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About
Hi, I'm Kristof
While this About section may start with me – it's actually about you.
Why? Because I know what you're looking for:
State-of-the-art machine learning (ML) models.
You want to achieve breakthroughs by running rapid ML experiments.
You want to apply continuous training on your models.
Your models constantly need clean datasets to keep improving.
And as a result, you need robust data pipelines to ensure your ML models' reliability.
How do I know this?
I specialized in Big Data and Bioinformatics at university and then gained experience on real-world projects.
Why should you work with me?
Here Are The Top 10 Reasons:
So what's my story?
I've always been an IT geek.
I enjoyed programming as a high-school student, but I didn't know anything about how to do it professionally.
So I did what any aspiring young professional would do, and enrolled in university to study the craft of professional software engineering.
I spent 5 years in the (sometimes) painful process of learning how to build quality software – attending every student conference, reading every research paper, competing at every student competition, and learning from mentors. I made my fair share of mistakes along the way, too...
But software engineering is a skill, and it can be learned. When I eventually mastered this skill, I worked for the:
As difficult as the projects at these organizations were, they were just as exciting. What I enjoyed most was seeing how the application of computer science in the field of healthcare can result in solutions that help healthcare professionals save more lives.
My experiences involved cleaning and visualizing healthcare data, running simulations on these datasets, analyzing the results with descriptive statistics, building optimization engines, and designing databases.
The project I'm most proud of is the optimization engine created for the Spanish National Kidney Exchange Programme – they still use it to this day. It automates manual data management tasks, which eliminates errors and saves a lot of time (and headache) for staff.
I'm confident that working for the above organizations helped me a lot to learn about IT in healthcare and it also allowed me to hone my professional software engineering skills. But besides that, my colleagues and I really enjoyed seeing the positive impact our work had on healthcare institutions – which I truly believe matters the most.
If you've made it this far, thank you for taking the time to read about me. Writing this About section has been a chance to share a bit of who I am and what matters to me. I'd like to encourage you to take action – reach out to me – I'd be happy to hear about what you're working on and what can I help you with.
Because of my 100% Money-Back Guarantee: You have absolutely nothing to lose, and everything to gain.
Sincerely,
Kristof
Service
What do I provide for you?
If you're not absolutely satisfied with the solution I create for you, or if I fail to meet any of my promises listed here, I insist that you tell me and I will give you 100% of your money back.
Your peace of mind is my top priority. While you focus on improving your ML algorithms, I'll take care of transforming your data into training datasets for your next ML model.
For years, I have delivered software and data engineering solutions to world-class institutions. I guarantee that I'll solve your data problems – saving you both time and money.
Regardless of your decision after our first consultation, you’ll still receive my project draft, a quick quote, and clear next steps tailored to your needs – all without obligations and completely free.
Working with me is totally risk-free. I take maximum responsibility for my work with liability insurance, ensuring you're protected no matter what happens.
For a fast and time-efficient solution delivery, I offer my services fully remotely. Staying in touch with me is easy – you can reach me online through text messages or video calls, no matter where you are.
Portfolio
Featured Projects
From Messy to Clean:
Preparing CRISPR-Experiments Data for Machine Learning
Problem:
The CRISPR gene-editing technique (sometimes called the "molecular scissors") holds immense potential for driving breakthroughs in medicine. For example, we can use it to advance our understanding of gene functions by conducting gene-knockout experiments. But there is a significant issue: data of biological experiments can be very messy.
Solution:
This project presented a solution for a real-world messy dataset by transforming a tabular set of CRISPR knockout experiments into a clean format – ready to be used for training predictive machine learning models.
Contact
I'd Love To Hear From You
Book a free 20-minute first-consultation with me!