posted by organizer: pramsatriapalar || 1661 views || tracked by 1 users: [display]

MDOML 2021 : International Virtual Course on Multidisciplinary Optimization and Machine Learning for Engineering Design (MDOML-2021)


When Aug 5, 2021 - Jul 19, 2021
Where Online
Submission Deadline Jun 19, 2021
Notification Due Jun 23, 2021
Categories    optimization   design optimization   machine learning   data science

Call For Papers


International Virtual Course on Multidisciplinary Optimization and Machine Learning for Engineering Design (MDOML-2021)


to be held virtually on 19 July – 5 August 2021.


- Maziar Raissi (University of Colorado Boulder, USA)
- Nathalia Bartoli (ONERA, France)
- Joseph Morlier (ISAE-SUPAERO, France)
- Eky Valentian Febrianto (University of Cambridge, UK)
- Hemant Kumar Singh (University of New South Wales, Australia)
- Joel Henry (Monolith AI, UK)
- Koji Shimoyama (Tohoku University, Japan)
- Lavi Rizki Zuhal (Institut Teknologi Bandung, Indonesia)
- Lucia Parussini (University of Trieste, Italy)
- Pramudita Satria Palar (Institut Teknologi Bandung, Indonesia)
- Rhea Patricia Liem (The Hong Kong University of Science and Technology, Hong Kong)
- Rommel Regis (Saint Joseph's University, USA)


There is an increasing demand for engineers to improve the design of engineering products to stay competitive with competitors, especially in the current era of high-performance computing and abundant data. Engineers then resort to optimization techniques to help them find high-performance solutions and uncover salient design insight and features. Optimization techniques have been constantly evolving to adapt to modern engineering practices, challenges, and complexities. Especially in this age of data, mastery of optimization for engineering design has never been more important than before. The dawn of machine learning has also enabled a more efficient data-driven optimization by aiding optimization techniques to rapidly discover insight and knowledge from data. The intertwine between computer simulations, experimental data, and data-driven methods is now one of the building blocks of modern engineering. Fluency in optimization and machine learning is then becoming an important skill that must be possessed by students, practitioners, and researchers in design optimization to take advantage of the abundant amount of data.


This international virtual course (IVC) aims to equip students with basic and advanced introduction to multidisciplinary design optimization and machine learning. This course covers the introduction, important topics, and practical aspects of optimization and machine learning for engineering design. In this course, students will (1) learn the basic of optimization and how to formulate engineering design optimization problems, (2) learn various techniques that support engineering optimization (e.g. uncertainty quantification and data mining), (3) learn the complexities and challenges in deploying optimization techniques for real-world applications, (4) learn the basic of machine learning in the context of engineering design optimization, (5) learn how to use various optimization techniques (gradient-based, gradient-free, machine learning) to solve the formulated problems. Students will learn the theory and practice of optimization from world-class researchers and also through Python-based tutorials guided by tutors.


The virtual course offers three weeks of courses and tutorials with the topics including but not limited to:

1. Optimization methods and simulation

2. Challenges and consideration in real-world optimization

3. Multidisciplinary design optimization

4. Data science and machine/ statistical learning for design optimization


20 May 2021: Open registration

19 June 2021: Application deadline

23 June 2021: Announcement of selected participants

19 July 2021: First day of virtual course


This course is free for students from around the world!

Eligible participants:

1. Senior Undergraduate Students

2. Graduate Students

3. Non-Student Participants (Fees: 175 USD)

Participants can apply for this summer courses through the following link:

All participants are required to provide motivation statement (maximum two paragraphs) and student ID number (for students) to apply for this summer course. Please fill the information in the registration link given above.

This program offers two types of participation: Full participation and Listening/seat-in participation. Full participation will receive everything the program has to offer which requires active engagement from the participants. Seat-in participation provides a more flexible option for participants who would like to only enjoy the expert-given lectures.


1. Pramudita Satria Palar (Institut Teknologi Bandung)

2. Lavi Rizki Zuhal (Institut Teknologi Bandung)

3. Rhea Patricia Liem (The Hong Kong University of Science and Technology)

4. Koji Shimoyama (Tohoku University)

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
IJCAI 2022   31st International Joint Conference on Artificial Intelligence
IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
ICML 2022   39th International Conference on Machine Learning
IEEE COINS 2022   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems |
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
MLDM 2022   18th International Conference on Machine Learning and Data Mining
CONF-CDS 2022   The 4th International Conference on Computing and Data Science (CONF-CDS) Call for Papers
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
IJCNN 2023   International Joint Conference on Neural Networks