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Björn AhlView profile

Professor

• Constitutional Law
• Human Rights Law
• Immigration Law
• +7 more

Prof. Dr. Björn Ahl is a full Professor at the University of Cologne in the East Asian Seminar – China Studies department. His research focuses on Constitutional Law , Human Rights Law , Immigration Law , and Public International Law with particular emphasis on Chinese Fundamental Rights Jurisprudence and Migration Policy . He has extensive publications on Chinese judicial reforms , legal globalization , and authoritarian legal systems . Research Projects: Chinese Fundamental Rights Jurisprudence: Constitutional Development under One-Party Rule Chinese Immigration Law and Policy: Perspectives of Legislators, Administrators and Immigrants Current Publications: Law and Social Credit in China (2024) Due Process in Post-2013 China (2022) Chinese Court Reforms (2021) Contact: Email: bjoern.ahl@uni-koeln.de Phone: +49 221 470 5421 Office: Room 3.01, Dürener Str. 56-60 His research interests center around legal and constitutional developments in China, particularly focusing on judicial reforms , human rights compliance , and migration governance . The majority of his publications examine authoritarian legal systems , judicial transparency , and cross-border legal integration through frameworks like the Belt and Road Initiative . Key methodologies include data-driven analysis of court decisions and comparative treaty studies . Prof. Ahl's work intersects with broader debates on global constitutionalism and Chinese legal adaptation in international contexts. His projects often analyze Party-state legal dynamics , migration policy reforms , and data protection mechanisms in Chinese governance.

Cezary CzaplewskiView profile

Professor

• Coarse-grained molecular modeling
• Protein structure prediction
• Molecular dynamics simulations
• +3 more

Professor Cezary Czaplewski of the University of Gdańsk is a leading researcher in computational chemistry and coarse-grained molecular modeling. Affiliated with the Faculty of Chemistry and the Department of Theoretical Chemistry , he heads the UNRES server for protein simulations and contributes to projects like Multi-GPU UNRES and MAGENTA . His work bridges physics-based simulations with experimental data integration. Key roles : Professor, Computational Biophysics, University of Gdańsk Projects : EuroHPC PL, Harmonia9, Enerliq, Parylens Research Focus : His research spans coarse-grained modeling , protein structure prediction , and hydrophobic interaction studies , with applications in virology (SARS-CoV-2), antimicrobial peptide design, and large-scale biomolecular simulations. He specializes in GPU-accelerated algorithms and multi-GPU implementations for scalable systems. Teaching : Currently teaches Molecular mechanics & dynamics , Introduction to Python programming , and Parallel programming (Bioinformatics III) . Past courses include Electronic chemical diagnostics and Software in biomacromolecular calculations for PhD students. Scientific Contributions : Developed the UNRES server for polypeptide simulations, extended the UNRES force field to nucleic acids and membrane proteins, and participated in multiple CASP/CAPRI experiments for protein structure prediction. His 2025 publications focus on time-averaged restraints and multi-GPU scalability. Contact : cezary.czaplewski@ug.edu.pl Location : Room B328, Faculty of Chemistry, University of Gdańsk

• Environmental Microbiology
• Biogeochemical Cycles
• Microbial Genomics
• +3 more

Corinne PETIT is a 1st Class Research Officer at the Université Clermont Auvergne (UCA), affiliated with the CNRS UMR 6023 laboratory 'Microorganisms: Genome and Environment'. Her research focuses on microbial contributions to biogeochemical cycles, particularly the chlorine cycle in natural ecosystems, integrating genomic and environmental data to understand microbial roles in carbon sequestration and environmental preservation. Education: Doctorate in Parasitology (1993–1997), Université Clermont Auvergne, awarded highest honors. Research Themes: Environmental genomics, microbial ecology, biogeochemical cycles, and functional diversity of microorganisms in aquatic systems. Key Projects: Studying methane oxidation in Lake Pavin, developing DNA biochips for environmental monitoring, and analyzing microbial communities in volcanic soils and deep lakes. Awards: Highest honors with congratulations from the jury for her doctoral thesis on microsporidian genomics (1997). Grants & Funding: Technological Research Grant (BRT), Auvergne Region (2007). Prévoir regional grant (2006). Labs & Teams: Integrated Genomics of Microbial Interactions team (UMR CNRS 6023). Active in collaborative projects on microbial diversity and bioremediation.

• Data Management
• Data Warehousing
• Data Mining
• +7 more

**Laks Lakshmanan** is a Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS) and the Data Science Institute. His research focuses on data management, data mining, social network analysis, and applications of machine learning in NLP and recommender systems. He supervises PhD and Master's students in topics like influence maximization, graph algorithms, and LLM optimization. **Affiliations**: ICICS, Data Science Institute, CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action **Education**: Not explicitly stated in provided text, but inferred as资深学者 in computer science **Research Interests**: Data warehousing, social network analysis, recommendation systems, algorithmic social science, and computational methods for combating misinformation. His work bridges theory and practice, with contributions to dense subgraph discovery, viral marketing models, and efficient LLM routing. **Articles Trends**: Recent work emphasizes scalable graph algorithms (densest subgraph discovery), efficient LLM applications (cost-effective routing), and social influence dynamics. Cross-modal consistency in multimodal models and clinical event prediction in healthcare are emerging focuses. **Advising**: Supervised over 20+ doctoral/master's students since 2010, with topics ranging from NLP architectures to social network analytics. Current openings for students in Master's/PhD, plus visiting researchers.

Florian MischekView profile

Researcher

• Operations Research
• Scheduling Algorithms
• Artificial Intelligence
• +3 more

Florian Mischek is a researcher affiliated with TU Wien's Forschungsbereich Databases and Artificial Intelligence department. His work focuses on optimization problems in industrial scheduling, particularly in test laboratories and healthcare environments. He holds a BSc, Dipl.-Ing (engineer's diploma), and a Dr.techn (doctorate) in technical sciences. Research interests include scheduling algorithms, hyper-heuristics, constraint programming, reinforcement learning, and their applications to real-world challenges like test laboratory operations and pandemic-era healthcare staffing. His contributions span both theoretical advancements in optimization frameworks and practical system implementations for automated scheduling. Notable publications explore problem-independent hyper-heuristics, reinforcement learning for cross-domain optimization, and local search frameworks for industrial scheduling. He has collaborated extensively with Prof. Nysret Musliu on projects involving test laboratories and nurse rostering problems. His work often bridges mathematical programming and heuristic approaches, emphasizing practical applicability.

Karim ChattiView profile

Professor

• Pavement Engineering
• Transportation Engineering
• Mechanistic Modeling of Pavement Systems
• +4 more

Dr. Karim Chatti is a Professor of Civil and Environmental Engineering at Michigan State University's College of Engineering. He serves as Editor-in-Chief of the ASCE Journal of Transportation Engineering, Part B: Pavements and is an internationally recognized expert in pavement engineering. His research focuses on mechanistic modeling of pavement systems, including dynamics, vehicle-pavement interaction, pavement preservation, and long-term performance. Sponsored by agencies like FHWA and USDOT, his work addresses challenges in pavement design, maintenance, and sensor-based monitoring. Education: Ph.D., Civil Engineering, University of California-Berkeley (1992) M.S., Civil Engineering, Michigan State University (1987) B.S., Civil Engineering, Michigan State University (1985) Research Interests: Dr. Chatti's research spans pavement dynamics, mechanistic modeling, and sustainable infrastructure. He develops methodologies for predicting pavement response to traffic loads and environmental factors, with applications to continuous health monitoring and preservation strategies. His work integrates sensor technology and data analytics to enhance infrastructure resilience. Publications Trends: Recent articles highlight advancements in pavement monitoring techniques, rolling resistance measurement, and seasonal/diurnal effects on pavement performance. Collaborations with institutions like the FHWA and CalTrans underscore practical applications of his findings. Awards: Fellow, American Society of Civil Engineers (2018) Robert G. Packard Award (2008) ASCE/FHWA LTPP Data Analysis Award (2002) Grants and Service: Research funded by USDOT, FHWA, and NCHRP. Active in editorial roles and professional societies, including ASCE. His academic service includes contributions to curriculum development using LTPP databases.

• Obstetrics and Gynecology
• Endometriosis
• Gynecologic Ultrasound
• +3 more

Jayesh Tigdi serves as an Assistant Professor in the Department of Obstetrics & Gynecology at McMaster University, focusing on advancing diagnostic methodologies for endometriosis and improving patient-centered care through innovative ultrasound techniques. His clinical and research activities are centered within McMaster's gynecology division, where he contributes to both patient care and academic training. Dr. Tigdi's research program emphasizes endometriosis diagnostics via transvaginal ultrasound, with notable contributions including the development of the "Triangle Sign" for identifying obliterated cul-de-sac in retroverted uteri and standardized protocols for uterosacral ligament evaluation. His work extends to patient-reported outcomes through validated questionnaires assessing endometriosis ultrasound experiences, and surgical innovation in fibroid management during pregnancy. Recent publications demonstrate expertise in superficial endometriosis detection, surgical time prediction models, and educational interventions for gynecologic patient experiences. Analysis of his 15 most recent publications (2022-2025) reveals a cohesive trajectory toward integrating ultrasound precision with patient-centered metrics . Key trends include standardization of IDEA consensus protocols for endometriosis imaging, development of dynamic ultrasound maneuvers for complex pelvic anatomy, and rigorous validation of patient experience tools. His work bridges diagnostic accuracy with quality-of-life outcomes, particularly for endometriosis patients with mental health comorbidities. Dr. Tigdi maintains active collaboration with Mathew Leonardi, as evidenced by co-authored publications on endometriosis ultrasound techniques. While the VIVO database indicates no currently loaded grants, his publication velocity suggests ongoing research support. He contributes to medical education through initiatives like the "Speak With the Residents Week" program connecting residents with medical students. His laboratory and team affiliations are not explicitly documented, though his work implies integration within McMaster's gynecologic surgery and ultrasound research units.

• Auditing
• Audit Data Analytics
• Accounting Information Systems
• +2 more

Nuriddin Tojiboyev is an Assistant Professor of Accounting and Information Systems at the School of Business, The College of New Jersey (TCNJ), since 2022. He holds a Ph.D. in Management with an Accounting Information Systems concentration from Rutgers University (2022), a Master of Accountancy from West Virginia University (2014), and a Bachelor of Business Administration from the University of Westminster (Uzbekistan, 2010). As a Certified Public Accountant (CPA, Montana, 2015), his expertise bridges cost accounting, indirect tax accounting, and data analytics. Research Focus: Development of data analytics models to enhance auditing effectiveness and efficiency. Professional Experience: Lecturer at Rutgers University (2021-2022), Graduate Teaching Assistant at Rutgers (2017-2021), and teaching/industry roles in Uzbekistan and West Virginia (2010-2015). Notable Contributions: Collaboration with AICPA on research projects, training sessions for PCAOB Investigators on Audit Data Analytics, and publications on SQL applications in auditing and risk aggregation methods. He serves on TCNJ's Undergraduate Curriculum Committee, Graduate Curriculum Committee, Open Educational Resources Council, and Faculty Senate.

Andrea Espejo FreireView profile

Assistant Professor

• Oncology
• Sarcoma Research
• Translational Medicine
• +5 more

Andrea Espejo Freire, MD serves as Assistant Professor of Medicine in the Division of Hematology, Oncology, and Transplantation at the University of Minnesota Medical School. Her clinical expertise focuses on sarcomas and rare tumors, with active research in translational oncology and clinical trials for desmoid tumors, gastrointestinal stromal tumors (GIST), and leiomyosarcoma. Dr. Espejo Freire's research centers on multiomic characterization of sarcoma biology, particularly DNA damage response pathways, tumor immunology, and mechanisms of therapy resistance. Her work integrates genomic, transcriptomic, and clinical data to identify biomarkers for treatment selection and develop novel therapeutic strategies for rare malignancies. Key methodologies include national database analyses, circulating tumor DNA monitoring, and comparative effectiveness studies of systemic therapies. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) molecular subtyping of sarcomas by age group and anatomical origin, (2) optimization of targeted therapies (particularly sorafenib) for desmoid tumors, and (3) exploration of immunotherapy resistance mechanisms in leiomyosarcoma. Her work consistently bridges laboratory findings with clinical applications through retrospective cohort studies and single-institution case series.

Odysseas PapapetrouView profile

Assistant Professor

• Streaming Data Processing
• Big Data Management
• Approximation Algorithms
• +5 more

Odysseas Papapetrou serves as Assistant Professor in the Information Systems group within the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His academic journey spans multiple prestigious institutions across Europe, establishing him as a significant contributor to the field of data management and stream processing. Academic Credentials: BSc in Computer Science from University of Cyprus MSc in Advanced Information Technologies from University of Cyprus MSc in Computer Science from University of Saarland and Max Planck Institute (2005) PhD in Computer Science from University of Hannover and L3S Research Center (2011) Dr. Papapetrou's research focuses on cutting-edge challenges in data-intensive systems, with particular emphasis on real-time processing of streaming data. His work develops approximation algorithms for distributed Big Data management, distributed data streams processing, and sketching/summarization techniques. He has made significant contributions to cloud computing aspects including cloud federations, cloud-based stream processing, and privacy-preserving query execution. His research bridges theoretical foundations with practical implementations, addressing the growing need for efficient data processing in high-velocity environments. Analysis of his recent publications reveals a consistent focus on multivariate data analysis, with increasing specialization in spatial data streams and advanced similarity search techniques. His work demonstrates a progression from foundational stream processing to increasingly complex multivariate scenarios, reflecting the evolving challenges in Big Data analytics. The publications show strong collaboration patterns with researchers across Europe, particularly in the Netherlands and Switzerland. Dr. Papapetrou actively contributes to the academic community through service roles including frequent participation as PC member and reviewer for top-tier data management conferences and journals such as TKDE and VLDB Journal. He serves as a grants evaluator for the Hong Kong Research Grants Council (RCG) and other funding schemes. His current educational activities at TU/e include teaching Capita Selecta Databases, Seminar Datamanagement, Data-intensive Systems and Applications, Big Data Management, and Datamodeling and Databases. He currently leads research within the Database Group at TU/e, focusing on developing novel techniques for stream processing and Big Data management. His work has received funding from the European Union's Horizon Europe research and innovation program STELAR under grant agreement No. 101070122, supporting his research on advanced data stream processing techniques.

Talia KonkleView profile

Professor

• High-level vision
• Visual representation
• Object recognition
• +5 more

Talia Konkle is a Professor of Psychology in the Faculty of Arts and Sciences at Harvard University. She directs the Konkle Lab, which is part of the joint Harvard Vision Sciences Lab co-led with Professor George Alvarez. Her research focuses on understanding how the human visual system is organized and how it represents the world around us, with particular emphasis on high-level vision including object recognition, scene perception, and visual memory. William James Hall 780, 33 Kirkland St, Cambridge, MA Phone: (617) 495-3886 Email: talia_konkle@harvard.edu Professor Konkle's research explores how representational spaces of the mind are mapped onto the surface of the brain. Her work examines how we recognize and organize our knowledge about objects, scenes, and actions and events. She uses human functional neuroimaging as her primary methodology, combined with computational modeling and behavioral approaches. Her recent publications reveal a strong focus on the intersection of neuroscience and artificial intelligence, with particular emphasis on understanding how brain organization relates to machine learning models. The research trends show increasing integration of deep learning approaches to model visual representation, with significant work on cortical topography, visual memory capacity, and the relationship between real-world object properties and neural organization. Lila R. Gleitman Award from Cognitive Science Society Professor Konkle has mentored numerous graduate students, postdocs, and research fellows, including current members like Fenil Doshi, Jacob Prince, and Haley Keglovits. Her lab has received funding through various grants including those from the Kempner Institute for Natural and Artificial Intelligence. She collaborates extensively with Professor George Alvarez and other researchers in the Vision Sciences Lab. The Konkle Lab maintains several important resources for vision research, including the Reachspace Database with over 10,000 images of reachable environments, various stimulus sets for studying object recognition and visual memory, and computational tools for analyzing visual representations. Professor Konkle also values work-life balance, noting that she is a mother of two daughters (ages 7 and 9) and a cancer survivor.

• Database Systems
• Stream Processing
• Internet of Things (IoT)
• +7 more

Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.

Songmei YuView profile

Professor

• Web Mining
• Distance Learning
• Data Warehouse and Data Modeling
• +5 more

Dr. Songmei Yu is a Professor in the Department of Computer Science at Felician University’s School of Business and Information Sciences. She holds a PhD in Information Technology from Rutgers University (2006), an MS in Computer Science from SUNY Stony Brook (2000), and a BS in Civil Engineering from Sichuan University. Her academic positions at Felician University have included Professor (2021–present), Associate Professor (2013–2021), and Assistant Professor (2008–2013). Research Interests: Web Mining for community discovery, distance learning effectiveness, data warehouse optimization, and data modeling. Teaching Interests: Database systems, data mining, programming, game development, and web scripting. Her publications focus on social media analytics, educational technology, and spatial data warehouse optimization. She has collaborated extensively with Dr. Sofya Poger and others on adaptive teaching methods and influence modeling in mobile platforms. Contact: YuS@felician.edu.

Besim BilalliView profile

Researcher

• Machine Learning
• Data Governance
• Big Data
• +3 more

Dr. Besim Bilalli is a researcher at the Universitat Politècnica de Catalunya (Technical University of Catalonia) within the Barcelona School of Informatics (FIB) and the Department of Service and Information Systems Engineering . He contributes to the inSSIDE and DTIM research groups, focusing on data governance, machine learning, and software engineering for data-intensive systems. His research interests include: Fair and interpretable machine learning Knowledge graph integration with language models Automated data lifecycle management Pre-processing pipeline automation AI ethics in healthcare applications Key projects include: EXPeriment driven analytics Automated data governance frameworks Erasmus Mundus doctoral program AutoETL pipeline generation Contact: besim.bilalli@upc.edu | ORCID: 0000-0002-0575-2389

Silvia OttavianiView profile

Senior Lecturer

• Non-coding RNAs
• Pancreatic Cancer
• MicroRNAs
• +4 more

Dr. Silvia Ottaviani is a Senior Lecturer in Molecular Cell Biology at the Bioscience Department, School of Science and Technology, Nottingham Trent University (NTU), and Group Leader at the John van Geest Cancer Research Centre. She holds an Honorary Lecturer position at Imperial College London and is a Fellow of the Higher Education Academy (FHEA). Her academic journey includes a BSc in Medical and Pharmaceutical Biotechnology from San Raffaele University (Milan), an MSc in Molecular Medicine, and a PhD in Cancer Biology from Imperial College London. Her research focuses on non-coding RNAs (microRNAs and lncRNAs) in epithelial cancers, particularly pancreatic cancer. Key interests include: Regulation of metastatic processes through tumor microenvironment interactions Circulating microRNAs as clinical biomarkers Nanoparticle-based RNA therapeutics delivery Tumor heterogeneity via single-cell RNA sequencing COVID-19 biomarker discovery and therapeutic repurposing Current projects explore RNA-based interventions for advanced pancreatic cancer. Publication analysis reveals three primary domains: Pancreatic cancer biology (40% of recent works) COVID-19 therapeutics (35% since 2020) Novel drug delivery systems (25%) Her research consistently bridges molecular oncology with clinical translation, particularly in RNA therapeutics and biomarker development. Awards & Recognition: Finalist: COVID Unsung Hero Awards (Women in STEM) Grants & Collaborations: Secured £365k in funding including: British Society for Antimicrobial Chemotherapy (£25k, co-PI) Action Against Cancer (£340k, co-PI) Actively supervises postgraduate researchers and welcomes PhD applicants in molecular oncology. Leads an independent research group at the John van Geest Cancer Research Centre focused on translational RNA biology. Maintains editorial roles at Oncogene and Cancer Gene Therapy , and holds memberships in BACR, EACR, Biochemical Society, and FEBS.