Hi, I'm Dr. Alistair Tiefenbacher, a Data Scientist, Bioinformatician & Statistician living in Austria
Bridging cutting-edge research with real-world healthcare applications through AI and machine learning. Explore my journey from cancer genomics research to AI-powered medical devices.
About Me
With over a decade of experience in data science and bioinformatics at top institutions worldwide, I've led the development of AI-powered medical diagnosis systems achieving >90% diagnostic accuracy. My work spans cancer genomics research at Cambridge, protein structure prediction at Oxford, electronic health record screening at Symptoma, and now digital health innovation at Xund. I specialize in translating complex biological and medical data into actionable insights through machine learning, having published 15+ peer-reviewed papers in Nature, Cancer Research, and other leading journals. My unique strength lies in bridging the gap between cutting-edge research and real-world healthcare applications, from developing COVID-19 screening tools used by governments to pioneering personalized cancer treatment strategies.
Research
My research focuses on the intersection of artificial intelligence and healthcare, with expertise spanning three key areas: Medical AI Systems - developing and certifying diagnostic algorithms that meet regulatory standards; Cancer Genomics - analyzing tumor heterogeneity and drug response patterns using machine learning; and Digital Health Innovation - creating scalable solutions for population health monitoring and personalized medicine.
At Xund, I lead the company-wide transition to scalable ML frameworks to enable sustainable product development. Further, I provide the technical expertise for medical AI certification, ensuring our machine learning models meet the stringent requirements for clinical deployment. My approach combines rigorous statistical validation with real-world clinical testing, bridging the gap between research innovation and practical healthcare applications. I believe in developing AI systems that augment rather than replace clinical expertise, creating tools that empower healthcare providers to make better decisions for their patients.
Education
2012-2017: Doctor of Philosophy (PhD) in Statistics
Department of Statistics, University of Oxford, United Kingdom
2007-2012: Master of Chemical Physics, 1st Class Hons, Industrial Exp.
Department of Chemistry, University of Edinburgh, United Kingdom
2010-2011: International Exchange Student
Department of Chemistry, Nanyang Technological University, Singapore
Research Experience
2024 onwards: Senior Data Scientist, Xund, Vienna
Led company transition to scalable ML-frameworks for improved product development and medical product certification.
2021-2024: Chief Data Scientist, Symptoma, Vienna
Pioneered digital health solutions focusing on electronic health records and symptom checker development.
2020-2021: Senior Data Scientist, Symptoma, Vienna
Developed data-driven digital health products including COVID-19 diagnostic screening tools for government use.
2017-2020: Postdoctoral Researcher, CRUK, University of Cambridge
Multi-faceted investigation into breast cancer and its heterogeneity at the transcriptional level.
2013-2017: Doctoral Student, University of Oxford
Investigating the structural information contained within mRNA above and beyond the mere specification of the amino acid.
2015: Visiting Researcher, UCB
Building a web platform for the identification and tracking of disruptive technologies.
2013: Rotation Student, University of Oxford
Developing a new mathematical framework for cellular modelling that captures the physical properties of monolayers.
2011-2012: Masters Student, University of Edinburgh
Determination of the hydrogenation pathway of ethylene on a Pt(111) surface using first-principle techniques.
2011: Visiting Academic, UC Berkeley
Exploring the possibilities of using polarisation in ultrafast Raman spectroscopy to reduce the HL(III) and HL(IV) contributions to the spectrum.
2011: Student Researcher, A*STAR Institute of High Performance Computing
Theory and simulations of solute cage formation around dissolved gasses and determination of partial molar volumes.
2010-2011: Student Researcher, Nanyang Technological University
Theory and simulations of the effects of polarisation in ultrafast Raman spectroscopy on third-order cascade effects observed in deuterated chloroform.
2009: Research Assistant, Max-Planck Institute for Iron Research
Electrochemical characterisation via a scanning droplet cell of binary and tertiary metal gradients created by physical vapour deposition.
Publications
Cancer Research & Precision Medicine
Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Cancer Research, DOI: 10.1158/0008-5472.CAN-24-1703 (2025)
A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival
Molecular Oncology, DOI: 10.1002/1878-0261.70015 (2024)
Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response
Nature Communications, DOI: 10.1038/s41467-021-22303-z (2021)
Landscape of G-quadruplex DNA structural regions in breast cancer
Nature Genetics, DOI: 10.1038/s41588-020-0672-8 (2020)
Metabolic Imaging Detects Resistance to PI3Kα Inhibition Mediated by Persistent FOXM1 Expression in ER+ Breast Cancer
Cancer Cell, DOI: 10.1016/j.ccell.2020.08.016 (2020)
Shieldin complex promotes DNA end-joining and counters homologous recombination in BRCA1-null cells
Nature Cell Biology, DOI: 10.1038/s41556-018-0140-1 (2018)
Digital Health & Medical AI
Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)
Sensors, DOI: 10.3390/s24041101 (2024)
Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records
Cancers, DOI: 10.3390/cancers15102741 (2023)
An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease
Frontiers in Neurology, DOI: 10.3389/fneur.2023.1108222 (2023)
Correlating global trends in COVID-19 cases with online symptom checker self-assessments
PLOS ONE, DOI: 10.1371/journal.pone.0281709 (2023)
An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot
Scientific Reports, DOI: 10.1038/s41598-020-75912-x (2020)
Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
Journal of Medical Internet Research, DOI: 10.2196/21299 (2020)
Academic & Methodological
10 simple rules for surviving an interdisciplinary PhD
PLoS Computational Biology, DOI: 10.1371/journal.pcbi.1005512 (2017)
Variation and decomposition of the partial molar volume of small gas molecules in different organic solvents derived from molecular dynamics simulations
The Journal of Chemical Physics, DOI: 10.1063/1.4854135 (2013)
Theoretical simulation and preparation of binary and ternary combinatorial libraries by thermal PVD
Physica Status Solidi A, DOI: 10.1002/pssa.200983302 (2010)