Took part in the design and implementation of several modules of a nationwide Hospital Information Management System (HIMS) deployed in over 200 hospitals across Turkey. Engineered scalable, n-tier software modules for Surgery Management, Clinical Operations, Inventory and Accounting using Object-Oriented design principles, supporting thousands of concurrent users. (2008-2010)
Engineered a third-party financial analysis plugin tailored for the Metastock trading platform using a hybrid C++ (DLL) and C#.Net architecture. Beyond the core analytical engine, designed and deployed a complete commercial infrastructure, including a secure licensing system, integration with a virtual POS payment gateway, and automated installation packages. Also delivered comprehensive user support modules and documentation to ensure seamless onboarding. (2013)
Architected and developed a comprehensive smart home ecosystem for Gesislab Elektronik, supported by the TUBITAK 1501 program. Designed a centralized, web-based command hub comparable to industry leaders like Google Home, enabling seamless wireless device orchestration via the Zigbee protocol. Managed a heterogeneous technology stack, integrating low-level firmware (C, C++) with dynamic backend services (Python, C#.Net) and a hybrid database layer (PostgreSQL, NoSQL). (2015-2018)
Addressed the scarcity of annotated data in low-resource languages by developing an automated framework to generate Turkish sentiment lexicons from English resources. Implemented a novel triple unification process to map sense-level polarities to word-level scores and employed a multi-algorithm bilingual translation strategy to resolve semantic ambiguity. Supported by the TUBITAK 1001 research grant. (2016-2017)
Developed a specialized n-Gram extraction and text mining engine for the Hacettepe University Department of Linguistics. The tool utilized Python (NLTK, Zemberek) to process complex Turkish morphological structures, enabling advanced academic research on large text corpora. (2017)
Engineered a mission-critical laser marking automation solution for the Ford Sakarya Factory by reverse-engineering proprietary production protocols to synchronize real-time vehicle data with the manufacturing process. Built on a hybrid C++ and C#.Net architecture, the system orchestrates the precise operation of the laser unit and peripheral electromechanical components, delivering a high-stability solution that has operated continuously on the production line for years with zero downtime. (2017)
Designed and deployed a mission-critical media monitoring platform for the Turkish General Staff to automate the digitization and analysis of daily print media. Leveraging the Abbyy FineReader API for advanced OCR processing and C#.Net for a robust backend architecture, the system automatically detected and highlighted sensitive keywords within massive text data, streamlining the workflow for operators to compile daily intelligence briefings. (2017-2021)
Developed a high-security monitoring platform for the Central Bank of Turkey’s Banknote Printing Plant to ensure end-to-end traceability of machinery, personnel, and sensitive raw materials. The system optimized production efficiency through real-time statistical analysis while safeguarding operations through an automated anomaly-detection and alert mechanism. (2017-2021)
Executed one of the first studies to fine-tune pre-trained Large Language Models specifically for Turkish emotion analysis using curated, domain-specific datasets. By leveraging novel transfer learning techniques, the research achieved state-of-the-art accuracy scores, surpassing traditional baselines. This work resulted in 3 published papers and established a new benchmark methodology for Turkish computational linguistics. (2017-2020)
Engineered an interactive educational robotics platform designed to democratize AI and programming concepts for beginners, supported by the TUBITAK 1501 R&D program. The system features a versatile dual-mode coding environment (Scratch and standard languages) integrated with hardware sensors (camera, microphone) for multimodal human-robot interaction, plus a modular AI interface for sentiment analysis, object tracking, and image recognition. (2020-2021)
Spearheaded the development of an automated question generation system for OSYM (Student Selection and Placement Center). The project utilized a sophisticated NLP pipeline, fine-tuning BERT and T5 architectures for context-aware question generation and paraphrasing, employing semantic word embeddings to engineer plausible multiple-choice distractors, and was delivered as a scalable Machine-Learning-as-a-Service API (PyTorch, Transformers, Django, PostgreSQL). (2021-2022)
Contributing to a major EU-funded initiative (Horizon Europe) designed to address data scarcity and privacy bottlenecks in medical AI. The project is developing a trustworthy platform that generates high-fidelity synthetic medical datasets via controlled data synthesis, integrating novel anonymization pipelines and attribute-based privacy measures to provide a compliant, scalable data ecosystem for healthcare data engineers and practitioners. (2023-Present)
Directed the R&D and AI strategy for an indigenous robotic system designed to automate high-risk chemotherapy drug preparation, mitigating critical safety risks including contamination and foaming. The system ensures end-to-end medication traceability by integrating directly with Turkey’s national health ecosystems (ITS and e-Prescription). Supported by the TUBITAK 1501 program. (2023)
Spearheaded the Machine Learning and Analytics architecture for a major R&D initiative to transform legacy national-scale health solutions (EHR, Drug Tracking, e-Prescription) into a globally scalable, cloud-native ecosystem. Engineered a resilient microservices infrastructure orchestrated via Kubernetes and Apache Kafka, integrating a centralized Data Warehouse for unified analytics. Supported by the TUBITAK 1501 program. (2023-2024)
Directed the R&D and AI strategy for an advanced predictive analytics platform designed to safeguard the pharmaceutical supply chain. The system leverages Time-Series Foundation Models (Amazon Chronos) for high-precision forecasting and unsupervised learning (Local Outlier Factor) to detect stock anomalies in real-time, integrated into GIS-enabled dashboards for health authorities. Supported by the TUBITAK 1501 program. (2023-2024)
Spearheaded the R&D and AI strategy for Mobithera, Turkey’s first MDR-certified Digital Therapeutic (DTx) for remote physiotherapy. Engineered highly optimized, proprietary Edge-AI Human Pose Estimation models running locally on mobile devices for real-time, privacy-centric (HIPAA/GDPR) biofeedback. The platform leverages Unity-based gamification to drive patient adherence and has achieved FDA Approval, EU MDR Certification, and NHS ORCHA validation. Supported by the TUBITAK 1501 program. (2023-Present)
Directed the R&D strategy for a comprehensive automated anonymization solution designed to secure sensitive data across high-regulation sectors (Health, Banking, Public). Utilizing a multi-modal AI pipeline (advanced NLP and image processing), the platform automatically classifies and de-identifies PII within unstructured text, tabular data, and images, balancing GDPR/KVKK compliance with data utility. Supported by the TUBITAK 1507 program. (2023-2025)
Led the development of a clinical decision support system designed to optimize post-operative follow-up for Descemet Membrane Endothelial Keratoplasty (DMEK). Deployed advanced deep learning architectures (Operational Neural Networks and CNNs) to analyze Anterior Segment OCT imagery, automatically assessing graft health and triaging patients for advanced confocal microscopy. Supported by the TUBITAK 1507 program. (2024-2025)
Directed the R&D and AI strategy for Fallower, a non-intrusive safety monitoring system utilizing Ultra-Wideband (UWB) radar technology. Engineered a privacy-preserving alternative to camera-based or wearable solutions, employing machine learning algorithms to classify human movement patterns and detect fall anomalies with high precision, triggering instant alerts to caregivers. Supported by the TUBITAK 1501 program. (2024-Present)
Spearheaded the R&D strategy to transform legacy, passive Personal Health Records (PHR) into a proactive, AI-driven health management ecosystem. The platform leverages Graph Machine Learning to model patient-disease relationships and family history for chronic disease risk stratification, and Reinforcement Learning to optimize the timing and content of personalized health interventions. Supported by the TUBITAK 1501 program. (2024-Present)
Directed the R&D strategy for a deep-learning-based radiotherapy planning system designed to automate precise segmentation of Target Tumor Volumes and Organs-at-Risk. Leveraging multi-modal imaging (CT/MRI), the platform optimizes radiation dose distribution to maximize therapeutic efficacy while minimizing toxicity, reducing manual contouring workload and inter-observer variability. Supported by the TUBITAK 1501 program. (2024-Present)
Directed the R&D strategy for a Generative AI platform designed to revolutionize the pre- and post-examination patient journey. The system utilizes Large Language Models to analyze natural language patient complaints, performing automated clinical triage to route patients to the appropriate specialty and assess eligibility for telemedicine, optimizing scheduling and follow-ups. Supported by the TUBITAK 1501 program. (2024-Present)
Contributing to a Horizon Europe (CL4) initiative designed to revolutionize the Software Development Lifecycle through Agentic AI. The project introduces an adaptive LLM-based multi-agent framework that facilitates collaboration between human developers and autonomous AI agents, assigning distinct, role-based personas to AI to advance responsible, human-centric software engineering while maintaining ethical oversight and code quality. (2025-Present)
Directing the R&D strategy for a sensor-less, mobile tele-rehabilitation platform designed to treat cervical musculoskeletal disorders caused by sedentary lifestyles. The solution leverages real-time computer vision (MediaPipe) to analyze patient movements via standard smartphone cameras, and integrates Promptable Game Models to dynamically generate personalized gamification content, boosting patient adherence. Supported by the TUBITAK 1501 program. (2025-Present)
NanoLoom aims to revolutionize drug discovery and molecular diagnostics by developing an imaging-based microfluidic platform that analyzes DNA-protein interactions at the single-molecule level. It combines nanoimprint-based microfluidic chips for DNA stretching and imaging, TIRF microscopy for visualizing molecular dynamics, and AI-powered software tools for quantitative analysis of protein binding and drug effects, providing a high-throughput, cost-efficient alternative to existing biochemical assays. Funded by the Eurostars-3 programme by Horizon Europe. (2025-Present)
Published in 8th European Conference on Data Mining, 2014
Recommended citation: Akba, F., Uçan, A., Sezer, E. A., & Sever, H. (2014, July). Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. In 8th European Conference on Data Mining (Vol. 191, pp. 180-184). https://www.doi.org/10.13140/2.1.1205.3286
Published in MSc Thesis, 2014
MSc Thesis
Recommended citation: Ucan, A. (2014). Automatic sentiment dictionary translation and using in sentiment analysis. MSc, Hacettepe University, Ankara, Turkey. http://hdl.handle.net/11655/2626
Published in International Conference on Text, Speech, and Dialogue, 2015
Recommended citation: Naderalvojoud, B., Sezer, E. A., & Ucan, A. (2015, September). Imbalanced text categorization based on positive and negative term weighting approach. In International Conference on Text, Speech, and Dialogue (pp. 325-333). Springer, Cham. https://www.doi.org/10.1007/978-3-319-24033-6_37
Published in 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016
Recommended citation: Ucan, A., Naderalvojoud, B., Sezer, E. A., & Sever, H. (2016, January). SentiWordNet for new language: automatic translation approach. In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 308-315). IEEE. https://www.doi.org/10.1109/SITIS.2016.57
Published in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2018
Recommended citation: Naderalvojoud, B., Ucan, A., & Sezer, E. A. (2018, October). HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 182-188). https://www.doi.org/10.18653/v1/W18-6225
Published in 27th Signal Processing and Communications Applications Conference (SIU), 2019
Recommended citation: Uçan, A., & Sezer, E. A. (2019, April). A New Approach on Emotion Analogy by Using Word Embeddings. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. https://www.doi.org/10.1109/SIU.2019.8806475
Published in Türk Dili Araştırmaları Yıllığı - Belleten, 2020
Recommended citation: UÇAN, A., SEZER, E. A.,(2020) Türkçe bilgisayarlı dil bilimi çalışmalarında his analizi. Türk Dili Araştırmaları Yıllığı-Belleten, Sayı 70, ss 193-210 https://doi.org/10.32925/tday.2020.48
Published in PhD Thesis, 2020
Recommended citation: Ucan, A. (2020). Use of Optimization and Pretrained Models in Turkish Emotion Analysis. PhD, Hacettepe University, Ankara, Turkey. http://hdl.handle.net/11655/23185
Published in Journal of Information Science, 2021
Recommended citation: Uçan A., Dörterler M., Sezer E. A., (2021) A Study of Turkish Emotion Classification with Pretrained Language Models, Journal of Information Science https://www.doi.org/10.1177/0165551520985507
Published in Concurrency and Computation Practice and Experience, 2021
Recommended citation: Uçan A., Dörterler M., Sezer E. A., (2021) An Emotion Analysis Scheme Based on Gray Wolf Optimization and Deep Learning, Concurrency and Computation Practice and Experience https://doi.org/10.1002/cpe.6204
Published in Change and Adaptation (book), Holistence Publications, 2022
Recommended citation: Uçan A., Çankal A., (2022) Determining The Factors Affecting the Monthly Unemployment Rate Forecasting: The Case of Turkey, Change and Adaptation, Holistence Publications, 201-210
Published in Archives of Computational Methods in Engineering, 2023
Recommended citation: Gharehchopogh F. S., Uçan A., Ibrikci T., Arasteh B., Isik G., (2023) Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications, Archives of Computational Methods in Engineering, 30(4), 2683-2723 https://doi.org/10.1007/s11831-023-09883-3
Published in Assistive Technology, 2024
Recommended citation: Değerli M. N. Ö., Şahin S., Altuntaş O., Uyanık M., Yılmaz A. A., Yiğit A. Y., Uçan A., Yapar İ., (2024) The effect of CLOSER-computer-based exercise program in older adults with a history of falls: a pilot study, Assistive Technology, 36(4), 302-308 https://doi.org/10.1080/10400435.2024.2315412
Published in 15. Türkiye Tıp Bilişimi Kongresi (15th Turkish Congress of Medical Informatics), 2024
Recommended citation: Yalıç H. Y., Usta A. E., Atıla Ü., Uçan A., Yılmaz A. A., Yiğit A. Y., (2024) TheraPose: A Large Video Dataset for Physiotherapy Exercises, 15. Türkiye Tıp Bilişimi Kongresi, 88-97
Published in Cornea, 2024
Recommended citation: Karaca E. E., Bulut Ustael A., Keçeli A. S., Kaya A., Uçan A., Kemer Ö. E., (2024) Predicting Success in Descemet Membrane Endothelial Keratoplasty Surgery Using Machine Learning, Cornea https://pubmed.ncbi.nlm.nih.gov/38913970/
Published in 4th International Congress on Artificial Intelligence in Health, 2024
Recommended citation: Yaşar E., Yalıç H. Y., Datlar B., Uçan A., Yiğit A. Y., Yılmaz A. A., (2024) Innovative Remote Neck Pain Relief using MOBITHERA: Leveraging AI-based Face Mesh on Mobile Devices, 4th International Congress on Artificial Intelligence in Health
Published in International Ophthalmology, 2025
Recommended citation: Ersarı B., Kola M. G., Karaca E. E., Işık F. D., Kemer Ö. E., Keçeli A. S., Kaya A., Gürgen Erdoğan T., Uçan A., (2025) Denoising diffusion-based anterior segment optical coherence tomography (AS-OCT) image generation, International Ophthalmology, 45, 431 https://doi.org/10.1007/s10792-025-03821-x
Published in 2025 Medical Technologies Congress (TIPTEKNO), 2025
Recommended citation: Yalıç H. Y., Dörterler M., Uçan A., Yiğit A. Y., Yılmaz A. A., (2025) Autononym: Multimodal Anonymization of Health Data using Named Entity Recognition and Structured Medical Data Processing, 2025 Medical Technologies Congress (TIPTEKNO) https://doi.org/10.1109/TIPTEKNO68206.2025.11270116
Published in 2025 Medical Technologies Congress (TIPTEKNO), 2025
Recommended citation: Yalıç H. Y., Uçan A., Yiğit A. Y., Yılmaz A. A., (2025) Real-time 3D Human Pose Estimation Model for Physiotherapy Exercises on Mobile Devices, 2025 Medical Technologies Congress (TIPTEKNO) https://doi.org/10.1109/TIPTEKNO68206.2025.11270124
Published in Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 2025
Recommended citation: Ela M., Çankal A., Uçan A., Dörterler M., (2025) A Universal Model for Debt Transparency Based on The Forecast of The Ratio of Debt to GDP, Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(4), 1239-1268 https://doi.org/10.30798/makuiibf.1490441
Published in International Journal of Imaging Systems and Technology, 2026
Recommended citation: Karacan L., Yalıç H. Y., Uçan A., Yiğit A. Y., Yılmaz A. A., (2026) Adapting 2D Vision Transformer Backbones for 3D Thoracic Multi-Organ Segmentation, International Journal of Imaging Systems and Technology, 36(2), e70318 https://doi.org/10.1002/ima.70318
Published in Informatics for Health and Social Care, 2026
Recommended citation: Şahin S., Temizkan E., Baysal Yiğit A., Arslan B. Ç., Uçan A., Yapar İ., Yiğit A. Y., Yılmaz A. A., Aki E., (2026) The usability and effectiveness of the Mobithera application on musculoskeletal pain and physical function in adult caregivers of oncology patients: a single-group pilot study, Informatics for Health and Social Care, 1-10 https://doi.org/10.1080/17538157.2025.2611120
Published in Signal, Image and Video Processing, 2026
Recommended citation: Sabaz F., Atıla Ü., Dörterler M., Uçan A., (2026) Challenges and enhancements in Turkish automatic lip reading using deep learning models, Signal, Image and Video Processing, 20(4), 237 https://doi.org/10.1007/s11760-026-05252-2
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Undergraduate course, Korkut Ata University, Management Information Systems, 2021
Temel bilgi teknolojileri dersi bilişim teknolojilerine güncel bir bakış açısı kazandırmayı amaçlar. Ders içerisinde öğrenciler sunum yapmayı ve ofis programları kullanmayı öğrenir. Hazır araçlar ile kişisel web sitesi yapma ve yayınlama deneyimi elde eder. Ders sayesinde dijital araçlarla kendi kendine öğrenebilme farkındalığı oluşur.
Undergraduate course, Korkut Ata University, Management Information Systems, 2021
Dersin amacı PYTHON dilinde web tabanlı programlama ve tasarım yapmaktır. Dersi alanlar web sayfasının bileşenleri, PYTHON dilinin yapısı, basit veritabanı tasarımı hakkında bilgi sahibi olur. Ders kapsamında web arayüzünden veri ekleme, düzenleme, silme uygulamaları yapılacaktır.
Undergraduate course, Korkut Ata University, Management Information Systems, 2021
Dersin amacı java dilinde nesneye yönelik programlama yapmaktır. Dersi alanlar java bileşenleri, sınıf ve nesne yapısı hakkında bilgi sahibi olur.
Undergraduate course, Korkut Ata University, Computer Engineering Dept., 2022
Bu dersin amacı, öğrencilere bilgisayar grafiğinin teorisi ve uygulaması konularında giriş düzeyinde bilgi ve beceri kazandırmaktır. Bilgisayar grafiğinin temel konuları, matematik ilkeleri, algoritmalar ve veri yapılarını kapsar.
Undergraduate course, Korkut Ata University, Management Information Systems, 2022
Dersin amacı PYTHON dilinde web tabanlı programlama ve tasarım yapmaktır. Dersi alanlar web sayfasının bileşenleri, PYTHON dilinin yapısı hakkında bilgi sahibi olur. Ders kapsamında web arayüzünden veri ekleme, düzenleme, silme uygulamaları yapılacaktır.
Undergraduate course, Korkut Ata University, Management Information Systems, 2022
Dersin amacı Yapay Zeka ve Makine Öğrenmesi konularında uygulamalı eğitim yapmaktır. Dersi alanlar Numpy, Pandas, Scikit Learn, Matplotlib gibi python kütüphaneleri kullanmayı öğrenir. Bu esnada Veri dönüşümleri, öznitelik mühendisliği, değerlendirme metrikleri, sınıflama, kümeleme, regresyon ve yapay sinir ağları hakkında bilgi sahibi olur.
Undergraduate course, Korkut Ata University, Computer Engineering, 2022
Dersin amacı Python dilinde nesneye yönelik programlama yapmaktır. Dersi alanlar python bileşenleri, sınıf ve nesne yapısı hakkında bilgi sahibi olur.
Undergraduate course, Korkut Ata University, Computer Engineering, 2023
Dersin amacı Yapay Zeka ve Makine Öğrenmesi konularında uygulamalı eğitim yapmaktır. Dersi alanlar Numpy, Pandas, Scikit Learn, Matplotlib gibi python kütüphaneleri kullanmayı öğrenir. Bu esnada Veri dönüşümleri, öznitelik mühendisliği, değerlendirme metrikleri, sınıflama, kümeleme, regresyon ve yapay sinir ağları hakkında bilgi sahibi olur.
Undergraduate course, Korkut Ata University, Computer Engineering, 2023
Bu proje dersinin amacı; öğrencilerin bireysel veya grup olarak bir sistematik içerisinde verilen bir konuyu araştırma, kavram geliştirme, gerekirse uygulamaya dönüştürme, raporlama ve sunma becerilerini geliştirmek, karşılaşılabilecek olumsuzlukları/riskleri azaltma ve en önemlisi alınan ödevi belirli bir proje planı kapsamında takip ederek zamanında bitirme alışkanlıklarının güçlenmesini sağlamaktır. Bu ders ile öğrencinin mühendislik çerçevesinde proje geliştirmesi amaçlanır. Projeler; yazılım, donanım, bilgisayar bilimleri, iletişim ve kontrol gibi alanları kapsar.
Undergraduate course, Ufuk University, Computer Engineering, 2026
Ayrık matematik, bilgisayar mühendisliğinin dilidir. Bu ders bir matematik dersi gibi değil, bir mühendislik dersi gibi işlenir: her hafta önce meslekte karşımıza çıkan somut bir problemle başlarız, sonra o problemi çözen matematiği öğrenir ve kodunu yazarız.