Hi, I’m Tomasz Lasota—a scientist, data geek, and AI enthusiast with a unique blend of expertise in Molecular Biology and Computer Science. With a PhD in Molecular Biology and an MSc in Computer Science specializing in Data Analytics, I focus on bridging the gap between wet-lab experimentation and computational insight.
My career began in the lab developing PCR-based diagnostics and nucleic acid extraction technologies, but I quickly became fascinated by the stories hidden in biological data. That curiosity drew me into the world of Python, machine learning, and statistical modeling. Whether it’s building classification models for PCR amplification curves, detecting anomalies in microfluidic pressure profiles, or interpreting metabolomics datasets using dimensionality reduction and clustering, I enjoy applying ML to solve meaningful biological problems.
My MSc research used artificial neural networks to model human biomechanics, but these days I'm more interested in making sense of high-dimensional biological data—especially in diagnostics and omics applications. I’m passionate about turning noisy lab readouts into actionable insights through clean, reproducible analysis pipelines.
Outside of science, I enjoy tinkering with 3D printing, automation projects, and cloud-native tools to accelerate discovery. And none of this would be possible without my dog, Lilly, who ensures I stay balanced with regular walks and reminders to unplug.
During my MSc, I developed expertise in machine learning, statistical modelling, and data-driven decision-making, with a strong foundation in software development and cloud computing. My coursework covered advanced topics in artificial intelligence, big data processing, and predictive analytics, equipping me with the skills to handle complex datasets and derive meaningful insights.
For my MSc project, I focused on Human Activity Recognition (HAR) using smartphone sensor data during weight training. I designed and implemented a machine learning model to classify different weightlifting exercises based on accelerometer and gyroscope data. The project explored feature engineering techniques, deep learning architectures, and real-time data processing to enhance recognition accuracy and provide insights into movement patterns. This experience reinforced my ability to apply data science techniques to real-world problems, particularly in wearable technology and fitness analytics.
My doctoral research focused on Loop-Mediated Isothermal Amplification (LAMP) technology and Bioluminescent Assay in Real-Time (BART) to develop a novel ultra-fast method for microRNA detection and hepatitis diagnosis. I worked on optimizing nucleic acid amplification techniques to enhance sensitivity and specificity, allowing for rapid and reliable molecular diagnostics. This research contributed to advancements in point-of-care testing by integrating isothermal amplification with real-time bioluminescence detection, facilitating faster disease diagnostics in clinical and low-resource settings. My work also involved assay development, quantitative data analysis, and validation of new molecular detection platforms.
My research focused on developing novel methods for nucleic acid purification from biological samples exposed to challenging environmental conditions typically encountered at crime scenes. I explored innovative extraction and stabilization techniques to enhance DNA and RNA recovery from degraded forensic evidence, improving the reliability of genetic profiling in forensic investigations.
This work involved optimizing sample preservation strategies, evaluating the impact of environmental factors on nucleic acid integrity, and applying molecular biology techniques to forensic casework. My research contributed to advancing forensic DNA analysis, ensuring more robust and accurate results in crime scene investigations.