Katarzyna Oszust-Polak is an Assistant Professor at the Center of Polish Language and Culture for Polonia and Foreigners at Maria Curie-Skłodowska University. With a background in Russian philology, she has focused her career on Polish language teaching methodology for non-native speakers and translation-based language acquisition. PhD in Humanities (2012) with dissertation on translation exercises in Russian language acquisition Postgraduate qualifications in teaching Polish as a foreign language (2016-2017) Research Focus: Her work examines translation's role in foreign language education, emphasizing: Translation didactics Intercultural competence development Polish language acquisition strategies Language exercise design Multimedia teaching techniques Student feedback analysis Publication Trends: Spanning 2007-2017, her 15 most recent works analyze: Translation's pedagogical value Language learning error patterns Cultural mediation in translation Language warm-up techniques Translation exercise typologies Intercultural communication frameworks Teaching Activities: She offers courses in: Practical Polish language (all proficiency levels) Multimedia techniques for language teaching Integrated language skills for Russian philology students
Dr. Izabela Olszak serves as an Assistant Professor in the Department of Applied Linguistics at the Institute of Linguistics, Faculty of Humanities, John Paul II Catholic University of Lublin, Poland. Her academic profile centers on innovative language education research addressing diverse learner populations including deaf and hard of hearing students, bilinguals, and trilinguals. Her research program integrates Second Language Acquisition with inclusive pedagogy , focusing on learning strategies for hearing-impaired learners and multilingual populations. Recent work critically examines Artificial Intelligence 's role in academic writing instruction while advancing Service Learning methodologies to enhance cultural awareness in language classrooms. This dual trajectory bridges theoretical psycholinguistics with practical classroom applications. Dr. Olszak's scholarly output reveals a clear evolution from foundational work on reading comprehension strategies (2015-2018) toward contemporary investigations of AI-mediated language learning and inclusive education frameworks. Her publications consistently address accessibility challenges while exploring technological innovations in language pedagogy. Scientific recognition includes: Individual Award of the Second Degree by the Rector of KUL for the best scored publication in 2024 Research leadership is demonstrated through international project coordination including the LANGSKILL initiative (learning styles of deaf students) and UNISERVITATE (Service-learning in Catholic Higher Education). She actively organizes scholarly forums such as the Langskills conference series and participates in global collaborations with institutions in Malta, Sweden, Italy, and the USA. As a member of the international research group "English as a Foreign Language for Deaf and Hard of Hearing People" (EFL DHH), she contributes to cross-border knowledge exchange in inclusive language education methodologies.
Dr. Kamila Tomaka serves as an Assistant Professor in the Department of Dutch Language at the Institute of Linguistics, Faculty of Humanities, John Paul II Catholic University of Lublin. Her academic profile bridges Dutch philology with linguistic theory, specializing in particle studies, comparative linguistics (Dutch-Polish), language change, and language education. She maintains active research connections between contemporary Dutch linguistic phenomena and ancient Greek linguistic traditions. Her research portfolio demonstrates remarkable coherence across several key domains: Particle Studies - In-depth analysis of Dutch particles' semantic values, interpretative challenges, and translational possibilities, with comparative frameworks examining parallels in Polish and Greek linguistic traditions Language Change - Investigation of contemporary shifts in Dutch usage, particularly the increasing acceptance of 'hun' as a subject pronoun and societal attitudes toward linguistic variation Comparative Linguistics - Systematic examination of grammatical structures across Dutch and Polish, especially word order patterns and grammatical constructions Language Pedagogy - Research on common errors made by Polish students learning Dutch and development of effective teaching methodologies Historical Connections - Exploration of links between ancient Greek linguistic concepts and modern Dutch linguistic phenomena Dr. Tomaka's scholarly trajectory reveals a consistent focus on the intricate relationship between linguistic structures and communicative functions, with increasing sophistication in cross-linguistic comparative frameworks. Her work on particles creates a distinctive bridge between Dutch linguistics and ancient Greek studies, demonstrating how pragmatic-discursive approaches can illuminate both historical and contemporary linguistic phenomena. Her notable scholarly recognitions include: Ministry of Education and Science scholarship for outstanding young scientists (18th edition, 2023) Rector's Award - team award, 1st degree Diplomatic distinction from the Ambassador of the Republic of Poland in The Hague (2013) Flemish Government Research Scholarship for research at KU Leuven Dr. Tomaka actively contributes to academic administration through supervision of diploma theses, development of the Dutch philology program, and membership in the Faculty Commission for Quality of Education. She has established significant international collaborations, including a cotutelle doctoral program agreement with KU Leuven. Her outreach efforts include co-organizing visits by prominent Dutch scholars (Prof. Ad Foolen, Prof. Roel Vismans), organizing the Comenius Summer Course in Dutch Language and Culture, and promoting Dutch studies through initiatives like 'Get in Touch with the Dutch' and representation at meetings with the Ambassador of the Kingdom of the Netherlands to Poland.
Anna Czapla , Assistant Professor at the Department of Polish Language, Faculty of Humanities, John Paul II Catholic University of Lublin, specializes in onomastics, historical linguistics, and Polish language pedagogy. Her work focuses on Polish-Ukrainian borderland naming patterns, urban toponymy, and language education for foreign students. Research Areas: Onomastics, Sociolinguistics, Polish-Ukrainian Linguistic Borderlands, Language Teaching Methodology Key Projects: Analysis of historical poviat toponymy, Lublin street name evolution, linguistic image of Polish-Ukrainian regions Publications Trends (2025-2001): Over 20 years, her scholarship examines place names across Polish-Ukrainian territories, linguistic changes in border regions, and language acquisition challenges. Notable subfields include historical cartography, ethnic identity through naming, and pedagogical approaches for Polish clergy and international students. Scientific Awards: Internal Individual Award II degree (2021), Outstanding Doctoral Dissertation Award (2009) Educational Services: Conducted preparatory courses for international candidates at John Paul II Foundation since 2001 Awards: Recognized for organizational and scientific popularization efforts
Assoc. Prof. Natasha Radusin-Bardic is affiliated with the Department of Romance Studies at the Faculty of Philosophy, University of Novi Sad, where she has held academic positions since 2004. She earned her PhD in 2014 with a dissertation on interrogative modality in French within the context of teaching French as a foreign language. PhD: Interrogative Modality in French (2014) MS: Modern Colloquial French (2007) BS: French Language & Literature (2001) Her research focuses on phonetics/phonology of French , language teaching methodology , and contrastive analysis between French and Serbian . She has published works on interrogative structures, phonodidactics, and linguistic variation in modern French. Recent publications analyze phonological reduction phenomena in language learning, radio French variation , and interrogative adverbs in language pedagogy . These works demonstrate her focus on practical applications of phonetic theory in foreign language instruction. Contact: natasa.radusin.bardic@ff.uns.ac.rs
Natalia Evnitskaya is a Professor at the Department of Applied Linguistics within the Institute for Multilingualism at Universitat Internacional de Catalunya (UIC Barcelona). She holds a doctoral contract (Contractada Doctora) and specializes in research areas including Content and Language Integrated Learning (CLIL), bilingual education, classroom interaction analysis, and educational linguistics. Her research focuses on optimizing language and content integration in multilingual classrooms, particularly in science and history disciplines. She has led projects such as the GRAM research group (Grup de Recerca en Adquisició Multilingüe) and contributed to initiatives like the Trans-CLIL project analyzing curriculum transitions in bilingual education. Her work frequently employs conversation analysis and multimodal interaction studies to explore pedagogical strategies. Dr. Evnitskaya teaches courses across undergraduate and postgraduate programs, including Methods for Teaching English as a Foreign Language and Master's Dissertation supervision in the Master's Degree in Language Acquisition and Teaching English as a Foreign Language. She has also developed specialized programs such as the Expert in TEFL in Early Childhood Contexts. Her recent publications address topics like peer interaction repair mechanisms, cognitive discourse functions in CLIL, and the role of non-verbal communication in teaching. She co-authored influential studies on CLIL inequities, teacher smile usage, and sociolinguistic factors influencing educational choices in multilingual contexts.
Michael P.H. Rodgers is an Associate Professor in the School of Linguistics and Language Studies at Carleton University, Canada. He concurrently serves as Director of the School and co-Editor-in-Chief of the Canadian Journal of Applied Linguistics. His academic journey includes a PhD in Applied Linguistics from Victoria University of Wellington (2013), a M.Sc. in TESOL from Temple University (2006), and a B.A. in Education from the University of Lethbridge (1992). His research focuses on two core areas: (1) language learning through media consumption (television, movies, video games), emphasizing vocabulary acquisition and comprehension processes, and (2) second language acquisition pedagogy, particularly in workplace and academic contexts. Notable contributions include studies on caption effects on TV comprehension, lexical coverage in media, and formulaic language instruction. Recent work explores multimodal input processing using eye-tracking methodologies, with publications in Studies in Second Language Acquisition and TESOL Quarterly . He actively supervises graduate research on topics like video game vocabulary, workplace pragmatics, and formulaic sequence acquisition. Current Grants: SSHRC Aid to Scholarly Journals (2022–2025) for Canadian Applied Linguistics Association support Key Affiliations: ACLA Executive Council member, TESL Canada Journal Review Board Teaching expertise spans EFL/ESL methodology, second language acquisition theories, and vocabulary pedagogy. His blended curriculum development focuses on supporting language learners in workplace and academic settings.
Shin Hwei Tan is an Associate Professor (Gina Cody Research Chair) at the Department of Computer Science and Software Engineering , Concordia University, Montreal, Canada. Previously, she worked as an Assistant Professor at the Southern University of Science and Technology (SUSTech) since June 2018. Her academic journey includes a PhD from the National University of Singapore under Abhik Roychoudhury and MS/BS degrees from the University of Illinois at Urbana-Champaign, co-advised by Darko Marinov and Lin Tan. Education: PhD in Computer Science (National University of Singapore, 2018) MS in Computer Science (University of Illinois at Urbana-Champaign, 2012) BS in Computer Science (University of Illinois at Urbana-Champaign) Her research interests span Automated Program Repair , Software Testing , Open-source Software Development , Genetic Improvement , Program Analysis , and Comment Analysis . She actively applies machine learning and formal methods to improve software reliability. Recent work includes LMDefects (Codex-generated bugs dataset), Droix (Android app repair), and Codeflaws (program repair benchmark). Her research outputs demonstrate expertise in automated patch generation , static analyzer testing , and quantum software verification . Key projects include Concoction (vulnerability prediction), SAScope (static analysis framework testing), and COMFUZZ (compiler fuzzing). She has secured significant grants including a NSERC Discovery Grant (2024–2028) and ECR Young Researcher Fund (2019). Scientific Awards: ACM-W Rising Star Award (2025) ICSE/SANER Distinguished Paper Awards (2023–2025) Google Anita Borg Memorial Scholarship (2015) David J. Kuck Outstanding MS Thesis Award (2013) Multiple Best Reviewer Awards She serves as General Chair for FSE 2026 , Guest Editor-in-Chief for TOSEM , and workshop co-chair for APR@ICSE series. Her teaching focuses on software engineering practices with real-world open-source projects. She also co-founded the ACM-W Montreal Professional Chapter in 2025.
Xiaoyu Sun is a Lecturer in the School of Computing at Australian National University (ANU), specializing in Software Engineering with a focus on Mobile Software Engineering and Intelligent Software Engineering. She holds a PhD from Monash University (2023) and a Bachelor's degree in Computer Science from Beijing Normal University (2016). Her research emphasizes applying static code analysis, dynamic testing, and NLP techniques to enhance software security and reliability, particularly in Android systems. Current projects include tools for detecting compatibility issues and privacy leaks in mobile apps. Her research interests span code generation frameworks (e.g., A^3-CodGen), security management in open-source projects, and AI-enhanced software development practices. Collaborations with tech giants like Bytedance and Alibaba highlight her industry engagement. Xiaoyu is Co-Investigator in the Tech4HSE project (2025-2027), developing AI-based monitoring systems for workspace safety. She has published in top venues including ICSE, ASE, and IEEE Transactions on Software Engineering. Her work bridges academia and industry, addressing challenges in mobile app security, code reuse efficiency, and developer toolchain innovation. Ongoing efforts focus on AI-driven solutions for software engineering tasks and fostering transparent privacy practices in open-source AI applications.
İlkay Öksüz is an Associate Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), where he has been serving since 2021, following his appointment as a Doctoral Academic Staff member in 2020. He previously held research positions at King's College London (2017–2020), The University of Edinburgh (2016), and Yale University (2015–2016). He earned his Ph.D. in Computer, Decision and Systems Science from IMT School for Advanced Studies Lucca, Italy, in 2017, under the supervision of Prof. Sotirios Tsaftaris. B.Sc. in Electronics Engineering, Istanbul Technical University (2010) M.Sc. in Electrical-Electronics Engineering, Bahçeşehir University (2013) Ph.D. in Computer, Decision and Systems Science, IMT School for Advanced Studies Lucca (2017) His research focuses on medical image analysis, particularly in cardiac MRI, with core interests in image segmentation, registration, quality assessment, and reconstruction using deep learning. He also explores electricity price forecasting and explainable AI. His work bridges machine learning with clinical applications, aiming to improve diagnostic accuracy and automation in radiology. The 15 most recent publications highlight a strong trend in applying deep learning to medical imaging, especially cardiac and prostate MRI, mammography, and ECG analysis. A significant emphasis is placed on explainability , optimization , and clinical applicability , with frequent participation in MICCAI challenges and collaborations with radiology departments. Projects also extend into energy forecasting, showing interdisciplinary versatility. Scientific awards include: 2024 ITU Young Scientist Award in Engineering 2024 Parlar Research Incentive Award He leads the Predictive Intelligence and Medical Imaging (PIMI) Lab at ITU, mentoring graduate students and managing multiple funded projects, including the TUBITAK International Fellowship. His lab has achieved top placements in national and international AI competitions such as Teknofest and MICCAI challenges. He serves as principal investigator (PI) on several active grants focused on interpretable deep learning for medical imaging and electricity forecasting. The PIMI Lab, under his leadership, actively participates in high-impact medical AI challenges and has consistently achieved top rankings in competitions related to prostate cancer detection, breast imaging, and cardiac MRI reconstruction, demonstrating strong translational research impact.
Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.
Marcell Richard Fekete is a Research Fellow at Aalborg University Copenhagen's Department of Computer Science within The Technical Faculty of IT and Design. Funded by the Carlsberg Foundation, his work focuses on multilingual modeling for resource-poor languages under Professor Johannes Bjerva's supervision. Education MA in Human Language Technology from Vrije Universiteit Amsterdam (2022) BA in Linguistics from University of Cambridge (2018) His research explores multilinguality, language typology, parameter-efficient fine-tuning methods, and computational linguistics interpretability. He investigates how language models represent linguistic knowledge and compares human-AI language understanding paradigms. Recent publications focus on adapter modules for cross-lingual transfer, phonetic similarity in toponym matching, and creole language benchmarks. His work demonstrates strong connections to machine translation, language modeling, and computational linguistics subfields. Active in academic dissemination, he has presented at major conferences like ACL and NAACL, participated in workshops, and engaged in international collaborations including a guest researcher position at Hungary's Research Centre for Linguistics.
Patrick Cox is an Assistant Professor at Lehigh University, specializing in Cognitive Psychology and Neuroscience . His research explores variability in perception and attention across contexts and expertise levels, with applications in training strategies, autism treatments, and computer vision. Education: Ph.D. in Neuroscience from Georgetown University Previous Position: Postdoctoral Fellow at George Washington University Research interests include Visual Search , Attention , Computational Cognitive Neuroscience , and Object Perception . His interdisciplinary approach combines behavioral testing , EEG , and computational modeling to address both basic and applied questions in cognition. Recent articles focus on experimental design biases , auditory-visual integration , expert performance , and fatigue-induced cognitive decline . Applied work spans autism research , airport security training , and biologically-inspired algorithms . Contact: pac323@lehigh.edu | Chandler-Ullmann Room 109
Sergei Savitsky is a Professor at Wedel University of Applied Sciences, where he serves as Head of the Bachelor of Computer Science program and Senate Chairman. He has been a university lecturer at the institution since October 2008, following professional experience at NXP Semiconductors (2006-2008) and Philips Research Europe (2001-2006). His educational background includes: Doctorate in Engineering (Dr.-Ing.) from Technical University of Dresden (2002) with distinction "summa cum laude" Habilitation (Dr.-Ing. habil.) from Technical University of Dresden (2024) with teaching authorization in "Technical Computer Science" Diplom-Informatiker (equivalent to MSc) in Computer Science from Technical University of Dresden (1998) Savitsky's research focuses on reconfigurable computing systems , with particular expertise in FPGA design, hardware acceleration, and error correction coding. His work bridges theoretical computer science with practical hardware implementation, resulting in numerous patents and publications in top venues. He has made significant contributions to the development of adaptive hardware architectures for forward error correction, which are critical for modern communication and storage systems. His recent publications demonstrate a strong trajectory in optimizing hardware design processes, with particular focus on applying machine learning techniques like self-organizing maps and gradient descent algorithms to improve FPGA placement efficiency. His research spans both theoretical foundations and practical applications, with patents filed in collaboration with industry partners like NXP Semiconductors and ST-Ericsson. Among his recognitions is the Best Paper Award at CENICS 2019 for his work on accelerating FPGA placement algorithms. As an educator, Savitsky teaches courses related to digital system design, including "Computer-aided design of digital systems," where he emphasizes algorithmic aspects of Electronic Design Automation beyond basic digital technology concepts.
Suyong Song is an Associate Professor in both the Department of Economics and the Department of Finance at the Tippie College of Business, University of Iowa. He is also a Henry B. Tippie Research Fellow, recognizing his contributions to research in economics and finance. His academic appointments reflect a strong interdisciplinary profile bridging econometric theory and financial applications. Education: Ph.D. in Economics, University of California-San Diego (2010) M.A. in Economics, Korea University (2004) B.A. in Economics, Korea University (2002) Suyong Song's research lies at the intersection of econometrics, corporate finance, and machine learning. His work focuses on developing and applying advanced statistical methods to economic and financial problems, particularly in the areas of quantile regression, measurement error models, network analysis, and non-Euclidean data. He investigates how social and interfirm networks influence corporate decisions, and applies machine learning techniques to diverse domains such as body shape-income relationships and social media sentiment analysis. His methodological rigor is evident in his publications in top-tier econometrics and finance journals. The recent publications highlight a consistent trend toward integrating modern data science tools—especially machine learning and network analytics—into traditional economic frameworks. His work spans corporate governance, supply chain resilience, monetary policy evaluation, and labor economics, demonstrating broad applicability of his methodological innovations. The articles reflect a strong emphasis on causal inference, robust estimation, and the use of novel data sources such as social media and biometric data. Scientific Awards and Recognitions: Henry B. Tippie Research Fellow Suyong Song actively engages in research advising and has collaborated with numerous scholars across institutions. While specific grant details are not listed, his publication record in high-impact journals suggests successful funding and research leadership. His interdisciplinary collaborations indicate involvement in team-based research projects, particularly in econometrics and applied finance. There is no mention of formal student advising in the provided text, but his role as a research fellow and associate professor implies mentorship responsibilities. He is involved in the broader research ecosystem at the Tippie College of Business, contributing to seminars and research initiatives in business analytics and econometrics. His work with the Tippie Analytics Cooperative and participation in research seminars suggest engagement with data-driven research platforms and academic discourse. His research program appears poised to continue advancing econometric methods and their application to pressing economic and business challenges.
