جستجوی استاد راهنمای دانشگاهی

• Computational Genomics
• Gene Regulation
• RNA Biology
• +۴ مورد دیگر

Susanne Bornelöv is a Professor in the Department of Biochemistry at the University of Cambridge. Her research focuses on computational genomics and gene regulation, particularly exploring posttranscriptional mechanisms such as codon optimality-mediated mRNA decay and transposon silencing. She uses computational methods, ribosome profiling, and Drosophila models to study how codon usage, tRNA availability, and RNA modifications influence gene regulation and genome evolution. Her work integrates artificial intelligence (AI) and comparative genomics to model gene regulatory processes and design novel regulatory elements. Key research areas include piRNA clusters' roles in suppressing retroviruses, codon usage bias in pluripotent stem cells, and the interplay between mRNA methylation and protein synthesis. The Bornelöv Group collaborates widely, including with institutions like Cold Spring Harbor Laboratory, to advance understanding of fundamental gene expression principles. Publications highlight contributions to topics like deep learning in genomics, evolutionary conserved piRNA mechanisms, and transcriptional regulation. She leads a group open to interns, students, and researchers, fostering interdisciplinary approaches to address complex biological questions.

• Artificial Intelligence
• Reinforcement Learning
• Evolutionary Computation
• +۱ مورد دیگر

Tim Rocktaschel is a Professor of Artificial Intelligence in the Department of Computer Science at University College London (UCL), where he has been working since 2018. He was promoted to Professor in October 2023, having previously served as an Associate Professor (2021-2023) and Lecturer (2018-2021) at the same institution. His educational background includes a Doctorat from University College London (2017) and a Diplom Informatiker from Humboldt-Universitat Berlin (2012). Rocktaschel's research focuses on the cutting edge of artificial intelligence, with particular emphasis on reinforcement learning, evolutionary computation, and open-ended learning systems. His work explores how AI systems can learn more efficiently through better exploration strategies, environment design, and the integration of language models with reinforcement learning frameworks. His recent publications reveal a strong trend toward developing more efficient and generalizable AI systems. The research spans unsupervised environment design, prompt engineering for self-improving systems, exploration strategies in reinforcement learning, and the application of language models to enhance policy learning. His work often bridges theoretical AI concepts with practical implementations, as evidenced by tools like GriddlyJS for reinforcement learning development. Rocktaschel maintains an active presence in the AI research community with numerous publications in top venues including NeurIPS, ICML, and the Journal of Artificial Intelligence Research. His work on zero-shot generalization, pragmatic understanding in language models, and open-ended learning environments has garnered significant attention in the field. He is actively involved in developing tools and datasets for the AI community, such as the large-scale NetHack dataset, which provides a complex environment for testing reinforcement learning algorithms. His research continues to push the boundaries of what's possible in artificial intelligence, particularly in creating systems that can learn and adapt in complex, open-ended environments.

Alison Cribbمشاهده پروفایل

پژوهشگر ارشد

• Marine Ecosystem Engineering
• Paleoclimatology
• Climate-Related Stress Responses
• +۳ مورد دیگر

Dr Alison Cribb is an 1851 Research Fellow in palaeoceanography and palaeoclimatology at the University of Southampton's Ocean and Earth Science department. Her research focuses on reconstructing the evolution of marine ecosystem engineers (e.g., bioturbators, reef builders) using the fossil record, particularly their responses to climate-related stress and mass extinctions. She explores how these organisms influenced ecological and evolutionary dynamics in ancient ecosystems, with implications for modern climate change impacts. Education: PhD in Geological Sciences, University of Southern California (2018–2023) BA Hons in Earth and Environmental Sciences, Vanderbilt University (2014–2018) Her research interests span marine ecosystem engineering, paleoclimatology, and the recovery of ecosystems after mass extinctions. Current projects include studying bioturbation patterns via machine learning and contrasting post-Triassic extinction dynamics between terrestrial and marine environments. Dr Cribb's recent publications highlight interdisciplinary approaches, combining fossil data with computational methods. Her work is funded by the Royal Commission for the Exhibition of 1851.

Roderich Grossمشاهده پروفایل

مدرس ارشد

• Swarm Robotics
• Modular Robotics
• Computational Intelligence
• +۵ مورد دیگر

Roderich Gross is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. He is also a Visiting Scientist at CSAIL, MIT, and leads the Enabling Technologies theme at Sheffield Robotics. His academic journey includes a Ph.D. in engineering science from Université libre de Bruxelles (2007), followed by postdoctoral fellowships as a JSPS Fellow (Tokyo Institute of Technology), Research Associate (University of Bristol), and Marie Curie Fellow (EPFL & Unilever). Research Interests : Swarm robotics, self-reconfigurable robots, multi-robot coordination, robotics software/tools (human-robot interaction interfaces, formal design tools), machine learning for behavior inference (Turing Learning, GANs), autonomous systems, natural computing (swarm intelligence, evolutionary algorithms). Scientific Contributions : Inventor of Turing Learning, a machine learning method for behavior inference. Key work includes swarm coordination, self-assembly, fault-tolerant quadcopters, energy-efficient drone delivery, and infrared-based swarm communication. Scientific Recognition : Held prestigious fellowships including JSPS and Marie Curie, and served as Associate Editor for leading robotics journals (IEEE Robotics and Automation Letters, Swarm Intelligence) and conference roles (General Chair DARS 2016, Program Co-Chair GECCO 2018). Grants : Principal Investigator on Horizon Europe OpenSwarm (£463,699), EPSRC Core Capital (£104,196), DSTL Multi Robot Systems (£98,781), and industry-funded modular robotics projects. Labs : Affiliated with the Natural Robotics Lab at the University of Sheffield.

Alexander J. Stewartمشاهده پروفایل

دانشیار

• Mathematical Biology
• Computational Social Science
• Cultural Evolution
• +۷ مورد دیگر

Alexander J. Stewart is an Associate Professor of Informatics at Indiana University Bloomington and an Honorary Professor of Mathematics at the University of St Andrews. He holds a PhD in Mathematical Biology from University College London (2010), and earlier degrees in Physics (Imperial College London) and Mathematical Biology (University College London). His research focuses on mathematical and computational models of social learning in online ecosystems, addressing topics such as misinformation, polarization, and cooperative behavior through lenses of game theory and evolutionary dynamics. Key themes include cultural evolution's interaction with algorithms, social network structures' impact on collective decisions, and cognitive mechanisms shaping social interactions. His work has been published in high-impact journals like Nature , PNAS , and Science Advances , addressing topics ranging from information gerrymandering to the evolution of stereotyping. He leads projects funded by the John Templeton Foundation, exploring cultural evolution and online social contracts. Advising over 7 graduate students and postdocs, he has mentored researchers now in academic and industry roles globally. His teaching includes advanced courses on game theory and social media dynamics. Media contributions include analyses on polarization's societal impacts and the dangers of algorithmic misinformation. Current research initiatives include modeling behavioral motivations for cooperation and investigating eco-evolutionary trade-offs in pathogen competition. His labs focus on interdisciplinary approaches to understanding complex social and biological systems.

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۶ مورد دیگر

Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.

Carola Leichtمشاهده پروفایل

دانشیار

• Organisational Behavior
• Diversity in the Workplace
• Social Psychology
• +۴ مورد دیگر

Dr. Carola Leicht is a Reader in HRM/Organisational Behaviour at Kent Business School, University of Kent. Her research focuses on social psychological processes influencing workplace decisions, particularly around diversity, stereotypes, and leadership evaluations. She applies these insights to areas such as women and minorities in leadership and the cultural context of science-religion interactions. Current affiliation: University of Kent, Kent Business School Prior role: Post-Doctoral Researcher at Coventry University Her research combines social cognition, stereotype analysis, and intergroup dynamics to examine how diversity experiences and implicit biases shape organisational behavior. She has secured significant funding, including a £2 million Templeton Religion Trust grant as Co-Investigator for a public perceptions study on science and religion. Recent publications highlight her interdisciplinary approach, blending organisational behavior with sociology and psychology to analyze leadership prototypicality, science-religion narratives, and stereotype mitigation. Her awards include a Professional Fellowship from the Advanced HE Academy . Carola is active in academic societies, including the Society for Personality and Social Psychology and the European Association of Social Psychology. She serves on the advisory board of the International Research Network for the Study of Science and Belief in Society.

Pier Palamaraمشاهده پروفایل

دانشیار

• Statistical Genetics
• Population Genetics
• Genomics
• +۶ مورد دیگر

Pier Palamara is an Associate Professor of Statistical and Population Genetics at the University of Oxford's Department of Statistics, affiliated with the Centre for Human Genetics. He holds a PhD in Computer Science from Columbia University (2014) and completed postdoctoral training at Harvard Chan School of Public Health and the Broad Institute of MIT and Harvard. His research integrates statistics, computer science, and genetics to develop methods for analyzing large genomic datasets, focusing on evolutionary parameters, demographic history, complex trait genetics, and disease variation. Key research interests include reconstructing population movements via genetic data, studying natural selection and mutation rates in human genomes, and developing scalable algorithms for genomic analysis. He leads the Palamara Lab, which collaborates on projects like the Genomics England haplotype reference panel and the UK Biobank imputation. His lab's work is supported by grants and partnerships, and they develop software tools such as ASMC, Quickdraws, and Threads. Recent publications highlight contributions to Indo-European genetic origins, scalable mixed-model association methods, and ancient DNA analysis of European farmers. He advises graduate students and has mentored researchers in computational biology and statistical genetics.

John Clarkمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.

Irene Nobeliمشاهده پروفایل

مدرس ارشد

• Bioinformatics
• Transcriptomics
• Regulatory RNAs
• +۳ مورد دیگر

Dr. Irene Nobeli is a Senior Lecturer and Education Lead (PG taught courses) at the School of Natural Sciences, Birkbeck, University of London. She specializes in computational biology and chemoinformatics, focusing on regulatory RNAs, transcriptomics in health/disease, brain disorders, and small molecule roles in biology. Her work bridges bioinformatics tools development and translational research. Administrative roles include directing the MSc Bioinformatics program and organizing the Sequence Analysis and Omics module. She collaborates with research centers like the Birkbeck Institute for Data Analytics (BIDA) and the Institute of Structural Molecular Biology (ISMB). Research interests span bioinformatics methodologies, Mycobacterium genomics, neurodevelopmental disorders, and computational drug design. Notable contributions include developing flexiMAP (alternative polyadenylation analysis) and baerhunter (bacterial non-coding RNA detection). Publications highlight her expertise in transcriptomics, bacterial pathogenesis, and neurobiological mechanisms. She actively engages in advancing computational methods for genomic data interpretation and translational medicine.

• Cybersecurity
• Machine Learning
• Data Analytics
• +۵ مورد دیگر

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

Stuart Gibsonمشاهده پروفایل

مدرس ارشد

• Digital Image Processing
• Machine Learning
• Forensic Science
• +۵ مورد دیگر

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

Damian Smedleyمشاهده پروفایل

استاد

• Computational Genomics
• Genotype-Phenotype Analysis
• Rare Disease Genetics
• +۳ مورد دیگر

Professor Damian Smedley is a Professor of Computational Genomics at Queen Mary University of London, affiliated with the William Harvey Research Institute's Clinical Pharmacology and Precision Medicine department. His research focuses on integrating clinical and model organism phenotype data to elucidate human disease mechanisms, particularly through initiatives like the International Mouse Phenotyping Consortium (IMPC) and the MorPhic project. He leads the development of the Exomiser software, a critical tool for prioritizing genetic variants in rare disease diagnostics, widely used in global projects such as the UK's 100,000 Genomes Project and NHS Genomic Medicine Service. His work bridges computational biology, genetics, and clinical translation, with collaborations spanning academia and industry. Key research areas include genotype-phenotype associations, precision medicine, and federated machine learning applied to multiomics data. Funded by NIH, MRC, Horizon Europe, and Barts Charity, his team collaborates with institutions like the Berlin Institute of Health and the University of Colorado. Notable contributions include advancing diagnostic pipelines for rare diseases and understanding the role of missense variants in genetic disorders. His group's work has been featured in high-impact studies, such as identifying novel disease genes through cross-species phenotype comparisons and optimizing variant prioritization algorithms. External collaborations include Prof. Peter Robinson (Berlin) and Dr. Chris Mungall (Lawrence Berkeley Lab), reflecting his global impact in computational genomics.

• Cloud Computing
• Fog Computing
• Edge AI
• +۷ مورد دیگر

Sukhpal Singh Gill is an Assistant Professor (Lecturer) in Cloud Computing at the School of Electronic Engineering and Computer Science, Queen Mary University of London (UK). He holds a PhD, ME, and BE in Computer Science and is a Fellow of the Higher Education Academy (FHEA). His roles include Programme Director for MSc Advanced Computer Science and MSc Business Analytics, as well as Deputy Chair of the Main Misconduct Panel. He leads the GillNet Research Lab and is the Editor-in-Chief of the International Journal of Applied Evolutionary Computation (IJAEC) , with editorial roles in IEEE IoT, Nature Scientific Reports, and other journals. Education: PhD in Computer Science ME in Computer Science BE in Engineering Research Interests: Focus on Cloud Computing, Edge AI, IoT, Energy Efficiency, and Quantum Computing. His work bridges theoretical advancements with practical applications in healthcare, smart cities, and sustainable computing. Notable projects include AI-driven frameworks for carbon-neutral cloud resource management, blockchain-empowered healthcare systems, and quantum cloud computing models. Teaching: Teaches modules such as Cloud Computing (Postgraduate), Fundamentals of Web Technology (Undergraduate), and Semi-structured Data and Advanced Data Modelling (Postgraduate/Undergraduate). He emphasizes inclusive curriculum design and innovative teaching tools like the Q-Module-Bot for AI-supported learning. Awards & Grants: Recipient of 12,500+ citations and an H-index of 54 (Google Scholar). Secured grants for projects on edge AI, federated learning, and sustainable cloud computing. Winner of awards including the IEEE IT Professional Magazine Outstanding Reviewer Award (2024) and Elsevier's Best Paper Award (2023). Labs & Teams: Leads the GillNet Research Lab , focusing on cutting-edge research in cloud-edge computing, AI, and quantum systems. Collaborates with industry partners on projects like HealthEdgeAI and AIoT-driven smart healthcare systems .

Ferrante Neriمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization
• +۵ مورد دیگر

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.