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 computational statistics. We bridge foundational statistical methodologies with applied data science, placing a particular emphasis on healthcare and public policy. I am actively seeking driven students and industry partners to collaborate with our group. Inquiries can be directed to ahmadhakiim[at]upm[dot]edu[dot]my.

Symbolic Data Analysis (SDA)

  • Overview: As datasets scale beyond the limits of efficient storage and processing, novel modelling paradigms are required. We extend Symbolic Data Analysis (SDA) approaches to model classical data directly through aggregate summaries. By focusing on optimal designs and likelihood-based frameworks for symbolic data, 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, leading to the failure of standard machine learning algorithms. Our group develops meta-learning frameworks to systematically address imbalanced classification, ensuring robust 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 specific topological dataset characteristics.
  • Algorithmic-Level Approaches: Beyond resampling, our research explores algorithmic interventions, particularly cost-sensitive learning, to penalise misclassifications mathematically 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.

Computational Statistics

  • Overview: Computational statistics leverages high-performance computing to analyse and interpret complex data through advanced algorithms and simulations. Our group focuses on advancing these computational techniques, primarily utilising R and Python, to deliver efficient, scalable processing for real-world statistical models and sophisticated data analysis challenges.

Consulting & Industry Translation

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

National AI Policy & Strategy

  • National Artificial Intelligence Office (NAIO), Ministry of Digital Malaysia: Served as the Education Sector Lead for the AI Talent Working Group. In this capacity, I contributed to shaping Malaysia's national AI landscape and collaborated on developing comprehensive AI literacy frameworks for the education sector.

Healthcare AI & Clinical Analytics

  • Sengkang General Hospital, Singapore: Appointed as an Artificial Intelligence Research Fellow (2025) 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 implementation of AI and analytics initiatives to enhance hospital IT infrastructure and patient outcomes.
  • Award-Winning Healthcare Collaboration: Awarded the Silver Medal at X-CIPTA (2025) for collaborative healthcare innovations developed alongside UNSW Sydney, Hospital Dalat, and UPM.
  • SalamPro, Malaysia: Acted 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: Served as a Project Advisor for the Work Integrated Learning (WIL) programme (2024), mentoring students on industry-integrated data analysis projects utilising SAS enterprise tools.
  • Corporate Analytics: Leveraged foundational industry experience from my tenure as a Technical (Analytics) Accountant at RHB Banking Group, utilising large-scale data to drive strategic financial insights.

Group Members & Supervision

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)
  • Sheriff A. Bah (Computational Mathematics, Universitas Brawijaya, Indonesia)
    Role: Co-supervisor (with Assoc. Prof. Dr. Syaiful Anam)

Honours Students

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

Research Interns

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