I am a Ph.D. candidate in Computer Science at the University of Massachusetts Amherst. Currently, I am working on developing new deep learning methods for multi-resolution and irregular time-series data with missing values. My general interests are in applications of machine learning in Health Care, Computational Biology, and Natural Language Processing.


  • Ph.D. in Computer Science (Sep 2018 - June 2024)
    University of Massachusetts Amherst, GPA: 4/ 4
    Current Research: Deep Learning models for heterogeneous time-series data
  • M.Sc. in Computer Science (Sep 2018 - Dec 2021)
    University of Massachusetts Amherst, GPA: 4/ 4
  • 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)
    University 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

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.


  • Zero-shot micro-climate prediction with deep learning.
    Iman Deznabi, Peeyush Kumar, Madalina Fiterau
    Tackling Climate Change with Machine Learning workshop NeurIPS (2023)
  • MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Timeseries Forecasting and Prediction.
    Iman Deznabi, Madalina Fiterau
    Conference on Health, Inference, and Learning (CHIL 2023)
  • 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
    Methods in Ecology and Evolution journal (2022)
  • Predicting in-hospital mortality by combining clinical notes with time-series data
    Iman Deznabi, Mohit Iyyer, Madalina Fiterau
    Association for Computational Linguistics (ACL-IJCNLP 2021) Findings
  • 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
    ACM (2020), Digital Government: Research and Practive, COVID-19 Commentary
  • DeepKinZero: zero-shot learning for predicting kinase–phosphosite associations involving understudied kinases
    Iman Deznabi, Busra Arabaci, Mehmet Koyutürk, Oznur Tastan
    Bioinformatics Journal (2020) Also presented at the ICML 2020 Workshop on Computational Biology
  • Personalized Student Stress Prediction with Deep Multitask Network
    Abhinav Shaw, Natcha Simsiri, Iman Deznabi, Madalina Fiterau, Tauhidur Rahaman
    ICML 2019, Adaptive and Multitask Learning Workshop
  • Multi-resolution Attention with Signal Splitting for Multivariate Time Series Classification
    Rheeya Uppaal, Bryon Kucharski, Bhanu Pratap Singh, Iman Deznabi, Madalina Fiterau
    ICML 2019, Time-Series Workshop
  • An Inference Attack on Genomic Data Using Kinship, Complex Correlations, and Phenotype Information
    Iman Deznabi, Mohammad Mobayen, Nazanin Jafari, Oznur Tastan, Erman Ayday
    IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)
  • MEMNAR: Finding Mutually Exclusive Mutation Sets through Negative Association Rule Mining
    Iman Deznabi, Ahmet Alparslan Celik, Oznur Tastan
    ISMB/ECCB Workshop on Machine Learning in Systems Biology (2017)
Under Review
  • Dynamic Clustering via Branched Deep Learning Enhances Personalization of Stress Prediction from Mobile Sensor Data
    Iman Deznabi, Yunfei Lou, Abhinav Shaw, Natcha Simsiri, Tauhidur Rahman, Madalina Fiterau
    Under Review at Scientific Reports (2023)
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

Selected Projects

SpaceX Rocket Lander
  • Environment: Python, Gym
  • Keywords: Reinforcement Learning, RNN, LSTM, Proximal Policy Optimization (PPO)
Inference attack on human genome
  • Environment: Matlab
  • Keywords: Matlab, Belief propagation, Machine Learning, Genomic Privacy
Automatic Music Composer
  • Environment: C#, SQL server.
  • Keywords: Machine Learning, Hidden Markov Models
Life Experiment
  • Environment: C#, BrainNet
  • Keywords: Multi-agent Reinforcement Learning, Reinforcement Learning, Neural Networks
Iman Deznabi

iman at cs.umass.edu

Computer Science Department
University of Massachusetts Amherst
Amherst, MA 01003, US

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