CADA is the research and technology-transfer laboratory of the Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University, Hanoi. We build exact algorithms, metaheuristics and learning-based methods for hard combinatorial decision problems — and we ship them as software that hospitals, universities, utilities and logistics operators run every day.
Who we are and what the lab exists to do.
CADA — the Center for Applied Data Science and Artificial Intelligence — is a research unit of the College of Technology at National Economics University. It works on machine learning, optimization, big data and AI, and turns that work into deployed technology for education, healthcare, logistics, finance and manufacturing.
The lab sits at the intersection of two disciplines that rarely share a corridor. On one side is operations research: mixed-integer programming, constraint programming, branch-and-cut, branch-and-price and large-neighbourhood metaheuristics — methods that come with proofs, bounds and guarantees. On the other is modern machine learning: reinforcement learning for dynamic decision problems, learned heuristics, forecasting, and predictive modelling on real operational and clinical data. Most of our contributions live where the two meet, because that is where the hard industrial problems actually are.
Alongside publication, CADA runs a deliberate technology-transfer programme. Faculty, researchers and students build production systems together — an ERP suite for higher education, optimization engines for staff rostering, production planning and delivery scheduling — and hand them to partner organisations. The lab is intended to act as a bridge between the university and industry, moving research ideas into operation rather than leaving them in a proceedings volume.
It is also a training environment. More than twenty students work inside the lab at any time, co-authoring papers with faculty, writing solver code, and shipping modules that real users depend on. Several of the lab's international publications carry student names as first authors.
Six active lines, each led by a member of the faculty team.
The lab's programme spans exact combinatorial optimization through to applied business analytics. Each direction is anchored by published work and, in most cases, by a deployed system.
Exact and hybrid methods for NP-hard problems: mixed-integer linear programming formulations, branch-and-cut, constraint programming with general bounding mechanisms, matheuristics, and adaptive large neighbourhood search. The methodological core the rest of the lab builds on.
Lead: Hà Minh Hoàng
Vehicle and arc routing under realistic constraints — synchronization, priority rules, time-dependent travel times, energy limits — and the emerging class of truck-and-drone systems: parallel drone scheduling, collective drones, two-echelon routing with drones, and school-bus rescheduling.
Lead: Hà Minh Hoàng
Rostering and resource allocation where the schedule is the product: online dynamic home health care scheduling, machine scheduling with conflicting jobs, and duty-roster systems in hospitals and utilities. A deployed roster optimizer cut scheduling time by more than 99% at a partner site.
Lead: Hà Minh Hoàng
Reinforcement learning for stochastic and dynamic operational problems, learning-guided heuristics, privacy-preserving federated learning over mobile edge computing, and applied predictive modelling — clinical risk prediction and satellite-imagery crop mapping among them.
Lead: Hà Minh Hoàng · Lê Duy Khánh
Applying optimization and AI inside the software stack itself: AI-assisted software engineering, DevOps and infrastructure automation, cloud and distributed systems, and the full-stack engineering of the lab's ERP and optimization products.
Lead: Trần Quang Toàn
Enterprise data within management information systems: decision support systems, business intelligence, digital transformation, financial time-series analysis and forecasting, and customer-journey analytics for marketing effectiveness.
Lead: Nguyễn Thị Minh Trang · Ngô Thái Hòa
Five permanent members of the Faculty of Data Science and AI. Every contact address is on the institutional domain.
The core team combines an internationally published operations-research group leader with early-career faculty trained in the UK, France and Vietnam across AI, software engineering and business analytics.
Dean, Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University · Head of CADA Lab · Head of ORLab
hoanghm@neu.edu.vnHà Minh Hoàng holds a PhD in Operations Research from École des Mines de Nantes (2012), a research master's in Systems Optimization and Security from the University of Technology of Troyes (2009), and an engineering degree from Hanoi University of Science and Technology (2008). He was a postdoctoral researcher at CIRRELT, École Polytechnique de Montréal (2014–2015), Visiting Scholar at Liverpool John Moores University (2017) and Visiting Professor at the University of Technology Sydney (2019).
He has published close to fifty peer-reviewed articles in venues including the European Journal of Operational Research, Transportation Science, Transportation Research Part C/E, Health Care Management Science, Networks and Scientific Reports, with conference papers at ICAPS, GECCO and CP. He serves as Associate Editor of the Journal of Combinatorial Optimization (Springer) and International Transactions in Operational Research (Wiley), and co-founded the Vietnam Operations Research Network (VORN) and the Southeast Asian Operational Research Network (SAORN).
Faculty of Data Science and Artificial Intelligence, College of Technology, NEU
tqtoan@neu.edu.vnMSc in Computer Science, Phenikaa University (2024); BSc in Information Technology, VNU University of Engineering and Technology (2020). His research combines optimization and artificial intelligence in software engineering and systems analysis, building solutions that improve operational efficiency and support decision-making in supply chains, software systems and technology infrastructure.
Faculty of Data Science and Artificial Intelligence, College of Technology, NEU
khanhld@neu.edu.vnLecturer in the Faculty of Data Science and Artificial Intelligence and a member of the CADA research and technology-transfer laboratory, contributing to the lab's machine learning and applied data science work.
Faculty of Data Science and Artificial Intelligence, College of Technology, NEU
ntmtrang@neu.edu.vnMSc in Management and Business Analytics, Nottingham Trent University, UK (2023); BSc in Banking and Finance, University of Lincoln, UK (2021). She joined the faculty in December 2024. Her research mines and analyses enterprise data within management information systems to produce decision-support solutions that improve managerial effectiveness and business operations.
Faculty of Data Science and Artificial Intelligence, College of Technology, NEU
hoatn@neu.edu.vnMSc in Applied Artificial Intelligence (2022) and MSc in Business Analytics (2020), Aston University, UK; BA in Banking, National Economics University (2016). He joined the faculty in 2024, bringing experience from market research, marketing analytics, and product and innovation projects in both corporate and start-up environments in Vietnam and abroad.
More than twenty students work inside the lab at any one time.
CADA is a working laboratory, not a seminar room. Over twenty undergraduate, Master's and doctoral students are embedded in project teams alongside faculty, and are expected to produce the same two outputs the faculty do: a paper and a running system.
Students join from the Faculty of Data Science and Artificial Intelligence's undergraduate programme and from Master's and PhD cohorts supervised by lab faculty. Each is assigned to a project team mapped to one of the six research directions, with a faculty lead responsible for their supervision. Teams meet weekly for a technical seminar; code is reviewed before it reaches a partner's system.
Student authorship is normal here rather than exceptional. Papers from the group in Transportation Science, the European Journal of Operational Research, Health Care Management Science and at ICAPS and GECCO carry student and early-career first authors, working with international collaborators in Canada, France, Brazil, Australia, Singapore and the UK.
Truck-and-drone delivery models, benchmark generation, exact and heuristic solvers for VRP variants.
Duty-roster and production-planning engines, including the systems running at partner hospitals and utilities.
Reinforcement learning and learned heuristics for dynamic and stochastic operational problems.
Full-stack development of the education ERP suite: research management, admissions, LMS, student affairs.
Forecasting, business intelligence and customer analytics on enterprise and financial data.
Pipelines, cloud infrastructure, model deployment and monitoring for the lab's production systems.
Recent peer-reviewed work from the group, chosen to show the range.
The group publishes in the leading operations research and transportation journals, and at top-tier AI planning and evolutionary computation conferences.
Products built in the lab and running at partner organisations.
Two product lines carry the lab's research into production: an ERP suite for higher education, and a family of optimization engines for operational planning.
Funded projects, training programmes and community work.
Current and recent lab activity, from funded research through to enterprise training.
Design and delivery of the software system managing research activity across National Economics University. Funded and deployed by NEU.
Research project funded by the Vingroup Innovation Foundation (VinIF), combining behavioural demand modelling with supply chain optimization.
Lab-led projects funded by Vietnam's National Foundation for Science and Technology Development and international sponsors, on optimization and AI methods.
Delivered by lab faculty to teaching staff at National Economics University.
Applied AI training delivered to Bình Phát Joint Stock Company.
Training for teachers and students at Đỗ Mười High School, extending the lab's work into secondary education.
TPC Co-Chair. Lab members presented and published in the conference proceedings.
TPC Co-Chair.
Optimization solutions for vehicle routing, scheduling and logistics, delivered to SIFT (Singapore), Pixelz (Denmark), Viettel and FPT.
How the lab intends to use a Claude Team plan.
CADA is applying to Anthropic's Team plan for scientists. The lab's work sits squarely in computer science, mathematics and engineering — combinatorial optimization, algorithm design and machine learning — and the seats would be used by named academic staff and supervised student researchers on that work.
| Seat group | Count | Role |
|---|---|---|
| Lab head | 1 | Assoc. Prof. Hà Minh Hoàng — administrator and principal investigator |
| Faculty | 4 | Lecturers and lab members supervising the six research directions |
| Student researchers | 20 | Undergraduate, Master's and doctoral researchers in the six project teams |
| Total requested | 25 | All invited from @neu.edu.vn institutional addresses |
Institutional affiliation. Every seat is held by a member of the Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University, and every invitation is issued to an address on the neu.edu.vn domain. No personal email accounts are used. Sensitive partner or clinical data is not entered into the tool without the data owner's approval.
Room 1601, A1 Building, National Economics University, 207 Giải Phóng, Đồng Tâm, Hai Bà Trưng, Hanoi, Vietnam.