Find Academic Supervisors

Clare BradshawView profile

Professor

• Marine Ecology
• Ecotoxicology
• Marine Pollution
• +7 more

Clare Bradshaw is a Professor in the Department of Ecology, Environment and Botany at Stockholm University. She is a marine ecologist and ecotoxicologist specializing in human impacts on marine ecosystems, with expertise in radioactive contaminants and bottom trawling effects. Her research focuses on how ecological processes determine the overall effects of disturbance, particularly in the Baltic Sea and Kattegat regions. Dr. Bradshaw's research interests span marine ecology, ecotoxicology, and sediment biogeochemistry, with a strong emphasis on understanding how bottom trawling affects seabed processes, sediment dynamics, and contaminant suspension. She has extensive experience studying marine pollution, particularly radioactive contaminants, and has evolved from earlier work on coral bleaching in the Indian Ocean and bottom trawling effects in the Irish Sea to her current focus on Baltic Sea ecosystems. Her recent publications reveal a strong focus on carbon cycling in marine environments, sediment biogeochemistry, and the cumulative impacts of human activities on seabed integrity. The research shows increasing attention to policy implications, with several publications addressing how scientific findings can inform marine conservation and fisheries management practices, particularly regarding carbon storage capacity in marine environments and the long-term recovery of benthic ecosystems. Member of the Bradshaw Research Group at Stockholm University Course organizer for the Marine Biology summer course Actively seeking Master's students for research on Baltic benthic macrofauna (as of Sept 2023) Dr. Bradshaw collaborates extensively with multiple institutions including SLU Aqua, DHI, Aquabiota, University of Cagliari, Edith Cowan University, IAEA Marine Lab Monaco, and NIVA. Her research has significant implications for marine conservation policy, fisheries management, and understanding the role of marine ecosystems in global carbon cycling.

Boro JakimovskiView profile

Assistant Professor

• Grid computing
• High-performance computing
• Parallel and distributed processing
• +1 more

Boro Jakimovski is an Assistant Professor at the Institute of Informatics, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University in Skopje. He specializes in Grid computing, High-performance computing, and Parallel and distributed processing, with significant contributions to international projects like the European Grid Initiative and SEE-GRID series. PhD in Informatics (2010) - Thesis: Modelling and verification of Grid workflows MSc in Informatics (2004) - Thesis: Communications in Grids BSc in Informatics (2000) - Graduated top of class His research focuses on optimizing computational grids through adaptive workflows and genetic algorithms. He teaches courses such as Distributed Operating Systems and Software Construction, while previously tutoring foundational informatics topics including Algorithms and automata, Data structures, and Parallel processing. Publications span 18 international conferences and 1 journal article, with a notable awarded paper at MII 2003 . Key projects include FP7 coordination for Pan-European infrastructure and leadership in SEE-GRID initiatives as Grid Infrastructure Manager for Macedonia. Scientific Awards : Awarded paper at MII 2003 International Conference Grants : Participated in 15+ international projects (FP6, FP7, TEMPUS, UNESCO, DAAD, INTERREG III) International Engagement : Lectured at 20+ workshops, visited 10+ universities/summer schools Specializations include Grid infrastructure development, having completed professional visits to institutions like CERN (Switzerland), Humboldt University (Germany), and Software Technology Research Laboratory (UK). He chairs Macedonia's coordination in European Grid Initiative projects and contributes to curriculum development across SEE countries.

• Sleep
• Circadian rhythms
• Bioinformatics
• +4 more

Ron C Anafi serves as Associate Professor of Medicine (Sleep Medicine) at the Hospital of the University of Pennsylvania and holds key affiliations as Faculty Scholar at the Penn Institute for Bioinformatics, Co-Investigator at the Center for Subcellular Genomics, and member of both the Chronobiology and Sleep Institute and ITMAT. His work bridges computational biology with clinical sleep medicine within the Perelman School of Medicine. His educational trajectory includes a B.S.E. in Biomedical Engineering and Philosophy from Tulane University (1995), followed by a Ph.D. in Applied Mechanics (2002) and M.D. (2004) from the University of Minnesota. This dual training underpins his unique approach to circadian research. Dr. Anafi's laboratory pioneers computational methods to decode sleep and circadian influences on human physiology, focusing on neurodegeneration and cancer. By applying machine learning and systems biology to large-scale omics data, his team reconstructs molecular rhythms from cross-sectional human tissue databases—overcoming the critical barrier of limited longitudinal sampling in patients. His research demonstrates how circadian disruptions in ribosomal function link to Alzheimer's pathology and how tumor clock strength predicts breast cancer outcomes. Analysis of his 15 most recent publications (2022-2025) reveals intensifying focus on cell-type-specific circadian disruptions in neurodegenerative diseases, with significant expansion into cancer chronobiology and contactless sleep monitoring technologies. His methodological innovations like CYCLOPS enable rhythmic signal extraction across diverse tissues—from brain to skin—while increasingly integrating multi-omics layers to identify therapeutic targets. As mentor in the Genomics and Computational Biology and Neuroscience graduate groups, he guides students through rotation projects at his Translational Research Laboratories facility (Suite 2100, 125 South 31st Street). His research is supported by NIH grants investigating circadian mechanisms in Alzheimer's disease and cancer progression, with recent work exploring sex-specific metabolic impacts of circadian genes and non-invasive sleep monitoring.

Kobus BarnardView profile

Professor

• Computer Vision
• Machine Learning
• Scientific Applications
• +3 more

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

• Financial Engineering
• High Frequency Trading
• Algorithmic Trading
• +3 more

David Lariviere is a Clinical Professor in the Industrial & Enterprise Systems Engineering (ISE) Department at the Grainger College of Engineering , University of Illinois at Urbana-Champaign. He is also the Founder and Director of the FinTech Lab at the university and affiliated with the Master of Science in Financial Engineering (MSFE) Program through both the Grainger College of Engineering and the Gies College of Business Finance Department. Research Interests include high-frequency trading technologies, algorithmic market microstructure, FPGA applications in financial trading, low-latency networking, and quantitative finance. His work bridges computer science, electrical engineering, and financial markets, focusing on real-world trading system design and optimization. Teaching Highlights : Directed 21 student groups in Spring 2025 with projects spanning agentic AI trading systems, FPGA development, crypto arbitrage, and GPS timing servers Received 8 teaching excellence awards (2019-2024) including Outstanding recognition for FIN556 Designed courses integrating cutting-edge technologies like OneTick databases, Strategy Studio backtesters, and PyTorch-based ML models Awards & Patents : 2007 Andrew P. Kosoresow Memorial Award 7 issued/filings including latency determinism enforcement and FPGA-based trading architectures

• Information and Knowledge Management
• Product Configuration Systems
• Supply Chain Management
• +5 more

Anders Haug serves as Associate Professor at the Department of Business and Sustainability (DBS) within the University of Southern Denmark's Kolding campus. Having joined the university in 2008 as Assistant Professor in the Department of Entrepreneurship and Relationship Management before transitioning to his current role in 2010, his academic career spans over 15 years of research and teaching in operations, supply chain, and digital transformation contexts. His work bridges theoretical rigor with practical industry applications, particularly in engineer-to-order manufacturing and logistics sectors. Education: PhD in communication, representation and automation of design knowledge (2005-2007) Haug's research centers on information and knowledge management systems, with deep expertise in data quality frameworks, knowledge-based configuration, and digitalization of business processes. His fingerprint reveals distinctive contributions to product configuration systems, digital twin applications, and supply chain resilience—particularly examining how configurators transform warehouse services, manufacturing processes, and product-service ecosystems. Recent work increasingly addresses sustainability through green dynamic capabilities frameworks and life cycle assessment tools, maintaining strong empirical grounding via case studies in Danish manufacturing. Analysis of his 2024-2025 publications shows converging trends: digital technologies (configurators, digital twins) are examined through operational performance lenses while addressing sustainability imperatives. These works span operations management, information systems, and strategic management disciplines but consistently prioritize practical implementation frameworks for manufacturing SMEs. The research demonstrates methodological diversity—from conceptual modeling to empirical case studies—with strong industry relevance in logistics, engineering-to-order contexts, and manufacturing digitization. Scientific Awards: Top read paper in Business 2017/18 (Wiley) (2019) Haug has supervised 34 teaching courses between 2018-2024 covering business information systems, digitalization projects, and supply chain management. His academic service includes extensive peer reviewing for conferences like NOFOMA and DRS, plus organizational roles in Nordic business research networks. While specific grant details aren't provided, his 175+ research outputs and industry collaborations (evidenced by consultant work since 2006) indicate substantial research funding engagement. Media contributions on 3D printing and business process efficiency demonstrate effective knowledge transfer to practitioners. Though no dedicated research lab is specified, Haug's extensive co-authorship network—including collaborations on projects like digital twin implementation and configurator development—reveals embeddedness in multiple research collectives. His industry-facing approach manifests through case studies with logistics providers, manufacturer partnerships, and practical frameworks for warehouse service design and supply chain resilience.

Qiang ZhuView profile

Professor

• Data Science
• Data Management
• Machine Learning
• +3 more

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Maria Del Rio-ChanonaView profile

Assistant Professor

• Network Science
• Agent-Based Modeling
• Large Language Models
• +7 more

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

Maxime GobertView profile

Researcher

• Database Systems
• Hybrid Polystores
• Database Schema Evolution
• +4 more

Maxime Gobert is a researcher at the University of Namur's Faculty of Computer Science, specializing in database systems and software engineering. Having completed his PhD in March 2023 titled 'Design, Manipulation and Evolution of Hybrid Polystores,' Gobert has established himself as an expert in hybrid database systems, particularly focusing on the HyDRa framework for modeling and evolving polystores. His research interests span database systems, hybrid polystores, database schema evolution, software engineering, data-intensive systems, and static program analysis. Gobert's work bridges theoretical database concepts with practical applications, particularly in NoSQL databases like MongoDB and complex hybrid data storage environments. His publication record demonstrates a clear trajectory from his 2013 Master's thesis on database reverse engineering through to his recent work on database testing best practices and sign language processing applications. His research shows strong collaboration with colleagues at the University of Namur, particularly with Professor Cleve A., and extends to international collaborations as evidenced by his 2016 guest researcher position at the University of Geneva. Best New Idea and Emerging Results (NIER) Paper Award at the 20th IEEE Working Conference on Source Code Analysis and Manipulation (SCAM 2020) Jean Fichefet 2013 award Gobert has contributed significantly to the development of the HyDRa framework for hybrid polystore management and has extended his research into sign language processing through collaborative projects creating bilingual sign language dictionaries and parallel corpora. His work demonstrates both technical depth in database systems and a commitment to applying this expertise to accessibility-focused applications.

Manos AthanassoulisView profile

Associate Professor

• Data Systems
• Cloud Data Management
• Database Systems
• +7 more

Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.

Baochun LiView profile

Professor

• Cloud Computing
• Distributed Systems
• Federated Learning
• +10 more

Baochun Li is a Professor and Associate Chair, Research at the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto , with a cross-appointment to the Department of Computer Science. He holds the Bell Canada Endowed Chair in Computer Engineering since 2005. Education: B.Engr. from Tsinghua University (1995), M.S. and Ph.D. from University of Illinois at Urbana-Champaign (1997, 2000) His research interests span cloud computing , distributed systems (including federated learning), security and privacy , and networking , often integrating control theory , game theory , and network coding into practical systems. Recent work focuses on asynchronous federated learning , secure mechanisms for UAV teams , and transformer-based distributed inference . His 15 most recent publications emphasize federated learning , security in distributed systems , and transformer optimization , reflecting trends in large language models and edge computing . Awards and Recognitions: IEEE Fellow (2014) IEEE INFOCOM Achievement Award (2024) Fellow of the Canadian Academy of Engineering (2023) University of Toronto McLean Award (2009) He has contributed extensively to teaching , including launching the first Canadian university course on Rust programming in Fall 2024 and receiving a Departmental Teaching Award (2011). As a professional service leader , he has chaired major conferences like IEEE INFOCOM and IEEE ICDCS.

Lev MichaelView profile

Professor

• Anthropological Linguistics
• Linguistic Typology
• Language Documentation
• +10 more

Lev Michael is a Professor of Linguistics at the University of California, Berkeley, in the Department of Linguistics within the College of Letters and Science. His research focuses on anthropological linguistics, typology, and the documentation and description of Amazonian languages, particularly in Peru. He is actively engaged in fieldwork, language revitalization, and the study of South American historical and contact linguistics. Research Interests: Lev Michael's work spans a wide range of topics in linguistic anthropology and descriptive linguistics. He specializes in the documentation of endangered Amazonian languages such as Iquito and Nanti, examining grammatical phenomena like evidentiality, negation, and subject-verb agreement. His research integrates fieldwork with computational methods, particularly in phylogenetic classification of language families like Tupí-Guaraní and Arawakan. He is deeply committed to language revitalization and community-based lexicography, contributing to school dictionaries and literacy materials. Recent Research Trends: His recent publications (2017–2022) reflect a strong emphasis on lexicography, computational phylogenetics, and the sociocultural dimensions of language use. Articles span from dictionary compilation to Bayesian modeling of language evolution, demonstrating a unique blend of traditional fieldwork and cutting-edge quantitative analysis. There is a consistent focus on Arawakan and Tukanoan languages, evidentiality, poetic structure in oral traditions, and the historical relationships among South American language families. Scientific Contributions: Co-developer of lexicographic resources for Iquito and other endangered languages Pioneer in applying computational phylogenetics to South American language classification Contributor to the understanding of evidentiality and reported speech in Amazonian languages Advocate for language revalorization and community-led documentation Advising and Grants: While specific students and funded grants are not listed in the provided text, Lev Michael frequently collaborates with students and community members on documentation projects. His extensive co-authorship with scholars like Christine Beier and Jaime Pacaya Inuma suggests a collaborative, team-based research model involving students, local speakers, and interdisciplinary partners. His work likely involves external funding given the scale of fieldwork and publication output, though specific grants are not mentioned. Labs and Research Teams: Lev Michael is associated with the Fieldwork and Language Documentation group and the Language and Social Context faculty at UC Berkeley. His collaborative projects involve multidisciplinary teams including linguists, anthropologists, and native speaker consultants, particularly from Peruvian Amazonian communities. He contributes to digital language archiving and lexicography using tools like FLEx (FieldWorks Language Explorer), indicating involvement in computational language documentation initiatives.

Mohan SunthaView profile

Professor

• Radiation Oncology
• Head and Neck Cancer
• Esophageal Carcinoma
• +3 more

Mohan Suntha, MD, MBA, serves as Professor of Radiation Oncology at the University of Maryland School of Medicine and holds a secondary appointment in Otorhinolaryngology-Head & Neck Surgery. He concurrently serves as President and Chief Executive Officer of the University of Maryland Medical System (UMMS), a role he assumed in 2019 after previously leading the University of Maryland Medical Center since 2016. His educational background includes: BA in Biology from Brown University (1986) MD from Jefferson Medical College (1990) Residency in Radiation Oncology at University of Maryland School of Medicine (1994) Clinical Oncology Fellowship in Head & Neck Malignancies (1994-1995) MBA from The Wharton School (2009) Dr. Suntha's research focuses on carcinomas of the head, neck, and esophagus, with emphasis on optimizing combined modality approaches through investigator-initiated clinical trials. His work investigates concurrent chemoradiation protocols, neoadjuvant strategies, and surgical integration to improve therapeutic outcomes. Current investigations include tumor volume prognostic factors, molecular imaging applications, and quality indicators for surgical oncology procedures. His publications demonstrate consistent focus on head and neck cancer disparities, esophageal carcinoma management, and quality metrics in radiation oncology. Key trends include translational research connecting surgical pathology metrics with oncologic outcomes, prognostic biomarker identification, and health equity analysis in cancer care delivery. Award highlights include: Five-time 'Top Doctor' in Radiation Oncology by Baltimore Magazine (2007-2016) 2022 Influential Marylander Honoree in Healthcare by Maryland Daily Record Administratively, Dr. Suntha has held progressive leadership roles including Vice Chair of Radiation Oncology, Associate Director of Clinical Affairs at UMGCCC, Vice President for System Program Development at UMMS, and CEO of St. Joseph Medical Center. His current UMMS presidency encompasses oversight of Shock Trauma Center, Children's Hospital, and Greenebaum Comprehensive Cancer Center. While maintaining active clinical faculty status, his executive responsibilities focus on system-wide strategic development and healthcare delivery innovation.

Patrick MunkView profile

Associate Professor

• Antimicrobial Resistance
• Genomic Epidemiology
• Metagenomics
• +12 more

Patrick Munk is an Associate Professor at the National Food Institute, Technical University of Denmark (DTU), where he leads research in the Research Group for Genomic Epidemiology and contributes to the DTU Microbes Initiative. His work integrates genomics, metagenomics, and bioinformatics to study antimicrobial resistance (AMR) across human, animal, and environmental systems. He is actively involved in global surveillance efforts and is currently accepting PhD students. PhD, MSc Member, The Danish Young Academy (2024–2029) Patrick Munk’s research focuses on antimicrobial resistance, genomic epidemiology, metagenomics, and bioinformatics . His work leverages large-scale sequencing of sewage, livestock, and environmental samples to understand the global spread of resistance genes. He has led and contributed to major international studies, including the global sewage surveillance project across 101 countries, and has developed key genomic resources such as the PanRes and MetalResistance databases. His recent publications span high-impact journals like Nature Communications and Nature Microbiology , with themes centered on sewage-based surveillance, fecal resistomes in livestock, source attribution of microbial communities, and novel sequencing methodologies . His research has been widely covered in media and policy documents, reflecting its societal impact. Genomic analysis of sewage from 101 countries (Nature Communications, 2022) Time-series metagenomics for source-attributed surveillance (Nature Communications, 2024) High-throughput single-cell metagenomic sequencing (Frontiers in Microbiology, 2025) Patrick Munk has received recognition through significant media coverage, policy citations, and widespread academic engagement. His work has been highlighted in over 40 news outlets, referenced in policy sources and patents, and discussed across social media and academic platforms like Mendeley and Wikipedia. He supervises multiple PhD students and is a Principal Investigator or Supervisor on several active and completed projects, including Bioinformatic methods for multi-metagenome integration and Host range and epidemiology of antibiotic resistance . He is also a co-creator of key datasets that support open science in AMR research. His lab is part of a broad collaborative network involving DTU, international consortia, and public health agencies.

Yoseph BarashView profile

Associate Professor

• Machine Learning
• Computational Biology
• Bioinformatics
• +4 more

Yoseph Barash is an Associate Professor at the University of Pennsylvania, jointly appointed in the Perelman School of Medicine's Department of Genetics and the School of Engineering and Applied Science's Department of Computer and Information Science. He leads the BioCiphers Lab, integrating machine learning with experimental biology to decode RNA biogenesis and splicing regulation in human disease. Education: B.Sc. in Physics and Computer Science, Hebrew University Ph.D. in Machine Learning, Hebrew University (2006) His research spans Machine Learning , Computational Biology , and Bioinformatics , focusing on predictive models for RNA splicing and its role in diseases like cancer and neurological disorders. He pioneered the splicing code (Barash et al., Nature 2010) and extended it to genetic variations (Xiong et al., Science 2015). Recent publications emphasize RNA splicing variations in cancer, tool development (e.g., MAJIQ V3, MAJIQ-CLIN), and machine learning for drug discovery (e.g., trametinib sensitivity in AML). His work bridges computational innovation with wet-lab validation, enabling novel high-throughput assays. Scientific Awards: Lap-Chee Tsui Publication Award (2010) NSERC EWR Steacie Fellowship Canadian Institute for Advanced Research Fellowship He advises companies in RNA therapeutics and has licensed splicing quantification tools to Pfizer, GSK, and Biogen. His lab collaborates extensively, training students and postdocs in interdisciplinary approaches to RNA biology.