By Associate Professor Gelareh Mohammadi

Preprints

Zhou Y; Yuan M; Xu H; Chen J; Wen D; Pan S; Xu X; Zhang W; Quigley A; Xing Z; Mohammadi G, 2026, LEGOUI: Designing with UI-DSL Bricks to Balance Transparency and Controllability, http://dx.doi.org/10.48550/arxiv.2608.04293

Yuan M; Chen J; Hu Y; Feng S; Xie M; Mohammadi G; Xing Z; Quigley A, 2025, Towards Human-AI Synergy in UI Design: Supporting Iterative Generation with LLMs, http://dx.doi.org/10.48550/arxiv.2412.20071

de Belen RAJ; Mohammadi G; Sowmya A, 2025, GazeDETR: Gaze Detection using Disentangled Head and Gaze Representations, http://dx.doi.org/10.48550/arxiv.2508.12966

Yuan M; Chen J; Xing Z; Quigley A; Luo Y; Luo T; Mohammadi G; Lu Q; Zhu L, 2024, DesignRepair: Dual-Stream Design Guideline-Aware Frontend Repair with Large Language Models, http://dx.doi.org/10.48550/arxiv.2411.01606

Hu Y; Zhang D; Yuan M; Xian K; Elvitigala DS; Kim J; Mohammadi G; Xing Z; Xu X; Quigley A, 2024, Investigating the Design Considerations for Integrating Text-to-Image Generative AI within Augmented Reality Environments, http://dx.doi.org/10.48550/arxiv.2303.16593

Somarathna R; Mohammadi G, 2024, Exploring Emotions in Multi-componential Space using Interactive VR Games, http://dx.doi.org/10.48550/arxiv.2404.03239

Morgenroth E; Somarathna R; Van De Ville D; Mohammadi G; Vuilleumier P, 2024, Emotion is a multi-componential experience guided by appraisal: evidence from multi-level annotation during naturalistic stimulation

Darejeh A; Marcusa N; Mohammadi G; Sweller J, 2024, A critical analysis of cognitive load measurement methods for evaluating the usability of different types of interfaces: guidelines and framework for Human-Computer Interaction, http://dx.doi.org/10.48550/arxiv.2402.11820

Liu CY; Mohammadi G; Song Y; Johal W, 2023, Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents, http://dx.doi.org/10.48550/arxiv.2309.09346

Alzubaidi F; Mostaghimi P; Niu Y; Armstrong RT; Mohammadi G; Berg S; McClure JE, 2023, Effective permeability of an immiscible fluid in porous media determined from its geometric state, http://dx.doi.org/10.48550/arxiv.2208.08027

Somarathna R; Vuilleumier P; Mohammadi G, 2023, EmoStim: A Database of Emotional Film Clips with Discrete and Componential Assessment, http://dx.doi.org/10.48550/arxiv.2306.08196

Mohammadi G; Van De Ville D; Vuilleumier P, 2023, Brain networks subserving functional core processes of emotions identified with componential modelling, http://dx.doi.org/10.1101/2020.06.10.145201

Hu X; Aldausari N; Mohammadi G, 2022, 2CET-GAN: Pixel-Level GAN Model for Human Facial Expression Transfer, http://dx.doi.org/10.48550/arxiv.2211.11570

Spooner A; Mohammadi G; Sachdev PS; Brodaty H; Sowmya A, 2022, Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease, http://dx.doi.org/10.48550/arxiv.2209.04793

Spooner A; Mohammadi G; Sachdev PS; Brodaty H; Sowmya A, 2022, Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers, http://dx.doi.org/10.48550/arxiv.2207.02380

Spooner A; Mohammadi G; Sachdev PS; Brodaty H; Sowmya A, 2022, Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery, http://dx.doi.org/10.48550/arxiv.2207.01822

Somarathna R; Vuilleumier P; Bednarz T; Mohammadi G, 2022, A Machine Learning Model for Analyzing the Multivariate Patterns of Emotions in Multi-Componential Framework with Personalization, http://dx.doi.org/10.2139/ssrn.4075454

Somarathna R; Bednarz T; Mohammadi G, 2021, Virtual Reality for Emotion Elicitation -- A Review, http://dx.doi.org/10.48550/arxiv.2111.04461

Aldausari N; Sowmya A; Marcus N; Mohammadi G, 2020, Video Generative Adversarial Networks: A Review

Mohammadi G; Vuilleumier P, 2020, A Multi-Componential Approach to Emotion Recognition and the Effect of Personality, http://dx.doi.org/10.48550/arxiv.2010.11370

Izhraqi MA; Mian M; Marcus N; Mohammadi G, Exploring Gender Differences in Cognitive Load and Stress Responses: A Machine Learning Approach using Physiological Data in Learning Environments, http://dx.doi.org/10.21203/rs.3.rs-6888326/v1

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