جست‌وجوی استاد و پژوهشگر

Uma Pradhanمشاهده پروفایل

دانشیار

• Anthropology
• Development studies
• Applied linguistics and educational linguistics
• +۶ مورد دیگر

Dr. Uma Pradhan is an Associate Professor at UCL's Institute of Education (IOE), Faculty of Education and Society, and serves as Deputy Programme Leader for BA Education, Culture, and Society. She also holds the position of Inclusion Co-Lead for the Department of Education, Practice and Society (EPS). At UCL, she is actively involved with the Centre for Education and International Development (CEID) and the Centre for the Study of South Asia and the Indian Ocean World. Prior to her current position, Dr. Pradhan was a Lecturer and Leverhulme Early Career Fellow at the University of Oxford from 2018 to 2022, and then a Lecturer at University College London from 2022 to 2024 before her promotion to Associate Professor in 2024. Dr. Pradhan's research spans multiple interconnected fields including Anthropology, Development Studies, Applied Linguistics, Humanitarian Disasters, Conflict and Peacebuilding, and Educational Technology. Her work particularly focuses on South Asian educational contexts, with extensive research on Nepal's educational landscape. She examines how educational policies intersect with social structures, linguistic diversity, and political transformations, with special attention to marginalized communities and post-disaster educational reconstruction. Her research demonstrates a consistent commitment to addressing educational inequality and exploring how education can serve as a vehicle for social justice. Dr. Pradhan's scholarly output reveals a strong focus on the intersections between language, technology, and education in South Asia. Her recent publications examine how artificial intelligence is being used for indigenous language preservation, the impact of the pandemic on educational systems across South Asia, and the bureaucratic mechanisms that can inadvertently reproduce social inequality through scholarship programs. Her work consistently connects theoretical frameworks with practical implications for educational policy and practice. Leverhulme Early Career Fellow Fellow of Higher Education Academy (FHEA) As an academic leader, Dr. Pradhan supervises research degrees and engages in collaborative projects and consultancy work. Her research aligns with several United Nations Sustainable Development Goals, particularly Quality Education (SDG 4), Reduced Inequalities (SDG 10), Peace, Justice and Strong Institutions (SDG 16), and Partnerships for the Goals (SDG 17). Her position at the intersection of multiple research centers at UCL enables her to foster interdisciplinary collaborations that address complex educational challenges in global contexts.

• Speech Recognition
• Speaker Verification
• Language Identification
• +۷ مورد دیگر

Professor Jan Černocký serves as Head of Department at the Department of Computer Graphics and Multimedia (DCGM) within the Faculty of Information Technology at Brno University of Technology (FIT VUT). With a professional email cernocky@fit.vut.cz and office L221.2, he maintains an active research profile with numerous publications spanning over 20 years in the field of speech processing and recognition. His work is well-documented through multiple research identifiers including ORCID iD 0000-0002-8800-0210, Scopus Author ID 6604040821, and Researcher ID M-7494-2019. Professor Černocký's research interests focus primarily on advanced speech processing technologies, with particular emphasis on speech recognition, speaker verification, language identification, and multimodal systems. His work demonstrates a strong trajectory from traditional speech processing techniques toward modern deep learning approaches, especially in self-supervised learning for speech applications. Recent publications show his leadership in developing benchmarks like TS-SUPERB for target speech processing and innovative methods for speaker verification using transformer models. His research group at BUT has made significant contributions to multi-channel speech processing, target speech extraction, and speaker diarization systems. The analysis of Professor Černocký's recent publications (2023-2025) reveals several key trends in his research direction. There's a clear shift toward self-supervised learning approaches for speech processing, with numerous papers exploring how pre-trained models can be adapted for speaker verification, target speech extraction, and multi-channel processing. His work increasingly incorporates transformer architectures and attention mechanisms, reflecting the broader trends in speech processing research. The 2024 publications particularly highlight work on multimodal analysis (BESST dataset for stress detection) and practical applications of speech technology for social inclusion. Throughout his career, Professor Černocký has maintained strong collaborative relationships with researchers across Europe and internationally, evidenced by his extensive publication record with co-authors from multiple institutions. His leadership role as Head of Department at DCGM places him at the center of speech processing research at Brno University of Technology, where his team continues to produce cutting-edge research in speech technology.

• Synthetic Biology
• Metabolic Engineering
• Yeast Cell Factories
• +۵ مورد دیگر

Verena Siewers is a Research Professor at the Department of Biology and Biological Engineering, Chalmers University of Technology. Her work focuses on synthetic biology and metabolic engineering of yeast cell factories for producing biofuels, pharmaceuticals, nutraceuticals, and bioplastics, with particular emphasis on developing biosensor tools for pathway optimization. Key research themes: yeast-based biosensors, lipid metabolism engineering, CRISPRi/a applications, and dynamic gene regulation Notable projects include: Development of acetic acid tolerance mechanisms Optimization of fatty acid ethyl esters production Engineering phosphoketolase pathways for acetyl-CoA overproduction Her recent articles reveal trends in: CRISPR-mediated pathway engineering Stress response transcriptional profiling Heterologous plant gene expression in yeast Promoter and transcription factor engineering Funding sources: VINNOVA Novo Nordisk Foundation Carl Tryggers Stiftelse EU Horizon grants Swedish Research Council (VR) Formas

• Computational Linguistics
• Machine Translation
• Text Simplification
• +۸ مورد دیگر

Sergiu Nisioi is an Associate Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with expertise in computational linguistics, machine translation, and text simplification. He bridges cognitive science with NLP through eye-tracking and EEG research, while also exploring sound art and digital autonomy via initiatives like HYPHA.ro. Current projects include PN-IV-P2-2.1-TE-2023-2007 (text complexity/readability), Legal Document Processing , and Europarl Dialectal Corpora Research spans computational psycholinguistics , LSTM-based translation models , and algorithmic composition for sound art His work integrates interdisciplinary methodologies, combining EEG signal processing for architecture data with the University of Architecture, and DSP for ecological projects at chlorophylla.live.

Jovan Stojkovicمشاهده پروفایل

استادیار

• Cloud Computing
• Datacenters
• Cloud-Native Workloads
• +۷ مورد دیگر

Jovan Stojkovic is an incoming Assistant Professor at the Department of Computer Science at the University of Texas at Austin, set to join in Fall 2026. Prior to his appointment at UT Austin, he will spend a year at Meta working with the AI and Systems Co-design group. His research focuses on cloud computing and datacenters, with particular emphasis on cloud-native workloads and machine learning inference. Education: PhD in Computer Science from the University of Illinois at Urbana-Champaign, advised by Professor Josep Torrellas Undergraduate studies at the School of Electrical Engineering, University of Belgrade, Serbia, where he was recognized as the best student of the Computer Engineering and Information Theory Department every year from 2017-2020 Research Interests: Jovan's research focuses on cloud computing and datacenters , with two primary domains: Cloud-native workloads , such as microservices and serverless computing. He investigates how to co-design novel hardware platforms and software systems that deliver orders-of-magnitude improvements in performance, energy efficiency, and resource utilization for these emerging workloads. Machine Learning (ML) inference , particularly large language models (LLMs). His work addresses the challenges of ML inference through smart scheduling, workload placement, and system-level configuration tuning to reduce energy, power, and thermal overheads while maintaining performance and accuracy guarantees. Publication Trends: Jovan's publications demonstrate a strong focus on optimizing cloud infrastructure for emerging workloads. His research spans across serverless computing, microservices, and large language model inference. A clear trend emerges in his work: addressing the performance, energy efficiency, and resource utilization challenges of modern cloud workloads through innovative hardware-software co-design approaches. His most recent work shows increasing focus on LLM inference optimization, particularly in the areas of thermal management, power efficiency, and scheduling for many-adapter environments. Awards and Honors: HPCA Best Paper Award (2025) IEEE MICRO Top Picks Honorable Mention (2024) 6 patents with IBM and Microsoft on: Serverless systems, Processor overclocking in the cloud, and Energy-efficient LLM inference W. J. Poppelbaum Memorial Award (2025) for hardware and architecture innovation Mavis Future Faculty Fellowship (2024–2025) Invited to present at 11th Heidelberg Laureate Forum (2024) Kenichi Miura Award (2022) for excellence in High Performance Computing Multiple student travel grants to ISCA, MICRO, ASPLOS, and HPCA Advising and Grants: Jovan is actively seeking prospective PhD students for his research group at UT Austin. His research has been supported through collaborations with major tech companies including IBM, Microsoft, and Meta. His six patents with IBM and Microsoft demonstrate the practical impact of his research in serverless systems, processor overclocking, and energy-efficient LLM inference. His work on serverless computing (MXFaaS, EcoFaaS) and LLM inference optimization has received significant recognition in top-tier computer architecture conferences. Research Groups: During his PhD at UIUC, Jovan worked with Professor Josep Torrellas on cloud infrastructure research. He has collaborated extensively with researchers at IBM Research (particularly Hubertus Franke) and Microsoft (particularly Íñigo Goiri and Ricardo Bianchini). His upcoming position at UT Austin will establish his independent research group focused on cloud computing and datacenter systems. His year at Meta working with the AI and Systems Co-design group will further strengthen his expertise in AI infrastructure.

• Medical Informatics
• Electronic Health Records
• Pediatric Emergency Medicine
• +۵ مورد دیگر

Allen Hsiao MD, FAAP, FAMIA is Professor of Pediatrics, Biomedical Informatics and Data Science, and Emergency Medicine at Yale School of Medicine. He serves as Chief Health Information Officer (CHIO) for Yale School of Medicine and Yale New Haven Health System, and as Vice Chair of Clinical Systems in Biomedical Informatics & Data Science. BA in Biomedical Ethics and MD from Brown University Pediatrics residency at Yale-New Haven Children's Hospital Fellowships in Pediatric Emergency Medicine and Medical Informatics at Yale His research focuses on leveraging health information technology to improve care delivery, with emphasis on electronic health records, clinical decision support, natural language processing, and AI applications. He actively explores how informatics can optimize systems, improve transitions of care, and advance health equity through diverse clinical trial participation. His work spans pediatric emergency medicine, gastroenterology, child abuse detection, and opioid safety. Dr. Hsiao's recent publications demonstrate leadership in AI-augmented clinical decision support, EHR-based machine learning models, and pandemic response infrastructure. His team has developed innovative tools for child abuse identification, gastrointestinal bleeding risk prediction, and collaborative research data platforms. 56 Hospital and Health System CMIOs and CNIOs to Know (Becker's Healthcare, 2023) Healthcare Diversity Leader Award (National Diversity Council, 2023) 48 CMIOs and CNIOs to Know (Becker's Hospital Review, 2022) Norman J. Siegel Faculty Award (Yale School of Medicine, 2022) As principal investigator and co-investigator on NIH and AHRQ-funded grants, he examines health information technology's impact on healthcare quality. He co-directs Yale's CTSA Informatics Core, working with Yale Center for Clinical Investigations to equip researchers with EHR (Epic) and clinical trials management (OnCore) tools. His leadership extends to national committees including American Academy of Pediatrics, HIMSS, and Children's Hospitals Association.

Andrew Z. Wangمشاهده پروفایل

استاد

• Cancer Research
• Nanotechnology
• Biomedical Engineering
• +۲ مورد دیگر

Andrew Z. Wang, M.D., is a tenured Professor and holds the A. Kenneth Pye Professorship in Cancer Research at the University of Texas Southwestern. He serves as Vice Chair for Translational Research and Commercialization in the Department of Radiation Oncology, having joined UTSW in August 2021. Previously, he held faculty positions at the University of North Carolina, where he rose from Assistant to Tenured Professor. Dr. Wang's research integrates biomedical engineering with oncology to develop innovative cancer diagnostics and therapeutics. His work focuses on: Nanotechnology-enabled drug delivery systems Cancer immunotherapy and vaccine development Biomaterial applications in oncology Liquid biopsy technologies for treatment monitoring Radiotherapy-enhancing strategies His 128+ publications demonstrate consistent focus on translational nanomedicine, with recent works advancing combination therapies (chemoradiation-immunotherapy), 3D-printed medical devices, and machine learning applications in oncology. Clinical publications emphasize optimizing radiation techniques for genitourinary/gastrointestinal cancers. Major scientific recognitions include: Fellow of the American Association for the Advancement of Science (AAAS) Fellow of the American Institute for Medical and Biological Engineering (AIMBE) Fellow of the American Society for Clinical Investigation (ASCI) A. Kenneth Pye Professorship in Cancer Research Dr. Wang leads a prolific research program generating 38 patents and founding three biotechnology startups. Clinically, he specializes in advanced radiation techniques (IMRT, SBRT, brachytherapy) for genitourinary and gastrointestinal malignancies.

• Product Development
• Systems Engineering Design
• Platform-based Development
• +۸ مورد دیگر

Professor Ola Isaksson is a faculty member in Product Development at Chalmers University of Technology, where he leads the Systems Engineering Design research group. With over 40 research projects nationally and internationally, his work bridges academic research and industrial application, particularly in aviation and transport-related manufacturing sectors. His expertise spans digitalization, sustainability, and advanced manufacturing methods in product development. Ola Isaksson received his PhD in Computer Aided Machine Design from Luleå University of Technology in 1999. Prior to his academic career, he had a specialist career at GKN Aerospace Engine Systems (formerly Volvo Aero) in Trollhättan, focusing on design and product development until 2015. Professor Isaksson's research focuses on developing new product development capabilities to address societal and industrial needs through digitalization and advanced manufacturing. His primary interests include platform-based development, Set Based Engineering, multidisciplinary engineering methods, Value-driven development, and knowledge-intensive system support. He has particular expertise in additive manufacturing integration, design space exploration, and sustainability transition in product development. Analysis of Professor Isaksson's recent publications reveals a strong focus on integrating digital technologies with sustainable manufacturing practices. His work demonstrates a progression from traditional design methodologies toward AI-assisted design, digital twins, and advanced data analytics. Key thematic areas include additive manufacturing implementation, design margin management, sustainability integration, and aerospace component optimization, reflecting his commitment to bridging theoretical research with industrial applications. Professor Isaksson is one of the founders of the Swedish Product Development Academy and maintains active membership in the Design Society, ASME, and SIG PM, reflecting his significant contributions to the field of engineering design. With over 100 scientific publications and leadership in more than 40 research projects, Professor Isaksson has established himself as a leading figure in product development research. His work frequently involves close collaboration with industry partners, particularly in the aviation sector, securing substantial research funding for projects addressing digitalization, sustainability, and advanced manufacturing challenges. Professor Isaksson leads the Systems Engineering Design research group at Chalmers University of Technology. His team focuses on developing methodologies for complex product development, with particular emphasis on digital tools, sustainability integration, and manufacturing innovation. The group maintains strong industry connections, especially with aerospace manufacturers, facilitating the translation of research into practical applications.

Philipp Koehnمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Artificial Intelligence
• +۲ مورد دیگر

Philipp Koehn is a Professor in the Department of Computer Science at Johns Hopkins University, with additional affiliation at the University of Edinburgh. His primary research focuses on statistical and neural machine translation, specifically developing methods to leverage large-scale digital information for cross-lingual communication. He leads the Machine Translation Research Group and maintains key resources like the Moses toolkit and Europarl corpus. His research interests span: Core machine translation techniques (statistical/neural approaches) Low-resource and unsupervised translation methods Cross-lingual representation learning Speech-to-speech translation systems Large-scale parallel data mining and alignment Evaluation methodologies for generated text Koehn's recent publications demonstrate strong focus on improving translation efficiency (dynamic compression, streaming models), robustness (noise handling, error correction), and accessibility (low-resource languages, radio speech processing). Key trends include multilingual generalization, document-level coherence, and human-centered evaluation. Significant scientific recognition includes: ACL Fellow (2024) IAMT Award of Honor (2015) European Inventor Award Finalist (2013) He currently advises PhD students Rachel Wicks, Elina Baral, Bismarck Odoom, and Weiting Tan. His Machine Translation Group develops widely-used open-source tools and organizes major conferences including WMT and MT Marathon.

• Coastal hydrodynamics
• Sediment transport
• Morphological changes
• +۴ مورد دیگر

Suzana Ilic is a Senior Lecturer at the Lancaster Environment Centre, Lancaster University, specializing in coastal processes and management. Her research focuses on understanding coastal hydrodynamics, sediment transport, and morphological changes along coastlines. She develops practical tools for coastal managers to predict future changes, addressing complex challenges of coastal structures' influence on natural systems including shoreline evolution, water quality, and habitat preservation. Dr. Ilic's research interests center on coastal hydrodynamics and sediment transport processes, with emphasis on developing predictive tools for coastal management. Her work aligns with major environmental initiatives including the Foresight programme on coastal flooding and erosion, Water Framework directives, and offshore sustainable energy development. She employs integrated methodologies combining field monitoring, numerical modeling, and community engagement to address contemporary coastal challenges. Her recent publications demonstrate a strong focus on coastal morphodynamics, with increasing attention to interdisciplinary approaches connecting physical processes with social dimensions of coastal management. The research spans multiple geographical contexts including the UK's Holderness coast and Morecambe Bay, as well as international sites like Croatia, reflecting her global perspective on coastal challenges. Dr. Ilic actively supervises PhD students including Juan Montalvan Lume and leads significant research projects such as NERCOUTE Inclusive Natural Environment Futures (2025-2026) and Adaptation Pathways (2025-2026). Her professional engagement extends to the North West Regional Flood and Coastal Committee and various workshops, demonstrating her commitment to translating research into practical coastal management solutions.

• Embedded Systems
• Compilers
• Hardware-Software Co-Design
• +۵ مورد دیگر

Prof. Dr. rer. nat. Rainer Leupers is a faculty member at RWTH Aachen University, chairing the Department of Software for Systems on Silicon. His research focuses on embedded systems, hardware-software co-design, virtual prototyping, and security in computing-in-memory architectures. He has published extensively on RRAM accelerators, logic locking, and neuromorphic security. Chair of Software for Systems on Silicon Research in hardware security and deep learning accelerators Recent publications on cross-tool virtual frameworks and thermal side-channel attacks His work bridges system-level modeling with practical security implementations, emphasizing reliability and performance in heterogeneous computing environments. Key trends in his 2025-2023 articles include compute-in-memory optimization, neural network inference efficiency, and security vulnerabilities in emerging hardware. Awards and formal recognitions are not explicitly detailed in the provided materials. He has not directly mentioned advising students or research grants in the given text fragments. The chair's contact information includes an office at ICT Cube 1, Electrical Engineering, Aachen, with direct email and website links.

• Genomics
• Computational Biology
• Cell Biology
• +۵ مورد دیگر

Gioele La Manno is an Assistant Professor (tenure track) at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Department of Life Sciences Engineering. He leads the Unité du Prof. La Manno (UPLAMANNO) and holds roles in teaching and doctoral education within the SSV-ENS and Programme doctoral Biologie computationnelle et quantitative . His research focuses on single-cell genomics, cellular heterogeneity, and systems biology approaches to study developmental and neural systems. La Manno’s educational background includes a strong foundation in computational and quantitative biology, reflected in his teaching of courses like Biological data science I: statistical learning and Scientific literature analysis in neuroscience . His lab explores interdisciplinary methods combining genomics, epigenomics, and machine learning to dissect cell fate decisions and tissue organization. He actively contributes to doctoral programs, supervising over a dozen PhD students and advising theses on topics ranging from nervous system development to drug sensitivity profiling. His work bridges basic research with translational applications, such as clinically compliant cell cryopreservation for regenerative medicine. La Manno collaborates across EPFL’s Life Sciences Engineering ecosystem, leveraging state-of-the-art technologies like mass spectrometry imaging and single-cell RNA velocity modeling. His lab’s recent studies address lipidome dynamics, glial diversity in the CNS, and the molecular regulation of midbrain dopaminergic neurons.

Bradley Hayesمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۵ مورد دیگر

Bradley Hayes is an Associate Professor in the Department of Computer Science at the University of Colorado Boulder, affiliated with the College of Engineering and Applied Science. He leads the Collaborative AI and Robotics (CAIRO) Lab, focusing on creating autonomous robots that collaborate effectively with humans through advances in explainable AI, machine learning, and human-robot interaction. His prior research includes foundational work at MIT's Interactive Robotics Group and Yale's Social Robotics Lab. Research interests span Explainable AI, Learning from Demonstration, Hierarchical Reinforcement Learning, Computer Vision, Natural Language Processing, and Cognitive Science. His work emphasizes making human-robot teams more efficient and safe through innovations like emotionally expressive robotic motion, socially aware navigation, and AR-based collaboration tools. Key contributions include techniques for robust robotic exploration, generative occupancy mapping, and systems for improving human trust through predictable robot behavior. His work has been applied to teleoperation training, surgical assistance, and space exploration scenarios. Grants and partnerships support development of assistive robotic canes and AR interfaces for collaborative tasks. Lab activities emphasize translating theoretical advancements into practical systems through close collaboration between researchers, engineers, and end-users. Education efforts include developing foundational robotics curricula addressing autonomy, perception, and control systems.

Adín Ramírez Riveraمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Representation Learning
• +۶ مورد دیگر

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.

• Biomarkers
• Epigenetics
• Aging
• +۳ مورد دیگر

Brian Chen is an Assistant Adjunct Professor at the Herbert Wertheim School of Public Health & Human Longevity Science at the University of California San Diego (UCSD). His research focuses on biomarkers of aging, epigenetics, and epidemiological studies , with a particular interest in translating these findings into clinical applications and population health strategies. Education: PhD in Epidemiology, University of California Los Angeles (UCLA) MPH in Epidemiology/Biostatistics, University of California Berkeley Bachelor of Science in Biochemistry/Cell Biology & Psychology, UC San Diego Research Interests: Brian's work centers on understanding biological aging mechanisms through epigenetic changes, particularly DNA methylation patterns. He explores how these biomarkers correlate with age-related diseases (e.g., dementia, cardiovascular disease) and health disparities. His studies often involve large-scale epidemiological cohorts and cutting-edge genomic technologies. Publications: His recent work emphasizes validation of aging biomarkers, epigenetic clock analyses, and translational strategies for clinical implementation. Key contributions include studies on racial/ethnic health disparities linked to epigenetic aging and the role of biomarkers in predicting cardiovascular outcomes. Collaborations: Brian frequently collaborates with leading researchers like Steve Horvath (epigenetic clock pioneer), Luigi Ferrucci (aging epidemiology), and Tamara B. Horvath (neuroendocrinology). His work has been cited over 5,000 times in high-impact journals. Future Directions: Current projects include developing multi-omic aging biomarker panels and investigating interventions to modulate epigenetic aging processes. He also advocates for standardized validation frameworks for aging biomarkers in clinical settings.