Sinan Kalkan

E-mail

skalkan@metu.edu.tr

Department

Department of Computer Engineering

ORCID

0000-0003-0915-5917

Scopus Author ID

23027462100

Web of Science Researcher ID

A-4843-2016

Machine learning based prediction of long-term energy consumption and overheating under climate change impacts using urban building energy modeling Canlı Akyol, Ilkim; Halaçlı, Eren Gökberk; Uçar, Şevval; Işeri, Orçun Koral; Yavuz, Feyza; Güney, Dilara; Gürsoy, Fatma Ece; Duran, Ayça; Akgül, ÇAĞLA; Kalkan, Sinan; Gürsel Dino, Ipek (2025-07-01)
A method for zone-level urban building energy modeling in data-scarce built environments Koral Iseri, Orcun; Duran, Ayca; Canlı, Ilkim; Akgül, Çağla; Kalkan, Sinan; Gürsel Dino, İpek (2025-06-15) Urban Building Energy Modeling (UBEM) is critical for improving the resilience of cities to climate change, but most regions lack of data sets necessary for its development. A bottom-up approach is a viable method to initi...
Urban-Scale Prediction of Heat Pump Energy Demand with Low Temporal Resolution Data Gürsoy, Fatma Ece; Canlı, İlkim; Demir, Ataberk Yasin; Kalkan, Sinan; Taylan, Onur; Gürsel Dino, İpek (2025-06-06)
Programming with Python for Engineers Kalkan, Sinan; Şehitoğlu, Onur Tolga; Üçoluk, Göktürk (Springer Nature, 2024-08-01)
XoFTR: Cross-modal Feature Matching Transformer Tuzcuoğlu, Önder; Köksal, Aybora; Sofu, Buğra; Kalkan, Sinan; Alatan, Abdullah Aydın (2024-06-16) We introduce XoFTR a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images TIR images are less susceptible to adverse lighting and weather conditi...
Uncertainty as a Fairness Measure Kuzucu, Selim; Cheong, Jiaee; Gunes, Hatice; Kalkan, Sinan (2024-01-01) Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been a...
FairReFuse: Referee-Guided Fusion for Multimodal Causal Fairness in Depression Detection Cheong, Jiaee; Kalkan, Sinan; Gunes, Hatice (2024-01-01) Machine learning (ML) bias in mental health detection and analysis is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multimodal methods work better than unimodal methods, there is m...
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection Cheong, Jiaee; Bangar, Aditya; Kalkan, Sinan; Gunes, Hatice (2024-01-01) Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multi-task approaches often work better than unitask approaches, there is minimal work inves...
BASENET: A Learning-based Mobile Manipulator Base Pose Sequence Planning for Pickup Tasks Naik, Lakshadeep; Kalkan, Sinan; Sorensen, Sune L.; Kjaergaard, Mikkel B.; Kruger, Norbert (2024-01-01) In many applications, a mobile manipulator robot is required to grasp a set of objects distributed in space. This may not be feasible from a single base pose and the robot must plan the sequence of base poses for grasping ...
An explainable two-stage machine learning approach for precipitation forecast Senocak, Ali Ulvi Galip; Yılmaz, Mustafa Tuğrul; Kalkan, Sinan; Yücel, İsmail; Amjad, Muhammad (2023-12-01) A common post-processing approach to improve precipitation forecasts is to use machine learning models such as artificial neural networks (more specifically, multi-layer perceptrons) as black-box systems. These models util...

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