caffe software (

Prof Jagath C Rajapakse
Classification of skin cancer from skin lesion images

Skin cancers are the most common human malignancies in fair skin populations.  Though melanoma has the highest mortality, other non-melanoma cancers are more common. The aim of this project is to classify cancerous skin lesions into different cancer types from skin lesion images by using deep convolution neural networks.

The aims of this project are

  1. to extract potential features from skin images for cancer classification
  2. to develop deep a deep convolutional neural network to classify cancerous skin lesions into different cancer types.​

The student will develop deep neural networks by using Caffe software (

Asst Prof Ke Yiping, Kelly
Effective Deep Techniques for Domain Adaptations


​Domain Adaptation Aims To Generalize A Model Learnt From A Source Domain To A Target Domain, Where The Labeled Data In The Target Domain Is Scarce And The Data Distributions In The Two Domains Are Different. Recently, Deep Methods Have Emerged As Promising Techniques For Domain Adaptation. This Project Is To Study The Problem Of Domain Adaptation, Identify The Key Challenges, And Develop Effective Deep Techniques To Solve It. The Student Is Required To (1) Collect And Pre-Process Real Data For Domain Adaptation, (2) Design And Implement Effective Deep Algorithms For Domain Adaptation, (3) Evaluate The Performance Of The Algorithms And Compare With State-Of-The-Art Baselines.

Prof Guan CuntaiDeep Learning Framework for EEG Classification of electroencephalographic (EEG) signals with high accuracy is an important aspect of modern Brain Computer Interfacing systems. This project aims to develop novel deep learning methods for classification of motor imagery based EEG signals. Small sample size, high dimensionality, low SNR and high inter-subject variability will be the challenges in this task. Students having previous experience with python and deep learning frameworks will be preferred.
​​Prof Guan CuntaiExploration of ear-EEG sensors for BCI applications
​​ sensors measure the electroencephalographic (EEG) signals form electrodes placed in-the-ear. This system provides a simple, non-invasive way for continuous monitoring of brain signals but its viability for BCI applications remains unexplored. This project aims to investigate the suitability and possible applications of ear-EEG for continuous signal monitoring. The project will involve designing of BCI experiment, data collection and analysis using ear-EEG.

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