Curriculum Vitae

Extension of smoking detection research focusing on improved object detection in challenging lighting conditions. Developing a YOLO-based architecture with specialized components addressing the unique challenges of low-light environments. GitHub

• Designing custom loss functions specifically optimized for low-light object detection scenarios
• Implementing attention mechanisms to focus on key visual features in varying illumination conditions
• Optimizing upsampling techniques to preserve fine details in dark environments
• Integrating lightweight low-light enhancement module into the detection pipeline

Technologies: PyTorch, YOLO, Computer Vision, CUDA, Attention Mechanisms

Status: Ongoing (April 2025)

Related publications: YOLOv11‑LCDFS: Enhanced Smoking Detection With Low‑light Enhancement (manuscript in preparation)

Developing medical image analysis systems for cancer research. GitHub

• Designing medical image segmentation algorithms for hepatocellular carcinoma and renal cell carcinoma
• Designing methods for integrating clinical tabular data with imaging features for comprehensive analysis
• Implementing multi-modal fusion techniques for combining different CT scan phases
• Developing 3D volumetric segmentation approaches for comprehensive anatomical analysis

Technologies: Python, Deep Learning, 3D Segmentation, Multi-modal Fusion, PyDicom, NumPy

Status: Ongoing (April 2025)

Related publications: Multi‑modal medical image segmentation and fusion (manuscript in preparation)

Developed deep learning models for automated disease detection and classification from medical imaging data, with a focus on diabetic foot ulcer detection. GitHub

• Implemented and compared multiple YOLOv8 architecture variants enhanced with advanced attention mechanisms (GAM, CBAM, ECA, CoordAtt) for precise localization of diabetic wounds in clinical images
• Designed and evaluated custom YOLOv8 architectures integrating novel upsampling techniques including CARAFE and DySample for improved feature map resolution
• Developed innovative triplet-based loss functions and Inner-CIoU mechanisms to enhance detection accuracy for wounds of varying sizes and appearances
• Employed dynamic convolution techniques to adaptively capture wound features across diverse clinical settings and lighting conditions
• Constructed and curated specialized datasets of diabetic foot ulcers for training and comprehensive validation
• Achieved significant improvements in both detection accuracy and inference speed compared to baseline models, with particular gains for small and atypical lesions
• Implemented model explainability techniques to visualize feature importance and attention maps for clinical interpretation

Technologies: PyTorch, YOLO, Attention Mechanisms (GAM/CBAM/ECA), Custom Loss Functions, Feature Upsampling Techniques, Dynamic Convolutions

Date: August 2024

Developed an advanced machine learning system for Twitter content quality assessment and spam detection, achieving over 88% accuracy in distinguishing between legitimate tweets and spam content. GitHub

• Implemented comprehensive data preprocessing techniques for Twitter data, including text normalization, feature extraction, and handling of missing values
• Engineered complex features by combining user metrics (follower count, following count) and behavioral patterns to enhance classification performance
• Applied sentiment analysis and content analysis techniques to identify quality patterns in tweets
• Developed and compared multiple machine learning models to optimize classification accuracy
• Created data visualizations to communicate findings and identify key patterns in Twitter content quality assessment

Technologies: Python, NLTK, scikit-learn, Pandas, Matplotlib, Seaborn

Date: August 2024

Developed a comprehensive data analysis and machine learning system to predict Airbnb listing prices in New York City based on various property features and location data. GitHub

• Performed extensive exploratory data analysis on NYC Airbnb dataset with over 48,000 listings, creating visualizations to reveal pricing patterns across neighborhoods
• Implemented geospatial analysis to visualize property distribution and identify high-value areas using Python GIS libraries
• Engineered relevant features by transforming categorical variables and creating new metrics to better capture pricing factors
• Developed a RandomForest regression model to predict continuous listing prices with optimized hyperparameters
• Created a classification model to categorize listings into price brackets, achieving high accuracy through model tuning with RandomizedSearchCV
• Built interactive visualizations to help hosts and travelers understand pricing determinants in the NYC short-term rental market

Technologies: Python, Pandas, Scikit-learn, GeoPandas, Matplotlib, Seaborn, RandomForest

Date: January 2024

Learning to analyze airline customer sentiment from Twitter data using natural language processing techniques. GitHub

• Studying text preprocessing methods for social media data (handling hashtags, mentions, emojis, URLs)
• Learning about different feature extraction approaches including TF-IDF vectorization and N-gram analysis
• Working with various classification models and comparing their performance
• Practicing error analysis to understand patterns in misclassified tweets
• Creating visualizations using Matplotlib, Seaborn, and Plotly to understand sentiment trends

Technologies: Python, NLTK, spaCy, scikit-learn, Pandas, Matplotlib, Seaborn, Plotly, WordCloud

Date: December 2023

Undergraduate thesis project combining YOLO object detection with MediaPipe skeletal tracking to accurately identify and classify smoking gestures in video streams. GitHub

• Utilized YOLO object detection to identify cigarettes and related objects in video footage
• Implemented MediaPipe for real-time skeletal tracking and pose estimation
• Designed algorithms to recognize characteristic smoking hand-to-mouth gesture patterns

Technologies: YOLO, MediaPipe, Pose Estimation, Action Recognition, PyTorch, OpenCV

Date: April 2023

Outcomes: Patent application (202310277784.1), 3 software copyrights, thesis received outstanding evaluation

Learning to analyze health indicators for heart disease prediction using machine learning approaches. GitHub

• Working with health metrics from the CDC's BRFSS survey dataset
• Learning about feature selection and preprocessing for health indicators
• Studying both traditional machine learning models and deep learning approaches
• Practicing model evaluation using cross-validation and performance metrics
• Creating visualizations to understand relationships between health factors

Technologies: Python, TensorFlow 2.11.0, scikit-learn, Pandas, NumPy, Matplotlib, Seaborn

Date: May 2022