Lucas Vinh Tran
Director of AI/ML, JPMorganChase
About Me
[LinkedIn] [Google Scholar][Github]
Biography: I am currently a Director of AI/ML and Principal Research Scientist at JPMorganChase (JPMC). I am a hands-on senior technical executive; leading various tent-pole projects towards commerce, shopping, ads & marketing, digital, personalization and broader AI/ML initiatives across JPMorganChase products, turning Chase into a leading personalization platform. I have a track record of leading and building large-scale distributed AI/ML systems, especially in the context of personalization, ranking, and recommender systems (on prem and in the cloud).
Previously, I was at Apple Services Engineering / Apple Media Products (AMP) AI/ML, 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 Dr. Gao Cong (NTU) and Dr. Xiaoli Li (A*STAR), and also Dr. See-Kiong Ng (NUS) earlier. 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 (but not limited to): (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 personalization and recommender systems (personalized and group recommendation).
Work Experience
JPMorganChase
Consumer & Community Banking (CCB) -- Personalization & Insights (P&I)Director of AI/ML / Principal Research Scientist. London, United Kingdom. Nov. 2022 - Present
Connected Commerce / Customer Acquisition and Marketing Platforms (CAMP) / Digital Technology
CCB AI/ML Cross-Functional Solution Design (Omnichannel; Platformization; Annotation; Feature Platform & Model Experimentation; GenAI / LLMs; and MLOps / LLMOps & Model Serving)
Apple
Apple Services Engineering -- Personalization & RecommendationSenior / Staff Applied Research Scientist. Singapore, Singapore. May. 2020 - Nov. 2022
Apple Media Products (AMP) AI/ML (App Store, Music, Video (TV App), Podcasts and Books)
Agency for Science, Technology and Research (A*STAR)
Institute for Infocomm Research (I2R) -- Personalized and Group RecommendationMachine Learning Research Scientist. Singapore, Singapore. Aug. 2016 - May. 2020
Data Analytics / Machine Intellection
Meta
APAC Facebook Global Gaming -- Dashboard PersonalizationGaming Data Scientist. Singapore, Singapore. May. 2015 - Aug. 2015
Data Science & Visualization
Education
Nanyang Technological University
School of Computer Science and Engineering (SCSE) -- Data Management and Analytics Lab (DMAL) / Data Management @ Nanyang Tech (DANTE) GroupPh.D. in Computer Science. Singapore, Singapore. Aug. 2016 - May. 2020
Nanyang Technological University
School of Physical and Mathematical Sciences (SPMS) -- Division of Mathematical SciencesB.Sc. in Mathematical Sciences. Singapore, Singapore. Aug. 2012 - May. 2016
Executive Leadership
Nanyang Technological University
Nanyang Business School (NBS) -- Nanyang Executive EducationExecutive Certificate for Engineering Leadership. Singapore, Singapore. Apr. 2022 - Jun. 2022
Publications
* : equal contributionPeer-Reviewed Conferences:
A Centralized Configuration Driven Modeling Framework for Personalization in Banking and Finance. Lucas Vinh Tran, and Jay Katukuri. In The AltRecSys Workshop on Alternative, Unexpected, and Critical Ideas at The 18th ACM Conference on Recommender Systems (RecSys), 2024. [PDF]
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%)
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)
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%)
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%)
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:
CCB AI/ML Centralized Platform - Deploying State-Of-The-Art Personalization Models. Lucas Vinh Tran. JPMC AI Innovation Week London, 2024
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
Personalized Travel Assistant: Empowering Bespoke Vacation Experiences through Large Language Models (LLMs). Lucas Vinh Tran, Yihui Tang, and Davood Shamsi. JPMC AI Summit, 2023
Recurring Transaction Detection with Time Series Embeddings and Word2Vec Based Merchant Embeddings. Chunpai Wang, Eddie Haam, and Lucas Vinh Tran. JPMC AI Summit, 2023
Luke Modeling Framework. Lucas Vinh Tran, Jay Katukuri, Ryan Kellerman, Shamanth Kumar, and Anjul Tyagi. JPMC AI Summit; and JPMC Innovation Week, 2023
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
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.
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
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:
"A Centralized Configuration Driven Modeling Framework for Personalization in Banking and Finance". RecSys'24. Bari, Italy. Oct. 18, 2024.
"CCB AI/ML Centralized Platform - Deploying State-Of-The-Art Personalization Models". 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
[all names redacted]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:
2025: ICLR, ICML, AISTATS, AAAI, WSDM, WWW, IJCAI
2024: ICLR, ICML, WWW, WSDM, 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]
Contact
Email: A@B, where A=lucasvinhtran and B=gmail.com