Personal vid Högskolan
Sepideh Pashami
Universitetslektor
( Akademin för informationsteknologi )
Arbetar med
Sepideh is associate professor in Machine Learing at the Center for Applied Intelligent Systems Research, Halmstad University and a senior researcher at RISE. She received her PhD degree from AASS Research Centre, Örebro University, Sweden, in 2016. Sepideh enrolled as the Technology Area Leader for Aware Intelligent Systems part of the Intelligent Systems and Digital Design department in 2020. Her research interests include predictive maintenance, interactive machine learning, causal inference, and representation learning. She has been involved as a researcher and research leader in several projects (e.g. FREEPORT, EVE, In4Uptime, ARISE, and HEALTH) together with Volvo Group AB, applying machine learning techniques for predictive maintenance of heavy-duty vehicles. She served on the organizing committee of the workshop such as ECAI 2023, SAIS 2022, DSAA 2021, ECML/PKDD 2019,2020, 2022, IDM-WSDM 2019 workshop, industry days at IJCAI-ECAI 2018.
Personlig
Senaste publikationer
Konferensbidrag
Conference paper
Towards Explainable Deep Domain Adaptation
(2024)Mind the Data, Measuring the Performance Gap Between Tree Ensembles and Deep Learning on Tabular Data
(2024)A Review of Randomness Techniques in Deep Neural Networks
(2024)Invariant Feature Selection for Battery State of Health Estimation in Heterogeneous Hybrid Electric Bus Fleets
(2024)Evaluating Multi-task Curriculum Learning for Forecasting Energy Consumption in Electric Heavy-duty Vehicles
(2024)Understanding Survival Models through Counterfactual Explanations
(2024)Improving Concordance Index in Regression-based Survival Analysis : Discovery of Loss Function for Neural Networks
(2024)Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach
(2023)XAI for Predictive Maintenance
(2023)Analysis of Statistical Data Heterogeneity in Federated Fault Identification
(2023)Toward Solving Domain Adaptation with Limited Source Labeled Data
(2023)Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data
(2023)An Explainable Knowledge-based AI Framework for Mobility as a Service
(2022)Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models
(2022)A Fault Detection Framework Based on LSTM Autoencoder : A Case Study for Volvo Bus Data Set
(2022)Towards Geometry-Preserving Domain Adaptation for Fault Identification
(2022)SurvSHAP : A Proxy-Based Algorithm for Explaining Survival Models with SHAP
(2022)Hierarchical Multi-class Classification for Fault Diagnosis
(2021)Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection
(2021)Extracting Invariant Features for Predicting State of Health of Batteries in Hybrid Energy Buses
(2021)Forklift Truck Activity Recognition from CAN Data
(2021)Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters : Using Clustering and Rule-Based Machine Learning
(2020)Baysian Network for Failure Prediction in Different Seasons
(2020)Bayesian network for failure prediction in different seasons
(2020)Modeling turbocharger failures using Markov process for predictive maintenance
(2020)Stacking Ensembles of Heterogenous Classifiers for Fault Detection in Evolving Environments
(2020)Predicting state of health and end of life for batteries in hybrid energy buses
(2020)Decentralized and Adaptive K-Means Clustering for Non-IID Data using HyperLogLog Counters
(2020)IDM-WSDM 2019 : Workshop on Interactive Data Mining
(2019)Eliciting Structure in Data
(2019)Avoiding Improper Treatment of Persons with Dementia by Care Robots
(2019)Interactive feature extraction for diagnostic trouble codes in predictive maintenance : A case study from automotive domain
(2019)Incremental causal discovery and visualization
(2019)Predicting Air Compressor Failures Using Long Short Term Memory Networks
(2019)Causal discovery using clusters from observational data
(2018)
Artikel i tidskrift
Article in journal
Rolling The Dice For Better Deep Learning Performance : A Study Of Randomness Techniques In Deep Neural Networks
(2024) PublishedThe Concordance Index Decomposition : A Measure for a Deeper Understanding of Survival Prediction Models
(2024) PublishedA Knowledge-Based AI Framework for Mobility as a Service
(2023) PublishedFast Genetic Algorithm for feature selection — A qualitative approximation approach
(2023) PublishedMulti-Domain Adaptation for Regression under Conditional Distribution Shift
(2023) PublishedMaterial handling machine activity recognition by context ensemble with gated recurrent units
(2023) PublishedWhy Is Multiclass Classification Hard?
(2022) PublishedSemi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals
(2022) PublishedParallel orthogonal deep neural network
(2021) PublishedAI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control
(2021) PublishedStacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life
(2020) PublishedEarly Prediction of Quality Issues in Automotive Modern Industry
(2020) PublishedWarranty Claim Rate Prediction using Logged Vehicle Data
(2019) PublishedMode tracking using multiple data streams
(2018) Published
Manuskript (preprint)
Manuscript (preprint)
Adversarial Contrastive Semi-Supervised Domain Adaptation
(2022)