جستجوی استاد راهنمای دانشگاهی

Erdem Yörükمشاهده پروفایل

استاد

• Social Welfare and Social Policy
• Social Movements
• Political Sociology
• +۲ مورد دیگر

Erdem Yörük is a Professor in Sociology at Koç University , with additional affiliations as Associate Member at the University of Oxford's Department of Social Policy and Intervention, and Affiliated Faculty at the Ford Institute of Human Security (University of Pittsburgh, Central European University). As Director of the Center for Computational Social Sciences at Koç University, he leads major research initiatives including the ERC-funded projects Emerging Welfare and Politus , along with the H2020 Social Comquant project. Ph.D. in Sociology from Johns Hopkins University (2012) M.A. in Sociology from Johns Hopkins University (2009) M.A. in Sociology from Boğaziçi University (2006) B.Sc. in Electrical and Electronics Engineering from Boğaziçi University (2002) His research integrates Computational Social Sciences with Political Sociology to analyze the interplay between Social Movements and Welfare Policy . Current work focuses on creating cross-national datasets ( Global Welfare Dataset (GLOW) and Global Contentious Politics Database (GLOCON) ) to explore how governments utilize social assistance as both Mobilization and Containment mechanisms in response to grassroots political activity. Major findings from his publications in journals like World Development , Governance , and Politics & Society demonstrate that Emerging Market Economies have developed distinct Populist Welfare State Regimes through interactions between Structural Pressures , Institutional Frameworks , and Political Agency . Notable among these is his 2022 book The Politics of the Welfare State in Turkey (University of Michigan Press), which presents a political explanation for Turkey's shift from employment-based social security to poverty-targeted assistance. He has organized EU-funded Training Workshops on topics including Social Media Data Research , Network Analysis with R , and Digital Trace Data applications. His methodological contributions span Random Sampling techniques for protest event coding, Multilingual Annotation protocols, and Machine Learning Integration with expert rule systems.

Fethiye Ecem Edisمشاهده پروفایل

استاد

• Building Science
• Construction Technologies
• Building Envelope
• +۹ مورد دیگر

Prof. Dr. Fethiye Ecem Edis , a faculty member at the Department of Architecture in Istanbul Technical University , specializes in Building and Construction Technologies/Systems . With a PhD in Building Science from the same institution, her career spans over 25 years, including roles from Research Assistant (1998-2015) to Associate Professor (2015-2024) and Professor (2024-present). She also served as Deputy Head of Department (2022-2023). Education : PhD in Building Science (2002-2006), MSc in Building Information with Thesis (1995-1997), BArch (1990-1995) – all from Istanbul Technical University. Edis' research focuses on building envelope performance , climate change adaptation , life cycle assessment , and machine learning applications in architectural design. Her work addresses thermal behavior of materials , façade durability , and energy-efficient systems , particularly in Mediterranean and hot-humid climates. Recent publications highlight trends in automated defect detection using thermal imaging, multi-objective optimization for retrofits, and LCIA-based material selection . These align with her projects on shape memory alloys in shading systems and thermal comfort indicators with machine learning integration. Notable Projects : 2025: Thermal Comfort Metrics with Machine Learning 2024: Low-Cost Infrared Moisture Detection 2022: Shape Memory Alloys in Shading Components

Mehmet Gönenمشاهده پروفایل

استاد

• Machine Learning
• Computational Biology
• Cancer Biology
• +۳ مورد دیگر

Mehmet Gönen is a Professor in the Department of Industrial Engineering at Koç University's College of Engineering. His academic career spans multiple disciplines at the intersection of engineering, computer science, and biomedical research. He maintains an active research program with significant contributions to machine learning applications in biological and medical contexts. Education: PhD, Boğaziçi University (2010) MSc, Boğaziçi University (2005) BS, Boğaziçi University (2003) Professor Gönen's research primarily focuses on developing and applying machine learning methodologies, particularly multiple kernel learning techniques, to solve complex problems in computational biology and medicine. His work bridges theoretical algorithm development with practical applications in cancer biology, infectious disease modeling, and drug discovery. He has made significant contributions to single-cell multiomics analysis, antibiotic resistance research, and cancer genomics. His methodological innovations in kernel-based machine learning have found applications across diverse biological domains, demonstrating the versatility and power of his computational approaches. Analysis of his recent publications (2022-2025) reveals a consistent research trajectory centered on applying advanced machine learning techniques to pressing biomedical challenges. His work demonstrates strong interdisciplinary collaboration, spanning computational methods development, clinical applications, and biological discovery. Key thematic areas include cancer genomics (particularly pathway analysis and biomarker discovery), infectious disease modeling (with emphasis on antibiotic resistance mechanisms), and methodological innovations in kernel learning for biological data integration. His research has practical implications for precision medicine, drug discovery, and healthcare analytics. Professor Gönen has maintained a robust publication record with significant contributions to both methodology development and domain-specific applications. His work on scMKL for single-cell multiomics analysis represents cutting-edge integration of computational techniques with modern biological data. The consistent focus on interpretable machine learning methods suggests an emphasis on creating tools that provide biological insights rather than just predictive accuracy. His research group appears to collaborate extensively with domain experts in microbiology, oncology, and clinical medicine, ensuring that computational approaches address real-world biomedical challenges.

Matteo Poggiمشاهده پروفایل

استادیار

• Stereo matching
• Multi-View Stereo
• Optical flow
• +۷ مورد دیگر

Matteo Poggi is a Tenure-Track Assistant Professor at the University of Bologna, where he teaches courses on Logic Circuits and Computer Architectures. His academic career is deeply rooted in computer vision and 3D perception, with a focus on stereo vision, depth estimation, and SLAM systems. Dr. Poggi earned his MSc and PhD degrees from the University of Bologna in 2014 and 2018 respectively, working on stereo vision under the supervision of Prof. Stefano Mattoccia. His educational background has provided a strong foundation for his current research endeavors in computer vision and 3D scene understanding. Dr. Poggi's research primarily focuses on stereo matching, multi-view stereo, optical flow, single-image depth estimation, online adaptation, federated learning, and neural SLAM. His work bridges the gap between theoretical computer vision and practical applications, with particular emphasis on robustness in real-world scenarios. His recent publications demonstrate a growing interest in 3D Gaussian splatting, neural radiance fields, and environmental monitoring applications like river plastic detection. His scholarly output shows a consistent trajectory of high-impact publications at top computer vision conferences including CVPR, ICCV, and ECCV. The trend in his work reveals an evolution from traditional stereo vision techniques toward more advanced neural representations and applications in environmental monitoring. His research has significant implications for autonomous systems, robotics, and environmental science. Area Chair for CVPR 2026 Associate Editor for IJCV Outstanding Reviewer at CVPR 2025 Dr. Poggi actively collaborates with researchers across the globe, as evidenced by his extensive publication record. His GitHub profile shows active engagement with the research community through open-source implementations of his work. While specific grant information isn't provided in the available texts, his numerous publications in top venues suggest successful funding for his research endeavors. His work appears to be conducted within a vibrant research group at the University of Bologna, with connections to other researchers in the computer vision community as seen through his GitHub followers and collaborators. His research has practical applications in autonomous driving, robotics, and environmental monitoring.

• Additive Manufacturing
• CAD/CAM
• Computational Geometry
• +۳ مورد دیگر

Ulaş Yaman is an Associate Professor in the Department of Mechanical Engineering at Middle East Technical University (METU) in Ankara, Turkey. He received his B.Sc. in Mechanical Engineering (2007), M.Sc. in Mechanical Engineering (2010), and Ph.D. in Mechanical Engineering (2014) from METU, with a minor in Mechatronics. His academic career includes roles as a Visiting Assistant Professor at Purdue University (2014-2015) and research positions at METU. His research specializes in additive manufacturing, CAD/CAM systems, computational geometry, and embedded control systems. Key areas include 3D printing optimization, FDM process enhancements, CNC command generation, and hybrid manufacturing technologies. Recent work explores Industry 4.0 integration, structural optimization for 3D-printed artifacts, and novel fabrication pipelines like LIPRO. Yaman's publications demonstrate strong focus on manufacturing innovations, with recurring themes in real-time command generation, material behavior in 3D printing, geometric algorithms for production, and educational tools for control engineering. His work frequently combines hardware implementation with computational efficiency. Scientific Awards: BEST PAPER AWARD - IEEE 8th Workshop on Intelligent Solutions in Embedded Systems (2010) Research Grants: TÜBİTAK 1001: A Smart- and Hybrid Manufacturing System Utilizing Additive Manufacturing Methods and Machining (2017-2020) TÜBİTAK 3001: A Novel Design and Fabrication Pipeline for 3D Printers: LIPRO (2017-2018) ODTÜ-BAP: Improving the Dimensional Accuracy of 3D Printed Artifacts (2016) He leads the Laboratory for Intelligent Production Systems (LIPRO) with six research assistants, focusing on additive manufacturing, computational geometry, and embedded systems. Current projects include developing multi-axis hybrid manufacturing systems and novel CAD/CAM architectures.

Sukru Kitisمشاهده پروفایل

استادیار

• Medical Electronics
• Biomedical Signal Processing
• EEG and ECG Analysis
• +۵ مورد دیگر

Assistant Professor Sukru Kitis is a dedicated academic at Kütahya Dumlupınar University's Simav Faculty of Technology, Department of Electrical-Electronics Engineering. With over 15 years of academic experience, he has progressed from Instructor (2009-2019) to Doctor Lecturer (2019-present), currently serving as Deputy Head of Department since 2022. His academic journey includes a Bachelor's and Master's in Electrical-Electronics Engineering from Niğde University, followed by a PhD from Sakarya University. His educational background includes: 1999-2004: Bachelor's in Electrical-Electronics Engineering, Niğde University 2004-2007: Master's in Electrical-Electronics Engineering, Niğde University 2008-2019: PhD in Electrical-Electronics Engineering, Sakarya University Professor Kitis's research spans two primary domains with significant interdisciplinary connections: biomedical engineering and renewable energy systems. His biomedical work focuses on advanced signal processing techniques for EEG and ECG analysis, novel filter design for medical applications, and increasingly, machine learning approaches to medical diagnostics including thyroid cancer examination, obesity stage detection, and neurological disorder assessment. His energy research centers on geothermal heating system automation, solar energy applications, and innovative vehicle design using alternative energy sources. These research strands converge in his recent work on providing energy for biomedical devices using renewable resources. His publication record demonstrates strategic evolution from circuit design and biomedical instrumentation (2012-2017) toward data-driven healthcare solutions (2018-2025), while maintaining parallel expertise in sustainable energy systems. This dual focus creates unique opportunities for students interested in the intersection of healthcare technology and sustainable engineering. Professor Kitis has received recognition for his scholarly contributions, most notably the Best Paper award in 2017. His research impact extends through: 93 publications including journal articles and conference proceedings 3 major research projects related to forest fire monitoring, electric vehicle design, and geothermal system automation 2 scientific awards including Best Paper 2017 As an educator and mentor, Professor Kitis supervises numerous graduate students working on cutting-edge projects at the intersection of electrical engineering, biomedical applications, and renewable energy. His administrative roles as Department Head (2019-2021) and current Deputy Head of Department demonstrate leadership within his academic community while maintaining active research and teaching responsibilities. His lab environment emphasizes practical application of theoretical concepts, with students working on real-world problems in medical device development, energy system optimization, and data-driven healthcare solutions. The interdisciplinary nature of his research creates a dynamic environment where electrical engineering principles meet medical and environmental challenges.

• Information Systems
• Image Processing
• Machine Learning
• +۱ مورد دیگر

Dr. İdris Kahraman serves as a Lecturer in the Department of Computer Engineering at Karabuk University's Faculty of Engineering, where he has held academic positions since 2010. His current role as Doctor Research Assistant (2024-present) follows over a decade as a Research Assistant, during which he completed his PhD while teaching undergraduate courses including Algorithms, Introduction to Computer Engineering, and Engineering Ethics. His educational background demonstrates progressive specialization: PhD in Computer Engineering, Karabuk University (2014-2019) Master's Degree in Computer Engineering, Karabuk University (2010-2014) Bachelor's Degree, Turkish Military Academy Command (2003-2007) Dr. Kahraman's research bridges theoretical computer science and practical geospatial applications, with four core pillars: Information Systems development, Image Processing techniques, Machine Learning implementations, and Artificial Intelligence solutions. His work consistently applies these to satellite imagery analysis, 3D modeling, and real-time detection systems, creating tangible outputs like AR tourism applications and automated protozoa identification tools. Publication trends reveal evolving expertise from foundational 3D campus modeling (2011-2013) through satellite road detection systems (2015-2017) to current biomedical and urban analytics applications. The 2024 Applied Sciences paper on real-time protozoa detection represents his most sophisticated integration of deep learning with practical instrumentation. Research funding shows sustained activity through university-supported projects since 2011: Current: Principal researcher on 3D building modeling project (2024-2026) Recent: Principal investigator for neural network road detection project (2017-2018) Historic: Multiple roles in satellite imagery and 3D campus system projects (2011-2024) Though no formal laboratory is documented, Dr. Kahraman operates within Karabuk University's collaborative research ecosystem, maintaining a decade-long productive partnership with Prof. İsmail Rakıp Karaş and contributing to the institution's geographic information systems research cluster through both technical projects and curriculum development.

• Computer Science
• Algorithms
• Data Structures
• +۲ مورد دیگر

Mehmet Evren Coşkun is an Assistant Professor in the Department of Computer Engineering at TED University's Faculty of Engineering. His academic profile includes teaching core computer science courses such as Data Structures and Algorithms II, Computer Organization, and Software Engineering across multiple semesters. Research Focus: His work spans foundational areas of computer science, including: Algorithm design and analysis Data structure implementation Computer system architecture Software engineering principles No information is available regarding awards, grants, lab affiliations, or supervised students.

• Hadith Studies
• Islamic Theology
• Arabic Language and Rhetoric
• +۱ مورد دیگر

İbrahim SAĞLAM currently serves as a Lecturer at the Department of Basic Islamic Sciences , Faculty of Islamic Sciences , Kırşehir Ahi Evran Üniversitesi since 2021. He holds a PhD in Hadith from Ankara University (2017) and an MA in Hadith from Gazi University (2008). Education : PhD (Hadith), Ankara University (2017) MA (Hadith), Gazi University (2008) BA (Theology), Ankara University (1999) Research Interests focus on Hadith Studies , Islamic Theology , and Arabic Linguistics , with particular emphasis on: Textual criticism of Prophetic traditions Applications of artificial intelligence in Hadith analysis Prophetic models for social cohesion Historical evolution of Hadith compilation Publications demonstrate expertise in: Hadith hermeneutics Medieval Islamic textual practices Modern reinterpretations of classical sources Intersections between Islamic tradition and contemporary issues Awards : Achievement Certificate (2014) His academic career includes extensive work on hadith criticism , Qur'anic exegesis , and contemporary Islamic sociology , with a notable focus on how traditional Islamic concepts address modern social challenges.

• Data Structures and Algorithms
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Mustafa AKSU is a Lecturer at Ahi Evran University, Faculty of Engineering and Architecture, Department of Computer Engineering. He holds a Doctorate (2016) from İnönü University in Computer Hardware, a Master's (2004) in Electrical and Electronics Engineering from Kahramanmaraş Sütçü İmam University, and a BSc (1998) in Computer Science Engineering from Kocaeli University. Current Position: Doctor Öğretim Üyesi (Lecturer) since 2022 Previous Role: Full-time Teaching Staff at Kahramanmaraş Sütçü İmam University (2001-2022) His research focuses on Data Structures and Algorithms , with significant contributions to skip ring/skip list innovations and their applications in robotics, artificial intelligence, and image processing. Key interests include: Algorithm optimization Machine learning applications Image segmentation techniques Mobile system development Renewable energy simulations Publications showcase expertise in data structures, robotics, and energy systems, with a recent 2024 paper on COVID-19 prediction algorithms and a 2023 book chapter on robotic simulation importance . Collaborations span institutions including İnönü University, University of Turku, and Gaziantep University. As a thesis advisor, he has guided three Master's students in 2024. Professional experience includes department chairmanship (2011-2014) and extensive teaching roles since 2001.

• Tax Law
• Comparative Law
• Corporate Taxation
• +۳ مورد دیگر

Prof. Dr. Neslihan Karataş Durmuş serves as Dean of Ankara Yıldırım Beyazıt University Faculty of Law and Professor in the Department of Financial Law since 2024, following her promotion from Associate Professor (2018-2024). Her academic career is anchored in comparative tax law with extensive expertise in Turkish-French legal systems. PhD in Tax Law, Université Paris 1 Panthéon-Sorbonne (2013) Master's in Tax Law, Université Paris 1 Panthéon-Sorbonne (2004) LL.B, Gazi University Faculty of Law (2000) Her research focuses on digital transformation of tax administration , fundamental rights in taxation , and comparative corporate tax systems . Recent works analyze AI implementation in tax authorities, social media income taxation, and European Court of Human Rights jurisprudence affecting tax policy. She pioneered studies on tax audit reforms during pandemics and gender-specific tax advantages. Her 57 publications demonstrate consistent output in high-impact journals including Türkiye Adalet Akademisi Dergisi and MALIYE DERGISI. Current research trajectories emphasize algorithmic tax governance , cross-border digital taxation , and constitutional limits on tax enforcement . As academic advisor, she has supervised 5 theses covering corporate group taxation, digital evidence in tax crimes, and comparative merger taxation. Her editorial leadership as First Editor of Yıldırım Beyazıt Law Journal since 2022 shapes scholarly discourse in Turkish legal academia. Key professional engagements include membership in the Interuniversity Board Legislation Commission (2024-present) and prior service as Education Counselor at the Turkish Embassy in Brussels (2019-2022). She has presented research at UN and OSCE institutions, demonstrating policy-relevant scholarship.

• Geodesy
• Space Geodesy
• GPS Applications in Earth Sciences
• +۶ مورد دیگر

Professor Haluk Özener is a distinguished geodesy expert at Boğaziçi University's Kandilli Observatory and Earthquake Research Institute in Istanbul, Turkey. With over two decades of research experience, he specializes in monitoring crustal deformation and earthquake hazards along Turkey's major fault systems, particularly the North Anatolian Fault Zone. His research spans geodesy, space geodesy, GPS applications in earth sciences, and geodynamics. Özener has made significant contributions to understanding seismic risks in the Marmara region and has developed methodologies for rapid earthquake hazard assessment. His work combines terrestrial and satellite-based geodetic techniques to monitor crustal movements and fault behavior. Analysis of his recent publications reveals a strong focus on seismic hazard assessment, particularly through GPS-derived strain rates and velocity fields. His research has evolved to incorporate advanced geospatial technologies, machine learning applications, and multi-hazard vulnerability assessments including nuclear accident risks and tsunami warning systems. Best Paper Award at TIEMS 2009 16th Annual Conference Extensive collaboration with international research networks including WEGENER Key contributor to European strong-motion data initiatives Professor Özener has supervised numerous research projects related to crustal deformation monitoring and has been instrumental in establishing geodetic networks across Turkey. His work with the KOERI seismic network has significantly advanced Turkey's earthquake monitoring capabilities. Recent research has expanded into social vulnerability mapping and nuclear accident risk assessment, demonstrating the interdisciplinary nature of his scientific contributions.

Mustafa ULAŞمشاهده پروفایل

استادیار

• Data Mining
• Big Data
• Machine Learning
• +۷ مورد دیگر

Mustafa ULAŞ is an Assistant Professor in the Software Engineering Department at Fırat University, Turkey. He also serves as a University Advisor and Coordinator of the Digital Transformation and Software Office at Fırat University since October 2020. With academic roots entirely at Fırat University, he has established himself as a prominent researcher in data science and software engineering. Born in November 1981 in Elazığ, Turkey PhD in Electrical-Electronics Engineering (2011) Master's in Computer Engineering (2006) Bachelor's in Electrical-Electronics Engineering (2003) Dr. ULAŞ's research spans multiple domains of computer science and engineering with particular emphasis on practical applications. His work bridges theoretical computer science with real-world problems in healthcare, finance, and industrial systems. Recent publications reveal a strong focus on machine learning applications, especially in medical diagnostics and explainable AI, while maintaining his longstanding interest in VLF signal analysis for earthquake prediction. His publication record shows a clear evolution from foundational work in database systems and medical imaging to cutting-edge research in deep learning and explainable AI. The most recent articles (2024-2025) predominantly focus on healthcare applications of machine learning, particularly diabetes and cancer diagnosis, while maintaining parallel research streams in industrial applications, financial analytics, and drone network optimization. This multidisciplinary approach demonstrates his ability to adapt core computational techniques to diverse problem domains. Dr. ULAŞ has been actively involved in numerous research projects, including TÜBİTAK-funded initiatives such as the 'Enriched Virtual Laboratory' and 'A New Approach in Teacher Education: Effective Blended Learning.' His project portfolio spans infrastructure development, educational technology, and advanced research applications. As an educator, he teaches courses including C Programming and Algorithms, Internet-Based Programming, Server Operating Systems, and Web Project Management. His administrative roles include serving as University Advisor and Coordinator of the Digital Transformation and Software Office at Fırat University since 2020.

SADIK ALASHANمشاهده پروفایل

دانشیار

• Hydraulic Engineering
• Hydrology
• Water Resources
• +۴ مورد دیگر

Dr. Sadik Alashan serves as Associate Professor in the Faculty of Engineering and Architecture at Bingol University, Turkey, specializing in Civil Engineering disciplines through teaching courses including Hydraulics, Hydrology, Fluid Mechanics, and Water Resources Structures. His academic journey spans over a decade at Bingöl University with progressive appointments from Research Assistant to current Associate Professor status. Education: Ph.D. in Hydraulic and Water Resources Engineering, Istanbul Technical University (2011-2016) M.S. in Hydraulic Engineering, Firat University (2008-2011) B.S. in Civil Engineering, Dicle University (1999-2003) Research Focus: Dr. Alashan pioneers statistical methodologies for hydrological trend analysis, particularly advancing Sen's Innovative Trend Analysis (ITA) into frameworks like S-IPTA and ITA-NF. His work bridges climate science and water engineering through drought assessment in Mediterranean regions, earthquake recurrence modeling in Eastern Turkey, and machine learning applications for hydraulic structures. Recent publications demonstrate evolution from foundational ITA refinements (2016-2020) toward integrated climate-hydrology frameworks (2023-2025), with increasing international collaboration. Scientific Awards: No major scientific awards listed in the provided information Academic Contributions: Dr. Alashan has co-supervised Master's research on climate impacts to river systems and serves as reviewer for leading journals including Water Resources Management and Natural Hazards. His publication trajectory shows consistent output with 15+ articles in the last five years, primarily in Q1 hydrology/climate journals. While no formal lab is specified, his computational research group focuses on methodological innovation for water resource challenges in semi-arid regions. Research Environment: Based in Bingöl (Eastern Turkey), his work addresses regional water security through earthquake-prone basin studies and Mediterranean drought analysis. His recent shift toward machine learning integration (2025 publications) indicates evolving technical capacity within his research group, leveraging computational approaches to solve practical hydraulic engineering problems.

Enes Algülمشاهده پروفایل

مدرس

• Graph Machine Learning
• Geometric Deep Learning
• Bioinformatics
• +۵ مورد دیگر

Enes Algül serves as a Lecturer in the Faculty of Engineering and Architecture at Bingol University, bringing deep expertise in Graph Machine Learning and Geometric Deep Learning developed through his PhD at the University of York and postdoctoral work at the University of Copenhagen. His research bridges theoretical computer science with practical applications in bioinformatics and computer vision. His educational foundation includes: PhD in Computer Science, University of York, UK (2017-2022) MSc in Software Engineering, University of Hertfordshire, UK (2016-2017) Bachelor's in Computer Engineering, Ankara University, Turkey (2009-2014) Research Interests: Algül pioneers methods for transforming complex data into graph and 3D point cloud representations, applying Graph Kernels and Graph Neural Networks to solve challenging problems in RNA structure classification and face recognition. His work uniquely combines graph theory, deep learning, and domain-specific knowledge in bioinformatics, with recent focus on novel graph representations for RNA molecules based on sequence free energy and 3D structural properties. This interdisciplinary approach enables breakthroughs in analyzing biological data and visual recognition systems. Scientific Recognition: Turkish Ministry of National Education Scholarship (2014) - Awarded to top 900 undergraduate students nationally Academic Contributions: Algül teaches advanced courses including Software Engineering, Natural Language Processing, and Object-Oriented Programming at Bingol University. His graduate teaching experience at York demonstrates strong pedagogical skills across Python, Java, and data science curricula. The scholarship supporting his entire graduate education reflects exceptional academic promise, and his recent publication surge in 2023 indicates active research momentum with potential for significant future contributions in geometric deep learning applications. Research Environment: While no dedicated lab is specified, Algül's work spans multiple high-impact domains including computational biology and computer vision. Students would engage with cutting-edge techniques in graph representation learning and neural network architectures applied to real-world data challenges.