Overview

Data analytics is an interdisciplinary field that aims to draw meaningful insight underlying data using various techniques based on theories from many branches including mathematics, probability, statistics, optimization, and computer science. The techniques can be applied to various domains including Electrical Engineering, Biomedical Engineering, Financial Engineering, Agriculture, Transportation, Network Engineering and Atmospheric Sciences. Our graduate program in data analytics aims to introduce essential techniques and tools used for students who are upon successfully completing this program, to be able to

Understand natural characteristics of data collected with the presence of randomness; clean up and manage the data appropriately using prior knowledge of the application domain of interest.
Formulate quantitative models to address research or practical questions in engineering applications.
Apply appropriate quantitative and statistical methods to make inference or prediction driven by data analysis.
Provide critical evaluation of the statistical methods available in various domains.
Communicate key knowledge and interpret information learned from the data to broad audiences.

Why studying Data Analytics at CUEE ?

Various available courses across many disciplines. In addition to available courses by CUEE, Participants can also enroll in relevant courses from many other departments or faculties in Chulalongkorn university, ranging from mathematics, applied statistics, or computer science.
Comprehensive foundation on mathematics and analysis. The key ingredient behind the success of statistical learning tools is from theories supporting statistical models and methods. We focus on developing analytical skills for participants to be able to construct predictive models and develop suitable statistical/numerical methods to the problem.
Exposure to diverse engineering applications. Participants can explore research projects across various fields including renewable energy application, smart health technology, multimedia signal processing, embedded and control systems, and telecommunication and transportation networks. Students can conduct collaborative research with five Chulalongkorn research units (RUs) focusing on artificial intelligence, machine learning, smart grid technology, intelligent control, wireless network and future internet, smart light and lighting technology.

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Research Highlights

Smart energy applications: solar power/solar irradiance/load forecasting using statistical methods and machine learning, home and building energy management, analysis of electrical energy yield of micro-inverter PV systems, estimation of transformer life-time using power quality data.

Smart health applications: Statistical models or analysis of biological time series (ECG, PPG, EEG, fMRI), Brain connectivity analysis, Causality analysis of physiological time series, QRS detection, Arrhythmia Detection, Atrial Fibrillation, Heart rate variability, Epileptic EEG seizure detection, Cuff-less Blood pressure estimation, Signal denoising in ultrasound and CT scans, white-blood-cell image classifications.

Traffic engineering applications: Reinforcement learning in road and telecommunication network applications, vehicular mobility classification, network traffic anomaly detection.

Speech enhancement: acoustic echo cancellation and background noise reduction in hands-free communications, noise reduction with dentist-drill applications, wavelet thresholding, signal decorrelation, and by adaptive filtering applications.

Image processing and recognition: face recognition, image segmentation, image classification, medical image processing, Hand-writing character recognition, Multi-focus image fusion for computer vision, Mosquito species classification, Dangerous object detection in X-ray.

Video processing and analytics: real time video processing in Single Board Computers/FPGA, video analytics in surveillance applications; face recognition, human action/gain recognition, image/video super resolution, anomaly detection in video scenes.

Sparse representation techniques: Signal/Image denoising, Wavelet applications, Sparse estimation using regularizations, sparse optimization.

Deep learning techniques: video coding and analytics, predictive maintenance for generator air cooler, music genre classification, lane/object detection, grid lock prediction in urban road network,

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Available Courses

2102500*	Linear Algebra for Electrical Engineering Normed linear space; vector and matrix norms; inner-product space; orthogonality; block matrix and its inverse; symmetric matrix; quadratic form of vectors; positive definite matrix; Schur complement; eigenvalue and important properties of some common matrices (orthogonal, unitary, nilpotent, permutation); matrix decomposition: LU, SVD, Cholesky; solving linear equations with different structures; derivatives of vector-valued functions; solving nonlinear equations.	(1 Credits)
2102501*	Random Processes for Electrical Engineering Random vector; Gaussian vectors and properties; transform of random variables; transform methods; probablilistic measures in random processes; stationarity; power spectral density matrix; autocorrelation function; linear system with random input.	(2 Credits)
2102503*	Time Series Analysis Data cleansing; down-sampling, up-sampling; correlation analysis; stationarity test; stationary models; maximum likelihood estimation; applications in anomaly detection and forecasting.	(1 Credits)
2102504	Introduction to Mathematical Analysis Mathematical proofs; basic set theory; the real number system; topology on the real line; sequence and convergence; limit and continuity of functions; vector spaces and linear operators; normed linear spaces; bounded operators; inner-product spaces; orthogonality and orthonormal bases; adjoint operators; applications to electrical engineering topics.	(3 Credits)
2102508*	Optimization Concepts and Applications General setting; formulating optimization problems; overview of problem types; brief introduction to convex programs; applications in engineering; overview of available methods; essential considerations of algorithms; applying optimization softwares to solve common problem types.	(1 Credits)
2102509*	Introduction to Optimization Techniques One-dimensional optimization; line search; unconstrained optimization; gradient descent; Newton method; trust-region; Levenberg-Marquardt; quasi-Newton; conjugate gradient; interior-point methods; methods for solving quadratic programming; constrained optimization; KKT conditions for nonlinear optimizations.	(2 Credits)
2102510*	Linear Programming Standard form; formulating applied problems in LP form; basic feasible solutions; the simplex method; duality and sensitivity analysis; integer linear programming: relaxation, cutting-plane, branch and bound algorithm.	(1 Credits)
2102511*	Optimization Methods for Engineering and Machine Learning Recent applications in engineering and machine learning; first-order methods for large-scale optimization; duality theory; convex optimization algorithms.	(2 Credits)
2102512*	Heuristic Optimization Introduction to optimization; evolutionary and swarm intelligence algorithms; particle swarm optimization; ant colony optimization; genetic algorithm; decision tree; dynamic programming; applications of operations research.	(2 Credits)
2102513*	Basic Image Processing Sampling and resolution; geometric transform; enhancement; restoration.	(1 Credits)
2102514*	Advanced Image Processing Segmentation; morphology; image description and representation; transforms in image processing; filters; applications.	(2 Credits)
2102515*	Digital Video Processing Analog and digital video; video signal analysis; frequency response of the Human Visual System (HVS); video models; two dimensional motion estimation; foundation of video coding; image and video coding standards.	(2 Credits)
2102516*	Adaptive Signal Processing Linear optimal filter, Wiener filter; adaptive filtering, adaptive algorithms (LMS, RLS); frequency-domain adaptive filtering; applications of adaptive signal processing.	(1 Credits)
2102517*	Wavelet Transform Short-time Fourier transform; 1D and 2D wavelet transform; filter banks; Harr, Daubechies and other wavelets; programming examples.	(1 Credits)
2102518*	Neural Networks and Deep Learning Introduction to deep learning; neural networks basics; shallow neural networks; deep neural networks.	(1 Credits)
2102519*	Reinforcement Learning and Applications Overview of reinforcement learning; introductory example: multi-armed bandit problem; Markov decision process; temporal-difference learning; reinforcement learning implementation with cloud technology; engineering applications.	(1 Credits)
2102521*	System Identification Dynamical models; input design; persistent excitation; constrained least-squares; prediction error method; subspace method; model selection and validation.	(2 Credits)
2102523*	Estimation Theory Properties of estimator; asymptotic distribution of estimators; minimum mean-square estimation; maximum likelihood estimation; Fisher information matrix; Cramer-Rao bound; maximum a posteriori estimation; applications on linear additive models.	(2 Credits)
2102571	Multimedia Communication Multimedia communication system; multimedia compression technology and standards (image, video, audio); multimedia communication protocols; telecommunication Infrastructure and mobile network ecosystem; multimedia communication applications (Internet of Things (IoTs); intelligent transport system (ITS) and autonomous vehicles; smart health, smart surveillance in smart cities; multimedia data analytics.	(3 Credits)
2102575	Statistical Inference and Modeling Procedures in statistical modeling; supervised learning; unsupervised learning; ensemble learning; regression models; classfication; clustering; model selection; model validation; engineering applications.	(3 Credits)

Cluster Coordinator

Data Analytics (DTA)
_{Coordinator for Master/
Ph.D. international applicants}

Supavadee Aramvith
Supavadee.A@chula.ac.th

Data Analytics (DTA)

Jitkomut Songsiri
Jitkomut.s@chula.ac.th

Professor in the Cluster	Research Interests
David Banjerdpongchai [DBP]	Industrial Automation, Advanced process control, Convex optimization, Robust control design, Control Application to Flexible Robot Arm, Distillation Column, Boiler, HVAC System; Energy management system, Energy Efficiency, Load and RE Forecast
Thavatchai Tayjasanant [TTN]	Power Quality Analysis, Power Quality Impacts of Distributed Generations and Smart Grid, Power quality data analytics, Smart Road Lighting, Horticultural Lighting, Human Centric Lighting, Intelligent lighting control
Naebboon Hoonchareon [NHC]	Power System Dynamics and Control, Energy Management Systems in Smart Grid, Solar Forecast and Applications
Nisachon Tangsangiumvisai [NTS]	Speech Enhancement, Noise Reduction, Adaptive Filtering, Acoustic Echo Cancellation, Wavelet Thresholding, Signal Decorrelation
Supavadee Aramvith [SAV]	Computer vision and deep learning techniques for surveillance applications, Image and Video Super Resolution, Video Anomaly Detection, Video Coding and Transmissions, Implementation of computer vision and deep learning model on SoC and FPGA.
Chaodit Aswakul [CAK]	Reinforcement Learning in Road and Telecommunication Network Applications, Vehicular Mobility Classification, Network Traffic Anomaly Detection, Machine Learning in Building Energy Management System Data and Mobile Telecommunication Network Data with Differential Privacy Protection, Network Security by Software-Defined Networking, Traffic Engineering Optimisation in Wireless Mesh Network, Network Function Virtualisation in Container Orchestration Cloud
Supatana Auethavekiat [SAK]	Medical image processing and recognition, image segmentation, image classification
Wanchalerm Pora [WPR]	Smart Devices, Smart Grid, Renewable Energy, EMS, AI, Weather Forecast, Mosquito Species Image Classification, Dangerous Object Detection in X-ray images
Charnchai Pluempitiwiriyawej [CPW]	Image Processing, Medical Image Segmentation (CT Scan, MRI), 3D image Reconstruction and Modeling, Face Recognition, White blood cell image classification, Gait recognition.
Chaiyachet Saivichit [CSC]	Communication Networking, Intelligent methods for Comm Networking, Graph Theoretical Networking, Telecom Economics, Network Reliability, Survivability and Trustworthiness
Jitkomut Songsiri [JSS]	system modelling, statistical learning, non-smooth optimization, sparse system identification, sparse optimization, solar power forecasting, brain connectivity, Granger causality, causality learning in time series
Widhyakorn Asdornwised [WAS]	5G and Beyonds, 5G Private Networks, Signal Processing for Communications, Staistical Inference and Deep Learning, Sparse Representation, Digital Signal Processing, and Image Processing
Manop Wongsaisuwan [MWS]	Control Systems Theory, Robotics, Computer Application in Control Engineering, Machine Learning
Suree Pumrin [SPR]	Image processing, Computer Vision, Embedded Systems, Microscopy Image, Plant Phenotyping
Apiwat Lek-uthai [ALT]	Medical & Physiological Signal Processing
Teerapol Silawan [TSL]	Graph network and its applications, Combinatorial optimization, Metaheuristic and heuristic optimization (Swarm intelligence, Evolutionary algorithm, search based optimization, etc.)

Lifelong Learning

แนะนำวิชา Optimization Methods for Engineering and Machine Learning

แนะนำวิชา Reinforcement Learning and Applications

แนะนำวิชา Heuristic Optimization

Data Analytics – DTA

Overview

Why studying Data Analytics at CUEE ?

Research Highlights

Available Courses

Cluster Coordinator

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