جست‌وجوی استاد و پژوهشگر

Sudhir Kumarمشاهده پروفایل

استاد

• Molecular Evolution
• Phylomedicine
• Functional Genomics
• +۵ مورد دیگر

Sudhir Kumar is a Professor and Principal Investigator at Temple University, leading a research laboratory focused on molecular evolution, phylomedicine, and functional genomics. His lab develops mathematical methods, computational algorithms, and software packages for analyzing genomic variation across populations, pathogens, tumors, and species. Key contributions include the widely used MEGA software (www.megasoftware.net) for molecular evolutionary analysis and the TimeTree knowledge-base (www.timetree.org) that synthesizes evolutionary knowledge on species divergence times. Dr. Kumar's research interests center on integrating mathematical and computational techniques into evolutionary biology and biomedicine. His lab pursues a holistic paradigm where evolutionary and genomic patterns are discovered through comparative analysis of big datasets, then used to reveal underlying biological processes and develop predictive models. His work spans phylomedicine of genetic diseases, molecular phylogenomics, and the timetree of life, with recent innovations including Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, and pathogenic mutations. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to evolutionary genetics, with multiple 2025 papers focused on sparse learning techniques, transformer-based models, and AI-assisted analytical protocols. His work increasingly bridges evolutionary biology with cancer genomics and precision medicine applications. His scientific achievements have been recognized with the prestigious 2025 George W. Beadle Award from the Genetics Society of America, which honors his "efforts to democratize evolutionary genetics." Dr. Kumar has mentored numerous doctoral candidates, postdoctoral researchers, and graduate students, many of whom have gone on to faculty positions at institutions including Oakland University and universities in Brazil. His lab includes current doctoral candidates working in bioinformatics and statistical molecular evolution, supported by technical staff including programmers, genome tech specialists, and informatics specialists. The Kumar Laboratory operates as an interdisciplinary research hub with multiple projects including MEGA (Molecular Evolutionary Genetics Analysis), TimeTree, myPEG (web-based evolutionary tools), and FlyExpress (a knowledge base for Drosophila melanogaster embryo images). The lab emphasizes green computing efforts aimed at democratizing scientific practice and making big data analytics more accessible.

Seyed Mojtaba Hoseyniمشاهده پروفایل

مدرس

• Process Safety
• Hydrogen Safety
• Risk Assessment
• +۶ مورد دیگر

Dr. Seyed Mojtaba Hoseyni is a Lecturer in Process Safety and Loss Prevention at the School of Chemical, Materials and Biological Engineering, University of Sheffield. Previously a Postdoctoral Research Associate at the same institution (2022-2024), he holds a PhD in Energy Engineering from Politecnico di Milano (2021). His research focuses on enhancing system resilience, risk assessment, and decision-making under uncertainty in engineering systems, particularly for decarbonization applications. Royal Academy of Engineering Global Talent (Exceptional Promise) in Chemical and Process Engineering His work spans hydrogen safety, climate change risk, nuclear engineering safety, and predictive maintenance. Recent publications emphasize integrating resilience metrics into HAZOP analysis and optimizing sensor placement for risk-informed decision-making. Teaching activities include the Hazards and Protections module (CPE61020). Specializes in RAMS (Reliability, Availability, Maintainability, and Safety) analysis Develops safety frameworks for hydrogen energy systems Applies advanced computational techniques to nuclear and industrial safety

• Computer Supported Cooperative Work (CSCW)
• Human-Centered Computing
• Human-Computer Interaction
• +۷ مورد دیگر

Pernille Bjørn is a Professor in Computer Supported Cooperative Work (CSCW) at the Department of Computer Science , University of Copenhagen (DIKU), where she has been since May 2015. Her research investigates collaborative work practices to design cooperative technologies, focusing on domains like healthcare, global software development, startup companies, and digital fabrication. Faculty of Science, University of Copenhagen Human-Centred Computing Section Research Interests : Bjørn’s work spans CSCW , Human-Computer Interaction , and Digital Fabrication , with applications in healthcare systems, cross-cultural software development, and inclusive technology design. She explores collaborative virtual reality training, FemTech, and crisis computing. ACM Distinguished Member (2024) Publications : Published in top venues like ACM Transactions on Computer-Human Interaction , CSCW , and CHI , her recent work examines hybrid work asymmetry, neurodiverse accessibility, and art-driven collaborative research.

Anupriya Tuliمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction (HCI)
• Global Health
• Feminist Technology Design
• +۵ مورد دیگر

Anupriya Tuli is a Postdoctoral Research Fellow at the KTH Royal Institute of Technology , affiliated with the Division of Media Technology and Interaction Design . She collaborates with Madeline Balaam and Airi Lampinen to examine ethics in data collection for intimate topics and designs interactions fostering trust and collective care in reproductive/menstrual journeys. Research Focus : Intersection of Human-Computer Interaction (HCI), global health, taboo, marginality, and activism Expertise : Qualitative research, feminist design, health equity Education : PhD in Human-Centered Design from IIIT-Delhi, India Their work emphasizes technology as a tool for equity and empowerment , particularly for marginalized communities. Recent publications explore themes like intimate technologies, post-growth HCI, and menstrual mobilities. Awards include fellowships from the Indian Government, Google, and the MARFATIA AWARD (2018). Scientific Awards Fellowship from the Indian Government (for doctoral research) Google Fellowship (for doctoral research) MARFATIA AWARD (2018)

Qi Yuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

Florian Börgelمشاهده پروفایل

پژوهشگر

• Climate Science
• Oceanography
• Computational Modeling
• +۳ مورد دیگر

Dr. Florian Börgel is a Senior Research Scientist and leader of the Junior Research Group Large-Scale Circulation and North European Climate Variability at the Leibniz Institute for Baltic Sea Research (IOW) in Warnemünde. He holds a Dr. rer. nat. in Physical Oceanography (2020) from IOW, an M.Sc. in Engineering Physics from the University of Oldenburg (2017), and a B.Eng. from the University of Applied Sciences Münster (2014). His research focuses on large-scale ocean-atmosphere interactions, regional climate dynamics in Northern Europe and the Baltic Sea, and the application of machine learning to improve climate models and river runoff forecasting. Key areas include Atlantic Multidecadal Variability (AMV), teleconnections between the North Atlantic and regional climates, and the impacts of climate drivers on marine environments. Notable achievements include the Outstanding Early Career Scientist Award (EGU 2023) and membership in expert groups such as the UN’s World Ocean Assessment III and the CMIP7 Data Analysis Working Group. He co-leads the Baltic Earth Teleconnections Working Group, focusing on North Atlantic-Baltic Sea linkages. Awards: EGU Early Career Award (2023), Fulbright Scholarship (2015-16) Grants/Positions: IOW appointee to Deutsches Klima-Konsortium (DKK), Parental Leave (2019-2020) Labs/Teams: Leads the Large-Scale Circulation group; contributes to the Baltic Sea Model Intercomparison Project (BMIP) and Validator tool development

Masooda N Bashirمشاهده پروفایل

دانشیار

• Privacy Protection
• Cybersecurity
• Cloud Computing
• +۷ مورد دیگر

Masooda N Bashir is an Associate Professor at the University of Illinois, affiliated with the School of Information Sciences, Informatics, Information Trust Institute, Industrial and Enterprise Systems Engineering, Coordinated Science Lab, Health Care Engineering Systems Center, and European Union Center. Her work focuses on privacy protection, cybersecurity, and ethical use of technology in health and AI systems. She explores user privacy in mobile apps, trust in autonomous systems, and compliance with privacy standards. Research interests include privacy policy analysis, human-robot trust dynamics, and cross-cultural privacy norms. Her recent work examines privacy risks in health apps, AI governance challenges, and cloud security certifications. Collaborations span cybersecurity ethics, human factors in automation, and societal impacts of emerging technologies. Her articles highlight trends in privacy engineering, trust mechanisms in autonomous systems, and compliance strategies beyond legal minimums. She emphasizes user-centric privacy design and interdisciplinary approaches to bridging technical and managerial challenges in privacy implementation. Affiliated with multiple interdisciplinary centers, her work integrates engineering, policy, and social science perspectives. She contributes to public discourse on privacy through media engagement and academic outreach.

Christopher M. Sheaمشاهده پروفایل

استاد

• Implementation Science
• Telehealth Adoption
• Organizational Readiness for Change
• +۳ مورد دیگر

Christopher M. Shea, PhD, is a Professor in the Department of Health Policy and Management and Adjunct Associate Professor in Social Medicine at the University of North Carolina’s Gillings School of Global Public Health. He serves as the PhD Program Director, leading interdisciplinary training in public health policy and management. His work focuses on implementation science, organizational readiness for change, and telehealth adoption, with expertise in mixed-methods research. Dr. Shea holds a PhD in Public Administration (2008) from North Carolina State University, an MPA (2004) and MA in English (1998), and a BBA in Finance and English (1995). His research bridges organizational behavior, health informatics, and quality improvement, particularly in healthcare innovation adoption. His research interests include assessing organizational preparedness for change, telehealth policy, and strategies to support 'innovation champions.' Key projects involve evaluating telehealth utilization in rural settings and analyzing how financial factors influence telemedicine adoption for critical care in hospitals. Dr. Shea’s recent work explores maternal telehealth access disparities, opioid use disorder treatment policies, and the role of social needs screening in primary care. His articles often address implementation barriers and strategies in healthcare systems, with a focus on translating research into practice through mixed-methods approaches. He has contributed to initiatives like the Gillings Innovation Labs, emphasizing practical solutions for public health challenges. His grants and collaborations focus on improving healthcare equity through evidence-based strategies, such as community resource referral systems for cardiovascular disease in rural North Carolina.

Antje Heinrichمشاهده پروفایل

مدرس ارشد

• Speech Perception
• Auditory Processing
• Hearing Loss
• +۴ مورد دیگر

Dr Antje Heinrich is a Senior Lecturer in Audiology/Hearing Science at the University of Manchester, affiliated with the Division of Psychology Communication and Human Neuroscience (L5). Her research focuses on understanding how different groups of listeners process speech, including younger/older native English speakers, non-native listeners, and those with hearing loss. Key research areas include listening effort, cognitive-auditory interactions, and translating findings into clinical diagnostics/training. Education: PhD (University of Toronto, 2005), MSc (Technische Universität Dresden, 2001), MA (University of Toronto, 2000). Previous roles include positions at the MRC Institute of Hearing Research (2011–2017), University of Cambridge (2008–2010), and the MRC Cognition and Brain Sciences Unit (2005–2007). Research emphasizes speech perception mechanisms, auditory processing, and hearing loss impacts. Recent work explores data standards in audiology and health informatics applications. Collaborations span international teams and interdisciplinary fields like cognitive science and public health. Advising: Supervised 3 research projects. Part of the Manchester Centre for Audiology and Deafness (ManCAD), contributing to hearing impairment research and clinical translation.

Shuangquan Wangمشاهده پروفایل

استادیار

• Mobile & Wearable Computing
• Activity Recognition
• Smart Health
• +۳ مورد دیگر

Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.

Mark Gothamمشاهده پروفایل

مدرس ارشد

• Computational Music Theory
• Digital Humanities
• Music Corpus Creation
• +۴ مورد دیگر

Mark Gotham is a Senior Lecturer in Cultural Computation at King’s College London’s Department of Digital Humanities. He holds a unique position bridging STEM and the humanities, with prior roles as an Assistant Professor of Computer Science at Durham University and a Professor of Music Theory at Technische Universität Dortmund. His research focuses on computational methods for music theory, corpus creation, and accessibility. Gotham completed a Ph.D. in Music Theory at the University of Cambridge, an MMus in Composition at the Royal Northern College of Music, and a First-Class Bachelor’s in Music from the University of Oxford. His work spans computational musicology, including projects like the OpenScore initiative, which digitizes and opens music scores. He is affiliated with King’s Computational Humanities Research Group and the Centre for Digital Culture. Gotham’s compositions, such as the award-winning CD *Utrumne est Ornatum*, blend theoretical rigor with creative expression. He collaborates with institutions like Deutsche Telekom on projects like *Beethoven X* and leads the Music Computing Lab at King’s. Gotham’s research emphasizes interdisciplinary approaches, using computational tools to explore musical structures and democratize access to music theory. His contributions include frameworks for aligning symbolic music, standards for harmonic analysis, and pedagogical innovations in music education.

George Hripcsakمشاهده پروفایل

استاد

• Biomedical Informatics
• Electronic Health Records (EHRs)
• Data Mining
• +۴ مورد دیگر

George Hripcsak is the Vivian Beaumont Allen Professor of Biomedical Informatics and Director of Medical Informatics Services at New York-Presbyterian Hospital, Columbia University. He holds affiliations with the Vagelos College of Physicians and Surgeons and the Data Science Institute (DSI). His expertise spans clinical informatics, electronic health records (EHRs), and medical knowledge representation standards. Hripcsak earned degrees in chemistry, medicine, and biostatistics, and is a board-certified internist. Research focuses on leveraging EHR data for clinical research and patient safety through data mining and causal inference techniques. Notable contributions include the Arden Syntax (a national standard for medical knowledge representation) and leadership in the Observational Health Data Sciences and Informatics (OHDSI) network. He chairs the AMIA Standards Committee and has advised federal health informatics policies under HIPAA. His academic awards include Fellowships in the American College of Medical Informatics (1995) and New York Academy of Medicine. Current projects emphasize federated learning, genomic risk prediction, and large-scale real-world evidence analysis through initiatives like LEGEND-T2DM and All of Us Research Program. Educations: MD, Biostatistics, Chemistry Labs/Teams: OHDSI, DSI, Medical Informatics Services Grants & Funding: Not explicitly listed in provided texts

Tarek Alskaifمشاهده پروفایل

دانشیار

• Energy Informatics
• Smart Grids
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Tarek Alskaif is an Associate Professor of Energy Informatics at Wageningen University & Research, specializing in the intersection of information technology and energy systems. He leads research on smart energy systems, focusing on electricity markets, distributed energy resources, and AI-driven solutions. His work integrates modeling, optimization, and big data analytics to advance the sustainable energy transition. Education: PhD in Energy Informatics (2012–2016, Cum Laude) from Universitat Politècnica de Catalunya, Spain. Postdoc at Utrecht University’s Copernicus Institute (2016–2020). Current roles include coordinating the BSc Data Science Minor and teaching Python and Big Data courses. Research interests emphasize leveraging digitalization for energy systems, including smart grids, electric mobility, and battery storage. Notable projects include HighLO Energy Markets (EU-funded, using particle physics and AI for market transparency) and MESSM (coordinated via TKI Urban Energy). He also leads the AI ELSA Lab (NWO-funded). Editorial roles include Associate Editor for IEEE Transactions on Smart Grid and IEEE Power Engineering Letters . Member of IEEE, the Netherlands Institute for Research on ICT (4TU.NIRICT), and the Technical Program Committee for IEEE SmartGridComm and PSCC 2026. Has supervised over 50 students (MSc/BSc) and 7 PhDs. Projects address challenges like grid congestion, EV charging optimization, and decentralized energy trading. His work bridges academic research with industry collaborations, including partnerships with CERN and ACER.

Özlem Uzunerمشاهده پروفایل

استاد

• Natural Language Processing
• Health Informatics
• Public Health Informatics
• +۲ مورد دیگر

Özlem Uzuner is a tenured Professor and Department Chair of Information Sciences and Technology at George Mason University's College of Engineering and Computing. She is an expert in Natural Language Processing and its applications to healthcare and policy, with a focus on translating fragmented clinical narratives into actionable data. Research Interests: Her work spans Health Informatics, Public Health Informatics, Mental Health Informatics, and Computational Social Science. She develops algorithms for information extraction, semantic representation, and applications in phenotype prediction, fraud detection, and topic modeling. Funding & Affiliations: Uzuner's research has been supported by the National Institutes of Health, National Library of Medicine, National Institute of Mental Health, Office of the National Coordinator, and industry partners. She is also a research affiliate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Scientific Awards: NSF grant for supporting socioeconomically disadvantaged students

Luis Amaralمشاهده پروفایل

دانشیار

• Information Systems
• Public Administration
• E-Government
• +۳ مورد دیگر

Luis Amaral is an Associate Professor at the School of Engineering, University of Minho, where he has served since 1998. He holds a PhD in Computer Science (Information Systems) from the University of Minho (1995) and has extensive experience in teaching, research, and administrative leadership. His research focuses on Information Systems in social and organizational contexts, particularly in public administration, with over 400 publications including books, journal articles, and conference papers. Key roles include Vice-Rector for Organizational Transformation and Administrative Simplification, Director of the Information Systems Department (2005–2006, 2010–2012), and leadership in projects like the Virtual Campus (e-UM). He has held numerous administrative positions, including President of the School of Engineering’s Council (2013–2016) and Pro-Rector (2006–2009). His work emphasizes e-government, digital transformation, and public procurement systems. Recent research trends include digitalization of public services, regulatory compliance (e.g., GDPR in higher education), and optimization of renewable energy systems. He has contributed to international initiatives such as the ICEGOV conference and projects in Mozambique and Timor-Leste. His publications highlight innovation in administrative processes, citizen engagement, and institutional digital readiness. Luis Amaral has coordinated postgraduate programs, including the Master’s in Information Systems (2001–2002, 2016–2017), and led research centers like IDITE Minho. His career reflects a balance between academic rigor and practical impact in technology-driven governance and organizational change.