The ESD Summer Conference is a student-led event, originated as a venue for first- and second-year PhD students to share their Summer Project Work and at the same time gain the experience of attending and presenting at a scientific conference, thus preparing them for larger conferences in the future.

After the success of the first iteration in 2018, this year ESD SummerCon will also be open to researchers outside SUTD ESD. The conference is a place for the ESD community, as well as our colleagues and friends from other institutions, to learn more about each other’s work, exchange ideas, and foster collaborations.

Conference Details

We welcome all contributions related to the design and operations of engineering systems such as water, transportation, finance, health-care, etc. Methodological contributions such as applied mathematics, statistics, game theory, operations research, and economics are strongly encouraged.

Conference Format

The Scientific Programme consists of a Plenary Session, followed by oral presentations, flash talks, and poster sessions. Each presenter can choose one of two modes of delivery:

(i) a 15-minute oral presentation, or

(ii) a 5-minute flash talk followed by a poster session; all posters will be printed by the organizers at no cost for the presenter.


Plenary (09:30 - 10:30)

Towards robust machine learning for transportation systems

Associate Professor Justin Dauwels, NTU


The field of machine learning has progressed rapidly in the recent years, fueled especially by new developments in deep learning. While such technologies are often hyped in the media, weaknesses of deep learning systems are starting to become obvious, potentially spelling trouble for mission-critical systems. Most current deep learning systems are brittle, since they typically do not encode or learn information about the physical world. For instance, state-of-the-art deep learning based object detection systems can potentially distinguish hundreds of animals, but do not necessarily know that birds fly or fish swim. In that sense, they are far from intelligent. The next generation of deep learning systems will be more robust, by letting them learn about the physical world. How such prior information can be encoded into the deep learning networks is an emerging area of research.

In recent work, we have shown that convolutional neural networks for objection detection in images can be made substantially more robust to image transformations (occurring in real-world applications) and to adversarial attacks by incorporating prior knowledge about the physical world. We encode physical properties of objects by means of hidden variables, and let the model infer what physical transformations have taken place in a given scene. As an illustration, we will present the Affine Disentangled Generative Adversarial Network (ADIS-GAN). On the MNIST dataset, ADIS-GAN can achieve over 98 percent classification accuracy within 30 degrees of rotation, and over 90 percent classification accuracy against FGSM and PGD adversarial attack, outshining systems trained through data augmentation.

We will also briefly outline ongoing application-oriented machine learning projects in our team related to intelligent transportation systems. At the end of the talk, we will explore future research directions.

About the speaker

Dr. Justin Dauwels is an Associate Professor of the School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore. He also serves as Deputy Director of the ST Engineering – NTU corporate lab, which comprises 100+ PhD students, research staff and engineers, developing novel autonomous systems for airport operations and transportation.

His research interests are in data analytics with applications to intelligent transportation systems, autonomous systems, and analysis of human behaviour and physiology. He obtained his PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. Moreover, he was a postdoctoral fellow at the RIKEN Brain Science Institute (2006-2007) and a research scientist at the Massachusetts Institute of Technology (2008-2010). He has been a JSPS postdoctoral fellow (2007), a BAEF fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited fellow (2010, 2011). He is quite active in the IEEE community, as conference chair, associate editor, and other roles. He is co-founder of the spin-off companies Vigti and Mindsigns Health.

His research on intelligent transportation systems has been featured by the BBC, Straits Times, Lianhe Zaobao, Channel 5, and numerous technology websites. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation, logistics, and digital health.

Invited talk (13:30 - 14:00)

Statistics lie—so does data visualization. And that's perfectly fine.

Assistant Professor Ate Poorhuis, SUTD

About the speaker

Ate Poorthuis is an Assistant Professor in the Humanities, Arts and Social Sciences at Singapore University of Technology and Design. His research explores the possibilities and limitations of big data, through quantitative analysis and visualization, to better understand how our cities work. He has a particular interest in the practical application of these academic insights within urban planning and policy.



The first two prizes shall be determined by popular votes and are open to all presenters. The third one shall be reserved for Summer Project students and determined by a panel of judges consisting of ESD faculty members. Each prize is worth a $100 book voucher.


If you are keen to participate, please register.



0930 – 1030 Plenary
Towards robust machine learning for transportation systems Dr. Justin Dauwels (NTU)
1030 – 1100 Tea break
1100 – 1230 Session 1
1100 – 1115 Dynamic pricing of repeated recruitment on mobile crowdsourcing Shugang
1115 – 1130 Competitive analysis in duopoly information marketing of mobile crowdsensing Hong Shu
1130 – 1145 Product description and consumer reviews in Omni-channel retailing Deng Qiyuan (SMU)
1145 – 1200 Anywhere but a Nash equilibrium: Follow-the-Regularized-Leader in zero-sum games (The Stochastic Case) Sai
1200 – 1215 Reasoning on Knowledge Graph with Dependent Type Theory Zhangsheng
1215 – 1230 Assisted algorithm design with dependent type theory Jin Xing
1230 – 1330 Lunch
1330 – 1500 Session 2
1330 – 1400 Invited talk: Statistics lie – so does data visualization. And that's perfectly fine. Dr. Ate Poorthuis (SUTD)
1400 – 1415 Application of queueing theory in patients' decision Yufeng
1415 – 1430 Appointment systems with the effect of consumer risk preferences on no shows Zhang Ruijie (SMU)
1430 – 1445 Performance analysis of the greedy algorithm for maximum weighted matching Shuqin
1445 – 1500 New bounds for pairwise independent Bernoulli random variables Arjun

A 15-minute intermission follows Session 2

Flash talks (1515 – 1615)

Each talk is five minutes; we will follow this order.

1 Wealth inequality and the price of anarchy Barnabé
2 Predicting commercial vehicle parking duration using Generative Adversarial Multiple Imputation Networks Raymond
3 Exploring the potential for crowdshipping using public transport Meijing
4 Representing reservoir storage dynamics and operations in the Variable Infiltration Capacity (VIC) model Thanh and Kamal
5 Reservoir regulation could significantly influence flooding dynamics in the Chao Phraya Delta Dung
6 Understanding impacts of transmission capacity on the power system performance in Laos Rachel
7 Spatial-temporal variability of streamflow in monsoon Asia over the past eight centuries and links to climate drivers Hung
8 Can long-range streamflow forecasts increase hydropower production? Jia Yi

Poster session: 1615 – 1745

Refreshments will be served during the poster session


Nguyen Tan Thai Hung, Ng Jia Yi, Rachel Koh Zhi Qi, Barnabé Monnot, Lim Jin Xing, Vu Trung Dung, Arjun Ramachandra, Sai Ganesh Nagarajan

Please send your inquiries to