phd machine learning new zealand

Yun Sing Koh Profile page

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• Advanced Machine Learning and Data Analytics Research (MARS) Lab
• Yun Sing's Website

Yun Sing Koh, a Professor at the School of Computer Science at the University of Auckland, New Zealand, is an expert in Artificial Intelligence (AI) and Machine Learning (ML). Her pioneering work delves into foundational AI research, focusing on continual learning and adaptation, domain generalization, transfer learning, anomaly detection, and data stream mining. Passionate about harnessing machine learning for societal benefit, Yun Sing's research has catalyzed interdisciplinary applications in environmental and health domains. She has published over 150 peer-reviewed publications in esteemed conferences and journals like IJCAI, IEEE ICDE, IEEE ICDM, Machine Learning Journal, and Journal of Artificial Intelligence. Yun Sing and her students' research excellence has been recognized with numerous awards, including Best Demo Paper for ACM MM 2023, Best Demo Paper for IEEE ICDE 2023, and Best Research Paper at IJCAI 2022 and DSAA 2022. She has various prestigious grants, including the New Zealand Royal Society Fast-Start Marsden funding, the United States Office of Naval Research Grant, MBIE Catalyst, and MBIE Endeavour Funds. Internationally, Yun Sing has held pivotal roles, including General Co-Chair at the IEEE International Conference on Data Mining in 2021 and the Australasian Data Mining Conference in 2023 and 2022. She has also served as Workshop Co-Chair at the ECML/PKDD conference in 2021, Program Co-Chair of the Australasian Data Mining Conference in 2018, and Program Co-Chair of the Australasian Joint AI Conference in 2024. Additionally, she has contributed as the Workshop Co-Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining. At the helm of the Advanced Machine Learning and Data Analytics Research (MARS) Lab and as the Director of the Centre of Machine Learning for Social Good, Yun Sing Koh continues to drive innovation, shaping the future of AI for the betterment of society. Explore and potential project at https://ml4sg.auckland.ac.nz/. Distinctions/Honours: Awards • Best Demo Paper award ACM MM (Core ranked: A*) 2023 (Main Author Aaron Keesing (PhD Student)) • Best Demo Paper award IEEE ICDE (Core ranked: A*) 2023 (Main Author Ben Halstead (Research Fellow)) • Best Research Paper award Australasian Joint AI Conference. 2022 (Main Author Bowen Chen (Masters Student)) • Best Research Paper award IEEE DSAA (Core ranked: A) 2022 (Main Author Ben Halstead (PhD Student)) • Finalist Women in AI Australia - NZ 2022 • Best Research Paper award Australasian Joint AI Conference. 2018 (Main Author Monica Bian (PhD Student)) Funding • MBIE 2020 Catalyst Strategic NZ-Singapore Data Science Programme “Advanced Graph Analytics for Human Brain Connectivity” 2020 -2023, Key Researcher • MBIE Data Science Programme “Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science” 2020 -2027, Key Researcher • MBIE Endeavour Research Programme “Our Generation, our Voices, all our Futures”, 2020 - 2025, Key Individual • Office Naval Research Grant “Prediction in Evolving Data Streams using an Adaptive System” 2019 – 2020, PI • Marsden Fast-Start 2018 “An Adaptive Predictive System for Life-long Learning on Data Streams” 2019 – 2021, PI • Precision Driven Health *A deep learning platform for GP referral triage 2018 -2020, Named collaborator • Vice-Chancellor’s Strategic Development Fund “Building resilience at the weakest link: A user-aware interactive and intelligent security system” 2018-2019, PIs: G. Russello, P. Corballis, Y.S. Koh, D. Lottridge • AUT University Vice-Chancellor Emerging Researcher Award (2009), Auckland University of Technology Committee/Professional groups/Services. • General Chair IEEE ICDM 2021, AusDM 2022 • Workshop and Tutorial Chair ECML/PKDD 2021 • Steering Committee Member: AusDM • Senior PC Member: AAAI, ECML, IJCAI, PAKDD • PC Member: SIGKDD Qualifications: PhD (University of Otago); Masters of Software Engineering (University of Malaya); BSc (First class Hons) Computer Science (University of Malaya) Areas of Expertise: • Machine learning, specifically in: • unsupervised learning and weak supervision, • data stream mining and continual learning, • anomaly detection, and • transfer learning.

UNIVERSITY OF AUCKLAND APPOINTMENTS

• Professor Faculty of Science, School of Computer Science, New Zealand

FACULTY / INSTITUTE

• Faculty of Science

GRADUATE SUPERVISION

• PhD/Doctoral Accredited Supervisor
• Has Current Supervision Opportunity

MEDIA ENQUIRIES

• Available for media enquiries

FIELDS OF RESEARCH

• Machine learning
• Stream and sensor data
• Artificial intelligence
• Deep learning
• Data mining and knowledge discovery
• Data engineering and data science
• Machine learning not elsewhere classified

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Artificial Intelligence

Why study Artificial Intelligence?

The growth of available data (“Big Data”) across companies, industry, government agencies and departments, iwi and community organisations has meant more and more of them are adopting AI into their mainstream activities. This has led to an increased demand for graduates with advanced skills in AI.

Studying AI at the University of Waikato will give students advanced skills and techniques to better leverage AI for the benefit of our communities.

The University of Waikato is well known worldwide for its computer science research and for development of  open source software  such as WEKA, MOA and ADAMS, with more than 10 million downloads. The University has recently  invested in New Zealand’s most powerful computer for AI , it is also the home of  Te Ipu o te Mahara, The Artificial Intelligence Institute .

Students have the opportunity to work alongside some of the world's leading AI researchers, with particular expertise in machine learning, deep learning, real-time analytics and open source software.

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• Master of Science
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Senior Research Officer / Research Officer (Artificial Intelligence and Machine Learning )

intelligence and machine learning technologies, to accelerate our research impact. The successful candidate will have completed a PhD in artificial intelligence or computer science or a related field with strong

Research Fellow, Computer Science

also have some knowledge in one or more of the following areas: Human-Computer Interaction ; Software Security Engineering; Artificial intelligence and machine learning . For more information about this

HPC Research Fellow/ Data Scientist

be responsible for designing and implementing machine learning algorithms and models to predict and classify patterns in sensor data. The ability to be able to analyse complex data sets, including

Lecturer in Fluid Mechanics

Department of Mechanical Engineering School of Engineering, Computer and Mathematical Sciences The Department of Mechanical Engineering within the School of Engineering, Computer and Mathematical

Lecturer in Dynamics

Senior lecturer/associate professor - management and governance.

Building, which provides state-of-the-art teaching and learning facilities. UABS is accredited by all three of the world’s top business school accrediting bodies (AACSB in the US, EQUIS in Europe and AMBA in

Lecturer/Senior Lecturer in Mechatronics Engineering

Senior research officer/research officer (artificial intelligence and machine learning ).

Zealand food sector. We are seeking an ambitious researcher who has the passion and potential to apply rapidly emerging Artificial Intelligence and Machine Learning technologies to solve complex food and

Lecturer/ Senior Lecturer/ Associate Professor in Thermodynamics

The Department of Mechanical Engineering within the School of Engineering, Computer and Mathematical Sciences (SECMS) at AUT is seeking to appoint a permanent staff in either at Lecturer or Senior

Research Software Engineer / Senior Research Software Engineer

machine learning .  We adopt the latest software development practices for undertaking work and making our work open-source. Mōu | Who You Are You must hold a Bachelor's degree in a relevant discipline (e.g

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DATA SCIENCE & ARTIFICIAL INTELLIGENCE PROGRAMME

The Data Science & Artificial Intelligence (AI) Programme is designed for IT and non-IT professionals who are interested to transition their career into data science, analytics, machine learning and artificial intelligence. The programme includes practical training, industry award and job placement support to help professionals secure a job as a Data Analyst or Data Scientist.

Download Course Outline

The Data Science & AI Programme can be taken in either 24 weeks part time or 12 weeks full time and is designed for career-driven professionals to transition into the big data and AI industry. The programme includes pre-work, intensive practical training, industry award and a job outcomes programme in collaboration with industry partners. The course content has been tailored to give you the skills that are demanded by businesses in today’s rapidly evolving job market. Remote learning is available for this programme. The interactive remote format enabled by video conferencing technology is a rich and connected experience featuring virtual break-out rooms, shared screens, digital whiteboards, peer to peer collaboration and instructor support.

Discuss with a Career Consultant

You can schedule a call with a Career Consultant to discuss this course.

THE DEMAND FOR TECH TALENT

The opportunity is massive in aotearoa new zealand and around the world. according to the digital skills aotearoa report, local tech companies have been creating approximately 2,000 new jobs a year on average for the past 10 years. in 2019, 555 new companies were created in the nz tech sector alone, up three percent from 2018., what is practical data science training.

Practical data science training is an essential requirement to practice as a Data Scientist or Data Analyst in Aotearoa New Zealand. Learn from industry experts on how to apply machine learning and AI techniques for businesses and government organisations including business consulting and simulating commercial projects. This means you will be ready to work as a Data Scientist or Data Analyst.

AUT & Institute of Data

Welcome to Aotearoa New Zealand's most in-demand practical data science training programme.

Learn how to create predictive models by learning to wrangle, analyse, visualise, predict and find insight into data alongside the best data science trainers in Aotearoa New Zealand.

The Data Science & AI Programme is a structured training course designed to help you develop the practical skills you will need as a Data Scientist or Data Analyst. After completing your Data Science & AI Programme, you will receive an AUT Data Science & AI Certificate of Completion and Institute of Data Certificate of Achievement.

Hundreds of professionals choose the Institute of Data every year to prepare them for their future career after careful consideration of their desired career progression and to attain maximum return on investment for their education. Leading the future of the profession, the Institute of Data offers a collaborative and successful programme, driven to ensure you have the best start to your career.

Data Science & Artificial Intelligence (AI) Programme

The next programme starts, 20th may 2024 (full-time), 21st may 2024 (part-time), with the practical training programme you will..., be ready to practice data science.

The programme is designed to prepare students for real commercial work in the area of data science, AI and big data. It is focused on practical in-class training, labs and real-life projects.

Receive the best training and the latest curriculum

The programme trainers are actively working in the industry and will teach you how to practice data science and advanced analytics. The curriculum is developed by the Institute of Data Academic Committee with decades of experience working in the Data Science Industry.

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The programme results in an AUT Data Science & AI Certificate of Completion and Institute of Data Certificate of Achievement which signals to employers that you are ready to join the industry. Your resume and profile will be reviewed by our Job Outcomes team to give you the best possible chance of success.

Get more support and guidance

The member network offers support and guidance from mentors and direct connections to the industry. You will connect with employers at both exclusive and public events held for alumni.

Programme structure

Students complete curated pre-work as an interactive online learning experience designed to prepare every student with the required skills and knowledge to complete all coursework and training.

Remote or In Class Training

Students undertake a professional in-classroom course from a team of expert industry trainers from leading consultancies and enterprises to acquire the most sought-after practical training in the country.

Job outcomes

You will undergo a Job Outcomes Programme which will help position you for the best chance of a new job opportunity and/or internal promotion. Land a job in one of the most sought after fields in tech with the guidance of professional career coaches and access to our hiring network.

How will I be assessed?

The programme uses competency based assessment through the completion of labs and a final capstone project. There is no formal exam. The capstone project is a real-life project and is assessed against the competency based assessment standards set by the Academic Committee.

Industry Partners & Contacts

Post course.

We'll help you transition into the big data, data science and artificial intelligence industry. We have a 93% job outcome success rate within 180 days of graduation for active job seekers.

Syllabus Breakdown

• Course Introduction and Overview
• Programming Fundamentals for Data Science
• Maths & Statistics Fundamentals for Data Science
• Data Science Practice (ongoing)
• Data Cleaning and Quality Assessment
• Data Visualisation
• Statistical Testing
• Succeeding as a Data Scientist in Industry
• Mini Project 1 – Data Science Practice
• Critical Thinking Training
• Relational and Non-Relational Databases
• Application Programming Interfaces (APIs)
• Introduction to Machine Learning
• Feature Modelling
• Evaluating Regression Models
• Overfitting and Regularisation
• Questioning Technique Training
• Logistic Regression
• Evaluating Classification Models
• Support Vector Machines
• Bayesian Inference
• K-means Clustering
• Principal Components Analysis
• Research and Documentation Training
• Mini Project 2 – Data Science Practice
• Decision Trees
• Random Forests
• Bias and Variance
• Web Scraping
• Text Pre-processing
• Sentiment Analysis
• Text Classification
• Presentation Skills Training
• Introduction to Artificial Intelligence
• Reinforcement Learning
• Deep Feedforward Neural Networks
• Recurrent Neural Networks
• Convolutional Neural Networks
• Mini Project 3 – Data Science Practice
• Introduction to Cloud Computing
• Deployment of Machine Learning Models
• Cluster Computing
• Streaming Data
• Storytelling Training
• Capstone Project
• Job Outcomes Programme
• Consultation and Presentation training
• Data Science Practice

Speak to a career consultant about fee and fee support details.

• Bachelor degree in any field of studies
• Any previous qualification undertaken in English or obtained IELTS 5.5
• Complete the assigned online pre-work provided to you when you enrol

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Doctoral study in Finance

Why study with us.

• We are ranked first in New Zealand and in the top 100 globally for Accounting and Finance*
• We are a leading education provider in our subject in the Asia-Pacific region
• Postgraduate Research Student Support (PReSS) funding is available for research expenses

*QS World University Rankings by Subject, 2021

Research opportunities

When you undertake your doctorate in Finance at the University of Auckland Business School, you’ll join a high-calibre research community and have opportunities to publish papers, attend international conferences and develop a network in academia and industry.

Our researchers are currently examining how investors and firms allocate scarce resources over time. This covers corporate financial decision-making, investment analysis, portfolio management and financial instruments, and institutions and markets.

We invite research proposals in any of the following or related topics**:

• Investments, asset pricing, financial anomalies and machine learning applications in finance
• The cost of capital - The factors that determine the required rate of return on risky asset, topics related to capital budgeting, the valuation of firms and the regulation of pricing
• Initial public offerings of equity securities and seasoned equity offerings - The factors affecting under-pricing and the history of the under-pricing phenomenon
• Market microstructure - The impact of "market architecture" on the outcomes of the trading process
• Securities’ market regulation - The regulation of information disclosure in financial markets and its impact on the financial information environment
• Risk management - The identification and measurement of financial risks and the management of these risks using financial instruments
• Banking, financial institutions and regulation - Bank supervision and regulation; the global financial crisis

**Note: this is not an exhaustive list.

Pursue your topic with us and benefit from the support and supervision of internationally recognised researchers.

Professor Norman Wong (Head of Department): Research interests include financial accounting; capital markets and accounting information; governance issues in accounting; accounting policy choice; taxation and accounting; and research methods in accounting.

Professor Henk Berkman : Asset pricing, return predictability, forensic finance and investment management.

Dr Paul Geertsema : Asset pricing, return predictability, financial anomalies and machine learning applications in finance.

Dr Dulani Jayasuriya : Big data, international banking, social media, applications of machine learning in finance, applying deep learning techniques for socio-economic problem solving, blockchain applications in finance, crypto currencies.

Dr John Lee : Empirical asset pricing, return anomalies and beta estimation.

Dr Michelle Li : Corporate finance including CEO board involvement, CEO compensation, minority shareholder protection, and merger and acquisitions.

Dr Helen Lu : Asset pricing, machine learning and executive successions.

Professor Dimitris Margaritis : Corporate finance, financial econometrics, money and banking.

Associate Professor Alastair Marsden : Corporate valuation, cost of capital, impact of regulation on financial markets and equity issues.

Past research topics

• "Investor Decision Making" | Supervised by Professor Henk Berkman and Associate Professor Alastair Marsden
• "The Effect of Earnings Management Constraints on Management Earnings Forecasts: Evidence from Japan' | Supervised by Professor Norman Wong and Professor Steven Cahan
• "An Investigation into the Effects of Weight Constraints, Estimation Error and Taxes on the Benefits from International Diversification" | Supervised by Associate Professor Alastair Marsden and Dr John Lee
• "Essays on Mutual Funds" | Supervised by Professor Dimitris Margaritis and Dr John Lee

Scholarships and awards

There are several scholarships you may be eligible for when you decide to pursue your doctoral studies in Finance.

University of Auckland Doctoral Scholarships

Barry Spicer and Owen G Glenn PhD Scholarship

Phone the University

• 923 7186 (within Auckland)
• 0800 61 62 63 (outside Auckland)
• +64 9 373 7513 (overseas)

Apply for doctoral study

Doctoral programmes.

• Doctor of Philosophy

Related subjects

• Commercial Law
• Information Systems

Related links

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