Education

• Ph.D. in Computer Science (Sep 2018 - May 2022)
  University of Massachusetts Amherst, GPA: 4/ 4
  Current Research: Deep Learning models for multi-resolution and irregular time-series data
• M.Sc. in Computer Engineering (Sep 2015 - Jan 2018)
  Bilkent University, GPA: 4/ 4
  Thesis title: DeepKinZero: Zero-Shot Learning for Predicting Kinase Phosphorylation Sites
• B.Sc. in Information Technology Engineering (Sep 2010 - Feb 2015)
  BUniversity of Tabriz, GPA: 17.48 /20 (3.67 /4), Last two years GPA:3.95/4
  Thesis title: Algorithmic music composition according to human feelings with Hidden Markov Models

Publications

Peer-reviewed

• Population-level inference for home-range areas
  Christen H Fleming, Iman Deznabi, Shauhin Alavi, Margaret C Crofoot, Ben T Hirsch, E Patricia Medici, Michael J Noonan, Roland Kays, William F Fagan, Daniel Sheldon, Justin M Calabrese
• Predicting in-hospital mortality by combining clinical notes with time-series data
  Iman Deznabi, Mohit Iyyer, Madalina Fiterau
• Impact of the COVID-19 Pandemic on the Academic Community: Results from a survey conducted at University of Massachusetts Amherst
  Iman Deznabi, Tamanna Motahar, Ali Sarvghad, Madalina Fiterau, Narges Mahyar
• DeepKinZero: zero-shot learning for predicting kinase–phosphosite associations involving understudied kinases
  Iman Deznabi, Busra Arabaci, Mehmet Koyutürk, Oznur Tastan
• Personalized Student Stress Prediction with Deep Multitask Network
  Abhinav Shaw, Natcha Simsiri, Iman Deznabi, Madalina Fiterau, Tauhidur Rahaman
• Multi-resolution Attention with Signal Splitting for Multivariate Time Series Classification
  Rheeya Uppaal, Bryon Kucharski, Bhanu Pratap Singh, Iman Deznabi, Madalina Fiterau
• An Inference Attack on Genomic Data Using Kinship, Complex Correlations, and Phenotype Information
  Iman Deznabi, Mohammad Mobayen, Nazanin Jafari, Oznur Tastan, Erman Ayday
• MEMNAR: Finding Mutually Exclusive Mutation Sets through Negative Association Rule Mining
  Iman Deznabi, Ahmet Alparslan Celik, Oznur Tastan

Technical Reports

• Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT) Bhanu Pratap Singh, Iman Deznabi, Bharath Narasimhan, Bryon Kucharski, Rheeya Uppaal, Akhila Josyula, Madalina Fiterau

Work Experience

• Microsoft, Data Science Intern (Jun 2022 - Aug 2022)
  Developed an end-to-end system that forecasts the hourly number of requests on databases and scales them accordingly. This system significantly reduces database costs and reduces the amount of throttled requests.
• Kronos Incorporated, Data Science Intern (Jun 2019 - Aug 2019)
  Improved hierarchical forecasting of real sales by more than 60% using deep learning models.

Selected Projects

SpaceX Rocket Lander

Space exploration is one of the most expensive and sophisticated industries in the world, usually funded by country governments. SpaceX, a private space exploration company, aims to reduce the cost of space exploration by landing rockets on the ground and reuse them instead of abandoning them into the atmosphere. However, achieving this goal was not easy for SpaceX. Their rockets crashed many times before the successful landing of Falcon 9. In this project, we try to improve a reinforcement learning algorithm based on a neural network to land a rocket in a simulated environment. Our aim is to reduce the number of SpaceX landing failures. To achieve this goal, we have studied many algorithms and came up with Proximal Policy Optimization (PPO) algorithm to attack this problem. PPO is an algorithm that learns the policy (i.e. the function that maps a state to action) and the value of a state by using an artificial neural network. In this project, we tried to modify the neural network part to achieve better results, also we tried to modify the PPO algorithm to adopt RNN to consider the previous steps in each episode which is a new idea.
You can see the steps of the training of our best model and how it performs in different stages of training in the following link:
https://youtu.be/UaHEVTesnkk

Environment: Python, Gym
Keywords: Reinforcement Learning, RNN, LSTM, Proximal Policy Optimization (PPO)

Inference attack on human genome

Developed an algorithm in Matlab for the complex and novel problem of inferring missing SNPs in Genome based on belief propagation algorithm. Results of this project is submitted to IEEE/ACM Transactions on Computational Biology and Bioinformatics. (Link to paper)

Environment: Matlab
Project Link: https://github.com/ideznaby/Genomic_Inference
Keywords: Matlab, Belief propagation, Machine Learning, Genomic Privacy

Automatic Music Composer

Developed a program in C# language which composes a music that stimulates the given feeling in humans like happiness, sadness and etc. using selected combination of instruments. The program First finds the music from a database we gathered, according to user selections of music feeling and instruments. Then it uses an edited Hidden Markov Model algorithm to learn the structures of these music and create a new music accordingly.

Environment: C#, SQL server.
Project Link: https://github.com/ideznaby/Automatic-Music-Composer
Keywords: Machine Learning, Hidden Markov Models

Life Experiment

This project is a very simple simulation of world which consists of some creatures and food which distributed randomly. Every creature has its own mind which is a simple neural network and has random number of neurons and it can eat food or attempt to eat other creatures. The creatures learn to go after food and it shows how they interact with each other and their environment.

Environment: C#, BrainNet
Project Link: https://github.com/ideznaby/LifeExperiment
Keywords: Multi-agent Reinforcement Learning, Reinforcement Learning, Neural Networks