جستجوی استاد راهنمای دانشگاهی

• Criminal Law
• Criminal Procedure
• Forensic Science
• +۵ مورد دیگر

Dolotov Ruslan Olegovich is an Associate Professor at the Faculty of Law of the National Research University Higher School of Economics (HSE), where he has been working since 2014. He is affiliated with the Department of Criminal Law, Procedure and Forensic Science and serves as an expert at the Project-training laboratory of anti-corruption policy. His educational background includes a Candidate of Law degree (2009) from Saratov State Academy of Law with a dissertation on 'The mechanism of criminal-legal regulation in the sphere of criminal infringements on intellectual property,' and a Jurisprudence specialty (2006) with qualification as a Lawyer. He also holds additional qualifications as an English language translator in professional communication and has completed advanced training in Tax Advocacy and inclusive vocational education. Dolotov's primary research interests focus on criminal law, particularly in the areas of crime qualification, criminal law general part, the price of crime, and corruption. His work explores the intersection of criminal law with economic activities, intellectual property protection, and anti-corruption measures. He has developed expertise in analyzing the economic dimensions of crime and the effectiveness of criminal sanctions. His recent scholarly output demonstrates a strong focus on contemporary challenges in criminal law, particularly in the areas of economic crimes, intellectual property violations, and corruption. There is a clear trend toward examining the application of criminal law in business contexts, with increasing attention to bankruptcy fraud, market manipulation, and the protection of entrepreneurs' rights. His work increasingly incorporates interdisciplinary approaches, connecting criminal law with financial regulation, compliance mechanisms, and even artificial intelligence applications for corruption prevention. Certificate of Honor of the Higher School of Economics (December 2022) Gratitude from the Faculty of Law of the National Research University Higher School of Economics (February 2020) Rector's personal allowance (2016-2017) Academic Work Allowance (2017-2018, 2015-2016) Best Teacher (2025, 2018-2022) High Professional Potential Group (HSE Personnel Reserve) Category 'New Teachers' (2015-2016) Dolotov has successfully supervised doctoral research, including that of Gareev A.A. on 'Illegal participation in entrepreneurial activity: criminal law aspects.' He has secured multiple research grants throughout his career, starting from 2007, including several from the Russian Humanities Research Foundation (RGNF) on topics such as corruption's impact on environmental conditions, anti-corruption expertise of legal acts, trends in reforming Russian criminal legislation, and the methodology for determining the 'price of crime.' His grant work demonstrates a consistent focus on practical applications of criminal law theory to real-world problems, particularly in the economic and anti-corruption domains. As an expert at the Project-training laboratory of anti-corruption policy, Dolotov contributes to developing practical anti-corruption measures and educational initiatives. His work connects academic research with real-world policy implementation, particularly in the areas of business regulation and corruption risk mitigation. The laboratory's activities have expanded significantly over the years, with increasing student participation and the development of specialized master's programs.

Ted Pedersenمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Word Sense Disambiguation
• +۳ مورد دیگر

Ted Pedersen is a Professor in the Department of Computer Science at the University of Minnesota Duluth, where he is part of the Swenson College of Science and Engineering. He serves as the Study Abroad Advisor for the CS Department and was a member of the UMN AI Task Force from January 2025 to August 2025, where he addressed questions regarding AI use at the University of Minnesota. Dr. Pedersen's research focuses on computational linguistics and natural language processing, with particular expertise in word sense disambiguation, semantic similarity measurement, and biomedical text analysis. His work bridges theoretical computational linguistics with practical applications in healthcare informatics and social media analysis. He has developed several widely-used open-source tools including SenseClusters, WordNet::Similarity, and WordNet::SenseRelate that have become standard resources in the NLP community. His publication record shows a clear evolution from foundational work in semantic similarity and word sense disambiguation toward contemporary applications in social media analysis, bias detection, and multilingual processing. Recent work demonstrates increasing focus on ethical AI considerations, particularly in sentiment analysis, offensive language detection, and news credibility assessment. His research maintains strong connections between theoretical linguistic frameworks and practical computational implementations. Multiple publications in AMIA Annual Symposium Proceedings Extensive work on semantic similarity measures in biomedical contexts Development of open-source NLP tools adopted by the research community Active participation in SemEval shared tasks since 2017 Dr. Pedersen actively mentors students through research opportunities in natural language processing, with current opportunities available for both undergraduate and graduate students. His teaching portfolio includes courses on Computer Ethics, Algorithms, Race and Computing, and advanced Natural Language Processing topics. He has taught consistently since at least 1999, demonstrating a long-standing commitment to computer science education. His laboratory work focuses on developing computational approaches to linguistic problems, with particular emphasis on unsupervised and semi-supervised methods for semantic analysis. Current projects appear to focus on multilingual processing, social media analysis, and ethical considerations in AI systems.

• Information Security and Cryptology
• Artificial Intelligence
• Computer Software
• +۳ مورد دیگر

Mehmet Ali YALÇINKAYA is a full-time Lecturer in the Department of Computer Engineering at Ahi Evran University's Faculty of Engineering and Architecture. His academic journey includes a PhD (2020) and MSc (2015) in Computer Engineering from Süleyman Demirel University, and a BSc in Electronics and Computer Education (2012). PhD: Computer Engineering, Süleyman Demirel University (2020) MSc: Computer Engineering, Süleyman Demirel University (2015) BSc: Electronics and Computer Education, Süleyman Demirel University (2012) His research focuses on Information Security and Cryptology , Artificial Intelligence , Computer Software , and Cybersecurity . Recent work includes phishing detection systems, deep learning applications for security, and AI-driven educational tools. Key article trends: 2025: NLP for violence detection in healthcare 2024: Multilayer security frameworks, AI in education, and biometric authentication 2023: Phishing defense mechanisms and agricultural pest detection He has supervised four Master's theses and collaborated with 48 researchers. Notable projects include a BCI-supported deep learning model for motor/facial analysis (2025-2027) and AI-powered educational systems for primary schools.