Data Scientist | Machine Learning, Deep Learning, AI, Computer Vision, Bioinformatics

Fine tuning pre-trained transformer models in TensorFlow and in PyTorch for question answering

Use of pre-trained transformer models from the Hugging Face library for named entity recognition from resumes

Neural machine translation network with an attention mechanism and its application to date translation

Application of U-Net to semantic image segmentation of road images by pixel-wise classification

Recurrent neural network with GRUs for trigger word detection from an audio clip

Use of word embeddings to classify sentiments of sentences and automatically attach emojis

Use of word embeddings and document similarity to solve word analogy problems

Recurrent neural network for building a character-level language model and its application to generating new dinosaur names

Long short-term memory (LSTM) trained on a corpus of music to generate novel music

Neural style transfer to merge content and style images using pre-trained VGG19 model

Application of pre-trained FaceNet to face verification and face recognition problems face-recognition facenet keras-models triplet-loss face-verification inception-network face-encoding

Application of pre-trained YOLO object detection model to car detection for autonomous driving

Transfer learning with MobileNetV2 trained on ImageNet to classify alpaca vs non-alpaca from an image

Keras Functional API implementation of the 50-layer residual neural network (ResNet-50) and its application to sign language digit recognition

TensorFlow Keras Functional API implementation of a convolutional neural network to recognize sign language digits

TensorFlow Keras Sequential API implementation of a convolutional neural network to classify a face image as happy (smiling) or not

TensorFlow implementation of a deep neural network to recognize sign language digits

NumPy implementation of deep neural networks to classify an image as containing a cat or not

Material used for a course on transcriptomics, covering transcriptome assembly, transcriptome functional annotation, differential expression analysis, and functional annotation data mining

Config files for my GitHub profile.

Material used for a course on handling high-throughput genomic data handling, covering DNA sequence assembly, RNA-seq data analysis, metagenomics, and read alignment

Material used for a bioinformatics computer lab course, covering DNA/RNA sequence assembly and analysis, phylogenetic analysis, gene functional annotation, and metagenomic analysis

Material used for an introductory scripting course for people with biology background, covering Linux, Python, regular expressions, Perl, API access to databases, and R.