Research, Group & Industry Consulting

The Statistical Data Science Research Group focuses on solving complex data challenges through symbolic data analysis, imbalanced classification, and computational statistics. We bridge fundamental statistical methodologies with applied data science, particularly in healthcare and policy. I am actively seeking driven students and industry collaborators; please reach out via ahmadhakiim[at]upm[dot]edu[dot]my.

Symbolic Data Analysis

• The Impact: As datasets grow too large to store or process efficiently, we need new ways to model them. We extend novel Symbolic Data Analysis (SDA) approaches to model classical data directly using summaries. By focusing on optimal designs and likelihood-based frameworks for symbolic data, we make it possible to run sophisticated statistical methods (like mixture models) on massive, complex datasets without losing critical information.

Imbalanced Classification

• The Impact: In critical domains like medical diagnosis or fraud detection, the "event of interest" is often rare, causing standard AI algorithms to fail. Our group develops meta-learning frameworks to eliminate the trial-and-error in imbalanced classification, ensuring high accuracy for minority class predictions.
• Data-Level Approaches: We focus on mitigating class imbalance directly at the data level by developing and applying advanced resampling methods (such as oversampling, undersampling, and hybrid techniques) tailored to specific dataset characteristics.
• Algorithmic-Level Approaches: Beyond data-level resampling, our research actively explores algorithmic interventions, particularly cost-sensitive learning, to penalize misclassifications and directly optimize models for minority class recognition.
• Recommendation Systems for Complex Data: We are developing novel recommendation systems specifically designed to navigate class imbalance, especially when it is coupled with other challenging data issues.

Computational Statistics

• The Impact: Computational statistics involves developing computational methods to analyze and interpret complex data, often using algorithms and simulations. By leveraging high-performance computing, it enables efficient processing of statistical models for real-world applications. My group focuses on advancing these techniques, primarily using R and Python, to tackle sophisticated data analysis challenges.

Collaborators

Academic Collaborators

• Professor Dr. Shukor Sanim, UiTM, Malaysia
  Professor in Software Engineering; Expertise: software engineering, deep learning, machine learning, AI
• Professor Dr. Nor Idayu Mahat, UUM, Malaysia
  Professor in Statistics; Expertise: statistics, pattern recognition
• Professor Dr. Nur Chamidah, Universitas Airlangga, Indonesia
  Professor in Statistics; Expertise: statistics, healthcare modelling
• Professor Dr. Vita Ratnasari, Institut Teknologi Sepuluh Nopember, Indonesia
  Professor in Statistics; Expertise: categorical data analysis
• Associate Professor Dr. Syaiful Anam, Universitas Brawijaya, Indonesia
  Associate Professor in Computational Intelligence and Data Science; Expertise: Computational Intelligence, Data Science, Computer Vision, Machine Learning
• Andrea Tri Rian Dani, Universitas Mulawarman, Indonesia
  Assistant Professor in Statistics; Expertise: Nonparametric-Semiparametric Regression, Time Series, Hydrological Modeling

Industry Collaborators

• Dr. Izzun Nasheef M. Hasmuri
  Director of Hospital Dalat, Sarawak, Malaysia
• Dr. Naim Malek
  Deputy Director of Hospital Tengku Permaisuri Norashikin, Selangor, Malaysia
• Siti Nurfarahdillah Binte Abdul Razak
  Senior Podiatrist at Sengkang General Hospital, Singapore

Current Students

Honours Students

• 
  Nur Zafnazuhani Jailani
• 
  Giridarkhanna A/L Vijay Khanna
• 
  Kevin Clement
• 
  Lochanna Sengottaiyan
• 
  Sametha Sivalingam

Interns

• Hanisah
  BSc Data Science, University of Sheffield

Consulting & Industry Projects

Beyond fundamental academic research, I actively translate statistical data science and machine learning into actionable, high-impact solutions for government, healthcare, and enterprise sectors. My consulting and industry fellowships focus on building robust AI frameworks and solving 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 role, I contributed to shaping Malaysia's national AI landscape and collaborated on the development of 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 in clinical settings.
• Hospital Kajang & Hospital Dalat, Malaysia: Serving as an Artificial Intelligence Research Fellow (2025) focusing on the implementation of AI and analytics projects to improve hospital IT systems and patient outcomes.
• Award-Winning Healthcare Collaboration: Won the Silver Medal Award at X-CIPTA (2025) for collaborative healthcare innovation between UNSW Sydney, Hospital Dalat, and UPM.
• SalamPro, Malaysia: Acted as an Artificial Intelligence Medical Technology (AI MedTech) Trainer (2025) to upskill professionals in healthcare technology applications.

Corporate Data Science & Mentorship

• SAS Institute Australia & UNSW Sydney: Served as a Project Advisor for the Work Integrated Learning (WIL) program (2024), mentoring students on industry-integrated data analysis projects using SAS tools.
• Corporate Analytics: Brought foundational industry experience from my time as a Technical (Analytics) Accountant at RHB Banking Group, where I utilized data to drive financial insights.