Oleksandra K.

Project overview:

The project involved designing and implementing an AI system for automated generation of dental treatment plans by combining classical machine learning, Retrieval-Augmented Generation (RAG), and large language models (LLM). The solution was developed around medical datasets, doctor-validated clinical rules, and human-in-the-loop feedback to improve quality, safety, and practical adoption in clinical workflows.

Responsibilities:

• Designed and implemented a hybrid AI system combining classical ML, RAG, and LLM components.
• Built ETL pipelines for medical data cleaning, normalization, and augmentation.
• Developed a medical knowledge base with clinician-validated rules and constraints.
• Created prompts and system logic for domain-specific LLM use cases.
• Implemented LoRA fine-tuning on specialized medical datasets.
• Built a continual learning pipeline driven by doctor corrections and human-in-the-loop review.
• Collaborated with doctors and stakeholders to align the system with clinical needs.

Achievements:

Designed a robust medical AI architecture that combined LLMs, RAG, and structured clinical knowledge for treatment planning support. Built domain-specific data pipelines and introduced continual learning from clinician feedback. Strengthened medical relevance and safety by incorporating doctor-validated rules and constraints into the workflow.

Technology stack:

Project overview:

The project involved leading a scientific data science initiative focused on early detection of sleep disorders using real-time biosignals and clinical data. The solution was designed to support improved diagnostic insights by combining advanced healthcare analytics, signal processing, and machine learning with clinically relevant interpretation. The work integrated multiple biomedical data sources to identify meaningful patterns associated with sleep disorders.

Responsibilities:

• Planned and executed and led advanced scientific biosignal and clinical data research for early detection of sleep disorders.
• Analyzed real-time physiological and clinical data using time series methods, signal processing, and anomaly detection.
• Analysing and interpreting EHR data to support clinical hypotheses and diagnostic models.
• Conducted advanced healthcare data analytics
• Worked with PPG, EEG, ECG, biomarkers, vital signs, and electronic health records.
• Interpreted analytical findings to support clinical hypotheses and model development.
• Created high-quality visualizations and scientific reports for research and stakeholder communication.

Achievements:

Led an end-to-end research initiative in sleep disorder detection using biosignals and clinical inputs from scratch. Improved analytical depth through integration of multiple physiological data streams, including PPG, EEG, ECG, biomarkers, and EHR data. Delivered scientific analyses, visualizations, and stakeholder-facing outputs to support research and clinical collaboration. Independently planning, executing and managing research activities, identifying key insights and resolving data discrepancies.

Technology stack:

Project overview:

The project involved leading the scientific research and development of a camera-based health monitoring solution for remote vital signs tracking and stress detection. The system was designed to extract physiological signals directly from facial video and support health and wellness applications through non-contact monitoring. The work combined medical research, signal processing, healthcare analytics, and machine learning to build scalable algorithms for real-world device environments.

Responsibilities:

• Designed and led scientific research initiatives for camera-based vital signs monitoring and stress detection.
• Developed algorithms for biomedical signal extraction, time series analysis, anomaly detection, and pattern recognition, clinical data analysis.
• Worked with EHR, biomarkers, lifestyle data, biometrics, and physiological monitoring data to support research hypotheses.
• Conducted statistical analysis, medical data interpretation, and research validation for healthcare use cases.
• Collaborated with university research teams and scientific organizations on medical R&D activities.
• Mentoring and managing the interns
• Produced scientific reports, stakeholder materials, and research documentation.

Achievements:

Led the scientific direction of remote vitals and stress detection research for medical and wellness applications. Built robust algorithmic approaches for extracting and analyzing physiological indicators from video streams. Supported scalable AI deployment across multiple device types and research settings while collaborating with universities, clinics and scientific organizations.

Technology stack:

Project overview:

The project involved developing a scientific and analytical framework for disease prevention based on nutrigenomics, integrating genetic, biochemical, lifestyle, and biobank data. The solution aimed to generate personalized health recommendations and identify disease risks through combined bioinformatics, predictive analytics, and healthcare data science methods.

Responsibilities:

• Led scientific research in nutrigenomics and disease prevention.
• Analyzed genetic and genomic data to identify disease associations and health risk indicators.
• Integrated DNA, genotype, SNP, biomarker, lifestyle, and biobank data for personalized recommendations.
• Performed predictive analytics, anomaly detection, and data mining on biomedical datasets.
• Generated scientific visualizations and reports for research and presentation purposes.
• Conducted homology searching and bioinformatics-driven interpretation of genomic information.

Achievements:

Led research integrating genomic, lifestyle, and biochemical data into personalized disease prevention workflows. Built analytical approaches for identifying genetic associations with disease risk and improving specificity of health recommendations through multimodal data integration. Delivered visualizations and research outputs for scientific and stakeholder communication.

Technology stack:

Project overview:

The project involved scientific research and development of AI algorithms for detecting cardiovascular diseases from biomedical signals, medical images, and biobank data. The work covered end-to-end research design, experimentation, and algorithm development for healthcare applications, including cardiac image analysis and predictive models derived from physiological data.

Responsibilities:

• Planned and conducted advanced medical research experiments using biomedical signals, imaging, and biobank data.
• Developed algorithms for cardiovascular disease detection and predictive analytics.
• Applied computer vision techniques to detect and segment heart structures on 3D CT images.
• Performed statistical analysis, signal processing, anomaly detection, and time series analysis on medical datasets.
• Worked with MR and CT images, EHR data, ECG, PPG, biomarkers, and lifestyle data.
• Produced scientific publications, reports, and conference materials.
• Collaborated with universities, scientific organizations, and clinical research teams.

Achievements:

Developed disease recognition algorithms from scratch for cardiovascular healthcare use cases. Advanced research in computer vision for detection and segmentation of heart structures on 3D CT images. Supported preparation for clinical testing and FDA-related processes through scientific analysis and collaboration with research institutions.

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