Iman Deznabi

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Coauteurs

Madalina FiterauAssistant Professor, University of Massachusetts, AmherstAdresse e-mail validée de cs.stanford.edu
Oznur TastanAssociate Professor, Sabanci UniversityAdresse e-mail validée de sabanciuniv.edu
Erman AydayCase Western Reserve UniversityAdresse e-mail validée de case.edu
Mehmet KoyutürkComputer and Data Sciences, Case Western Reserve UniversityAdresse e-mail validée de case.edu
Tauhidur RahmanHalıcıoğlu Data Science Institute, University of California San DiegoAdresse e-mail validée de ucsd.edu
Mohit IyyerAssociate Professor, University of Massachusetts AmherstAdresse e-mail validée de cs.umass.edu
Mohammad MobayenjarihaniPhD Candidate, Manning College of Information & Computer Sciences, University of MassachusettsAdresse e-mail validée de cs.umass.edu

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Iman Deznabi

PhD candidate, Manning College of Information & Computer Sciences, University of Massachusetts

Adresse e-mail validée de cs.umass.edu - Page d'accueil

Machine Learning Deep Learning Time Series Analysis Computational Biology Natural Language Processing


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An inference attack on genomic data using kinship, complex correlations, and phenotype information I Deznabi, M Mobayen, N Jafari, O Tastan, E Ayday IEEE/ACM transactions on computational biology and bioinformatics 15 (4 …, 2017	48	2017
DeepKinZero: zero-shot learning for predicting kinase–phosphosite associations involving understudied kinases I Deznabi, B Arabaci, M Koyutürk, O Tastan Bioinformatics 36 (12), 3652-3661, 2020	34	2020
Predicting in-hospital mortality by combining clinical notes with time-series data I Deznabi, M Iyyer, M Fiterau Findings of the association for computational linguistics: ACL-IJCNLP 2021 …, 2021	21	2021
Impact of the COVID-19 Pandemic on the Academic Community Results from a survey conducted at University of Massachusetts Amherst I Deznabi, T Motahar, A Sarvghad, M Fiterau, N Mahyar Digital Government: Research and Practice 2 (2), 1-12, 2021	18	2021
Personalized student stress prediction with deep multitask network A Shaw, N Simsiri, I Deznaby, M Fiterau, T Rahaman arXiv preprint arXiv:1906.11356, 2019	12	2019
Population‐level inference for home‐range areas CH Fleming, I Deznabi, S Alavi, MC Crofoot, BT Hirsch, EP Medici, ... Methods in Ecology and Evolution 13 (5), 1027-1041, 2022	11	2022
Multi-resolution networks for flexible irregular time series modeling (multi-fit) BP Singh, I Deznabi, B Narasimhan, B Kucharski, R Uppaal, A Josyula, ... arXiv preprint arXiv:1905.00125, 2019	6	2019
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction I Deznabi, M Fiterau Conference on Health, Inference, and Learning, 509-525, 2023	1	2023
MEMNAR: Finding mutually exclusive mutation sets through negative association rule mining I Deznabi, AA Celik, O Tastan International Workshop on Machine Learning in Systems Biology, 2017	1	2017
Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data Y Luo, I Deznabi, A Shaw, N Simsiri, T Rahman, M Fiterau Scientific Reports 14 (1), 6631, 2024		2024
Zero-shot Microclimate Prediction with Deep Learning I Deznabi, P Kumar, M Fiterau arXiv preprint arXiv:2401.02665, 2024		2024
Multi-resolution Attention with Signal Splitting for Multivariate Time Series Classification R Uppaal, B Kucharski, BP Singh, I Deznabi, M Fiterau

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