Hi there, my name is
Duc Minh La (Bob)
I'm a researcher
and a software engineer

Know more

About me

Profile Image

I'm Duc Minh La, a PhD candidate at Monash University, specializing in large-scale transport modeling. My passion lies in the creation of smart systems, leveraging IoT and machine learning to realize the vision of a fully automated world. In our data-driven society, I believe in the transformative power of data. Understanding its multifaceted nature is essential to avoid bias and ensure that we control the data, rather than letting it control us.

Through my experiences, I've had numerous opportunities to dive into diverse technologies and fields. My experience spans a wide spectrum, from technical areas like application development to business-oriented topics such as requirement engineering and project management. I'm constantly seeking opportunities to apply and refine my skills, driven by both the desire to upskill and the enjoyment of tackling intellectual challenges.

To be more specific, my current reseach at Monash focuses on how to utilise different available data to model the complexity of our current transport systems and predict the effects of diffrent future scenarios.

View Resume

Projects/ Experience

A novel framework for Population Synthesis

Feb 2022 - Present

Synthesis (PopSyn) is a foundational step in Activity-Based Modelling (ABM), which represents the cutting edge in transport modeling. ABM seeks to simulate every possible individual (or agent) within a system and model their interactions within various scenarios, such as a travel network represented by a graph model. However, collecting data on every individual is impractical and raises privacy concerns. This is where PopSyn comes in, generating synthetic populations from publicly available data like Census information.

I developed a new framework from the ground up to address challenges in PopSyn, particularly in accurately synthesizing household relationships. My approach introduces a novel method that builds on and advances traditional techniques like CTGAN, VAE, and Bayesian Networks.

This work is currently under consideration for publication. In the meantime, a poster summarizing the project has been accepted and presented at TRC30, a conference celebrating 30 years of Transport Research Part C (Q1: SJR 2860).

Skills: Python, Pandas, Numpy, Bayesian Networks, Deep Learning, System Design, Prototyping

The poster Source Code

Understanding the EV ownership from a synthetic population

Feb 2023 - Present

Another part of my research is investigating the impact of Electric Vehicles (EVs) for our future. EV is one potential solution to decarbonisation the transportation sector, one of the biggest (if not the bigest) contributor to green house gas emission. However, a 100% EV penetration scenario can be quite far-fetched with all the complex impact EV has on our current network such as charging infrastructure and electricity grid. Thus, using a synthetic population and ABM, I aimed to predict which household are most likely to own an EV so we can generate the electricity demand spatially and temporally. The results then can be used to guide decision makers for potential adaptation such as building new charging places or pricing or Vehicle to Grid (V2G).

The work is still being polished and extended. There has been a poster with prelimanary results accepted for presenting at AITMP conference 2023.

Skills: Python, Pandas, Deep Learning, Regression Learning

Poster Source Code

Graduate Transport Analyst at Veitch Lister Consulting (VLC)

Jun 2023 - Sep 2024

One of the biggest achievement I had at (VLC) is created a completely new internal software (using Python) to accelerate the process of building the network model. It helps save 90% of run time (from 8 hours to only 30 minutes). It was built incorporating with the CI pipeline and help connect with another existing tool to maintain the data integrity.

Skills: Python, Pandas, QGIS, spatial analysis, proposal preparation

Technical Officer at Monash Smart Munafacturing Hub (MSHM)

Jan 2022 - Sep 2023

My biggest achievenment with MSMH successfully delivered an unified and comprehensive platform from data collection (from sensors) to the data lake, then process to store in SQL then visualise with different needs using the Metabase.

Skills: Python, SQL, IoT, data management, database building

Contact

Like what you see, contact me maybe?

Email me now!

or contact me via my list below