Skip to content
View kalfasyan's full-sized avatar
πŸ—ΊοΈ
πŸ—ΊοΈ

Highlights

  • Pro

Block or report kalfasyan

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
kalfasyan/README.md

AI & Machine Learning Research Scientist

For a summary of links to various online profiles, you can check out my linktree.

A multidisciplinary computational scientist at the intersection of AI, Machine Learning, and Bioscience Engineering, with a proven track record of translating cutting-edge research into impactful technological solutions. Leveraging advanced expertise in Neural Networks, computer vision, and data-centric AI, I specialize in developing innovative research and applied AI software solutions across domainsβ€”from neurophysiological data analysis to environmental monitoring. My professional journey spans academic research, industry applications, and strategic AI implementation, with a consistent focus on driving meaningful technological advancements that address real-world challenges.


Background

πŸŽ“ Studies

Keywords: Python Programming, Artificial Neural Networks, Spiking Neural Networks, Machine Learning

I studied Computer Science in the Aristotle University of Thessaloniki (Greece πŸ‡¬πŸ‡·) earning a solid basis around computing theory. Next, I finished my Master's in Machine Learning at KTH University (Stockholm, Sweden πŸ‡ΈπŸ‡ͺ) specializing in Computational Neuroscience (Spiking Neural Networks). For my thesis work, I simulated a small piece of the neocortex using the NEST simulator in Python to compare various columnar structure types and their activity. My academic journey continued with two years of research in a neurophysiology lab, exploring computational neuroscience. While I did not complete the initial PhD program, I subsequently earned a PhD in Bioscience Engineering, pivoting my research to focus on optical insect identification using artificial intelligence.

πŸ’Ό Professional Experience

Deep Learning in Neurophysiology at KUL (PhD researcher) 🧠

Keywords: Brain-Inspired AI, Visual System Research, Neural Activity Prediction, Computer Vision, Scientific Publishing

As a PhD researcher in the lab of Neurophysiology of KU Leuven for 2 years, I conducted in-depth studies on deep Convolutional Neural Networks and their resemblance to the visual system. My work included complex computer vision and regression tasks for predicting biological neuronal activity based on artificial neuron activations of various SOTA CNN models, leading to 4 scientific publications in renowned Neuroscience journals ([1][2][3][4]) and a poster presentation at VSS conference (Florida, USA), before exiting the programme.

Applied AI at Faktion (Data Scientist) πŸš€

Keywords: Practical Industry AI Solutions, Machine Learning Pipelines, Computer Vision, Cloud Technology, Hackathons

Having developed a passion for Deep Learning and its software ecosystem, I wanted to shift my focus from fundamental research to applied AI applications for which I could more clearly gauge their societal impact. Working as a Data Scientist at Faktion in Antwerp, I honed my skills in industry practices such as end-to-end ML pipelines, AI model training, Docker containers, and Cloud components. Notably, my team and I won a hackathon on Activity Recognition in video data, organized by Vinci Energies.

Data-centric AI at MeBioS, KUL (PhD researcher) 🐞

Keywords: Insect Recognition, Optical Sensors, Smart Monitoring Systems, Computer Vision, Sound Pattern Analysis, EU Projects, IoT Devices, Cloud Services

Motivated to pursue more applied research this time, and be closer to home, I returned to Leuven (and KUL) to obtain my PhD in Bioscience Engineering. My thesis topic was Optical Insect Identification using Artificial Intelligence and focused on 2 distinct insect recognition tracks based on:

  1. images, using Computer Vision, (example repo1, repo2, repo3)
  2. time-series (wingbeats), using Signal Processing. (example repo1, repo2)

The main objectives of my research were around data-centric AI and strict model validation to reveal the "true" model performance once deployed in the field. During my PhD I have developed software tools, GUIs Streamlit, Tkinter and AI models (YOLO, RCNN, 2-stage detectors, ...) which ran on IoT (e.g., RaspberryPi) devices, Linux/Windows desktops, and the cloud (AWS). One of my greatest achievements was a custom API server that still runs on AWS and serves our image classification model to external companies and collaborating research institutes (examples of device and software: 1, 2). Apart from the API (FastAPI), it incorporated a user-friendly GUI (Streamlit) to aid researchers with image annotation tasks.

Postdoctoral Researcher at MeBioS, KUL 🦾

Keywords: Research Mentorship, Advanced Imaging, Agricultural Technologies, Software Development, Data Management

As a Postdoctoral researcher at MeBioS (KUL), I got involved in multiple projects around AI in insect monitoring or agrifood applications. I guided PhD researchers and built software tools that aided in their research. Being more involved in Hyperspectral Imaging (HSI) projects, I familiarized myself with SOTA techniques to deal with complex hypercube data using AI. Moreover, I was the research data and software manager for our lab, being responsible on hosting and sharing our software/data using KUL's infrastructure and maintaining our research group's GitLab (here's its public profile, where you can see some of its content). Last but not least, I developed an image tiling library plakakia which helped researchers with image processing and object detection tasks.

Remote Sensing & AI Researcher at Vito πŸ›°οΈ

Keywords: Earth Observation, Environmental Monitoring, EU Projects, Sustainable Development, Geospatial Analysis, Cloud Services, Hadoop, Spark, AWS

Now, I'm taking my expertise to new heights as a remote sensing & AI researcher at Vito. My current role involves classifying the earth's land cover in a reliable and accurate way through the LCFM project of the EU commission (JRC), which continues the very successful work done for the ESA Worldcover products. This important work has real-world applications for environmental conservation, land use planning, and climate change mitigation. In this role, I leverage hyperspectral satellite data, computer vision techniques, and machine learning models to analyze and interpret complex environmental data. By integrating these advanced technologies, I contribute to the development of innovative solutions for monitoring and managing our planet's resources more effectively.

For an overview of Land Cover Mapping projects and their applications have a look at this well-structured blog post by the Land Carbon Lab.

Contact

🌱 I’m always interested to learn about how Artificial Intelligence can improve our lives.
πŸ’¬ Do you want to reach out? Send an email at kalfasyan[at]gmail[dot]com
πŸ”— Check my linktr.ee

πŸ“š Researcher profiles:
🧬 ORCID
πŸ”¬ GOOGLE SCHOLAR
πŸ“– RESEARCHGATE

🌐 Stay connected through the following social media channels:
πŸ“² BLUESKY
πŸ“² LINKEDIN
πŸ“² GITHUB

Pinned Loading

  1. plakakia plakakia Public

    Python image tiling library for image processing, object detection, etc.

    Python 12 3

  2. Home_Surveillance_with_Python Home_Surveillance_with_Python Public

    Motion detection using OpenCV (Raspberry Pi compatible), alerting through pushbullet, served with flask.

    Python 10 5

  3. pytorch-dl-tutorial-for-students pytorch-dl-tutorial-for-students Public

    Jupyter Notebook

  4. photobox photobox Public

    Insect Sticky Plate Imaging Software

    Jupyter Notebook 2

  5. undistort undistort Public

    Simple package to remove spatial distortion from images.

    Python

  6. streamlit-basic-image-processing streamlit-basic-image-processing Public

    Practicing MLOps

    Python