● Researched on paper about ELM (Extreme Learning Machine). ● Researched how to inject ELM in different models architecture such as CNN or Transformers (Encoder and Decoder both) to see the speed improvement.

● Developed a model that determines the binding parameter of epitope-paratope pair connection using various neural networks structures such as CNN, LSTM, RNN, GRU. ● Used BERT transformer to encode the data of epitope and paratope acid sequences and hereby the model accuracy increased by 15%. ● Suggested to use SMOTE technique to make balance in the target data and therefore F-1 score of our model increased by 6%.

● Created a CAT system to improve the student testing system. ● Learned new knowledge through reading multiple articles and gained math knowledge through understanding the math behind the model such as: Bayes Theorem, Point bi-serial coefficient, Estimation Maximization algorithm and Maximum Likelihood. ● Created a simple CAT model based on dataset taken from Kaggle and trained it using the data mined from results of tests taken by thousands students. ● Was engaged in algorithm developing of a model and used the predictive model to predicts the future scores of students.