Ristea Nicolae-Catalin
Mentor
(25 reviews)
US$20.00
For every 15 mins
51
Sessions/Jobs
ABOUT ME
Data Scientist@Microsoft and Teaching Assistant@UPB
Data Scientist@Microsoft and Teaching Assistant@UPB
I am a data scientist at Microsoft with a PhD from University Politehnica of Bucharest, specialized in Machine Learning and Signal Processing. I enjoy machine learning projects and I have been working as a data scientist in multiple companies by now. Due to the nature of my past research/work I have experience in several popular languages and technologies for data science, i.e. Python, MATLAB, PyTorch, Tensorflow.
I teach Machine Learning and Signal Processing Theory at University Politehnica of Bucharest for 3 years, where I guided several students for personal projects.
I have over 6 years of experience in coding. I love mathematics since collage, when I won multiple Olympiads and contests. Moreover, I have a pure passion about machine learning and algorithm.
Bucharest (+02:00)
Joined April 2022
EXPERTISE
4 years experience
5 years experience | 8 endorsements
4 years experience
5 years experience | 19 endorsements
I have been using Python heavily in my research as it is becoming the language of science. Particularly, in my research in Deep Learning,...
I have been using Python heavily in my research as it is becoming the language of science. Particularly, in my research in Deep Learning, this language is extremely popular.
3 years experience
REVIEWS FROM CLIENTS
(25 reviews)
Jeremy Boucher
June 2022
Ristea was very knowledgeable and open about the feasibility of the research project that he assisted me with. I would happily recommend him as a freelancer for jobs involving elements of machine learning and AI.
Nancy
June 2022
Great and helpful session
Sarper Arslan
June 2022
Thank you Mr.Cristea Nicolae-Catalin for help , he is professional and kind person.
He tried to understand problem and solved it , he is such a great problem solver
lpp
June 2022
Ristea is amazing! A top data science and machine learning expert, excellent engineer and awesome to work with!! Highly recommended!
Neda
May 2022
Ristea is very professional. He could quickly pick up the issues and provide solutions. I highly recommend him.
erlichbachmann
May 2022
Ristea immediately recognized my problem. He was very patient and gave me the knowledge I was missing.
very experienced in the field of deep learning, lstm, time series
Malv P
May 2022
Ristea is a super big help! Recommend him for ML topics in Python! He makes sure you understand everything during the session. Thank you again Ristea
marriam
May 2022
He was very cooperative, fast, and expert in machine learning, his explanation is very clear
Khalid
May 2022
Extremely knowledgeable in ML, professional and fast in responce. One of my fav mentor. Thank you!
Bryce Ronquille
May 2022
Ristea is very knowledgeable around the areas of deep learning, training models, styleGAN, and Tensorflow. Also a cool guy that is nice to talk to. He helped give me a really good idea of the best path forward for my new venture, and clearly answered any questions I had. Thank you so much! Highly recommended.
SOCIAL PRESENCE
sspcab
Python
24
6
cycle-transformer
Python
8
4
EMPLOYMENTS
Machine Learning Scientist
Microsoft
2021-11-01-Present
I work to develop the latest models for deep echo cancellation for Teams.
I work to develop the latest models for deep echo cancellation for Teams.
Python
C
Machine learning
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Python
C
Machine learning
Mathematics
Deep Learning
PyTorch
AI
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Data Scientist
Veridium
2019-05-01-2021-10-01
I brought contributions to a handling biometric approach based on Machine Learning models. I used handcrafted feature combined with deep ...
I brought contributions to a handling biometric approach based on Machine Learning models. I used handcrafted feature combined with deep features from neural models in order to attain the best possible results.
Python
Git
Machine learning
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Python
Git
Machine learning
Cassandra
Signal Processing
Docker
Kubernetes
Deep Learning
TensorFlow
PyTorch
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Machine Learning R&D
Xperi
2018-06-01-2019-07-01
I developed a CNN model for in-cabin emotion monitoring system for automotive industry. Moreover, I worked with several GAN architectures...
I developed a CNN model for in-cabin emotion monitoring system for automotive industry. Moreover, I worked with several GAN architectures (Cycle-GAN, WGAN, GAN, SimGAN) in order to perform style transfer for expanding automotive data sets.
Python
Git
Linux
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Python
Git
Linux
Image Processing
Machine learning
Deep Learning
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PROJECTS
Complex Neural Networks for Earthquake Source and Magnitude EstimationView Project
2021
In this project, I proposed a novel approach for estimating epicentral distance, depth, and magnitude directly from individual raw 3-comp...
In this project, I proposed a novel approach for estimating epicentral distance, depth, and magnitude directly from individual raw 3-component seismograms of 1-minute length observed by single stations. The proposed convolutional neural network-based method is able to handle complex-valued representations of the seismic data in the time-frequency domain by using dedicated convolutional and activation functions. The validation experiments were conducted over a publicly available and large database, STanford EArthquake Dataset (STEAD). This is part of a research paper published at IEEE Geoscience and Remote Sensing Letters, a top-tier journal in the geoscience domain.
Python
Git
Signal Processing
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Python
Git
Signal Processing
Deep Learning
PyTorch
AI
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Deep Learning Data Set Generator for Automotive Radar InterferenceView Project
University Politehnica of Bucharest
2021
A data set generator for radar interference mitigation. This is a solution to the lack of publicly available data sets. I proposed a solu...
A data set generator for radar interference mitigation. This is a solution to the lack of publicly available data sets. I proposed a solution based on MATLAB and Python, which generates a custom number of data samples, which mimic real radar data. This project could be used to train deep learning models as well as classical algorithms. This is part of two research papers that were published at VTC-Fall 2020 and CVPR Workshop 2021.
Python
Signal Processing
Deep Learning
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Python
Signal Processing
Deep Learning
AI
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