Grad Student @ TU Darmstadt | Passionate in Deep Learning Research
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TU Darmstadt
Data Scientist
June 22, 2026 – Present
CrossPoint
February 28, 2022 – April 27, 2023
Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)
View ProjectCD_HPE
September 1, 2021 – January 29, 2022
Official implementation of "Towards Accurate Cross-Domain In-Bed Human Pose Estimation" (ICASSP, 2022) https://arxiv.org/abs/2110.03578
View ProjectOmniglot-Dataset-Classification-using-Memory-Augmented-Neural-Networks
May 6, 2020 – May 10, 2020
An example of Few Shot Classification using Memory Augmented Neural Networks (MANN)
View ProjectCOVID-19-Cough-Detection
May 5, 2020 – May 30, 2020
An approach to detect people likely to have COVID - 19 in crowd using the cough sounds.
View ProjectQPSK-Encoding
March 30, 2020 – March 30, 2020
An Implementation of QPSK Encoding using Wolfram Mathematica
View ProjectNumerical-Solution-to-ODE
December 4, 2019 – December 4, 2019
Numerical Solution to Ordinary Differential Equations using Python
View ProjectTwitter-Sentiment-Analysis-Supervised-Learning
September 25, 2019 – September 25, 2019
A Twitter Sentiment Analysis model developed using python and NLTK (NLP Library)
View ProjectRealtime-Sign-Language-Translation-to-Speech-DNN
August 21, 2019 – December 25, 2021
A project carried out for the InnovateFPGA 2019 competition as a regional finalist in the AsiaPacific and Japan region and IntelliHack 2019 competition.
View ProjectCultural Fit Analysis
The candidate demonstrates a strong passion for Data Science and Machine Learning through numerous personal projects, including contributions to academic research. The breadth of technologies and problem domains explored (NLP, Computer Vision, Signal Processing) suggests a curious and versatile individual. The current role as 'Data Scientist' at TU Darmstadt aligns well with the target role. However, the lack of team-based projects or explicit collaboration experiences makes it difficult to fully assess cultural fit in a collaborative industry setting.
Soft Skills & Operational Fit
The candidate's project descriptions are clear and concise, indicating good communication skills. The diversity of projects suggests adaptability and a proactive learning attitude. However, without psychometric test results, it's difficult to assess logical reasoning, work attitude, stress handling, and team collaboration.