WHY.AI is dedicated to empowering companies with cutting-edge AI solutions through research, rapid prototyping, and consulting services. We specialize in helping organizations understand and leverage the power of artificial intelligence by conducting innovative research and delivering impactful proof-of-concepts (PoCs).What do we do?- AI PoC Development: Rapidly prototyping and building PoCs to demonstrate AI's value and impact in real-world scenarios.- AI Research: Our team of Ph.D. experts explores groundbreaking AI advancements, pushing the boundaries of what's possible.- Consulting: With extensive experience in patenting and writing EU projects, we provide strategic insights to help companies navigate complex processes.At WHY.AI, we are committed to driving innovation and delivering tailored solutions that help businesses thrive in the AI-driven future.

As a Research Assistant, my main goal is to produce scientific research related to big data usage in transport and transfer that knowledge to new generations of transport engineers. Main responsibilities:- Publishing relevant papers in the Transport Data Science research field.- Mentor for students' bachelor's and master's thesis.- Lecturer in course of Computer Science.- Lecturer in course of Algorithms and Programming.

As a Researcher on the project "DATACROSS - Advanced methods and technologies in Data Science and Cooperative Systems", I was conducting research related to applying machine learning algorithms to extract transport-related knowledge from large GPS datasets. The research resulted in producing a novel methodology for traffic state estimation named Speed Transition Matrix, which contributions were published in several WoS journals.Main responsibilities:- Writing a Ph.D. thesis.- Lecturer in the course Data Mining.- Lecturer in the course Algorithms and programming.

As the Artificial Intelligence Engineer, my main responsibilities are developing AI-based production-ready solutions focused on solving business cases related to sales forecasting, demand-based clustering, and route optimization challenges. Alongside R&D tasks, I am providing our team support in maintaining a knowledge base (Confluence), Jira board (SCRUM), and mentoring students at summer internships. Main responsibilities:- Development of dockerized deployment and inference AI-based sale forecasting solution.- R&D related to clustering of delivery locations based on demand and geo-location.- Development of scheduling service based on AirFlow.- Development of monitoring service based on Prometheus and Grafana.- POC development for various AI-based services like face recognition, object detection, route optimization, time-series clustering, etc.

As a Visiting Researcher, I was working two months on sharping my knowledge related to tensor decompositions and applying these methods on detecting anomalies in transport-related data. The visit resulted in the publishing of several research papers published at scientific conferences and two papers published in WoS journals.Main responsibilities:- Development of the method for traffic-related anomaly detection using tensor decompositions.- Learning about possible wider implementations of tensor-related methods for applying them on transport datasets like clustering, competition of sparse GPS datasets, etc.

As a junior developer at Audi, I was responsible for developing POCs related to Vehicle-to-everything (V2X) applications. Among several smaller data analysis projects, the two most important came along. The first was related to creating a C# application for managing data from multiple data stores (database, files, etc.) in one management console. The second one was supporting the project related to the simulation of communication protocols during the charging of the electric vehicle using Raspberry PI.

The main goal achieved within this internship was to develop a machine-learning model to detect IoT devices by searching through network traffic. The result was a Python application with the trained model used for identifying IoT devices from .pcap files collected using Wireshark.During this internship I learned about:- Using Wireshark to monitor packages.- Developing Python GUI app.- Presenting weekly team reports.- SCRUM methodology.

- Configuring and maintaining Active Directory environment on Windows Server 2012 r2 operating system. - Writing and testing Powershell scripts.

- Undergraduate assistant on course of C# Algorithms and Programming. - Helping students to understand course and achieve better test score.- Performing preparatory classes for exams.