Design as a Unifying Agent Among Disciplines​

Who we are

The ODDS-Lab is based in the School of Mechanical and Materials Engineering at University College Dublin. We view design as a unifying agent among disciplines that provides communities with a tool to formulate and work towards a joint objective by leveraging the group members’ unique expertise in a decision-centered framework. Specifically, we study statistical methods that fuse multimodal data sources to enable the design of increasingly more complex systems. 


Through continued digitization, designers have increased access to data to inform system design decisions. The integration of multimodal data sources in a statistically rigorous framework brings about multiple research questions that we are interested in studying. Examples of questions that we are interested in are: i) How can user, simulation, and experimental data be integrated to make design decisions that are optimal over the entire life cycle of a system? ii) How can experimental and simulation data be leveraged to accelerate the discovery of generalizable knowledge? iii) How to adequately quantify the interpolation uncertainty in machine learning models for big data? and iv) How to account for disparate interests of stakeholders so as to improve the effectiveness of system design decisions. What unites these questions is that they involve data-driven decision-making problems where we aim to leverage available data/resources to accelerate the development of complex systems. Our fundamental approach to addressing design-related questions is to describe them mathematically, make predictions for untested conditions/decisions, and discover new knowledge by empirically verifying the generalizability of the developed model. We strongly believe in scientific integrity and therefore do our part to make most of our developed codes and data available online (i.e., sharing to gain).


With the research performed in the ODDS-Lab we aim to discover generalizable knowledge that enables the design of complex systems that drive societal progress.


1st April 2023:Welcome Maryam Ghasemzadeh to ODDS-Lab to conduct research in the area of machine learning for knowledge discovery in pursuit of her doctoral degree.


28th March: ICASP paper on Bayesian optimization for stochastic functions has been accepted.


5th July 2022: The ODDS-Lab has an open Ph.D. position in the area of digital twin-enabled systems design or machine learning for knowledge discovery (call for applicants).


1st July 2022: The Optimization and Data for Designed System Laboratory (ODDS-Lab) has been established.


25th March 2022: IDETC paper on evolving cyber-physical-social systems has been accepted.