Lam Hoang

Title

Research Staff Member, Master Inventor

Bio

Welcome to my personal website. I earned my specialist degree with honors in applied mathematics and computer science from Lomonosov Moscow State University, Russia, in 2007. In 2013, I obtained my PhD in computer science with cum laude distinction under the supervision of Professor Toon Calders and Professor Paul De Bra at Eindhoven University of Technology (TU/e), the Netherlands. My recent research focuses on AI for science, including the development of foundational models for scientific data such as molecules and proteins.

Awards:

IBM Corporate Technical Award for AutoAI 2021
IBM Outstanding Technical Achievement Award for 'Toward automating the AI lifecycle with AutoAI' 2019
IBM Outstanding Technical Achievement Award for 'z AI and Modernization for the z15' 2019
IBM Master Inventor 2022
My PhD thesis was nominated by the department of Mathematics and Computer Science, Eindhoven University of Technology in 2014. for the best PhD thesis award Eindhoven University of Technology (TU/e) in December 2014
Nominated for the best paper award at SIAM Data Mining Conference SDM 2012.

Academic service:

Conference PC member: NeurIPS (since 2021), ICLR (since 2022), AAAI ( since 2020), IJCAI (since 2020), ICML ( since 2021),
I am a Kaggle competition master

Selected publications:

Raul Fernandez-Diaz, Hoang Thanh Lam, Vanessa López, Denis C. Shields. A new framework for evaluating model out-of-distribution generalisation for the biochemical domain. ICLR 2025
Thanh Lam Hoang, Marco Luca Sbodio, Marcos Martínez Galindo, Mykhaylo Zayats, Raúl Fernández-Díaz, Victor Valls, Gabriele Picco, Cesar Berrospi, Vanessa López: Knowledge Enhanced Representation Learning for Drug Discovery. AAAI 2024
Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo: Ensembling Graph Predictions for AMR Parsing. NeurIPS 2021: 8495-8505
Gabriele Picco, Marcos Martínez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa López, Thanh Lam Hoang: Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction. ACL (demo) 2023: 357-368
Young-Suk Lee, Ramón Fernandez Astudillo, Hoang Thanh Lam, Tahira Naseem, Radu Florian, Salim Roukos: Maximum Bayes Smatch Ensemble Distillation for AMR Parsing. NAACL 2022: 5379-5392
Hoang Thanh Lam, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, Horst Samulowitz: Automated Data Science for Relational Data. Demo at ICDE 2021: 2689-2692
Hoang Thanh Lam, Fabian Moerchen, Dmitriy Fradkin, Toon Calders: Mining Compressing Sequential Patterns. Nominated for best paper award at SIAM SDM 2012: 319-330
Hoang Thanh Lam, Toon Calders, Ninh Pham: Online Discovery of Top-k Similar Motifs in Time Series Data. SDM 2011: 1004-1015
Hoang Thanh Lam, Toon Calders: Mining top-k frequent items in a data stream with flexible sliding windows. KDD 2010: 283-292

IBM products:

My work on automated feature engineering for relational data is a core part of the IBM Watson Studio AutoAI product.

I am part of the following open-source initiative:

TabularFM A framework for building and benchmark foundational models for tabular data.
Graphene: graph ensemble learning for AMR parsing
ZShot: a Spacy plug-in for few and zero shot for named entity recognition and classification with textual descriptions.

Mentoring activities: