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Julia Wrobel is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University. Her research focuses on integrating biostatistical methods with bioinformatics and public health challenges, particularly in spatial data analysis, cannabis pharmacology, and functional data modeling. She holds a BA from Swarthmore and a PhD from Columbia University. Education: BA, Swarthmore PhD, Columbia University Research interests span spatial proteomics, cannabis effects on human physiology, and computational methods for single-cell and multiplex imaging data. Her work includes developing tools like scSpatialSIM and GammaGateR for analyzing spatial molecular patterns. Recent studies explore immune cell clustering in tumors and THC's impact on driving performance. Her 2024 Dean’s Pilot Innovation Award supported research into novel statistical methods for spatial and functional data. Collaborations focus on translational applications in oncology and public health, with publications bridging biostatistics, computational biology, and clinical toxicology. Grants/Advising: Recipient of 2024 Dean’s Pilot Innovation Award Focus on interdisciplinary collaborations in bioinformatics Labs/Teams: Active in Emory’s Biostatistics and Bioinformatics core, specializing in multiplex imaging analytics and cannabis-related health studies.

• Bioinformatics
• Biostatistics
• Public Health Informatics
• +4 more

Andrew Peacock is an Associate Professor at the School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University, and a member of the Institute for Sustainable Building Design. His research focuses on energy demand modelling, climate change mitigation, and sustainable development, particularly in the context of renewable energy systems and community-scale energy solutions. He contributes to UN Sustainable Development Goals, emphasizing clean energy and climate action. Key research interests include heat pump efficiency, demand forecasting, solar-powered irrigation in Nigeria, and the impact of climate change on urban environments. He has published extensively on energy policy, smart grids, and data-driven approaches to energy systems. Notable collaborations include work on India’s cooling demand and South Asian megacity resilience. Peacock has received awards for his research, including the Best Presentation Award (2019) and Rushlight Awards for Resource Innovation (2018) and Water Management (2018). His work integrates technical, behavioral, and policy dimensions to address energy challenges in communities globally.

• Sustainable Energy
• Climate Change Mitigation
• Renewable Energy Systems
• +3 more

Asghar Shams is an Associate Professor at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, affiliated with the Institute for GeoEnergy Engineering. His research focuses on reservoir characterization, seismic data analysis, and application of artificial intelligence in petroleum engineering. He has extensive experience in carbonate reservoir modeling, fluid flow dynamics, and integration of geophysical data with reservoir engineering workflows. Research interests include reservoir simulation, production optimization, fracture network modeling, and machine learning applications. His work spans global basins like the Sirt Basin (Libya), Gulf of Mexico, and Middle East carbonate systems. He has published over 52 peer-reviewed articles and actively participates in international conferences such as SEG and EAGE. Professional activities include membership in committees at Imperial College London and Italy's Istituto Nazionale di Oceanografia e di Geofisica Sperimentale. His research emphasizes bridging geoscience and engineering through advanced computational methods.

• Petroleum Geoscience
• Reservoir Engineering
• Seismic Data Analysis
• +3 more

Sonja Aits is an Associate Senior Lecturer and Research Team Manager at Lund University, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Cell Biology, and Environmental Science. She leads studies on lysosomal biology, cell death mechanisms, and their implications for human health and environmental sustainability. Her work bridges computational methods with biomedical applications, including AI-driven image analysis and text mining in medicine. Affiliations: Lund University Cancer Centre (LUCC), BECC (Biodiversity & Ecosystems), and multiple AI-focused profile areas (e.g., AI and Digitalization). Research Themes: Lysosome regulation, environmental toxin effects, AI in microscopy, and sustainable healthcare. Her research interests emphasize leveraging AI to analyze large-scale biomedical datasets, develop predictive models for protein function, and integrate multi-omics data. Collaborations include NEUBIAS, TRANSAUTOPHAGY, and international networks like eSSENCE and EpiHealth. Publications highlight contributions to high-content microscopy datasets, AI ethics in healthcare, and interdisciplinary AI education. She actively participates in conferences and public outreach on AI’s societal impact, particularly in Sweden. Grants and Projects: Over 22 funded projects, including FORMAS-supported studies on environmental toxins and AI-based text mining for pandemic response. Supervised one doctoral student to date. Labs/Teams: Manages research teams in Cell Death studies and coordinates initiatives like the 'Cell Death Census' and OpenChart-SE medical data projects.

• Artificial Intelligence in Medicine
• Cell Death Mechanisms
• Lysosomal Biology
• +8 more

Kaushik Sinha is an Associate Professor at the School of Computing, Wichita State University, part of the College of Engineering. His research focuses on machine learning, optimization, algorithms, and their applications in health informatics. He holds a PhD in Computer Science from Ohio State University and completed a postdoctoral fellowship at UC San Diego under Sanjoy Dasgupta and Kamalika Chaudhuri. His professional activities include serving as an Associate Editor for Neurocomputing (2015–2017) and Technical Program Committee member for conferences like KDD, ICML, and AAAI. He is an affiliated researcher at the Institute for Foundations of Machine Learning (IFML). Dr. Sinha actively mentors PhD students in theoretical and applied machine learning. He currently offers two PhD positions for Fall 2025 focusing on algorithm analysis and applied machine learning, requiring strong mathematical or programming backgrounds. His work emphasizes both foundational research and practical applications, including federated learning, nearest neighbor search, and health data analysis.

• Machine Learning
• Artificial Intelligence
• Optimization
• +5 more

Maria Olmedilla Fernandez is an Associate Professor at SKEMA Business School in Paris, France. She holds a Ph.D. in Strategic Management and International Business from the University of Seville (2017) and a Habilitation à Diriger des Recherches (HDR) from Université Paris Dauphine-PSL (2024). Her research focuses on eWOM communities, text mining, machine learning techniques, online user behavior, NLP, and artificial intelligence applications in marketing and digitalization. She is affiliated with the SKEMA Centre for Artificial Intelligence and serves as Academic Director at SKEMA since 2023. Her academic journey includes roles as a PhD Researcher at the University of Seville (2014–2017) and Assistant Professor at École de management Léonard de Vinci (2017–2019). She has received notable awards such as the 2024 Outstanding Paper Award and multiple Pedagogical Innovation awards from SKEMA. She actively contributes to peer-reviewed journals like Decision Support Systems and Technological Forecasting and Social Change , and serves on committees for conferences like CARMA and ICISDM. Her research explores the intersection of technology and consumer behavior, particularly in analyzing online reviews, predicting helpfulness, and detecting fake content using NLP and machine learning models. She has pioneered methodologies for harvesting Big Data in social science and improving educational innovation through gamification tools like Kahoot!.

• eWOM communities
• Text mining
• Machine learning techniques
• +5 more

Luca Di Grazia is a Researcher (Postdoctoral) in the STAR group at the University of Lugano (USI), Switzerland, supervised by Prof. Mauro Pezzè. He holds a PhD (summa cum laude) in Software Engineering from the University of Stuttgart, advised by Prof. Michael Pradel. Previously, he completed his Bachelor's and Master's degrees in Computer Engineering at the Polytechnic of Turin, Italy, with a minor in Embedded Systems. His research focuses on Generative AI, Program Repair, Software Evolution, and Code Search techniques. Education: Bachelor's and Master's in Computer Engineering, Polytechnic of Turin (Italy), with a thesis on "Protein classification using geometrical features for 3D face analysis". PhD in Computer Science (summa cum laude) from University of Stuttgart (Germany), thesis: "Supporting Software Evolution via Search and Prediction". Postdoctoral Researcher at USI, Switzerland. Research Interests: Generative AI for software testing and bug fixing (e.g., winning an Uber competition with a GenAI tool). Program Repair techniques, such as PyTy for Python type errors. Code Search and Change Retrieval (e.g., DiffSearch engine). Software Evolution and Type Annotation studies in Python. Achievements: ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2022 for work on Python type annotations. Second prize at ACM Student Research Competition at ICSE 2022. Won GenAI Uber competition (2023) with a project to boost developer productivity, beating 103 teams. Summa cum laude PhD (2024). Recipient of national scholarships during his studies at Polytechnic of Turin. Advising: Supervised seven students on projects including automated error repair and testing frameworks. Labs/Teams: STAR group at USI, collaborating with JetBrains and Uber.

• Software Engineering
• Generative AI
• Deep Learning
• +7 more

Andrew Skidmore is a Professor of Spatial Ecology at Macquarie University and holds an Honorary Professorship at the University of Twente. He specializes in hyperspectral and LiDAR remote sensing technologies for vegetation monitoring, biodiversity conservation, and climate change impact analysis. His research focuses on developing innovative image processing techniques and essential biodiversity variables (EBVs) for sustainable natural resource management. Previously, Skidmore worked for the Forestry Corporation of New South Wales and served as Head of the Department of Natural Resources at the University of New South Wales from 1997. He has directed the Centre for Remote Sensing and GIS and currently leads projects on coastal blue carbon ecosystems, forest community monitoring, and climate adaptation strategies in Australia and Europe. His research interests include canopy structure analysis, ecosystem resilience, and the integration of environmental DNA (eDNA) with satellite remote sensing. Skidmore has contributed to over 450 publications and collaborates internationally on biodiversity observation systems. He has received grants for projects such as 'Monitoring Blue Carbon Ecosystems' and 'Can coastal floodplains survive ferals and rising seas?' Key awards and recognitions are not explicitly listed, but his extensive editorial work includes roles at the International Journal of Applied Earth Observation and Geoinformation . His advisory efforts focus on leveraging remote sensing for ecological conservation and policy alignment with essential biodiversity variables.

• Hyperspectral Remote Sensing
• LiDAR Remote Sensing
• Vegetation Monitoring
• +3 more

Associate Professor Karin Reinke is affiliated with the School of Science at RMIT University, Australia. Her academic role includes supervision of Masters and PhD students in environmental sustainability research projects, such as future forests, wildfire surveillance algorithms, and geospatial AI applications. She holds a permanent full-time faculty position. Education and Industry Background: Prior to academia, she held professional roles in spatial analysis and environmental modeling across multiple organizations including the Department of Sustainability and Environment (Wilson’s Promontory National Park and Melbourne offices), Australian Surveying and Land Information Group, Bureau of Resource Sciences (Canberra), Geomatic Technologies (Melbourne), and RMIT University as a Postdoctoral Research Fellow. Research Focus: Her work emphasizes spatial data quality, uncertainty, and scale in environmental information systems. Key areas include vegetation assessment via remote sensing/GIS, wildfire management applications, and forest fuel structure modeling. She develops methodologies for satellite-based fire detection, flood modeling in data-scarce regions, and integrating low-cost remote sensing technologies for environmental monitoring. Supervision Trends: Recent supervision projects highlight innovations in geospatial AI for sustainability, active fire detection algorithms, and analyzing satellite data's influence on landscape perception. Projects often intersect satellite imagery applications with ecological recovery and human-environment interactions. Collaborations: Involved in cross-disciplinary teams including the Fuels3D project and AusCover CALVAL activities. Works closely with researchers like Simon Jones and Luke Wallace on wildfire surveillance and spatial resolution studies.

• Remote Sensing
• Wildfire Management
• Environmental Science
• +5 more

Professor Nick Pears is a faculty member in the Department of Computer Science at the University of York. He holds roles including Deputy Head of Department (Research) and Chair of the Departmental Research Committee. His academic career spans postdoctoral research at the University of Oxford, a Fellowship at the University of Cambridge, and progressive roles at York from Lecturer to Professor (since 2022). He holds a BSc in Engineering and a PhD in Robotics from 'Dunelm' (likely Durham University). His research focuses on 3D facial modeling, computer vision for autonomous systems, robotic manipulation, and medical imaging. He leads the Vision, Graphics and Learning research group and has contributed to advancements in morphable models, gaze estimation, and medical anomaly detection. His work bridges theoretical computer vision with practical applications in healthcare and robotics. Recent publications emphasize generative models for medical imaging, 3D face reconstruction techniques, and adaptive robotics. His research often intersects AI ethics, emphasizing safe and explainable systems for autonomous vehicles and surgical applications. He actively collaborates with clinicians and engineers to translate vision algorithms into real-world solutions. As a leader in academic research governance, he oversees York's REF submissions and departmental strategy, fostering interdisciplinary innovation. His lab maintains an open-access repository of 3D morphable models and benchmark datasets for robotic motion planning.

• 3D Face/Head Modeling
• Computer Vision for Self-Driving Vehicles
• Robot Manipulators
• +4 more

Dr Edward Smart is a Principal Research Fellow at the University of Portsmouth, affiliated with the Faculty of Technology and School of Electrical and Mechanical Engineering. He is also part of the Centre of Excellence in Defence, Risk & Resilience, Portsmouth AI and Data Science Centre, and the Centre for Operational Research & Logistics. He holds a PhD from the University of Portsmouth (2011) and an MMath from the University of Reading (2005). Education: PhD in Engineering, University of Portsmouth, 2011 MMath in Mathematics, University of Reading, 2005 Research Interests: Dr Smart focuses on learning algorithms for detecting rare or abnormal events in industrial data, particularly in manufacturing, computing, agri-tech, and transport sectors. His work emphasizes anomaly detection, condition-based monitoring, and predictive maintenance, with applications in maritime, railway, aerospace, and high-performance computing industries. He has secured over £6.8M in grants from Innovate UK, STFC, and DASA, collaborating with institutions like Southampton, Nottingham, UCLAN, and Brunel. Grants & Collaborations: Lead or co-investigator on 20+ grants totaling £6.8M (over £3.4M for Portsmouth) Collaborations with industry partners in maritime, railway, aerospace, and agri-tech sectors Labs & Teams: Active in the Centre for Operational Research & Logistics and the Food Cultures in Transition research cluster. He contributes to researcher development through the Researcher Concordat implementation and previously chaired the Researchers' Network.

• Data Analysis
• Machine Learning
• Anomaly Detection
• +3 more

Vass Verkhodanova is a Lecturer at the University of Groningen's Campus Fryslân, affiliated with the Language Technology and Culture (LTC) department. Her research focuses on phonetics, speech prosody, and Parkinson's disease speech analysis. She completed her PhD in 2021 with the thesis titled More than words: Recognizing speech of people with Parkinson's disease . Her work spans cross-linguistic studies of speech healthiness, dysarthric speech recognition, and perceptual effects of expertise. Recent publications (2024-2017) emphasize Dutch dysarthric speech recognition strategies, prosodic changes in Parkinson's patients, and vowel articulation in multilingual contexts. Key contributions include developing machine learning approaches for speaker-specific speech recognition and analyzing acoustic correlates of Parkinson's-related speech disorders. Media engagements highlight her work on speech technology for Parkinson's detection.

• Phonetics
• Phonology
• Speech Prosody
• +3 more

Bruno David Ferreira Saraiva is a Ph.D. candidate and invited lecturer at Universidade Lusófona, affiliated with the School of Communication, Arts and Information and the CICANT research center. His academic work bridges data science, AI, and applied communication, focusing on the dynamics of collective behavior in digital environments. His research interests lie at the intersection of data science , artificial intelligence , and complex network analysis . He investigates how interactions in online group conversations give rise to collective phenomena, using tools from machine learning and natural language processing. His work has practical applications in education and cultural industries, particularly in fostering critical thinking and supporting film production through audience data analytics. The two most recent publications in 2023 reflect a strong trend in computational analysis of social interactions, leveraging transformer models like BERT and developing specialized software tools for conversation dynamics. These works emphasize methodological innovation in studying online discourse structure and classification. Bruno has contributed to research projects funded by the Portuguese Foundation for Science and Technology and is currently involved in the EU-funded Crescine project. While no formal students are listed, his role as an invited lecturer indicates active participation in academic teaching and mentorship. He is part of a research lab at CICANT, working on data-driven applications in communication and culture, particularly through the Crescine project, which aims to transform film audience data into actionable insights for European cinematic production.

• Data Science
• Artificial Intelligence
• Complex Networks
• +7 more

Dr. Turgay KOÇ is a Lecturer at the Department of Electrical and Electronics Engineering, affiliated with the Faculty of Engineering and Natural Sciences at Middle East Technical University. He holds a doctoral degree in Electrical-Electronic Engineering from the same institution (2012) and a bachelor's degree from Nigde University (2001). His research focuses on Signal Processing, Speech Processing, and applications of AI in healthcare, including lung disease detection and vocal fold analysis. He has contributed to advancements in CNN-based denoising, medical image segmentation with U-Net architectures, and forensic audio analysis. His work spans over 15 years, with notable publications on nonlinear source-filter models and glottal inverse filtering techniques. Dr. KOÇ's research interests also encompass audio, image, and video processing techniques, particularly in biomedical contexts. His recent work (2020–2025) emphasizes AI-driven solutions for post-COVID-19 lung disease detection and spoofing attack identification. He has explored hybrid modeling approaches for signal denoising and developed algorithms for vocal fold segmentation in high-speed video imagery. His contributions bridge theoretical signal processing with practical medical and cybersecurity applications. No scientific awards or grants are explicitly mentioned in the provided texts. He has advised no listed students, and lab affiliations remain unspecified.

• Signal Processing
• Speech Processing
• Audio Processing
• +3 more

Dr. Shoaib Jameel is an Associate Professor at the University of Southampton, with a focus on interdisciplinary research at the intersection of Artificial Intelligence, Machine Learning, and Health Informatics. He is part of the Web and Internet Science research groups and the Centre for Machine Intelligence. His work emphasizes developing AI-driven solutions for social media analysis, mental health monitoring, and scientific collaboration assessment. Current research includes multimodal fake news detection, parameter-efficient large language models, and human-AI hybrid systems for scientometric analysis. Education and career details are not explicitly listed, but his profile highlights roles in supervising PhD students in Computer Science and active participation in cutting-edge AI research. He collaborates extensively with industry and academic partners, addressing real-world challenges through innovative machine learning frameworks. Research interests span natural language processing, deep learning architectures, and applications in healthcare and education. Notable contributions include frameworks like GAMED for fake news detection and CLEFT for analyzing user engagement in educational videos. His work is published in top venues such as the Artificial Intelligence Review, IEEE journals, and conferences like ACM. Dr. Jameel currently supervises three PhD students: Lingzhi Shen (Computer Science), Ubaid Azam (Computer Science), and Inamullah (Computer Science). He actively accepts new PhD applications in AI-related fields.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +4 more