Adeer Khan

I recently completed my MSc. in Civil and Environmental Engineering at KAIST, South Korea, where I worked at the Smart and Sustainable Environment Design Laboratory (SSEL). My research focused on automated building design and LLM integrated digital twin systems. I hold a BSc. in Civil Engineering (Gold Medalist) from Capital University of Science and Technology (CUST), Islamabad. I aim to advance the future of sustainable, intelligent, and human-centric built environments.

“All things excellent are as difficult as they are rare.” - Spinoza

Research Interests

Application of generative AI for automated design and optimization in the AEC industry
Leveraging large language models (LLMs) for intelligent digital twin management, adaptive control, and human–AI collaboration in architecture.

news

Sep 12, 2025	Paper accepted in IEEE Access.
Aug 31, 2025	Paper accepted in the Q1-ranked journal Automation in Construction.
Aug 30, 2025	Finished MSc. in Civil & Environmental Engineering at KAIST.
Feb 26, 2024	Started MSc. in Civil & Environmental Engineering at KAIST.
Oct 17, 2023	Started working as AI Research Engineer to lead the BIM Startup Craft AEC Gmbh, Germany.

selected publications

2025

Generative AI approaches for architectural design automation

Adeer Khan, Seongju Chang, and Hojong Chang

Automation in Construction, 2025

Abstract HTML

This review examines the potential and challenges of Generative Artificial Intelligence (AI) in automated building design within architectural practice. A comprehensive analysis of advanced generative models is conducted to evaluate their performance across eight architectural criteria. The qualitative assessment indicates that hybrid approaches combining diffusion models with autoregressive techniques provide the most promising outcomes for architectural applications. Despite advancements, significant challenges remain, including scalability limitations, fragmented workflow integration, and the lack of standardized evaluation frameworks. Potential solutions are identified through interdisciplinary collaboration and strategic research directions, such as developing unified evaluation metrics, enhancing model adaptability, integrating energy-optimized design generation for sustainability, and incorporating designer input in AI-driven workflows. This review provides a structured evaluation of current generative design approaches while proposing a roadmap for future research that bridges the gap between AI innovation and practical architectural implementation, ultimately advancing the field toward more efficient, creative, and sustainable building design automation.
Neural Network Coding Layer (NNCL): Enhancing Deep Learning Robustness against Feature Erasure

Chae-Seok Lee, Adeer Khan, Seong-ju Chang, and 1 more author

IEEE Access, 2025

Abstract

This paper proposes a novel Neural Network Coding Layer (NNCL) that applies network coding theory to provide structured redundancy and enable reconstruction of lost features, thereby mitigating information loss problems in deep learning models. Unlike existing skip connections or attention mechanisms, NNCL embeds reconstructible redundancy into intermediate features through a learnable coding process, providing an explicit algebraic restoration mechanism for the original features. This design adapts the core principle of network coding—encoding information recoverably across a channel—for application within the computational graph of a neural network. Extensive experiments on the CIFAR-10 and CIFAR-100 datasets validate NNCL’s effectiveness. Even under a more conventional feature erasure rate of 20%, NNCL consistently improved classification accuracy by up to 8.3 percentage points. The model’s robustness becomes even more pronounced under extreme conditions. In a deliberate stress test involving 60% feature erasure—a scenario where baseline model performance collapses—NNCL dramatically boosted accuracy by up to 40.3 percentage points (e.g., from 19.8% to 60.1% on CIFAR-100). The proposed layer is designed to be modular and has been successfully integrated into various modern architectures, including ResNet and EfficientNet, and Vision Transformer, proving its broad applicability.

2022

Machine learning-based monitoring and modeling for spatio-temporal urban growth of Islamabad

Adeer Khan, and Mehran Sudheer

The Egyptian Journal of Remote Sensing and Space Science, 2022

Abstract HTML PDF

LULC maps are important thematic maps that provide a baseline for monitoring, assessing, and planning activities. This study incorporates spatio-temporal land use/ land cover (LULC) monitoring (1991–2021) and urban growth modeling (2021–2041) of Islamabad, Pakistan to deduce the changes in various LULC classes in the past and the future by incorporating realistic influential thematic layers and Artificial Neural Network-Cellular Automata (ANN-CA) machine learning algorithms. Three decades of Landsat satellite imagery were used to classify LULC maps using a random forest algorithm with high Kappa indexes ranging from 0.93 to 0.97. Simulations for 2011 and 2021 were done for well-calibration of the model with Kappa (>0.85) and spatial similarity (>75%) using the MOLUSCE plugin in QGIS software. Future predictions were done for the years 2031 and 2041 to analyze and study the future urban growth patterns. The satellite-based LULC maps during 1991–2021 exhibited a 142.4 km2 increase in net urban growth. This had detrimental effects on other classes: net decrease of forests by 38.4 km2 and waterbodies by 2.9 km2. The projected increase of urban areas in 2021–2041 will be 58.2 km2. Visual urban sprawl assessment on LULC maps was done to highlight the type of sprawls. Overall, it was sensed that the city’s urbanization has been unplanned and erratic; leading to dire consequences on the environmental and urban systems. Therefore, the study necessitates better monitoring and better planning of urbanization by enforcing policies and necessary measures.