Lucas Vinh Tran

About Me

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Biography: I am a Director of AI/ML and Principal Research Scientist at JPMorganChase (JPMC). I am leading, building large-scale and distributed personalization, ranking, and recommender systems across JPMorganChase products, turning Chase into a leading personalization platform towards commerce, shopping, and broader AI/ML initiatives.

Previously, I was at Apple Media Products (AMP) AI/ML Personalization & Recommendation team, building large-scale personalization systems for App Store, Music, Video (TV App), Podcasts and Books. My team also powered content understanding and features discovery, as well as on-device and federated machine learning. 

Before that, I obtained my Ph.D. in Computer Science from Nanyang Technological University (NTU), under A*STAR Computing & Information Science Scholarship (ACIS) and A*STAR Graduate Scholarship (AGS), supervised by Prof. Gao Cong (@NTU) and Dr. Xiaoli Li (@A*STAR). At the same time, I was also affiliated with the Machine Intellection Department of Institution for Infocomm Research (I2R), A*STAR. Prior to that, my B.Sc. background was in Mathematical Sciences from the same university. 

Academic & Industrial Research: My main research interests include (applied) machine learning, (geometric) deep learning, differentiable generative models and high-dimensional statistics. In general, I am broadly interested in representation learning (Euclidean and non-Euclidean representation); on-device, privacy-preserving and federated machine learning; AI governance, risk and safety; Responsible and Explainable AI (XAI); MLOps/AIOps/ModelOps and ML infrastructure/framework; especially in the applications of recommender systems (personalized and group recommendation).

Work Experience

JPMorganChase 

Director of AI/ML / Principal Research Scientist. London, United Kingdom. Nov. 2022 - Present

Apple 

Senior / Staff Applied Research Scientist. Singapore, Singapore. May. 2020 - Nov. 2022

Agency for Science, Technology and Research (A*STAR)

Machine Learning Research Scientist. Singapore, Singapore. Aug. 2016 - May. 2020

Meta

Gaming Data Scientist. Singapore, Singapore. May. 2015 - Aug. 2015

Education

Nanyang Technological University

Ph.D. in Computer Science, Singapore, Aug. 2016 - May. 2020

Nanyang Technological University

B.Sc. in Mathematical Sciences, Singapore, Aug. 2012 - May. 2016

Executive Leadership

Nanyang Technological University

Executive Certificate for Engineering Leadership, Singapore, Apr. 2022 - Jun. 2022

Publications 

Peer-Reviewed Conferences:

1. HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation. Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, and Fan Li. In the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020. [PDF] (Acceptance rate: 147/555 = 26.47%)

2. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, and Xiaoli Li.  In the 13th ACM International Conference on Web Search and Data Mining (WSDM), 2020. [PDF] (Acceptance rate: 91/615 = 14.80%) (Best Paper Award Runner-Up)

3. Quaternion Collaborative Filtering for Recommendation. Shuai Zhang, Lina Yao, Lucas Vinh Tran, Aston Zhang, and Yi Tay. In the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. [PDF] (Acceptance rate: 850/4752 = 17.89%)

4. Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation. Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, and Xiaoli Li. In the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2019. [PDF] (Acceptance rate: 84/426 = 19.72%)

5. Holographic Factorization Machines for Recommendation. Yi Tay*, Shuai Zhang*, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, and Lucas Vinh Tran. In the 33th AAAI Conference on Artificial Intelligence (AAAI), 2019. [PDF] (Acceptance rate: 1150/7095 = 16.21%)

Internal Publications, Manuscripts and Others:

1. Enhancing Customer Online Experience and Account Origination Rate in CCB: A Deep Learning Approach to Personalized Product Recommendation. Lucas Vinh Tran, Chunpai Wang, Yihui Tang, Davood Shamsi, and Jay Katukuri. JPMC AI Summit, 2023

2. Personalized Travel Assistant: Empowering Bespoke Vacation Experiences through Large Language Models (LLMs). Lucas Vinh Tran, Yihui Tang, and Davood Shamsi. JPMC AI Summit, 2023

3. Recurring Transaction Detection with Time Series Embeddings and Word2Vec Based Merchant Embeddings. Chunpai Wang, Eddie Haam, and Lucas Vinh Tran. JPMC AI Summit, 2023

4. Luke Modeling Framework. Lucas Vinh Tran, Jay Katukuri, Ryan Kellerman, Shamanth Kumar, and Anjul Tyagi. JPMC AI Summit; and JPMC Innovation Week, 2023

5. Multi-Objective Ranking Using a Feedback Loop. Sofia Nikolakaki, Lucas Vinh Tran, Carlos Laguna Rueda, Lata Chari, and Rabi Chakraborty. Apple Machine Learning Summit (MLS). Recommendations and Personalization (R&P) Track, 2022

6. App Store: New "Apps You Might Like" Personalization on Apps Tab. Lucas Vinh Tran, Ishan Vashishtha, Surender Yerva, and Rabi Chakraborty. Apple Data Science Summit (ADSS) (Search and Recommendation Track); and Apple Machine Learning Summit (MLS) (Recommendations and Personalization (R&P) Track), 2022.

7. App Store: User Lifecycle Modeling. Lucas Vinh Tran, Ishan Vashishtha, Rabi Chakraborty, Puja Das, and Jagan Varadarajan. Apple Machine Learning Summit (MLS). Recommendations and Personalization (R&P) Track, 2022

8. Attention-based Models for Personalized Game Recommendation. Lucas Vinh Tran, Puja Das, and Jay Katukuri. Apple Machine Learning Summit (MLS). Recommendations and Personalization (R&P) Track, 2021

Honours & Awards

• Golden Gnomes Award 2023 Nomination (The Technology Innovator - JPMC Global Technology / London Technology Centre)

• ICML 2020 Top Reviewer Award

• WSDM 2020 Best Paper Award Runner-Up

• A*STAR Computing and Information Science Scholarship (ACIS) 

• ACM SIGIR 2019 Student Travel Grant

• A*STAR Graduate Scholarship (AGS)

Invited Speaker

Talks:

• "AI/ML products for the finance industry – common pitfalls and how to avoid them". AI Singapore (AISG) x Monetary Authority of Singapore (MAS), AI in Global Finance. Singapore Fintech Festival 2023. Singapore, Singapore. Sep. 25 and Nov. 15-17, 2023.

• "Recommender Systems". Singapore Institute of Technology (SIT). INF2008 Machine Learning. Singapore, Singapore. Mar. 16-31, 2023.

• "Recommender Systems: Breaking The Gap Between Academia and Industry". The University of New South Wales (UNSW Sydney). COMP9729 Recommender Systems. Sydney, Australia. Jun. 29, 2022.

• "Representation Learning for Recommender Systems". Spotify. Spotify Personalization. London, UK. Jun. 09, 2022.

• "Representation Learning for Recommender Systems". ShareChat. ShareChat & Moj Feed AI Personalization. London, UK. Mar. 11, 2022.

• "Group Recommender Systems". Nanyang Technological University. M.Sc. in Analytics. MH6301 Information Retrieval and Analysis. Singapore, Singapore. Mar. 11, 2021

• "Efficient and Effective Group Recommendation: Challenges and Solutions". Nanyang Technological University. Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU). Singapore, Singapore. Aug. 19, 2019

Conferences:

• "CCB AI/ML Centralized Platform". JPMC Innovation Week London 2024. London, UK. Jun 26, 2024.

• "Luke Modeling Framework". JPMC AI Summit 2023. AI Infrastructure Efficiency and ML Lifecycle Track. New York, USA. Nov 15, 2023.

• "Enhancing Customer Online Experience and Account Origination Rate in CCB: A Deep Learning Approach to Personalized Product Recommendation". JPMC AI Summit 2023. Personalization and Recommender Systems Track. New York, USA. Nov 16, 2023.

• "Personalized Travel Assistant: Empowering Bespoke Vacation Experiences through Large Language Models (LLMs)". JPMC AI Summit 2023. Personalization and Recommender Systems Track. New York, USA. Nov 16, 2023.

• "Luke Modeling Framework". JPMC Innovation Week London 2023. London, UK. Jun 14, 2023.

• "App Store: New "Apps You Might Like" Personalization on Apps Tab". Apple Data Science Summit (ADSS) (Search and Recommendation Track); and Apple Machine Learning Summit (MLS) (Recommendations and Personalization (R&P) Track). Cupertino, USA. Feb 09, 2022.

• "App Store: User Lifecycle Modeling". Apple Machine Learning Summit (MLS). Recommendations and Personalization (R&P) Track. Cupertino, USA. Feb 09, 2022

• "Attention-based Models for Personalized Game Recommendation". Apple Machine Learning Summit (MLS). Recommendations and Personalization (R&P) Track. Cupertino, USA. Jan. 26, 2021

• "HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems". Best Paper Award Session. WSDM'20. Texas, USA. Feb. 06, 2020

• "Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation". Recommendations Session. SIGIR'19. Paris, France. Jul. 22, 2019

Consultancy

• A stealth blockchain startup (acquired): Build AI/ML solutions to predict land acquisition on Decentralized market for LAND (i.e., Decentraland, NFTs, Land Metaverse). 2018 - 2019

• A bank: Advise on projects with the target to increase open, reaction, and redemption rate for bank offers to card holders. 2017 - 2018 

• A workforce technology company: Advise on AI/ML, engineering solutions and team expansion in Singapore, Malaysia and Vietnam. 2016 - 2017

• A SaaS company (appeared on Shark Tank): Design business model and engineering architecture, strategic advisor for fund raising in Singapore. 2014 - 2015.

Professional Services

Organizer / Program Committee (PC) / Invited Reviewer:

• Conference: 

  • 2024: ICLR, ICML, WWW, IJCAI, SIGIR, COLM

  • 2023: ICLR, NeurIPS, ICML, WSDM, ACL, EMNLP, IJCAI

  • 2022: ICLR, NeurIPS, ICML, AAAI, CIKM, ICDE, EMNLP, UMUAI

  • 2021: ICLR, NeurIPS, ICML, CIKM, AAAI, ACL, EMNLP

  • 2020: NeurIPS, ICML, WSDM, ACL, IJCAI

  • 2019: NeurIPS

• Internal Conference/Program: 

  • 2023: JPMC AI Summit, JPMC Innovation Week, JPMC DEVUP, Apple Scholars in AI/ML PhD Fellowship

  • 2022: Apple Machine Learning Summit (MLS), Apple Infrastructure Summit (InfraSummit)

  • 2021: Apple Machine Learning Summit (MLS), Apple Data Science Summit (ADSS), Apple Infrastructure Summit (InfraSummit), Apple InnerSource Summit (AISS)

  • 2020: Apple Data Science Summit (ADSS)

• Journal: 

  • Journal Editorial Board: Frontiers in Big Data

  • 2021: TKDE

External Reviewer (Before 2019):

• Conference: NeurIPS, KDD, WWW, SIGIR, WSDM, EMNLP, ACL, AAAI, IJCAI, ICDE, CIKM, ICDM, SIGSPATIAL, DASFAA

• Journal: TKDE, TIST, TOIS, TNNLS, JASIST

Miscellaneous

• Group Recommender Systems: Research Papers, Datasets and Source Code [Link]

• Deep Learning based Recommender System: A Survey and New Perspectives [Link]

• Libraries for deep learning based recommendation models [Link 1][Link 2][Link 3][Link 4][Link 5]

• Benchmark Public Datasets for Recommender Systems [Link 1][Link 2]

• Comprehensive reading lists for new ML/DL research students [Link]

• Computer Science Conference Ranking List [Link 1][Link 2]

Contact

• Email: A@B, where A=lucasvinhtran and B=gmail.com