Collaboration & Partnerships

At the AI & Health Epidemiology Advanced Data Lab (AHEAD Lab) at Nazarbayev University School of Medicine, we translate complex health data into actionable epidemiological insights and predictive AI models.

We actively partner with academic researchers, healthcare institutions, governmental bodies, pharmaceutical leaders, and international consortia to accelerate medical discovery, evaluate health technologies, and drive data-informed clinical decisions across Kazakhstan and Central Asia.

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Academic & Trainee Research Thesis support, co-supervision, and fellowships for PhD candidates, postdocs, and clinical fellows. Public Sector & Health Systems National grant partnerships, health technology assessment, and disease burden analytics for Kazakhstan. Industry & Real-World Evidence Contracted RWE studies, post-marketing surveillance, and bespoke analytics from Central Asian cohorts. International Research & Consortia Dual-cohort comparative studies and joint grant applications as the regional data hub for Central Asia.

Academic & Trainee Research

Empowering the next generation of clinician-scientists with advanced data science and epidemiological modeling.

We collaborate directly with PhD candidates, clinical fellows, and medical residents to integrate cutting-edge health informatics and machine learning into their doctoral research.

Potential collaborations

  • Thesis AI & Epidemiological Support — joint supervision and analytical consulting, providing custom machine learning pipelines, risk stratification models, and survival analysis tailored to your clinical cohort.
  • Research Fellowships — visiting opportunities (3–12 months) for regional and international postdocs and master's scholars to gain hands-on experience with health data architecture.
  • Joint publications and co-authorship — publication-ready statistical frameworks and rigorous methodology validation for thesis defenses.

Typical outcomes: co-authored manuscripts in peer-reviewed journals, enhanced epidemiological rigor for thesis defenses, and training in health informatics.

Target partners: PhD students, residency directors, clinical research fellows, academic universities.

Public Sector & Health Systems

Informing national health policy, evaluating medical technologies, and driving evidence-based healthcare.

As a health data analytics lab in Kazakhstan, AHEAD Lab offers technical and analytical expertise for national scientific initiatives, Ministry of Healthcare strategic programs, and highly specialized clinical centers.

Potential collaborations

  • Program-Targeted & Grant Funding (PTF / MSHE) — partnering as lead data center or co-investigators on national scientific grant applications and targeted health research programs.
  • Health Technology & Surgical Outcome Assessment — contracted evaluations of short- and long-term efficacy, safety, and cost-effectiveness for specialized medical interventions such as organ transplantation, advanced surgical technologies, and high-tech therapies.
  • Disease Burden & Policy Analytics — epidemiological forecasting, registries, and real-world disease mapping to guide national health strategies.

Typical outcomes: national program evaluations, disease burden reports, and registry-based evidence that informs health policy decisions.

Target partners: Ministry of Healthcare, Ministry of Science and Higher Education (MSHE), national scientific centers, university hospitals.

Industry & Real-World Evidence

Generating robust real-world evidence from Central Asian population data to advance precision medicine.

We partner with global and regional biotechnology, pharmaceutical, and medical device companies requiring real-world clinical insight and post-marketing observational data.

Potential collaborations

  • Real-World Evidence (RWE) Studies — observational studies evaluating drug utilization, real-world effectiveness, patient safety, adherence, and disease natural history across diverse Central Asian patient cohorts.
  • Post-Marketing Surveillance (PMS) — longitudinal tracking of therapeutic outcomes and adverse events using curated regional healthcare databases.
  • Custom Clinical Dashboards & Health Economics — contract research for cost-effectiveness analysis (CEA) and health economics dossiers tailored to market access requirements.

Typical outcomes: decision-grade RWE dossiers, safety and effectiveness profiles, and market access evidence for regional and global stakeholders.

Target partners: Pharmaceutical companies, CROs, medical device manufacturers, health economics research teams.

International Research & Consortia

Bridging Central Asian health data with global registries for high-impact international discovery.

AHEAD Lab serves as a regional data and AI hub, facilitating cross-border research initiatives, dual-cohort studies, and international grant applications.

Potential collaborations

  • Dual-Cohort & Cross-National Studies — comparative epidemiological analyses matching AHEAD Lab datasets against European, Asian, or North American national registries to study genetic, environmental, and healthcare delivery differences.
  • Global Research Consortia — joining international funding applications (e.g., Horizon Europe, NIH, Wellcome Trust) as the lead data center for Central Asia.
  • Data Harmonization & Model Validation — testing and validating global AI algorithms on local patient populations to ensure cross-population accuracy and fairness.

Typical outcomes: multi-center international publications, harmonized cross-national datasets, and validated AI models generalizable across populations.

Target partners: European and global universities, international research consortia, global health organizations.

Research Capabilities

  • Large-scale epidemiological study design and biostatistics
  • Machine learning pipelines for disease prediction and risk stratification
  • Survival analysis and clinical risk prediction models
  • Real-world data curation from national healthcare records
  • Health economics and cost-effectiveness analysis
  • Data harmonization with international registries and cohorts
  • Natural language processing and local-language LLMs for clinical text
  • Research data governance, ethics, and privacy-preserving analytics

How Collaboration Starts

  1. Tell us about the research question

    Share your idea, dataset, or project through the inquiry form below.

  2. We assess feasibility

    Our team reviews data availability, methodology, ethics requirements, and timelines.

  3. We define the collaboration

    We agree on roles, contributions, governance, and authorship or contractual terms.

  4. The project begins

    Joint work starts with agreed milestones, deliverables, and communication channels.

Track Record

Explore our Publications, Research Grants, Team and Collaborators.

Collaboration Inquiry

Fill in the form below and the relevant pathway team will review your inquiry.

Please do not include patient-identifiable information or detailed confidential datasets in this form.