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Saksham SuriView profile

Researcher

• Computer Vision
• Machine Learning
• Diffusion Models
• +12 more

Saksham Suri is a Researcher at Meta Reality Labs , focusing on efficient foundation models and computer vision . He earned his PhD in Computer Science from the University of Maryland, College Park , advised by Prof. Abhinav Shrivastava . Prior to this, he completed his B.Tech in Computer Science and Engineering at IIIT Delhi , where he worked at the IAB Lab and Precog research group. His research interests center around computer vision and machine learning , particularly in reducing supervision requirements and leveraging synthetic data (e.g., from diffusion models and GANs ) for tasks like object detection , image segmentation , lip synchronization , and video instance segmentation . He has proposed novel architectures and loss functions to enhance recognition through generation, addressing challenges in sparsely annotated object detection , autoregressive generative models , and efficient feature transforms for Vision Transformers . Notable publications include LARP (ICLR 2025, under review), UVIS (CVPR Workshop 2024), Gen2Det (CVPR Workshop 2024), LiFT (ECCV 2024), GRIT (WACV 2024), and Diff2Lip (WACV 2024). These works span topics in video tokenization , synthetic data for detection , lightweight feature transforms , and audio-conditioned lip synchronization . He has collaborated with researchers such as Rama Chellappa , Mayank Vatsa , Richa Singh , C.-C. Jay Kuo , and Larry Davis . His academic contributions include open-source code repositories for projects like LARP , SparseDet , ViT Analysis , and LiFT . He has also served as an Area Chair for CVPR (2018–2021) and contributed to educational resources for courses like CS188 at UC Berkeley.

• Artificial Intelligence in Humanities
• Digital Preservation of Cultural Heritage
• Data Curation and Sustainability
• +3 more

Thomas Asselborn is a Researcher at the University of Hamburg, affiliated with the Institute for Humanities-Centered Artificial Intelligence (CHAI) and the Artificial Intelligence in Humanities group under Prof. Dr. Möller. His work focuses on creating sustainable information systems and AI models for humanities research. Current projects include OCR error correction using transformer models Development of chatbots for tabular data evaluation Data curation frameworks for cultural heritage preservation His research intersects artificial intelligence and digital humanities, particularly in digitization and data sustainability challenges. Recent publications address data linking infrastructure, repository usability, and AI applications for manuscript analysis. Asselborn contributes to initiatives like the Excellence Cluster 'Understanding Written Artefacts' (UWA) and participates in workshops on language models in humanities research.

Souvika SarkarView profile

Assistant Professor

• Natural Language Processing (NLP)
• Information Retrieval (IR)
• Artificial Intelligence (AI)
• +3 more

Souvika Sarkar is an Assistant Professor at the School of Computing within the College of Engineering at Wichita State University. Her research focuses on enhancing AI and data science accessibility through interdisciplinary work at the intersection of Natural Language Processing (NLP), Information Retrieval (IR), and AI. She aims to develop context-aware and scalable AI systems capable of semantic understanding of natural language, particularly for broader societal benefit. Dr. Sarkar holds a Ph.D. from Auburn University, where she was honored with prestigious awards including the 100+ Women Strong Outstanding Departmental Annual Graduate Award and Auburn University’s Outstanding Doctoral Student Award. She also earned a master’s in software engineering from Jadavpur University. Prior to academia, she worked as an IT Analyst at Tata Consultancy Services, managing Microsoft SharePoint migrations and enterprise process workflows. Her research interests span NLP applications in education, multilingual AI systems, and ethical AI practices. Notable projects include developing conversational frameworks for K-12 physics education and analyzing annotator bias in hate speech detection systems. She has also explored deploying NLP models on embedded devices to address computational constraints. Publications highlight her work on digital twin security, LLM-driven meta-review systems, and Bangla language processing. Her industry experience bridges academic research with real-world enterprise solutions, reflecting a commitment to practical AI applications.

• Figurative Language in NLP
• Machine Learning
• Arabic NLP
• +3 more

Azza Abugharsa is an Adjunct Professor in the Department of Linguistics at Montclair State University, College of Humanities and Social Sciences. She holds a Ph.D. in TESL and Linguistics (2014, Oklahoma State University) and a Master's in Computational Linguistics (2022, Montclair State University). Her research focuses on applying machine learning to Arabic NLP, particularly metaphor detection in poetry and cross-cultural discourse analysis. Education: Ph.D. in TESL/Linguistics, Oklahoma State University, 2014 M.A. in Computational Linguistics, Montclair State University, 2022 Research interests include figurative language processing, Arabic dialect analysis, and usage-based language acquisition theories. Current projects involve developing LSTM models for metaphor detection in Misurata Arabic poetry and analyzing sentiment in dialectal poems using hybrid machine learning approaches. Her recent work on cross-cultural interactions emphasizes communicative competence in ESL settings. Her technical expertise spans NLP frameworks (Sklearn, Mazajak), deep learning architectures (CNN, LSTM), and discourse analysis methodologies. Ongoing projects include generating new Arabic poems via ML models and expanding her third book manuscript.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +7 more

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

• Industrial Cyber-Physical Systems
• Digital Twins
• Semantic Web Technologies
• +2 more

Gernot Steindl is a PostDoc Researcher at the Automation Systems group (E191-03) at Technische Universität Wien. His research focuses on digital twins, industrial IoT, and semantic technologies for cyber-physical systems. Research domains include: Semantic interoperability in industrial automation Model-based systems engineering Energy management systems Safety and security integration Recent publications demonstrate developments in semantic event-handling frameworks, OPC UA integration, and digital twin architectures. Research consistently applies semantic web technologies to enhance system interoperability and explainability in industrial contexts. Supervised student research includes work on multi-agent systems, fault detection models, and automation system interfaces.

Alexander PachaView profile

Lecturer

• Optical Music Recognition
• Deep Learning
• Music Information Retrieval
• +3 more

Dr. Alexander Pacha is an External Lecturer at TU Wien, specializing in Optical Music Recognition (OMR) and deep learning applications. His work focuses on advancing OMR systems through graph neural networks, dataset development (e.g., DeepScoresV2), and music score analysis. He leads projects like DeepOMR and contributes to the Mixed Reality Lab and Virtual Meeting Room initiatives. Key research areas include music symbol detection, staff/measure segmentation, and cross-document alignment in music scores. Research interests span computer vision, artificial intelligence, and software tools for music information retrieval. His work bridges theoretical advancements with practical applications in digital libraries and music technology. Notable contributions include self-learning OMR frameworks and benchmarking tools for music object detection. Labs & Teams: Mixed Reality Lab (exploring AR/VR in music contexts), Virtual Meeting Room (collaborative digital environments).

• Systems Engineering
• Internet of Things (IoT)
• Cyber-Physical Systems
• +3 more

Amirali Amiri is a PostDoc Researcher at TU Wien's Department of Automation Systems, part of the Faculty of Informatics. His work focuses on systems engineering, IoT integration, and cyber-physical systems. He teaches courses like Fundamentals of Digital Systems and Computer Engineering Project . His research emphasizes supply chain optimization, IoT performance, and IT/OT convergence security. Notable contributions include model-based approaches for IIoT lifecycle management and self-adaptive systems. Recent studies explore MQTT broker deployment architectures, semantic annotation for runtime models, and graph-based safety analysis. His work bridges theoretical frameworks with empirical validation in industrial automation contexts. Amiri has contributed to conferences such as IEEE ICM, ICPS, and ETFA, addressing challenges in cloud-based routing, cybersecurity, and DevOps integration.

• Worst-Case Execution Time Analysis
• Real-Time Systems
• Testing of Embedded Systems
• +2 more

Raimund Kirner is an academic at the Institute of Computer Engineering (E191-01) at Technische Universität Wien , specializing in real-time and embedded systems. His career spans research in Worst-Case Execution Time (WCET) Analysis , Time-Triggered Communication , and Automated Testing for safety-critical systems. He has contributed to projects such as ALL-TIMES, COSTA, and HINT, focusing on timing predictability and composability in real-time systems. Key Research Areas : WCET Analysis, Real-Time Systems, Embedded Testing, Time-Predictable Computing Notable Collaborations : Peter Puschner, Andreas Prantl, Martina Zolda Recent Publications analyze cybersecurity in automotive networks, quantitative interface evaluation for time-triggered systems, and synchronization trade-offs. His work bridges theoretical timing models with practical compiler and hardware solutions for deterministic execution. Awards : Recipient of the Mobilitätsstipendium der Creditanstalt AG (2003) for outstanding dissertation. Advising includes supervising 11 theses from 2002–2014 on topics like Automated Load Balancing , WCET Estimation , and Compiler Timing Models .

• Bioinformatics
• Proteomics
• Genomics
• +4 more

Rolf Apweiler serves as Director of the European Bioinformatics Institute (EMBL-EBI), part of the European Molecular Biology Laboratory, a position he has held since 2015. Previously, he was Associate Director (2012-2015) and led multiple critical protein resources including UniProt and InterPro. His leadership extends to directing Open Targets since 2018 and spearheading EMBL-EBI's contribution to the European COVID-19 Data Platform. Apweiler's research spans Bioinformatics, Proteomics, and Genomics , with significant contributions to protein annotation methods, proteomics data standards, and major biological databases. His work enables comprehensive analysis of proteome sets across entire organisms and has established foundational resources for the global scientific community. His publication portfolio demonstrates consistent leadership in developing critical bioinformatics infrastructure, with major contributions including UniProt, PRIDE, IntAct, and InterPro. These resources form the backbone of modern proteomics research and have been widely adopted across the life sciences. HUPO Distinguished Achievement Award in Proteomics (2004) EMBO Member (2011) ISCB Fellow (2015) President of Human Proteomics Organisation (2007-2008) As Director of EMBL-EBI, Apweiler oversees one of the world's leading bioinformatics institutions, managing extensive research programs, international collaborations, and major data infrastructure projects. His leadership in Open Targets demonstrates significant engagement with translational research connecting basic science to drug discovery. Apweiler's teams maintain several critical global resources including PRIDE (proteomics identifications), IntAct (molecular interactions), and UniProt (protein knowledgebase), forming an integrated ecosystem for biological data analysis.

• Genetic Epidemiology
• Metabolic Disorders
• Cardiometabolic Diseases
• +3 more

Jun Liu is a Novo Nordisk Postdoctoral Research Fellow at the Nuffield Department of Population Health, University of Oxford. His research focuses on integrating multi-omics approaches to study cardiometabolic diseases, neurological disorders, and liver diseases. He holds a clinical medicine degree from Shanghai Jiao Tong University (2013), an MSc in genetic epidemiology from Erasmus Medical Center (Rotterdam), and a PhD in multi-omics integration for type 2 diabetes research. His work utilizes large-scale datasets such as the UK Biobank to explore genetic, metabolic, and proteomic biomarkers. Liu's research interests include metabolic dysfunction-associated liver diseases, genetic epidemiology, and the interplay of metabolome and microbiome in neurological conditions. His recent studies highlight novel genetic insights into amino acid roles in liver disease and the impact of ethnicity on dementia risk. Collaborations with Professor Cornelia van Duijn and Dr. Joanna Howson focus on cross-omics analyses of non-alcoholic fatty liver disease. His publications span journals like Nature , medRxiv , and J Hepatol , reflecting contributions to omics integration, Mendelian randomization, and multi-ancestry genetic studies. Key themes include biomarker discovery, therapeutic target identification, and population health disparities.

Eduardo OriasView profile

Research Professor

• Genetics
• Genomics
• Molecular Biology
• +3 more

Eduardo Orias is a Research Professor and Professor Emeritus in the Department of Molecular, Cellular, and Developmental Biology at the University of California, Santa Barbara (UCSB). He earned his Ph.D. in Zoology from the University of Michigan in 1960, focusing on Tetrahymena genetics. His career spans over six decades, with visiting professorships at institutions such as the Carlsberg Foundation, Albert Einstein College of Medicine, and universities in Italy and Japan. He has held leadership roles in the Society of Protozoologists and editorial boards of journals like Journal of Protozoology . His research centers on Tetrahymena thermophila , a model organism for eukaryotic cell biology, genomics, and DNA rearrangement. Key contributions include mapping the Tetrahymena genome, coordinating the International Tetrahymena Genome Project, and investigating mating type switching and DNA rearrangement mechanisms. Collaborations, such as with Prof. Wei Miao’s group in China, have advanced understanding of genomic processes in ciliates. Dr. Orias has organized major international conferences on Tetrahymena genomics and pioneered genetic tools like gene knockout and RNAi in this organism. His work bridges fundamental biology with applications in comparative genomics and environmental science, as seen in studies on quantum dot toxicity in microbial systems.

Yuchao JiangView profile

Associate Professor

• Statistical Genomics
• Bioinformatics
• Single-cell Omics
• +3 more

Dr. Yuchao Jiang is an Associate Professor at Texas A&M University (TAMU), holding a joint appointment in the Department of Statistics (primary) and the Department of Biology within the College of Arts and Sciences. He joined TAMU in 2023 and previously served as an Associate Professor at the University of North Carolina at Chapel Hill. His academic background includes a B.Sc. in Biological Sciences from Cornell University (2012), an M.Sc. in Statistics from the Wharton School at the University of Pennsylvania (2014), and a Ph.D. in Genomics and Computational Biology from the University of Pennsylvania (2017). Dr. Jiang's research focuses on statistical and computational methods in genetics and genomics, particularly in analyzing large-scale cohort studies of human health and disease. His lab develops tools for detecting structural variants, assessing tumor heterogeneity, and studying genome-wide DNA damage and repair using single-cell omics approaches. Key projects include multi-omic analysis of circadian rhythms, HIV latency reversal, and tumor phylogeny inference. He currently co-directs the Center for Statistical Bioinformatics at TAMU and serves on the NIH's Cancer and Hematologic Disorders study section. His work is supported by grants like R35 GM138342 from the National Institute of General Medical Sciences. Notable contributions include the development of computational frameworks such as SCOPE, SCDC, and Destin for single-cell data analysis. His publications span journals like Cell Systems , PLOS Genetics , and Nature Communications .

Volkan AtalayView profile

Professor

• Machine Learning
• Bioinformatics
• Data Stream Analysis
• +3 more

Volkan Atalay is a Professor in the Department of Computer Engineering at Middle East Technical University (METU), Ankara, Turkey. His research focuses on machine learning applications in bioinformatics, data stream analysis, and drug discovery. He has held leadership roles including Vice President for Research at METU (2010–2016) and chaired the Department of Computer Engineering (2007–2008). Education: Ph.D. in Computer Science from Université Paris Descartes (1993), followed by postdoctoral work at Virginia Tech (2004–2005). Research Interests: Machine learning for bioinformatics (drug-target interaction prediction, protein function annotation), data stream clustering algorithms, and deep learning applications in drug discovery. Key projects include CROssBAR (biomedical knowledge graphs) and DEEPScreen (drug-target interaction prediction tools). Recent publications (2020–2023) emphasize hybrid deep learning models, data fusion for energy forecasting, and open-source protein annotation benchmarks. Awards include the Best Poster Award at GLBIO 2017 for iBioProVis visualization work. Current grants: SmartHome Consortium (data stream analytics) and projects on biomedical relation curation. Labs/Teams: Leads the Cancer Systems Biology Laboratory and collaborates with international consortia like the CAFA challenge.

Ed GehringerView profile

Professor

• Computer Science Education
• Peer Assessment Systems
• Educational Technology
• +1 more

Ed Gehringer is Professor in Electrical and Computer Engineering at North Carolina State University. Research specializes in educational technologies, particularly automated peer assessment systems and computer science pedagogy. Authored books on parallel processing and capability architectures. Recent publications focus on AI-enhanced assessment tools and software engineering education. Research trajectory shows progression toward intelligent tutoring systems and automated feedback mechanisms for programming education. Current projects develop natural language processing techniques to evaluate student submissions and peer reviews.