Mr Arijit De | Computational Modeling | Best Researcher Awardย
Senior Research Fellow at ย Jadavpur University ,Kolkata, India
Arijit De is a seasoned Machine Learning Engineer with over 6 years of expertise in the field. ๐ His skills span data science, Python programming, and SQL, adeptly applied in projects using PyTorch, TensorFlow, and OpenCV. Arijit has a strong background in Computer Vision and Natural Language Processing, contributing to end-to-end ML solutions. Currently, he leads deep learning pipeline development at mVizn Pte. Ltd., focusing on semantic segmentation of 3D point clouds. His career includes impactful roles at TCS and as a TCS Research Fellow at Jadavpur University, where he developed innovative ML solutions for healthcare and data quality enhancement projects.
Profile:
๐ Education:
Arijit De has pursued an extensive academic journey culminating in a pending PhD from Jadavpur University, focusing on cutting-edge research in Machine Learning. He holds an M.Tech in Computer Science & Engineering and a B.Tech from Techno India, Kolkata, showcasing his academic prowess with impressive GPAs. Arijit has augmented his academic achievements with certifications such as Deep Learning and TensorFlow from Deeplearning.ai, underscoring his commitment to staying at the forefront of technological advancements in AI. His academic and certification credentials solidify his expertise in applying theoretical knowledge to practical ML solutions, driving innovation in the field.
๐จโ๐ซ Professional Experience
As a Machine Learning Engineer at mVizn Pte. Ltd., Arijit spearheads the development of DL pipelines for semantic segmentation, optimizing data processing and deploying models in web applications. His tenure at TCS Research Fellow focused on Alzheimer’s disease classification and brain tumor detection using advanced ML techniques.
Skills and Technologies
Arijit’s proficiency extends across Python, Java, C++, and SQL, alongside technologies such as PyTorch, TensorFlow, and OpenCV. He leverages cloud platforms like Microsoft Azure and possesses theoretical expertise in Deep Learning, Computer Vision, and NLP.
Research focus:
Citations:
Citations: 42 ๐
Documents: 7 ๐
h-index: 2๐
Publication Top Notes:
- 3-D Segmentation of Large and Complex Conjoined Tree Structures (2024):
- This paper suggests involvement in methods related to segmenting complex structures, possibly in medical imaging or related fields.
- Sleep Apnea sub-type detection from Polysomnography signals (2024):
- This indicates research into detecting different sub-types of sleep apnea using signals obtained from Polysomnography.
- 3D Hippocampus Segmentation Using a Hog Based Loss Function with Majority Pooling (2023):
- Focus on developing techniques for segmenting the hippocampus in 3D using specialized loss functions and pooling methods.
- Brain Tumor Classification from Radiology and Histopathology using Deep Features and Graph Convolutional Network (2022):
- Research on classifying brain tumors using deep learning features and graph convolutional networks, integrating radiological and histopathological data.
- A Deep Graph Cut Model For 3D Brain Tumor Segmentation (2022):
- Development of a deep learning model for segmenting 3D brain tumors using graph cut techniques.
- DTI based Alzheimer’s disease classification with rank modulated fusion of CNNs and random forest (2021):
- Application of diffusion tensor imaging (DTI) for classifying Alzheimer’s disease, using fused CNNs and random forest models.
- Prefrontal haemodynamics based classification of inter-individual working memory difference (2020):
- Research involving classification based on prefrontal haemodynamics, possibly related to cognitive neuroscience or neuroimaging.