generated digits

[Source: rdn-consulting]







Last updated: 01/12/2020

Research scholarships at A2I2

A2I2 currently offers a number of full PhD scholarships (3-3.5 years). The scholarship generally covers tuition fees and living expenses.

Research areas
Selection criteria
What to send

Research areas

The Applied Artificial Intelligence Institute (A2I2), co-led by Professor Svetha Venkatesh, is an Human-centred AI research body, currently undertaking research in the following areas:

AI Fundamentals

We study all relevant aspects of intelligence and build systems that learn to read from text, perceive the world, build world model, remember the past, imagine the future, reason, collaborate with others and act on the world.  See this blogpost for our view on machine learning, and its recent developments. Major sub-areas:

» Grand unified cognitive architecture in what we aim to build a single principled computational architecture that can replicate and explain human cognitive capabilities across scales in time. The ultimate goal is exploring the limits of computational approach to self-awareness and consciousness.
» Deep learning 2.0 in that we find efficient ways for differentiable learning with minimal human supervision, towards System 2 capability. Our sub-areas include, but not limited to: generative models, graph neural networks, relational models, memory and attention, continual learning, machine theory of mind, and learning to reason. See this blogpost on why deep learning works, this post for a tutorial, and this page for works done so far by us.
» Reinforcement learning in that an agent acts on the world, interacts with others, builds theory of mind, imagines the future and receives feedbacks. Equipped with deep nets for perception, memory, statistical relational learning, and reasoning capabilities, we aim to bring RL to a new level.
» Build the world model from observations. Learning from large labelled data as we do today does not scale, and it is not anywhere near human intelligence. A better way is learning to represent the world just by perceiving the world, reading from text or watching videos with little hints from teachers. Intuitive physics, knowledge graphs and the like will naturally emerge, just like those discovered by infants.
» Value alignment in that we build intelligence and ensure that the machine respects human preferences, even if when the machine is capable of devising its own goals and aware of what it does. The day when machine and human co-exist and co-create seems far, but it is already here in every fabric of life.

AI Applications

We want to use AI to make the world better. Here are a few things we are passionate about:

» Computer vision in which we want to understand and reason about videos, talk about movies, read children stories, monitor streets, and understand human behaviors in video. See iCetana for our product in action to make a safer world.
» Natural language processing in which we aim to build agents that read text, answer multi-turn questions, hold a long conversation, share human values and talk about health. We also aim to leverage NLP techniques for scientific extraction from text and ideas emergence in which we learn to read the scientific texts, extract machine computable knowledge, model the emergence and dynamics of ideas over time, and support scientific reasoning and question answering.
» Healthcare in which we aim to (a) understand about patient's health, need, preference and intention through patient's diverse sources of data (medical records, -omics, user-generated media, medical imaging and biosignals); (b) acquire and reason about established medical knowledge; (c) have a meaningful dialog with patients; (d) recommend the personalized course of medical actions; (e) support doctors and hospital managers to improve their precision and efficiency. See this blogpost for some futuristic ideas and this page for works done by us so far. See our product Toby PlayPad for supporting learning in autistic children.
» Drug design in which we aim to model the entire drug-like space, compute bioactivity, predict binding to proteins, and best of all, generate new drugs with a given set of desirable properties.
» Structural bioinformatics in which we aim to unlock the one of the most mysteries of nature - our genomes, map genotype-phenotype, answer any genomic queries for a given sequence (DNA, MtDNA, RNA, etc), predict drug-protein interactions, and learn to generate proteins and DNAs.
» Accelerating scientific discovery in which we use AI to replace hard physical computation and experiments such as computing quantum chemical properties, predicting molecular properties & interactions, predicting chemical reactions, understanding the structure and characteristics of materials, searching for new alloys, and generating molecules & crystals. Part of these is accelerating new product development by finding best configurations for producing new products. See our recent work on a faster way to make new materials, and other works here.
» Smart homes in which we model human activities and sensing systems/IoT within a home to assist/empower the tenants in their everyday life. An important AI goal is to build a situated conversational agent that can hold meaningful conversations with tenants. A high impact environment will be aged care homes.
» Automated software engineering in what we want to read the code, fix the bugs, synthesize programs, translate between languages, automate the programming process, understand developer and support team management. See this page for the latest update on our research.
» Other cool apps: (A) Cybersecurity through anomaly detection in that we detect unusual behaviors without supervision. We aim to handle the complexity of data types, networks, relations and dynamics. (B) Recommender systems in that we deliver personalized content to user. See this post series for some of our works.

Selection criteria

There is no single selection criterion and we can only say that we look for candidates who have good potential to have a successful research career, not just a PhD degree. If you do not know what it takes for a research career, I recommend that you should spend time to read about it. See, for example, this blogpost for a view on how AI research these days is going. Sometimes it will help you decide whether you need a PhD or not. Here is the list of things we are looking for

» Thirst of knowledge and willing to pursue a research career. PhD is just a stepping stone, and a by-product of the learning process.
» Research experience (e.g. has published papers).
» Academic recognition: university prizes and awards. High quality honours at high school (such as members of national or international teams) may also count. 
» Bachelor/Master in CS/Maths/Physics with excellent academic performance from a well-recognised university. For those with Australian qualifications, the standard requirement is an Honours degree. 
» Good math background (we will use a lot of statistics, optimization and linear algebra).

Good programming skills. An ability to scale up to large datasets is a bonus.
» Good machine learning & AI background. As a general rule of thumb, a good AI/ML PhD student will be likely to have covered statistical machine learning & probabilistic graphical models. A prior exposure to deep learning is a great bonus.
» Good English (generally IELTS score 6.5, no bands below 6, is required for entry, but you should be aware that this score is insufficient for smooth communication). Good critical reading skills are vital because you will spend up to 50% of your time for reading. Technical writing is also important because it is often the end-product for assessment, and it accounts for 50% chance of acceptance.

What to send

The rule of thumb is that you should send whatever (electronic) documents you think that will support your future research career. Before you send these, I recommend that you spend time to study research areas and publications by A2I2. Documents are written in English and may include some of the following:

» A comprehensive resume (or CV);
» Academic transcripts;
» Theses;
» Published papers or technical reports (if written in languages other than English, titles and abstracts should be translated to English);
» A statement of purpose briefly describes your research intention and interests, general approach to reach your PhD/career goals;
Proposal for the intended thesis; and

Reference letters from your professors.

You can send your documents directly to A/Professor Truyen Tran.