جست‌وجوی استاد و پژوهشگر

Juergen Hahnمشاهده پروفایل

استاد

• Computational Systems Biology
• Data Science
• Autism Spectrum Disorder
• +۳ مورد دیگر

Juergen Hahn is a Professor and Department Head of Biomedical Engineering at Rensselaer Polytechnic Institute, with additional appointments in Chemical & Biological Engineering. He holds the Jackson Center for Biotechnology and Interdisciplinary Studies directorship and has significantly expanded RPI's biomedical engineering program, including establishing a new master’s degree in biomedical data science in 2024. Education : Diploma (RWTH Aachen, 1997), MS (University of Texas, 1998), PhD (University of Texas, 2002) Hahn's research focuses on computational systems biology and data science approaches for autism spectrum disorder (ASD) diagnosis and treatment, with over 170 peer-reviewed publications. His work includes developing physiological blood tests for ASD using metabolomics and machine learning, identifying autism subtypes through multimodal data analysis , and creating AI-driven tools for residential autism care optimization. Recent publications highlight advancements in circadian rhythm analysis (2025), robust medical imaging classifiers (2024), and Alzheimer's disease subtyping (2024). His autism research spans biomarker discovery (2023), gastrointestinal-autism connections (2022), and maternal metabolite analysis (2020-2021). Scientific Honors : European Academy of Sciences and Arts (2024) BMES Fellow (2022) AIChE Fellow (2020) AIMBE Fellow (2013) CAST Outstanding Young Researcher (2010) Multiple journal awards (2004-2007) Fulbright Scholar (1995) As an educator, Hahn has advised numerous researchers in multivariate data analysis , metabolic modeling , and AI applications in medicine . He serves as Deputy Editor-in-Chief for the Journal of Process Control and Associate Editor for multiple journals.

Ananda Mondalمشاهده پروفایل

استادیار

• Big Data Analytics
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Ananda Mondal is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). He previously held roles at Claflin University (2012-2018), where he was tenured and promoted to Associate Professor, and at the Bangladesh University of Engineering and Technology (1991-1997). He earned his Ph.D. in Computer Science from the University of South Carolina (2011). His research focuses on Big Data Analytics , Machine Learning (with emphasis on Deep Learning ), and Bioinformatics , particularly in cancer genomics, protein network analysis, and health disparity studies. He has developed undergraduate courses integrating research into bioinformatics and big data, mentoring students who published in IEEE BIBE, BIBM, and BIOCOMP conferences. Dr. Mondal’s work has garnered significant recognition, including the NSF CAREER Award (2017-2022) , the Innovative Scientific Research Award (2017), and multiple Research Excellence Awards (2015, 2017). His lab (Mondallab) focuses on interdisciplinary applications of machine learning in healthcare and genomics. Education : Ph.D. in Computer Science, University of South Carolina (2011) Professional Activities : Session Chair (IEEE BIBM, BIBE), Peer Reviewer (Bioinformatics, IEEE BIBM) Affiliations : ACM, IEEE, ISCB His recent publications highlight advancements in interpretable AI for cancer survival prediction, multi-omics integration frameworks (MOGAT), and studies on health disparities in lung cancer and skin tone classification.

Christian O'Reillyمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neurodevelopmental Disorders
• EEG Analysis
• +۴ مورد دیگر

Dr. Christian O'Reilly is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, with joint appointments at the Artificial Intelligence Institute (AIISC) and Carolina Autism and Neurodevelopmental (CAN) Research Center. He holds a B.Ing in Electrical Engineering (2008), M.Sc.A in Biomedical Engineering (2011), and Ph.D. in Biomedical Engineering (2012) from Polytechnique Montréal. His interdisciplinary research focuses on understanding the brain across spatial and temporal scales to address neurodevelopmental disorders like autism. Research approaches include: Analytical techniques: EEG source reconstruction and functional connectivity Computational modeling: Point neurons, morphologically-detailed neurons, neural masses Bayesian model-driven analyses to infer latent biological variables Novel applications of artificial intelligence in neuroscience Development of biologically-inspired neural networks His extensive publication record spans computational neuroscience, neurodevelopmental disorders, EEG analysis methods, and AI applications in neuroscience, with recent work focusing on multimodal data integration, neurobiological modeling, and translational applications for autism spectrum disorder. Dr. O'Reilly leads the Laboratory for Integrative Neuroscience Analysis (LINA) and has received the Penn Criminology Thesis Award for his work on defense counsel impacts on sentencing outcomes.

Megan Courtmanمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Megan Courtman is a Research Fellow at the Peninsula Medical School, University of Plymouth, specializing in applying machine learning to medical imaging for healthcare solutions. Her research focuses on explainable AI models for diagnostics, including stroke prediction and Parkinson’s disease detection in MRI/CT scans. She holds a PhD in Health Data Science (2024) and an MSc in Data Science & Business Analytics (2020), both from the University of Plymouth, alongside a BA in History from the University of York (2014). Education: Megan’s academic journey includes PhD: Health Data Science, University of Plymouth (2020–2024) MSc: Data Science & Business Analytics, University of Plymouth (2019–2020) BA: History, University of York (2011–2014) Her research interests integrate computational methods with clinical applications, emphasizing interpretability through explainable AI. Key areas include medical imaging classification, neural network development, and leveraging NLP for pathology analysis in scan descriptions. Her work aligns with UN Sustainable Development Goals targeting health and well-being. Megan’s publications and conference contributions reflect a growing emphasis on AI-driven medical diagnostics, particularly in neurological conditions. She has explored MRI-based detection of Parkinson’s and related disorders, CT aneurysm clip identification for imaging safety, and NLP techniques for preprocessing medical data. Awards and Recognition: 2024 Health and Care Research Conference poster prize 2023 University of Plymouth Three Minute Thesis winner 2022 Association of British Neurologists award for top poster Advising & Grants:Megan has secured grants from the MRC and EPSRC for healthcare intervention development and has led interdisciplinary projects. While no formal advisees are listed, her collaborative projects involve mentoring roles within research teams. Notable grants include MRC funding for stroke prediction AI (2024–2026) EPSRC PhD studentship (2020–2024) Affiliations: She is part of the SUSTAIN South West , Big Data group , and Applied Parkinson’s Research Group , alongside professional memberships in the European Stroke Organisation, International Parkinson and Movement Disorder Society, and British Institute of Radiology.

Ziyi Wangمشاهده پروفایل

مدرس ارشد

• Multivariate Statistics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Ziyi Wang is a Senior Instructor in Business Analytics at the CU Denver Business School, University of Colorado Denver. His industry background includes executive roles in marketing analytics and technology entrepreneurship, having co-founded and sold two technology companies. He holds patents in data science applications for homeland security and marketing analytics. Research interests span multivariate statistics, artificial intelligence, machine learning, business analytics, media mix modeling, large language models, and cloud technologies. His publications focus on analytical chemistry, chemometrics, and statistical modeling techniques.

Markus Wolfienمشاهده پروفایل

پژوهشگر

• RNA-Seq Data Analysis
• Workflow Development
• Omics Data Integration
• +۳ مورد دیگر

Dr. Markus Wolfien is a Researcher in Systems Biology and Bioinformatics at the University of Rostock's Faculty of Computer Science and Electrical Engineering. His research develops computational workflows for analyzing biological data, particularly RNA-Seq datasets. Research focuses on integrating omics data through customizable analysis pipelines using tools like Galaxy, Conda, and Docker. Key applications include: Stem cell research and cardiac regeneration Machine learning for biomedical data Workflow optimization for NGS data Epigenomic analysis in cancer Recent publications demonstrate cross-disciplinary approaches combining bioinformatics, machine learning, and systems biology. Current projects include developing deep learning methods for sequencing data and creating accessible tools for cancer genomics research.

• Resilient Infrastructure Systems
• Disaster Management
• Smart Cities
• +۵ مورد دیگر

Mohammad Ilbeigi is an Assistant Professor in the Department of Civil, Environmental and Ocean Engineering at Stevens Institute of Technology (2019–present), previously serving at Bowling Green State University (2017–2019). He holds a PhD and MS in Construction Engineering and Management from Georgia Tech (2017), and earlier degrees from Sharif University of Technology and the University of Tehran. His research focuses on resilient infrastructure systems, disaster management, smart cities, data-driven uncertainty analysis, and pedagogical innovations in construction engineering education. He has led NSF-funded projects totaling over $700K, including AI-enhanced educational systems and post-disaster damage assessment technologies. Dr. Ilbeigi serves on academic committees such as the Civil Engineering Program Committee and Teaching and Learning Excellence Committee. He has received awards including the 2021 Siemens Tech Sustainability Challenge and the 2017 ASCE Thomas Fitch Rowland Prize. His work spans 100+ peer-reviewed publications across journals like IEEE Transactions and ASCE series, with a focus on smart infrastructure systems and pedagogical innovations. He teaches courses in construction project management, infrastructure sustainability, and automation in construction. His research bridges engineering education, AI applications, and disaster resilience, with recent work emphasizing equitable energy access and gamified learning strategies.

Nelly Elsayedمشاهده پروفایل

استادیار

• Applied AI
• Machine Learning
• Healthcare Informatics
• +۷ مورد دیگر

Dr. Nelly Elsayed is an Assistant Professor at the School of Information Technology, University of Cincinnati. She leads the Applied Machine Learning and Intelligence (AMLI) Lab, focusing on AI, cybersecurity, healthcare informatics, and smart technologies. Her research emphasizes practical applications in IoT security, neural networks, and predictive modeling. Her work includes grants from the National Science Foundation, Procter & Gamble, and federal agencies, totaling over $600k. Notable projects include IoT security frameworks, mental health monitoring systems, and cyberbullying detection. Dr. Elsayed has received multiple recognitions, including the Golden Apple Award for Teaching and UCAADA Advising Excellence. She actively serves on technical committees for IEEE and ACM conferences, contributing to academic service and outreach. Research Interests: Applied AI & Machine Learning Cybersecurity for IoT/Healthcare Deep Learning Models (LSTM, CNN) Data Science & Pattern Recognition Smart Technologies for Accessibility Recent Research Trends: Her publications emphasize hybrid models (e.g., LSTM-CNN) for intrusion detection, cybersecurity frameworks, and AI-driven solutions for mental health and elderly care.

Tristan Millerمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing
• Lexical Semantics
• +۴ مورد دیگر

Tristan Miller is an Assistant Professor in the Department of Computer Science at the University of Manitoba, leading the Computational Linguistics at Manitoba (CLAM) Lab. His research focuses on computational linguistics, natural language processing, and humor analysis. He holds academic affiliations with institutions like the Austrian Research Institute for Artificial Intelligence (OFAI) and has contributed to projects like the JOKER Lab at CLEF. His work emphasizes lexical semantics, historical online corpora, and the computational detection/translation of wordplay. He organizes workshops on computational humor (CHum, JOKER) and serves as an editor for journals like Humor and European Journal of Humour Research . Key contributions include the JOKER corpus for multilingual wordplay analysis, tools like PunCAT for pun translation, and LaTeX classes for proposal formatting (Heria, SSHRC-Insight). His research bridges theoretical linguistics with practical NLP applications. He has held roles such as Senior Area Co-Chair at EMNLP 2025 and Faculty Advisor at ACL 2025. His funded projects include the FWF-funded "Computational PUN-DERSTANDING" and Horizon Europe proposal development initiatives.

Jeff Gilchristمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Health Data
• Neonatal Intensive Care
• +۳ مورد دیگر

Dr. Jeff Gilchrist is an Adjunct Research Professor at Carleton University's Faculty of Engineering and Design since 2013. He holds a Ph.D. in Systems and Computer Engineering from Carleton University. He currently works as a Senior AI Data Scientist at the Children’s Hospital of Eastern Ontario (CHEO) Research Institute, collaborating on machine learning applications in healthcare. His research focuses on predicting medical outcomes for neonatal intensive care unit (NICU) patients using AI, analyzing real-time patient data to develop clinical decision support systems. Key projects include 'Applying Data Preprocessing Methods to Predict Premature Birth' (2018) and 'Developing an Automated Clinical Trending Tool for the NICU' (2018). His work integrates machine learning with health data to improve care for infants and supports evidence-based decision-making for medical teams and parents. Dr. Gilchrist collaborates with BORN Ontario and contributes to precision child and youth mental health initiatives.

Vana Kalogerakiمشاهده پروفایل

استاد

• Distributed Systems
• Real-Time Systems
• Participatory Sensing
• +۸ مورد دیگر

Prof. Vana Kalogeraki is a Professor leading the Distributed and Real-Time Systems research group at Athens University of Economics and Business. Previously, she held faculty positions at the University of California, Riverside, and was a Research Scientist at Hewlett-Packard Labs. She earned her PhD from UC Santa Barbara in 2000, with earlier degrees from the University of Crete. Her research focuses on distributed systems, real-time computing, participatory sensing, and edge computing. She has published over 100 papers and co-authored the CORBA Dynamic Scheduling Standard. She has received notable awards including the Marie Curie Fellowship and best paper recognitions at IPDPS and SAINT conferences. Her work is supported by grants from the ERC, EU, NSF, and industry partners like SUN and Nokia. She has organized major conferences and serves on editorial boards for journals such as Transactions on Mobile Computing and Peer-to-Peer Networking. Education: PhD in Computer Science, University of California, Santa Barbara (2000) M.S. in Computer Science, University of Crete (Greece) B.S. in Computer Science, University of Crete (Greece) Research Interests: Prof. Kalogeraki explores distributed systems, real-time processing, and edge computing applications. Her work emphasizes resource management, fault-tolerance, and sensor network middleware. Recent projects include serverless computing optimizations, privacy-preserving mobile cloud frameworks, and anomaly detection in IoT environments. She also contributes to diversity initiatives in database conferences. Awards and Honors: Marie Curie Fellowship Best Paper Awards at IPDPS 2009 and SAINT 2008 Best Student Paper Award at SAINT 2011 UC Regents Fellowship Award ERC Starting Independent Researcher Grant Grants and Collaborations: Her research is funded by the European Union (including Thalis and Aristeia grants), NSF, and industry partnerships. She has led projects on edge AI, serverless pipelines, and participatory sensing systems. Labs and Teams: She directs the Distributed and Real-Time Systems Lab at Athens University, focusing on real-time distributed applications and edge computing innovations.

Akitaka Matsuoمشاهده پروفایل

مدرس

• Political Science
• Data Science
• Text Analysis
• +۳ مورد دیگر

Akitaka Matsuo is a Lecturer in the Department of Government at the University of Essex. His academic career includes roles as a Postdoctoral Fellow at the Institute for Analytics and Data Science (IADS) and a Research Fellow in Data Science at the London School of Economics and Political Science (LSE). He holds a Ph.D. in Political Science from Rice University (2012). His research focuses on computational social science, political communication, and text analysis, leveraging tools like R packages quanteda and spacyr . Matsuo’s work spans topics such as Brexit discourse, election forecasting, legislative sentiment analysis, and gendered language strategies in campaigns. He actively contributes to open-source software development for text analysis and has published widely on NLP applications in political science. His academic support hours are Tuesdays 3-5 PM (appointment-only). Grants and funding details are not explicitly listed in the provided texts.

• Health Informatics
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Gang Luo is an Assistant Professor in the Department of Biomedical Informatics at the University of Utah's School of Medicine. He holds a PhD in Computer Science from the University of Wisconsin-Madison with a minor in Mathematics. His research focuses on health informatics, machine learning, natural language processing, and database systems, with applications in clinical decision support, medical search engines, and healthcare data analytics. Dr. Luo has over 50 publications and 20+ patents, including inventions like the iMed intelligent medical search engine and systems for progress indication in database operations. His work integrates advanced computing techniques with healthcare challenges to improve patient care and public health outcomes. Education: PhD in Computer Science (University of Wisconsin-Madison), BS in Computer Science (Shanghai Jiaotong University). Research Interests : Health Informatics (software systems, data analytics) Machine Learning & AI for healthcare Data Mining and Databases Medical Search and Information Retrieval Decision Support Systems His patent portfolio includes innovations in database systems, real-time event detection, and medical search tools. Recent work explores computational approaches for asthma management, COPD phenotyping, and predictive modeling in clinical settings. His research emphasizes translating technical advancements into practical healthcare solutions.

Jerzy W. Grzymala-Busseمشاهده پروفایل

استاد

• Data mining
• Knowledge discovery
• Machine learning
• +۳ مورد دیگر

Jerzy W. Grzymala-Busse is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, where he has made significant contributions to data mining, machine learning, and rough set theory for several decades. Professor Grzymala-Busse's research focuses on data mining, knowledge discovery, machine learning, expert systems, reasoning under uncertainty, and rough set theory . He is the creator of the LERS (Learning from Examples based on Rough Sets) system, which induces rules from examples and classifies new examples using rough set theory. His work connects rough set theory with Dempster-Shafer theory, and he has explored various aspects of data preprocessing including discretization methods and handling missing attribute values. His research has demonstrated that expert systems with rule sets induced by LERS achieve significantly higher positive predictive values (59-93%) compared to traditional methods (17-38%) in medical applications like preterm labor risk assessment. Professor Grzymala-Busse has been actively teaching courses such as EECS 510, EECS 638 (Fundamentals of Expert Systems), EECS 645, EECS 690 (Introduction to Data Mining), EECS 837, and EECS 839. He authored the textbook "Managing Uncertainty in Expert Systems" published by Springer Verlag in 1991 and has supervised numerous graduate students on research spanning classification systems, data reduction techniques, and comparative studies of rule induction algorithms. His LERS system has found practical applications in diverse domains including finance (loan decision-making), medical areas (diagnostic decision support), and military applications (job performance evaluation). Notably, it has been used by NASA Johnson Space Center for developing expert systems for medical decision-making on board the International Space Station, and by the U.S. Environmental Protection Agency for facility compliance under Sections 311, 312, and 313 of Title III, the Emergency Planning and Community Right to Know Act.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۲ مورد دیگر

Dongjie Wang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, School of Engineering. His research focuses on artificial intelligence, machine learning, and data science applications in bioinformatics, transportation planning, and data-centric AI. Wang's research interests span several key areas: Development of reinforcement learning frameworks for biological pathway analysis and gene selection Generative AI applications in urban transportation systems Multi-modal representation learning for cross-domain retrieval Neuro-symbolic approaches for feature engineering His recent publications demonstrate a consistent focus on developing novel AI methodologies for real-world applications, particularly in transportation systems optimization, biological data analysis, and tabular data processing. The articles collectively show strong emphasis on reinforcement learning architectures, graph neural networks, and multimodal integration techniques. Wang advises graduate students in computer science and leads research in data-centric AI applications. His laboratory focuses on developing scalable machine learning solutions for complex systems.