Turning Point and Monash at Google

August 5, 2019

Turning Point colleague, Sam Campbell, and I went to the Google AI Impact Challenge Accelerator event in London (see the video here, I get a brief shots 0:14 and 0:20 and Sam gets an interview at 0:49).  It was an in depth technology review so we could help design our system.  We had a number of very clued-in Google experts supporting us in designing our architecture.  My systems and internet applications experience is over 20 years old, so I get the general ideas but don’t know the modern specifics!

Lots the the AI Impact folks attended and we all agreed the committment and support from Google was fabulous.

AI Impact Challenge Accelerator Tech Sprint

All the teams at the London tech event, 31/07/19.


Southeast Asia Machine Learning School

July 9, 2019

Very fortunate to be asked to give a lecture on “Foundations of Supervised Learning” at SEA-MLS in Jakarta on 8th July.  The school was co-organised by Google, so opening talk by Google and a member of the Indonesian government.  A big crowd too!  Everyones slides are up on the schedule page.

Never been to Jakarta so an exciting opportunity meet some colleagues, some students, in a lovely environment. Monash has a school at Malaysia so a few Malaysia Monash folks turned up too.  Here we are after my lecture.


Monash Malaysia and Melbourne students at SEA-MLS.


AI suicide surveillance with Turning Point

June 23, 2019

Wray, Dan and Debbie at Google Launchpad, 15/05/2019

Along with Prof. Dan Lubman’s team at Turning Point, and funded through Google’s innovative AI for Social Good programme, I’ll be developing an AI system to accelerate “coding” (a form of content analysis) of ambulance records so we can understand the nature of suicide in Australia.   The local press has it so:

Google taps local addiction service to build AI suicide surveillance system

As the Google blurb says:

By using AI tools to analyze these records, Turning Point, a national center within Eastern Health, will uncover critical suicide trends and potential points of intervention to better inform policy and public health responses.

For us researchers, it means unifying a bunch technologies that I’ve been working in for a while like active learning, multi-label classification, multi-task learning and crowd-sourcing.  But most importantly, all these need to be placed in the context of​ doing accurate and properly monitored coding while at the same time trying to minimise costly expert (human) effort.  This is important stuff for NGOs and health organisations so we’re really excited by the application opportunities this can give us all.

In mid May Dan Lubman, Debbie Scott and I flew off to Google’s Lauchpad Space in San Francisco to spend a week with other members of the programme for a bootcamp, to brainstorm about our project and get coaching from Google’s experts.  Google has a lot of other plans for us to, in terms of supporting the development, which we are very grateful for!


Visiting Helsinki

December 21, 2018

Visited my old workplace, University of Helsinki, where I visited various faculty like Prof. Petri Myllymaki, Teemu Roos, Arto Klami and others. Across the bay is Aalto University with folks like Prof. Sami Kaski.   The machine learning groups in broader Helsinki are very strong, with loads of researchers, quality students, and a very vigorous start-up and high-tech culture.

Gave a talk at Machine Learning Coffee Seminar on 17/12 (which serves porridge as well as coffee), and attended the AI Day on 12/12. The AI Day is organised by the recently convened Finnish Centre for Artifical Intelligence, and the speakers were senior researchers from Finland describing their broader vision and direction, so really interesting stuff and very educational. Lots of creativity in there, for instance combining simulation, machine learning, HCI and cognitive psychology.


Invited talk at ACML in Beijing

October 11, 2018

I’ve given an invited talk at ACML in Beijing November:  see Invited Speakers at the ACML website.

Wray's talk at ACML 2018

I talked about the state of Machine Learning, contrasting the old with the new, and discuss where we may head next.  Moreover, I gave some warnings about some problems we are currently facing.  PDF slides for the talk are here.  Abstract is given below.  Prof. Jun Zhu (Tsinghua U.) has had some similar ideas so we conferred afterwards.

Several of us from Monash went:  in the picture are Ye Zhu, Wray Buntine, Lan Du, Yuan Jin and He Zhao.Monash (past and present) at ACML 2018

Something Old, Something New, Something Borrowed, Something Blue

Something Old: In this talk I will first describe some of our recent work with hierarchical probabilistic models that are not deep neural networks. Nevertheless, these are currently among the state of the art in classification and in topic modelling: k-dependence Bayesian networks and hierarchical topic models, respectively, and both are deep models in a different sense. These represent some of the leading edge machine learning technology prior to the advent of deep neural networks. Something New: On deep neural networks, I will describe as a point of comparison some of the state of the art applications I am familiar with: multi-task learning, document classification, and learning to learn. These build on the RNNs widely used in semi-structured learning. The old and the new are remarkably different. So what are the new capabilities deep neural networks have yielded? Do we even need the old technology? What can we do next? Something Borrowed: to complete the story, I’ll introduce some efforts to combine the two approaches, borrowing from earlier work in statistics.


ECML-PKDD talk on Bayesian network classifiers

September 14, 2018

On the 14th September 2018 I presented the following paper at ECML-PKDD in Dublin.  The slides for the talk are here.

We figured out how to do good smoothing of Bayesian network classifiers.  The same technique works for decision trees, and in fact beats all known algorithms for smoothing/pruning!

Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes”, by François Petitjean, Wray Buntine, Geoffrey I. Webb and Nayyar Zaidi, in Machine Learning, 18th May 2018, DOI 10.1007/s10994-018-5718-0.  Available online at Springer Link.  Presented at ECML-PKDD 2018 in Dublin in September, 2018.



Fabulous data science tag cloud

June 2, 2018

This comes from PhD student Caitlin Doogan.

Tag Cloud on Data

Tag Cloud on Data by Caitlin Doogan