Lasse Ebdrup Pedersen is a Senior Researcher in the Biotherapeutic Glycoengineering and Immunology group at the Department of Biotechnology and Biomedicine, Technical University of Denmark. His research focuses on antibody engineering, CRISPR/Cas9 applications in cell line development, and glycoengineering for biotherapeutics. He actively supervises multiple PhD students in projects related to bispecific antibodies, stem cell-derived NK cell manufacturing, and machine learning applications in biotechnology. Accepting PhD students Active in multiple collaborative projects Expert in antibody engineering and cell line optimization His research interests span biotechnology, immunology, and genetic engineering with specific emphasis on CRISPR activation screening, antibody development, and computational analysis of binding interfaces. Recent publications highlight advancements in CHO cell engineering, phage display data mining, and bispecific antibody design. Scientific activities include participation in conferences like GlycoBioTec 2019 and Danish Conference on Biotechnology . Current projects focus on scalable manufacturing of iPSC-derived NK cells and Raman spectroscopy with machine learning models.
Mutian He is a PhD candidate and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, affiliated with the Idiap Research Institute and the School of Engineering. He is pursuing his doctoral studies in Electrical Engineering under the supervision of Phil Garner. He holds a B.E. from Beihang University (BUAA) and an MPhil from the Hong Kong University of Science and Technology (HKUST). B.E., Beihang University (BUAA), 2019 MPhil, Hong Kong University of Science and Technology, 2022 PhD Candidate, École Polytechnique Fédérale de Lausanne (EPFL), ongoing His research focuses on spoken language understanding, speech synthesis, and the intersection of speech and language processing with machine learning. He explores efficient model architectures, pretraining strategies, multilingual and low-resource modeling, and the use of large language models in speech tasks. His work spans both theoretical and applied aspects, including distillation to linear-complexity models, robust TTS, and commonsense reasoning via conceptualization. His recent publications at top venues such as ICLR, EMNLP, Interspeech, and KDD demonstrate a strong trend towards efficient and scalable models for speech and language, with increasing emphasis on multilingualism, knowledge transfer, and real-world deployment in low-resource settings. He has also contributed to open-source implementations and community tools like Speech Rankings. Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity (ICLR 2025) Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization (AIJ 2024) The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (Findings of EMNLP 2023) Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding (Interspeech 2023) Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis (2022) Mutian He has served as a teaching assistant for courses including Introduction to Natural Language Processing at HKUST and Introduction to Speech Processing at Idiap. He has also worked on speech synthesis at Microsoft, focusing on robustness and multilingual conditions. He is actively involved in research advising under Phil Garner and has collaborated with multiple researchers across institutions. He is affiliated with the LIDIAP (Laboratory of Intelligent Data Analysis and Pattern Recognition) at EPFL, where he contributes to research in deep learning for speech and language. His lab work involves developing novel neural architectures, conducting experiments on multilingual datasets, and open-sourcing code to promote reproducibility.
Lingkun Kong is a researcher at Rice University, United States, actively contributing to programming languages, compiler optimization, and high-performance computing. His work focuses on regular expression matching, bit-parallel algorithms, and hardware-software co-design. Recent Research Trends: Analysis of automata-based regex engines, GPU acceleration for pattern matching, and domain-specific languages for streaming data. Publications span venues like SPLASH, PLDI, and OOPSLA tracks.
Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.
Eric D. Widmer is a Professor in the Department of Sociology at the University of Geneva, where he conducts research on family and personal configurations, life course trajectories, and social networks. He is a member of I-DEMO and affiliated with the Swiss National Center of Competence in Research LIVES. Widmer developed the Family Network Method (FNM) and applies a configurational perspective influenced by Norbert Elias to study evolving family structures. University of Geneva – Department of Sociology Member, I-DEMO Affiliated with LIVES – Swiss Center of Expertise in Life Course Research Former Visiting Researcher, Research Group Demography and Inequality (2014) His research focuses on family diversity, gendered life courses, and methodological innovations such as multichannel sequence analysis. He investigates how family configurations evolve across the life course, how they relate to health and social capital, and how spatial dispersion is shaped by inclusiveness. His work emphasizes the historical and structural nature of family interdependencies. The most recent publications highlight trends in family ambivalence, conflict structures in older adults’ networks, spatial dispersion, and misleading social norms affecting life trajectories. His methodological contributions include optimizing sequence analysis techniques by borrowing from bioinformatics, enhancing the robustness of life course typologies. Scientific contributions include: Development of the Family Network Method (FNM) Advancement of configurational theory in family sociology Innovations in multichannel sequence analysis Typologies of male and female life trajectories Analysis of vulnerability and social norms across the life course Widmer has supervised and collaborated with numerous researchers across Europe, contributing to major publications and collective volumes. His work is supported by Swiss and European research networks. He has not disclosed specific grants, but his involvement with LIVES and the Swiss Household Panel indicates institutional funding. He is actively involved in research teams focusing on life course analysis, family configurations, and mental health trajectories. His lab-like collaborations include the Pavie group and interdisciplinary teams with bioinformaticians and statisticians, emphasizing methodological rigor and theoretical innovation.
Dr. Tayla Degan is a Research Fellow at the National Drug and Alcohol Research Centre (NDARC), University of New South Wales, and a registered Clinical Psychologist with the Psychology Board of Australia. She holds a Doctor of Philosophy (Clinical Psychology) and a Bachelor of Psychology (Honours) from the University of Wollongong. Current roles: Postdoctoral Research Fellow (NDARC), Adjunct Lecturer (University of Wollongong/Charles Darwin University) Specializations: Substance use disorder treatment, mental health services, health literacy, and stimulant research Key projects: The Tina Trial (mirtazapine for methamphetamine use), Continuing Care Project (telephone-based interventions) Research Focus: Tayla's work bridges clinical psychology and public health , examining how health literacy impacts treatment outcomes, developing technology-driven interventions for substance use, and evaluating continuing care models post-residential treatment. Her methodologies include randomised controlled trials , latent profile analysis , and systematic reviews . Publication Trends: Her recent articles (2025-2019) emphasize: Testing pharmacotherapies like mirtazapine for stimulant dependence Improving health literacy in dual-diagnosis populations Implementing telehealth interventions post-residential care Evaluating longitudinal recovery trajectories via statistical modeling Scientific Recognition: Australian Psychological Society Student Prize (2022) Publication of the Month Award (Illawarra Health and Medical Research Institute, 2022) University of Wollongong and Salvation Army Matching Scholarship (2018-2022) Education & Advocacy: Tayla's PhD (2020) focused on health literacy in mental health services. She has lectured at the University of Wollongong and Charles Darwin University, and contributed to health literacy policy , indigenous health initiatives , and prison mental health care .
Ouri Wolfson is the Richard and Loan Hill Professor of Computer Science at the University of Illinois at Chicago (UIC), with a joint appointment at the University of Illinois at Urbana-Champaign (UIUC). He earned his Ph.D. in Computer Science from NYU's Courant Institute in 1984 and has previously held faculty positions at Columbia University and Technion. His research focuses on database systems, distributed systems, mobile/pervasive computing, and computational transportation science. His work bridges theoretical foundations with practical applications in intelligent transportation, urban computing, and mobile data management. Wolfson has authored over 200 publications spanning databases, transportation systems, and computational neuroscience. His recent work demonstrates strong focus on: Spatio-temporal algorithms for transportation networks Intelligent urban mobility solutions Computational neuroscience applications Resource management in distributed environments Honors include: ACM Fellow AAAS Fellow IEEE Fellow University of Illinois Scholar (2009) ACM Distinguished Lecturer (2001-2003) He founded two technology companies (Mobitrac, Pirouette Software) and has secured significant research funding from NSF, DARPA, NASA, and others, including a $3.1M NSF grant establishing a Ph.D. program in Computational Transportation Science.
Chris Rasmussen serves as an Adjunct Professor in the Investigations Department within the Henry C. Lee College of Criminal Justice and Forensic Sciences at the University of New Haven. He brings extensive frontline experience from international financial crime prevention to his academic role. Professor Rasmussen's professional expertise spans critical areas in financial integrity: Anti-Money Laundering systems implementation Match-Fixing Detection in global sports markets Sports Betting Risk Management protocols Financial Crime Investigation techniques Odds Compilation and manipulation analysis His practical experience directly informs his academic work, with frequent media consultations on high-profile sports integrity cases across soccer, tennis, and handball competitions. Professor Rasmussen regularly provides expert commentary to major international publications including The New York Times, The Athletic, and European media outlets regarding suspicious betting patterns and match-fixing investigations.
Assoc. Prof. Dr. Ayça Türer is an Associate Professor of Cardiology at Yeditepe University Faculty of Medicine, Department of Cardiology. She has been serving as a Doctoral Academic Staff member since 2014 at Yeditepe University, Faculty of Medicine, Department of Internal Medicine Sciences, Cardiology Department. Dr. Türer's educational background includes undergraduate medical education from Ankara University Faculty of Medicine (1999-2005), medical specialization in cardiology at Istanbul Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital (2006-2011), and a doctorate in Molecular Medicine from Yeditepe University Institute of Health Sciences. Her research interests focus on cardiology, particularly myocardial infarction, heart failure, electrocardiography, coronary artery disease, and cardiac biomarkers. She has made significant contributions to understanding prognostic factors in acute coronary syndromes, cardiac imaging techniques, and the relationship between cardiovascular diseases and other systemic conditions. Dr. Türer has published numerous articles in high-impact cardiology journals, with her most recent work focusing on asymmetric dimethylarginine levels in pulmonary hypertension, biomarkers for left ventricular hypertrophy, and novel electrocardiographic patterns for diagnosing myocardial infarction. Her research demonstrates a strong emphasis on clinical applications and diagnostic improvements in cardiovascular medicine. She is an active member of professional organizations including the European Association of Cardiovascular Imaging (since 2018), European Society of Cardiology (since 2016), and Turkish Cardiology Association (since 2014). Dr. Türer has taught various cardiology courses at the undergraduate level, including Heart Failure, Cardiomyopathies, Valvular Heart Diseases, and cardiovascular examination techniques, all delivered in English. She has also authored several books in cardiology, including the "Cardiology Survival Guide" and "Heart Diseases in Women".
Sudeepa Roy is an Associate Professor of Computer Science at Duke University's Department of Computer Science within Trinity College of Arts & Sciences. She joined Duke in Fall 2015 after completing a postdoctoral research associate position at the University of Washington's Department of Computer Science and Engineering, where she worked with Professor Dan Suciu and the database group. Her educational background includes a Ph.D. in Computer and Information Science from the University of Pennsylvania, where she was advised by Professors Susan Davidson and Sanjeev Khanna. During her doctoral studies, she completed two internships at IBM Research, Almaden. Roy's research spans three interconnected thrusts in computer science: (1) Data management, focusing on repairing noisy data, data provenance, and tools for helping novices learn relational queries; (2) Data analysis, investigating interpretable causal inference techniques and meaningful explanations for data analysis pipelines; and (3) Database theory, exploring foundational problems at the intersection of databases, logic, and algorithms. Her work bridges theoretical rigor with practical applications across various domains. Her recent publications demonstrate a strong trajectory in causal inference, database theory, and privacy-preserving data analysis, with a notable emphasis on making complex database operations interpretable and accessible. The publications reveal increasing focus on causal explanations, differentially private query processing, and novel approaches to database repairs and query optimization. VLDB Endowment Early Career Research Contributions Award, 2022 NSF Career Award, 2016 Google Ph.D. Fellowship, 2011 (the first Google fellowship in Structured Data) SIGMOD Best Artifact Award - Honorable Mention, 2023 Roy has successfully mentored numerous graduate and undergraduate students, with former PhD students securing positions at institutions like Yale University, Simon Fraser University, and Megagon Labs. Her research has been supported by multiple significant grants including an NSF Award IIS-2147061 on "FAI: An Interpretable AI Framework for Care of Critically Ill Patients," an NSF Award IIS-2008107 on "Helping Novices Learn and Debug Relational Queries," and an NIH Award 1R01EB025021-01 on causal inference methods for big data. She is an active member of the Duke Database Group (Duke Database Devils) and has served in leadership roles for major conferences including as PC Co-Chair of ACM SIGMOD 2026 and PC Chair of ICDT 2025.
Tatiana Starikovskaya is an Assistant Professor (Maître de Conférences) in the Computer Science Department at École normale supérieure (ENS), Paris, France. She leads the PARSe project (ANR-20-CE48-0001) focusing on approximation and randomized string processing, and participates in AlgoriDAM (ANR-19-CE48-0016). Co-supervises PhD students T. El Ghazi and Gabriel Bathie Co-chaired CPM 2021; served on program committees for STACS, ESA, ICALP, and others Organized CPM summer school (2023) and 'New Horizons of Stringology' workshop (2024) Research Focus Her work centers on algorithms on strings , small-space algorithms , and streaming models , with applications in bioinformatics and data security. Recent research explores trade-offs between time/space complexity in pattern matching, wildcards, and error-tolerant string analysis. Publications Trends Her recent work spans streaming algorithms , compressed data processing , and approximate pattern matching , with collaborations on challenges like k-mismatch problems, Dyck edit distances, and language distance estimation. Education PhD in Mathematics, Lomonosov Moscow State University (2013) M.Sc. in Data Science (Moscow Institute of Physics and Technology/Yandex, 2009) M.Sc. in Mathematics, Lomonosov Moscow State University (2009)
Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks
Javier Courel Ibáñez serves as a Permanent Labor Professor (Associate Professor) in the Department of Physical Education and Sports at the Faculty of Education and Sports Sciences Melilla. His teaching responsibilities include structured tutoring sessions held Wednesdays from 9:00-12:00 and 15:00-19:00 in Office 007 across both academic semesters. His research spans Sports Science , Exercise Physiology , and Biomechanics , with concentrated expertise in athletic performance assessment, injury epidemiology, and evidence-based exercise interventions. Key domains include football (soccer) injury dynamics, padel performance analysis, rehabilitation protocols for tendinopathies, and physical activity applications for aging populations and post-COVID-19 recovery. His methodological approach integrates velocity-based resistance training, systematic literature reviews, and prospective cohort studies to address contemporary challenges in sports medicine. Analysis of his 2022-2025 publications reveals three dominant research trajectories: pandemic-related sports injury patterns (particularly in female football), technology-driven performance assessment (MyotonPRO reliability, resistance-band testing), and special population interventions (older adults, rheumatic conditions). His work consistently bridges laboratory findings with practical applications through EULAR collaborations and sport-specific training protocols.
Róbert Tornai serves as an Associate Professor in the Department of Data Science and Visualization at the Faculty of Informatics, University of Debrecen, Hungary. His institutional affiliation encompasses active participation in the department's core mission of advancing data processing, visualization, and computational methodologies within Hungary's academic landscape. His primary research focuses on high-performance data transfer in supercomputing environments, parallel data processing using memory-safe Rust programming, and virtual collaboration system development. These interconnected domains emphasize optimizing data-intensive workflows while ensuring system security and user accessibility, reflecting contemporary challenges in distributed computing infrastructure. Analysis of his 15 most recent publications reveals dominant trends in high-speed connectionless networking protocols (2020-2025), where he investigates performance optimization, error detection, and encryption for file transfer systems. Significant secondary themes include biometric security applications (iris/voice recognition) and GPU-accelerated image processing techniques leveraging WebAssembly and Vulkan API, demonstrating technical versatility across networking, security, and visualization domains. His scholarly output consistently addresses practical implementation challenges in data transfer and secure systems, with recent work extending into educational technology applications of 3D printing. This trajectory indicates sustained engagement with evolving computational paradigms while maintaining focus on real-world system performance and security requirements.
Furkan Eren Uzyildirim is a Research Fellow at the Department of Computer Engineering, Izmir Institute of Technology (IYTE), where he has worked since 2015. He earned his B.Sc. (2014), M.Sc. (2016), and Ph.D. (2022) in Computer Engineering from IYTE, graduating as a high honors student. His research focuses on Computer Vision and Deep Learning , with a particular emphasis on image segmentation, object recognition, and keypoint matching. He contributes to projects like Safe and secure autonomous driving technologies and Smart agricultural technologies using unmanned vehicle systems , both funded by TÜBİTAK 1512. His recent publications explore unsupervised learning for outdoor plane estimation, advanced keypoint matching algorithms, and 3D scene analysis. These works intersect with subfields such as Autonomous Driving , 3D Scene Understanding , and Feature Extraction . He teaches courses including Numerical Computing and Programming & Data Structures.
