The University of Southampton
Telephone:
+442380593715
Email:
ltt1m09@ecs.soton.ac.uk

Dr. Long Tran-Thanh

Academic Staff

Personal homepage

I'm a lecturer (Assist. Prof. equivalent) in Computer Science.

Awards

  • AAAI 2012 Outstanding Paper Award, honourable mention

  • ECAI 2012 Best Student Paper Award, runner-up

  • ECCAI Artificial Intelligence Dissertation Award (for the best European PhD thesis in Artificial Intelligence in 2012), honourable mention

  • CPHC/BCS PhD Dissertation Award (for the best Computer Science PhD thesis in the UK in 2012/2013), runner-up

Research

Research interests

 Sequential decision making

My primal research area is sequential decision making with constraints. More precisely, I am interested in multi-armed bandit (MAB) models where pulling an arm (i.e., making a decision) requires a cost and the total spending is limited by a finite budget. To tackle this problem, I have introduced a new model, called the budget-limited MAB, and have also proposed a number of arm pulling algorithms for which I have provided both theoretical and empirical performance analyses. I am also interested in applying this bandit model (or its variances) to other domains of AI, such as  (i) decentralised controlling for UAVs; (ii) information collection in wireless sensor networks; and (iii) budget-limited online keyword bidding.

 

Game theory

I mainly focus on large coalition formation games from both game theoretical and decision making perspective. In more detail, I look at systems where the number of participants is very large (typically thousands or more). Within these systems, calculating different solution concepts (e.g., the core, nucleolus, Shapley-value, etc.) are very hard. Given this, my goal is to identify approximation techniques that can efficiently provide high quality results. To do so, with some of my colleagues, we have introduced a novel, vector-based, representation model of the participating agents, with which we can calculate the abovementioned concepts in a significantly more efficient way. We have also analysed the error bounds of approximating the Shapley value in large games.

 

Staying within game theory, I also study different games with resource allocation from both aspects of classical and behaviourial game theory. In particular, I am interested in calculating different equilibria and price of anarchy. A preliminary version of work has been published at SAGT 2011. 

 

From the behaviourial game theory perspective, I aim to identify players' favourite strategies when they repeatedly play such games against different opponents (Repeated Colonel Blotto).

 

Crowdsourcing

More recently, I investigate the performance of different crowdsourcing systems from a theoretical perspective, aiming to provide rigorous performance guarantees for task allocation algorithms. 

 

Home energy management

I am heavily involved in the research work on home energy management. In particular, we aim to improve the energy consumption profile of home owners, in order to reduce the CO2 emission of the domestic energy sector. To do so, as the first step, we mainly focussed on the accurate learning and prediction of homeowners' habit, such as appliance usage and heating preferences. 

 

Other research interests

The cost of interference to closed evolving systems: We investigate what is the cost to interfere into closed systems, if we want the system to achieve some desirable states. As a first step, we look at evolving evolutionary games, where an external decision maker can invest his resources into the system (e.g., via a reward scheme) such that in the long term, the agents will follow a preferred behaviour. A preliminary result has been presented at COIN 2014 and NAG 2014.

 

Stochastic shortest path problems: This is an ongoing work on approximating the classical stochastic shortest path, defined by Frank (1969). In particular, so far I have developed a new concentration inequality that provides a lower bound on the probability mass on the probability P(X < a) with a < E[X]. Combining this new result with other existing concentration bounds, I could provide a O(n^3) running time algorithm that can achieve O(1/n) approximation for sure (where n is the number of vertices). In addition, the algorithm works for any type of bounded distribution. To the best of my knowledge (which can be obsolete), the current state of the art is pseudo-polynomial with 2/n approximation coefficient, which only works on discrete distributions. Currently I am aiming to improve the approximation coefficient of my algorithm.

 

Non-monetary referral incentives: I am also investigating how non-monetary referral incentivisation work in social networks. You can find a preliminary version of our work here. For more details, you can visit the website of our project, or watch a video about it. 

Grants

EPSRC-Singapore funded project on cyber security for smart traffic control systems (£500K for 2 years): 2016 - 2018

Professional

Professional activities

Current activities:

I am a PC member of IJCAI 2016

I am a co-organiser of SecMAS at AAMAS 2016
 
 
Past activities:

I was a PC member of AAMAS (2012-2014), AAAI (2014), IJCAI (2013), EC (2013), WCNC (2013) and several other workshops (OPTMAS, CoopMAS, etc...) in the last few years

I was a reviewer for AAMAS (2010-2011), ICML (2012-2014), NIPS (2012-2014), COLT (2012-2013), SAGT (2012), PRIMA (2013), Artificial Intelligence, Journal of Machine Learning Research, Theoretical Computer Science, Swarm Intelligence

I was a co-organiser/chair of the HAIDM workshop series.

I was a co-organiser and chair of the MassiveMAS workshop at AAMAS 2015

Conferences attended

AAAI 2010 (Atlanta), 2012 (Toronto), 2015 (Austin) 

AAMAS 2009 (Budapest), 2012 (Valencia), 2013 (St. Paul), 2014 (Paris), 2015 (Istanbul)

COLT 2011 (Budapest)*,

EC (2012)*,

ECAI 2012 (Montpellier),

ICC 2010 (Cape Town), 

ICML 2012 (Edinburgh)*, 2015 (Lille)

IFORS 2008 (Sandton), 

IJCAI 2013 (Beijing), 2015 (Buenos Aires)

NIPS 2015 (Montreal)

SAGT 2011 (Amalfi), 

UAI 2014 (Quebec city),

WCNC 2009 (Budapest), 2010 (Sydney)

*: no paper presentations

Duties

I am organising the AIC seminar series. If you are interested in giving a talk in Southampton in artificial intelligence, machine learning, or game theory related topics, please let me know.

I am also running the AIC reading group series, where we come together to discuss topics that we are interested in. Further details will be uploaded later.

Contact

Telephone: +442380593715

Email: ltt1m09@ecs.soton.ac.uk

Additional contact details

My personal homepage is at:

http://users.ecs.soton.ac.uk/ltt08r

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