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Machine Learning Engineer at Bosch
With a decade of experience in the diverse field of machine learning, I am an experienced professional who had the chance to push the boundaries of AI, working on interesting pre-development projects of self-driving in the automotive sector. As a dedicated engineer with a keen interest in business, I always strive to grasp the big picture in the projects I am involved with. Complementing the technical background, my MBA has equipped me with a deep understanding of business operations, financial analysis, and strategic planning. This dual perspective enables me to bridge the gap between technical teams and business stakeholders, fostering collaboration that drives organizational success. I especially enjoy working on challenging R&D problems in diverse, multinational teams, managing complex projects from concept phase to completion. I strive for excellence in my work and I never skip the chance to learn something new on the way.
Eötvös Loránd University
Master of Business Administration - MBA, Finance and Management
September 1, 2021 – July 1, 2023
Technische Universität Wien
Erasmus student, Machine Learning
January 1, 2016 – January 1, 2017
AIT Aquincum University of Technology
guest student, Biomathematics, Bioinformatics, and Computational Biology
January 1, 2016 – January 1, 2016
Budapest University of Technology and Economics
Master's Degree with Highest Honors, Computer Systems Networking and Telecommunications
January 1, 2015 – January 1, 2017
Budapest University of Technology and Economics
Bachelor's Degree, Electrical and Electronics Engineering
January 1, 2011 – January 1, 2014
Bosch
Senior Expert Machine Learning
January 1, 2026 – Present
CEN and CENELEC
Machine Learning expert
December 1, 2024 – Present
Hybrid
Bosch
Project Leader Artificial Intelligence
May 1, 2024 – Present
Bosch
Lead Expert Machine Learning
May 1, 2021 – December 1, 2025
Bosch
Research Development Product Owner
July 1, 2018 – May 1, 2021
Bosch
AI/ Deep Learning Research Engineer
June 1, 2017 – June 1, 2018
Nokia Networks
Working Student
October 1, 2014 – January 1, 2015
Budapest
BME TMIT - Speechlab
MSc student
September 1, 2014 – February 1, 2017
University Klagenfurt
Research Intern
July 1, 2014 – August 1, 2014
Klagenfurt
Public Lecture - 7th Deep Learning Meetup in Vienna; Title: Convolutional Neural Networks: Applications and a short timeline
December 1, 2016 – Present
Appeared as the main lecturer of 7th Deep Learning Meetup in Vienna. A 30-minute-long presentation was given about convolutional neural networks, with an emphasis on applications. Slides are available here: https://goo.gl/QU3Lvx
Public Lecture - 6th Deep Learning Meetup in Vienna; Title: Deep learning in practice - A text-to-speech scenario
October 1, 2016 – Present
Appeared as the main lecturer of 6th Deep Learning Meetup in Vienna. A 40-minute-long presentation was given about practical deep learning, especially of its importance in speech techology. Slides are available here: http://bit.ly/2dNk5L4
Skin cancer image classification using Deep Convolutional Neural Networks
May 1, 2016 – August 1, 2016
This project aims to build better performing models for various skin cancer detection and classification problems using deep neural networks. The performance improvement of deep convolutional architectures in the recent years has opened new possibilities for building models which may even surpass previous state of the art solutions for skin cancer classification.
Ensemble Deep Neural Network based Waveform-Driven Stress Model for Speech Synthesis
January 1, 2016 – May 1, 2016
The paper of this project was accepted for SPECOM 2016 International conference. I took part in developing the neural networks models, training the networks and evaluating the results. ------ Abstract. Stress annotations in the training corpus of speech synthesis systems are usually obtained by applying language rules to the transcripts. However, the actual stress patterns seen in the waveform are not guaranteed to be canonical, they can deviate from locations defined by language rules. This is driven mostly by speaker dependent factors. Therefore, stress models based on these corpora can be far from perfect. This paper proposes a waveform based stress annotation technique. According to the stress classes, four feedforward deep neural networks (DNNs) were trained to model fundamental frequency (F0) of speech. During synthesis, stress labels are generated from the textual input and an ensemble of the four DNNs predict the F0 trajectories. Objective and subjective evaluation was carried out. The results show that the proposed method surpasses the quality of vanilla DNN-based F0 models.
Approximation of human speech parameters with Deep Neural Networks, Phase II.
September 1, 2015 – January 1, 2016
A DNN-based model capable of modeling the fundamental frequency of speech was implemented. The results are comparable earlier approaches based on Hidden Markov Model, with a possibiliy of further improvement. The results were published in a Scientific Students' Associations (TDK) paper in November, 2015. The project continues with Phase III., which investigates the modeling of other speech parameters (e.g. mel-frequency cepstrum).
MOOC - an overview
September 1, 2015 – December 1, 2015
MSc short project. The goal of the project was to investigate the current state of MOOC platforms available to the wider public. The paper reviews the basic features of current MOOC platforms, also its advantages and drawbacks. The author compares MOOC with ordinary forms of education, and based on this knowledge, predicts the future of this educational systems.
Approximation of human speech parameters using Deep Neural Networks, Phase I.
February 1, 2015 – May 1, 2015
MSc project. The aim of this project was to write a reusable Python software which can create a Deep Neural Network training datasets from data gained from short recordings of human speech. With the datasets, it is possible to approximate certain characteristics of human speech (e.g. fundamental frequency) using the Neural Network.
Enhanced Text-to-Speech features on mobile devices
September 1, 2014 – January 1, 2015
BSc thesis project. The project presents a standalone Android application, which is designed to collaborate with the existing text-to-speech application developed by Budapest University of Technology and Economics SpeechLAB. The application implements an interface to community contribution based remote server, where "special word" database is stored to ease the work of the text-to-speech application.
Comparison of Smart Grid Microgrid simulaton systems
January 1, 2014 – May 1, 2014
BSc project. The goal was to create a report of the features of the most important Microgrid simulators currently available (commerical and non-commercial also.)
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from academic research in speech synthesis to industrial applications in automated driving and involvement in AI policy, demonstrates adaptability and a broad interest in the field. Their progression through various senior roles at Bosch, coupled with external lecturing and standardization work, suggests a strong drive for continuous learning and contribution. The MBA also indicates a business-oriented mindset alongside technical expertise, which can be valuable for strategic roles.
Soft Skills & Operational Fit
The candidate's experience as a lecturer, mentor, and internal consultant at Bosch suggests strong communication, leadership, and collaboration skills. Their involvement in strategic AI initiatives and standardization bodies indicates a proactive and influential approach to their work. The project descriptions, while not explicitly detailing soft skills, imply a capacity for independent research, problem-solving, and presenting complex technical topics.