Hakiim Jamaluddin

Hakiim Jamaluddin

he/him

Statistics & Data Science

Statistical Data Science Group

Research Focus and Areas

The Statistical Data Science Group is dedicated to addressing complex data challenges through symbolic data analysis, imbalanced classification and uncertainty quantification. We strive to bridge foundational statistical methodologies with applied data science, with a particular emphasis on improving healthcare and public policy. We are always eager to welcome driven students and industry partners to collaborate with us. Inquiries are warmly welcomed at ahmadhakiim[at]upm[dot]edu[dot]my.

Symbolic Data Analysis (SDA)

  • Overview: As datasets grow beyond the limits of traditional processing, novel modelling paradigms become essential. We extend Symbolic Data Analysis (SDA) approaches to model classical data through aggregate summaries. By focusing on optimal designs and likelihood-based frameworks, we enable sophisticated statistical methodologies—such as mixture models—to be applied to massive, complex datasets without critical information loss.

Imbalanced Classification

  • Overview: In high-stakes domains such as medical diagnosis and fraud detection, the event of interest is often rare, which can challenge standard machine learning algorithms. Our group is dedicated to developing meta-learning frameworks to systematically address imbalanced classification, ensuring robust and fair predictive accuracy for minority classes.
  • Data-Level Approaches: We mitigate class imbalance at the foundational level by developing and applying advanced resampling methodologies (including oversampling, undersampling, and hybrid techniques) tailored to the specific topological characteristics of a dataset.
  • Algorithmic-Level Approaches: Beyond resampling, our research explores algorithmic interventions, particularly cost-sensitive learning, to mathematically penalise misclassifications and optimise models directly for minority class recognition.
  • Recommendation Systems: We are pioneering novel recommendation architectures specifically designed to navigate class imbalance, particularly when compounded by other structural data irregularities.

Uncertainty Quantification (UQ)

  • Overview: While deep learning models, such as Convolutional Neural Networks (CNNs), offer remarkable predictive power, they often operate with inherent uncertainties. Our group is dedicated to developing and applying Uncertainty Quantification (UQ) techniques to measure and communicate the confidence of these models. By illuminating the "black box," we strive to make AI predictions more transparent, reliable, and trustworthy, particularly in high-stakes domains like healthcare and clinical diagnostics.

Consulting & Industry Translation

Beyond foundational academic research, our group is deeply committed to translating statistical data science and machine learning into actionable, high-impact solutions for the government, healthcare, and corporate sectors. Our industry engagements focus on building robust, ethical AI frameworks to solve complex, real-world challenges.

National AI Policy & Strategy

  • National Artificial Intelligence Office (NAIO), Ministry of Digital Malaysia: I had the privilege of serving as the Education Sector Lead for the AI Talent Working Group. In this capacity, I was grateful to contribute to shaping Malaysia's national AI landscape and collaborating on the development of comprehensive AI literacy frameworks for the education sector.

Healthcare AI & Clinical Analytics

  • Sengkang General Hospital, Singapore: Honored to serve as an Artificial Intelligence Research Fellow (2025), collaborating to drive advanced data solutions and analytics within clinical settings.
  • Hospital Kajang & Hospital Dalat, Malaysia: Serving as an Artificial Intelligence Research Fellow (2025), focusing on the thoughtful implementation of AI and analytics initiatives to enhance hospital IT infrastructure and patient outcomes.
  • Award-Winning Healthcare Collaboration: Deeply grateful to receive the Silver Medal at X-CIPTA (2025) for collaborative healthcare innovations developed alongside UNSW Sydney, Hospital Dalat, and UPM.
  • SalamPro, Malaysia: Had the pleasure of acting as an Artificial Intelligence Medical Technology (AI MedTech) Trainer (2025), upskilling healthcare professionals in the application of emerging clinical technologies.

Corporate Data Science & Mentorship

  • SAS Institute Australia & UNSW Sydney: Had the privilege of serving as a Project Advisor for the Work Integrated Learning (WIL) programme (2024), mentoring students on industry-integrated data analysis projects utilizing SAS enterprise tools.
  • Corporate Analytics: Drawing on foundational industry experience from my tenure as a Technical (Analytics) Accountant at RHB Banking Group, I strive to bring practical, large-scale data insights to our academic and industry collaborations.

Group Members & Supervision

I am deeply proud to mentor and collaborate with the next generation of data scientists and researchers. Our current and past group members include:

PhD Candidates

  • Dr. Ferwahn Fairis Ab Karim (Medicine & Health Sciences, UPM)
    Role: Co-supervisor (with Assoc. Prof. Dr. Mohd Rafee Baharudin)

Master's by Research Candidates

  • Muhammad Abbas (Statistics, UPM)
    Role: Co-supervisor (with Dr. Mohd Shafie Mustafa & Dr. Farid Zamani Che Rose)
  • Md Shakib (Statistics, UPM)
    Role: Co-supervisor (with Dr. Farid Zamani Che Rose)
  • Sheriff A. Bah (Computational Mathematics, Universitas Brawijaya, Indonesia)
    Role: Co-supervisor (with Assoc. Prof. Dr. Syaiful Anam)

Honours Students

Completed

  • Nur Zafnazuhani Jailani (BSc Statistics, UPM, Malaysia) – Completed 2025

Ongoing

  • Giridarkhanna A/L Vijay Khanna (BSc Statistics, UPM, Malaysia) – Started 2026
  • Kevin Clement (BSc Statistics, UPM, Malaysia) – Started 2026
  • Lochanna Sengottaiyan (BSc Statistics, UPM, Malaysia) – Started 2026
  • Sametha Sivalingam (BSc Statistics, UPM, Malaysia) – Started 2026
  • Arman Azad Shahrezad (BSc Statistics, UPM, Malaysia) – Started 2026
  • Bashiri Surya (BSc Statistics, Universitas Airlangga, Indonesia) – Started 2026

Research Interns

  • Nur Hanisah (BSc Data Science, University of Sheffield, United Kingdom) – Completed 2026