Founder - Emberis | AI & ML Researcher | Neuromorphic Computing Expert | Business Strategist | Cybersecurity Enthusiast | Kaggle Expert
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emberis.ai
ML Researcher
June 13, 2026 – Present
CycleGAN-Implementation-for-Image-To-Image-Translation
March 3, 2024 – March 3, 2024
🖼️ Our CycleGAN Implementation for Image-to-Image Translation project leverages PyTorch to seamlessly transform images between domains, all without paired examples. With a keen focus on innovation and effectiveness, we've explored CycleGAN's capabilities across various domains. Join us as we delve into the world of image translation technology! 🚀
View ProjectMulti-Class-Text-Classification-using-BERT-Model
February 1, 2024 – February 1, 2024
Predict consumer financial product categories using BERT, based on over two million customer complaints. This project involves data processing, model building with pre-trained BERT, and making predictions on new text data.
View ProjectLLM---Detect-AI-Generated-Text
November 3, 2023 – November 24, 2025
AI-Generated Text Detection: A BERT-powered solution for accurately identifying AI-generated text. Seamlessly integrated, highly accurate, and user-friendly.🚀
View ProjectFace-Recognition-System-in-Python-using-FaceNet
October 10, 2023 – March 3, 2024
Dive into the world of computer vision! Our Image Classification from Video project uses advanced techniques to identify faces in images and videos. Explore video processing, face extraction, and deep learning magic. Join the adventure now! 👩💻📸"
View ProjectMedical-Image-Segmentation-Deep-Learning-Project
September 23, 2023 – September 24, 2023
Our project uses state-of-the-art deep learning techniques to tackle a vital medical task: polyp segmentation from colonoscopy images. We harness the Unet++ architecture and a robust tech stack to precisely detect and isolate polyps, advancing healthcare diagnostics and patient care. 🏥💡
View ProjectMalware-Detection
September 16, 2023 – September 16, 2023
Welcome to the Malicious Executable Detection project! This repository explores the world of machine learning and clustering analysis to detect malicious executable files 🔥🔐
View ProjectDeep-Learning-for-EEG-Emotion-Classification
September 4, 2023 – September 4, 2023
This repository contains a Python code script for performing emotion classification using EEG (Electroencephalogram) data. Emotion classification from EEG signals is an important application in neuroscience and human-computer interaction. The code leverages deep learning techniques to analyze EEG data and predict emotional states.
View ProjectBrain-Tumor-Detection
August 28, 2023 – August 29, 2023
Brain Tumor Detection using CNN: Achieving 96% Accuracy with TensorFlow: Highlights the main focus of your project, which is brain tumor detection using a Convolutional Neural Network (CNN) implemented in TensorFlow. It also emphasizes the impressive achievement of reaching 96% accuracy, which showcases the effectiveness of your model.
View ProjectLung-Cancer-Detection-App
August 10, 2023 – August 29, 2023
🔍 Discover the future of healthcare with our Lung Cancer Detection Project. Using advanced machine learning techniques, we've achieved 92% accuracy in identifying lung cancer. Join us at the forefront of medical AI. 👩⚕️🌟 #AIHealthcare #LungCancerDetection
View ProjectMachine-learning-Pipeline
May 14, 2022 – August 29, 2023
Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"
View ProjectCultural Fit Analysis
The candidate's portfolio showcases a strong passion for Machine Learning and Deep Learning, with a wide array of personal projects covering different sub-fields. This diversity suggests an inquisitive and continuous learning mindset, which is positive for cultural fit in a research-oriented role. The current role as an ML Researcher further strengthens the alignment. However, the lack of team-based projects or contributions to open-source initiatives makes it difficult to assess collaboration and broader community engagement, which are often important for cultural fit in research environments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual with a strong interest in applying advanced ML techniques to real-world problems. The consistent use of Jupyter Notebooks suggests a preference for iterative development and experimentation. However, without specific psychometric or English test results, it's difficult to assess communication clarity, teamwork, or stress handling abilities. The quantity and quality of project descriptions are good, indicating a decent level of communication in written form.