جست‌وجوی استاد و پژوهشگر

• Cardiology
• Internal Medicine
• Hypertension Research
• +۴ مورد دیگر

Assoc. Prof. Dr. Erdoğan Sökmen serves as a full-time academician at Kırşehir Ahi Evran University Faculty of Medicine within the Department of Internal Medical Sciences . His clinical and research activities focus on cardiology, particularly coronary artery disease and electrophysiological assessments. His educational background includes a Medical Degree from Istanbul University Cerrahpasa Faculty of Medicine (2007) and Medical Specialization in Internal Medical Sciences from Health Sciences University (2012). He maintains professional certifications in Good Clinical Practices and holds advanced English proficiency (YDS 93). Dr. Sökmen's research centers on cardiovascular pathophysiology with emphasis on: Electrocardiographic parameters in myocardial infarction Inflammatory biomarkers in hypertension Coronary artery anomalies and reperfusion phenomena Metabolic interactions in cardiovascular disease His publication analysis reveals consistent contributions to understanding no-reflow phenomenon, ventricular repolarization abnormalities, and novel biomarkers like nesfatin-1 and omentin-1. He actively participates in academic service as a member of both the Turkish Cardiology Association and European Society of Cardiology since 2012. His mentorship includes supervising Samet Altunbaş's thesis on myocardial infarction biomarkers. With an h-index of 11 (UNIS) and 10 (WoS), his work demonstrates significant scholarly impact in cardiovascular medicine.

Matthew Collinsonمشاهده پروفایل

مدرس ارشد

• Logic
• Theoretical Computer Science
• Information Security
• +۱۲ مورد دیگر

Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.

• Internet of Things
• Fog Computing
• Edge Computing
• +۹ مورد دیگر

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

Olga Kurasovaمشاهده پروفایل

استاد

• Machine Learning
• Data Visualization
• Cybersecurity
• +۳ مورد دیگر

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

Ian Simpsonمشاهده پروفایل

استاد

• Biomedical Informatics
• Machine Learning
• Genomics
• +۳ مورد دیگر

Professor Ian Simpson is a faculty member at the University of Edinburgh's School of Informatics , holding the Chair in Biomedical Informatics and serving as Director of the UKRI Artificial Intelligence CDT in Biomedical Innovation . He teaches courses including Foundations in Biomedical Artificial Intelligence Research and supervises PhD students across multiple programs. Current Supervision: Aidan Marnane, Antoine Lain, Barry Ryan, Tariro Chatiza, Sebestyén Kamp, Maria Dolak, Hanane Issa, Juliana Rodriguez Cubillos Past Supervision: Xin He (2017), Maciej Pajak (2018), Emilia Wysocka (2019), Samuel Heron (2019), Alba Crespi (2022), Magdalena Navarro (2022), Michael Yates (2023), Jamie Campbell (2023), Nicholas Moir (2024) Research Focus: Developing computational and machine learning methodologies for biological systems analysis, particularly brain development and neurological diseases. Key interests include high-throughput brain data analysis, graph-based biological modeling, molecular phylogenetics, and brain disease molecular modeling. Publication Trends: Recent work emphasizes multi-omic data integration through graph neural networks (MOGDx), batch correction challenges in breast cancer datasets, and longitudinal stratification approaches for Parkinson's disease. His articles demonstrate applications of AI in heterogeneous disease diagnosis while preserving biological validity. Scientific Recognition: Fellow of the Alan Turing Institute (2021-) Member of RSE Young Academy of Scotland BBSRC Pool of Experts member (2021-) STEM Ambassador Laboratory Affiliations: Leads the Biomedical Informatics group within the Institute for Adaptive & Neural Computation, with affiliations to Edinburgh Neuroscience, Patrick Wild Centre, and the Simons Initiative for the Developing Brain.

• Public Health
• Integrative Medicine
• Complementary Medicine
• +۳ مورد دیگر

Professor Jon Wardle serves as Foundation Director of the National Centre for Naturopathic Medicine and holds the Maurice Blackmore Chair of Naturopathic Medicine at Southern Cross University. He maintains significant international influence through visiting positions at Boston University, University of Washington, and University of Oxford, while serving on the World Health Organization and National Health and Medical Research Council committees. His leadership spans public health policy, integrative medicine systems, and traditional knowledge protection. His educational foundation includes a Bachelor of Health Science (ACNM), Postgraduate Certificate in Health Economics, Master of Health and Medical Law (University of Melbourne), Master of Public Health (University of Queensland), and PhD (University of Queensland). These qualifications underpin his interdisciplinary approach to health systems research. Wardle's research centers on integrating traditional medicine into contemporary healthcare frameworks, with emphasis on regulatory systems, cultural safety, and health equity. His work critically examines therapeutic pluralism in global contexts, particularly regarding Indigenous knowledge protection, migrant health disparities, and pandemic response equity. He develops evidence-based frameworks like the Contemporary Implementation of Traditional knowledge and Evidence (CITE) to bridge traditional practices with modern health systems. Analysis of his 15 most recent publications reveals dominant themes: traditional medicine governance (35% of works), pandemic impacts on vulnerable populations (20%), complementary medicine regulation (15%), and women's health integration (10%). His methodological approach combines qualitative analysis with systematic reviews, focusing on policy implementation gaps across Australian, African, and global contexts. A notable trend is the increasing emphasis on decolonizing health research and stakeholder-driven policy development. As Editor-in-Chief of Advances in Integrative Medicine and editorial board member for seven other journals, Wardle shapes scholarly discourse in the field. His leadership extends to convening the Public Health Association of Australia's complementary medicine special interest group and chairing World Federation of Public Health Associations initiatives. Wardle actively supervises higher degree research students through Southern Cross University's Graduate School, focusing on integrative medicine policy and traditional knowledge systems. His NHMRC Natural Therapies Working Committee role directly influences Australian health funding priorities, while WHO collaborations drive global standard-setting for traditional medicine integration. The National Centre for Naturopathic Medicine operates as his primary research hub, facilitating collaborations with 14-country networks studying naturopathic practice patterns. Current projects include Sub-Saharan Africa traditional medicine governance frameworks and Australian military complementary medicine utilization studies, positioning the Centre at the forefront of evidence-based integrative health policy development.

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.

• Software Engineering
• Information Security
• Computer Systems
• +۵ مورد دیگر

Hironori Washizaki is a Professor at Waseda University's School of Fundamental Science and Engineering, Department of Information and Computer Science, and serves as Director of the Global Software Engineering Laboratory. He also holds a visiting professorship at the National Institute of Informatics and serves as outside director at SYSTEM INFORMATION CO.,LTD. and eXmotion Co., Ltd. With a Doctorate in Information and Computer Science from Waseda University (2003), he has established himself as a leading researcher with 384 publications and an h-index of 36 according to Google Scholar. His research spans multiple domains including software engineering methodologies, security patterns, programming education, and the application of machine learning to software development. His work has significantly contributed to the fields of software patterns, quality assurance, and educational tools for programming. With over 20 years of academic experience, his career progressed from Research Associate (2002-2004) to Assistant Professor (2004-2008), Associate Professor (2008-2016), and Professor (2016-present). Washizaki's recent publications demonstrate a strong focus on applying AI and machine learning techniques to software engineering challenges, including prompt engineering patterns, program repair methods, and vulnerability assessment. His work bridges theoretical research with practical applications in both educational and industrial contexts, particularly in B2B software development and programming education for diverse age groups. KDDI Foundation Award (2022) Spirit of the Computer Society Award (2022) Distinguished Contributor, IEEE Computer Society (2022) IEEE Computer Society Golden Core Member (2022) Fellow, International Academy, Research, and Industry Association (2022) Computer Research Contribution Award, APSCIT (2016) Washizaki has served as chair of the IEEE CS Japan Chapter and SEMAT Japan Chapter, director of ACM-ICPC 2014 Asia Regional Tokyo Contest, and Convenor of ISO/IEC/JTC1/SC7/WG20. His editorial work includes positions at IEICE Transactions on Information and Systems and International Journal of Software Engineering and Knowledge Engineering. His leadership extends to programming education initiatives like SamurAI Coding, demonstrating his commitment to developing the next generation of software engineers.

Gabor Kolohمشاهده پروفایل

استادیار

• Demographic History
• Social History
• Economic History
• +۳ مورد دیگر

Dr. Gábor Koloh is a contemporary Assistant Professor and Research Fellow at the Eötvös Loránd University (Department of Economic and Social History) and the HUN–REN RCH Institute of History . His scholarly work focuses on demographic history , social history , and rural economic structures , with particular emphasis on fertility transitions , household organization , and modernization processes in 19th-20th century Hungary. Education : Law (ELTE), Aesthetics (BA), MA and PhD in Economic and Social History (supervised by György Kövér) Research Themes : Demographic change in interwar Hungary Ethnic agricultural practices in Baranya Temporal/spatial patterns in peasant communities Social mobility of Protestant clergy Methodology : Micro-historical analysis, family reconstitution, manuscript source interpretation Current Projects : Interdisciplinary studies on pronatalist discourse, cooperative territorial development, and aristocratic demographic patterns

Matthias Lehmannمشاهده پروفایل

استاد

• Private International Law
• International Business Law
• Financial Market Law
• +۷ مورد دیگر

Matthias Lehmann is University Professor (Univ.-Prof.) at the University of Vienna, Faculty of Law, and Director of the Institute for European, International and Comparative Law. He teaches and researches in private international law, international business law, financial-market regulation, and the law of digital assets and blockchain technology. Education & Degrees: Dr. iur., Germany DEA, Université Panthéon-Assas (Paris II), France LL.M., Columbia Law School, USA J.S.D., Columbia Law School, USA Research Focus: Lehmann’s recent work concentrates on the intersection of private international law and financial technology. Major projects include the EU’s Markets in Crypto-assets Regulation (MiCAR), uniform prospectus-liability rules, blockchain-based arbitration, and the treatment of digital assets in insolvency proceedings. He coordinates several edited volumes and commentaries, such as the Münchener Kommentar zum BGB capital-markets volume and the Austrian standard treatise Grundriss des Bank- und Kapitalmarktrechts . Teaching & Moot Court Activities: He offers courses on U.S. law introduction, international financial law, comparative law, private international law, banking & capital-markets law, and runs the Willem C. Vis Moot as well as the PAX Moot Court on international civil procedure and private international law. Team & Supervision: Lehmann currently supervises six university assistants (prae- and post-doc) working on arbitration, banking-union completion, crypto-asset regulation, and comparative procedure. Former team members are listed as alumni without indication of emeritus status. Scientific Awards: No specific prizes or fellowships are mentioned in the supplied text. Contact & Office: Juridicum, Schottenbastei 10–16, Staircase I, 5th floor, Room 512, 1010 Vienna, Austria; phone +43 1 4277 35122; email matthias.lehmann@univie.ac.at.

Marco L. Della Vedovaمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +۷ مورد دیگر

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

• Digital Holography
• Laser Engineering
• Marine Technology
• +۶ مورد دیگر

Dr. Thangavel Thevar is a Senior Lecturer in the School of Engineering at the University of Aberdeen, where he has been teaching since 2005. He completed both his undergraduate degree (First Class Honours in Electrical Engineering) and PhD (in Laser Engineering) at the University of Aberdeen in 1989 and 1993 respectively. Prior to his academic career, he accumulated approximately 10 years of industrial R&D experience in the USA, working on solid-state laser development and holographic applications. Dr. Thevar's research focuses on several key areas: Digital holography for imaging of marine plankton and micro-particles Laser Induced Breakdown Spectroscopy (LIBS) for subsea applications Laser-based instrumentation development Development of solid-state lasers for scientific, industrial, and medical applications Engineering applications of holography His most notable recent achievement is leading a team that developed the weeHoloCam, a state-of-the-art ultracompact underwater holographic camera for imaging microorganisms. Weighing just 3.5 kg, this system is the lightest and most compact of its kind, capable of imaging 240 ml/s and continuously recording up to 200,000 holograms. The system incorporates a rapid hologram processor and an AI-based image classifier. This technology has significant applications in marine studies including spatial and temporal monitoring of plankton species, monitoring harmful plankton & micro-jellyfish, study of vertical transport of floc, and monitoring microplastic pollution in the ocean. Dr. Thevar has secured numerous research grants as Principal Investigator, including projects funded by Sustainable Aquaculture Innovation Centre (SAIC), BBSRC, DEFRA, and Defence & Security Accelerator (DSTL). His current research portfolio demonstrates strong interdisciplinary connections between optical engineering, marine science, and environmental monitoring. His scientific contributions include: Royal Academy of Engineering Visiting Teaching Fellow Award (2010-2013) US patent 8,494,012 B2 for Raman converters Development of alexandrite lasers and ruby holographic lasers during his industrial R&D period Work on US government contracts for non-destructive inspection methods for military aircraft and the space shuttle Sabbatical work at NASA Langley Research Centre developing diode pumped Thulium YALO lasers As an educator, Dr. Thevar has served as Coordinator of MSc Oil & Gas Engineering (2007-2020), Undergraduate Level 1 Coordinator, and has contributed to various committees including Quality Assurance and Students' Progression. He currently teaches courses including Principles of Electronics, Electrical & Mechanical Systems, Control Systems, and supervises individual projects at both undergraduate and postgraduate levels. He is accepting PhD students interested in Engineering research. Dr. Thevar is actively involved in professional organizations, serving as Technical Programme Chair for IEEE/OES Oceans Conference 2007, on organizing committees for various conferences, as a committee member of the Instrument Science and Technology Group (Institute of Physics), and as a member of both IET and IEEE. He also serves as a reviewer for optics-based journals.

Danijel Skočajمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

Jamal Atifمشاهده پروفایل

استاد

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +۴ مورد دیگر

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Raffi Khatchadourianمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Software Evolution
• +۵ مورد دیگر

Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College and the Graduate Center of the City University of New York (CUNY). His research focuses on techniques for automated software evolution, particularly automated refactoring and source code recommendation systems, with the goal of easing the burden associated with evolving large and complex software through automated tools. He also conducts research on the automated analysis of Object-Oriented programs. Ph.D., Computer Science & Engineering, Ohio State University (2011) MS, Computer Science & Engineering, Ohio State University (2010) BS, Computer Science, Monmouth University (2004) Khatchadourian's research spans multiple areas of software engineering and programming languages, with particular emphasis on automated software evolution techniques. His work addresses critical challenges in refactoring legacy systems to modern language constructs, optimizing parallel processing in Java 8 streams, and addressing technical debt in machine learning systems. His recent research has expanded into deep learning program transformation, where he develops techniques to convert imperative deep learning code to more efficient graph execution models while ensuring safety. His approach combines static analysis, program transformation, and empirical validation to create practical tools that developers can integrate into their workflows. Analysis of Khatchadourian's recent publications reveals a strong focus on bridging the gap between theoretical program analysis and practical software engineering challenges. His work increasingly intersects with machine learning systems, examining both how to improve ML code through refactoring and how to ensure safety in deep learning frameworks. The research demonstrates consistent evolution from foundational work on Java language features toward more complex systems involving concurrency, deep learning, and automated program transformation. Distinguished Paper Award at SCAM '18 for work on Java 8 stream optimization EAPLS Best Paper Award at FASE '20 for study on Java 8 stream usage EAPLS Distinguished Paper Award at FASE '25 for Deep Learning refactoring work Best Paper Award nominee at IJCAI '24 for AI safety framework Khatchadourian actively mentors graduate and undergraduate students, with several advisees going on to successful academic and industry positions. His former Ph.D. student Tatiana Castro Vélez accepted a tenure-track Assistant Professor position at the University of Puerto Rico. He has supervised numerous master's theses and undergraduate research projects, often resulting in co-authored publications at top software engineering venues. His research has been supported by various grants, though specific funding details are not prominently featured in the available information. Through his work on tools like Fraglight for aspect-oriented programming and Hybridize Functions for deep learning refactoring, Khatchadourian has established a research group focused on practical program analysis and transformation. His lab develops Eclipse plugins and other IDE-integrated tools that help developers with automated refactoring, bug detection, and code optimization. The group maintains active collaborations with researchers at other institutions and contributes to open-source projects on GitHub.