جستجوی استاد راهنمای دانشگاهی

Mario Edenمشاهده پروفایل

استاد

• Process Systems Engineering
• Process Synthesis
• Process Design
• +۴ مورد دیگر

Mario Eden serves as Dean of the Samuel Ginn College of Engineering and the Joe T. and Billie Carole McMillan Professor in Chemical Engineering at Auburn University. Appointed Dean in 2023 after leading the Chemical Engineering department, he holds Ph.D. and M.S. degrees from the Technical University of Denmark. Education: Ph.D. in Chemical Engineering, Technical University of Denmark M.S. in Chemical Engineering, Technical University of Denmark Research Focus: Dr. Eden's work centers on Process Systems Engineering , specializing in Process Synthesis, Design, Integration and Optimization and Product/Molecular Design . His research integrates machine learning, optimization algorithms, and sustainability principles to develop efficient chemical processes and green products, with particular emphasis on process intensification and solvent design. Publication Trends: Recent publications (2022-2024) reveal strong focus on sustainable process design through machine learning applications, ethylene oxide process intensification, and green solvent development. Key innovations include hybrid optimization frameworks for heat exchanger networks, interpretable toxicity prediction models, and NLP-based molecular property prediction. Scientific Recognition: Fellow of the American Institute of Chemical Engineers (AIChE) Author of top 1% most cited article in Industrial & Engineering Chemistry Research Leadership Impact: As Department Chair, he elevated Chemical Engineering to its highest U.S. News ranking, added 17 tenure-track faculty, and achieved record enrollment with top-tier student metrics. As Dean, he oversees the college's record $117.7 million FY24 research portfolio and spearheads major initiatives including the Gulf Coast Engineering Research Station. Research Infrastructure: His group contributes to Alabama's bioplastics initiative using agricultural waste and develops computational tools like ProCAFD for sustainable process synthesis, with research directly supporting the college's strategic focus on coastal environment studies through the new Orange Beach facility.

• Language Processing
• Information Theory
• Artificial Intelligence
• +۳ مورد دیگر

Richard Futrell is an Associate Professor in the Department of Language Science at the University of California, Irvine. He leads the Language Processing Group, focusing on understanding language processing in humans and machines through information theory and Bayesian cognitive modeling. His research also extends to NLP and AI interpretability, emphasizing the shared information-processing constraints between humans and language models. His work explores universals of language structure and their explanations through generic information-theoretic principles. Dr. Futrell's research spans computational linguistics, psycholinguistics, and cognitive science, with notable contributions to dependency length minimization, word order universals, and the efficiency of language design. He has received awards including the ACL Best Paper Award (2024) and the Best Paper Award for Computational Modeling of Language (2023). His group investigates topics such as code-switching, syntactic priming, and the cognitive foundations of language processing. His publications address language universals, computational models of syntax, and the interplay between linguistic structure and processing efficiency. Key contributions include studies on dependency locality, the role of memory and surprisal in sentence comprehension, and the application of information theory to adjective ordering and morphological fusion.

Paulo Costaمشاهده پروفایل

استاد

• Cybersecurity
• Decision Support Systems
• Probabilistic Ontologies
• +۴ مورد دیگر

Paulo Costa is a Professor and Chair of the Department of Cyber Security Engineering at George Mason University's College of Engineering and Computing. He also directs the Center of Excellence in C5I and serves as Vice President for Securing Automation at CyManII. His expertise spans cybersecurity, decision support systems, and probabilistic ontologies. Costa holds a PhD in Information Technology from GMU, with earlier degrees in Systems Engineering and Brazilian Air Force Academy training. Research focuses on Bayesian reasoning applications in cyber-physical systems, information fusion, and semantic technologies. Key projects include PR-OWL (probabilistic ontology language), ADS-Bsec (secure aviation communication), and cybersecurity frameworks for semiconductor manufacturing. He leads the ETUR Working Group on uncertainty reasoning and has pioneered tools like UnBBayes-MEBN for probabilistic reasoning. Grants: Includes $3.9M for cybersecurity scholarships and projects under DOE's Cybersecurity Manufacturing Innovation Institute. Awards: Peggy Brouse Cybersecurity Educator Award, multiple best paper awards, and the Eduardo Gomes Medal. Leadership: IEEE Senior Member, ISIF Board member, and affiliated with Brazilian universities. Teaching includes courses on cybersecurity, probabilistic reasoning, and systems engineering. Advises over 20 PhD students and has mentored teams in national competitions.

Muhammad Mahbubur Rahmanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۲ مورد دیگر

Muhammad Mahbubur Rahman is a tenure-track Assistant Professor in Pediatrics and Biostatistics/Bioinformatics at Children's National Hospital. He holds a PhD in Computer Science from University of Maryland, Baltimore County, with postdoctoral training at NIH and Johns Hopkins University in AI/ML and NLP. His research integrates artificial intelligence, natural language processing, and machine learning to address challenges in child mental health, including ADHD prediction, misinformation detection, and clinical decision support. His work applies advanced computational techniques to healthcare data and social media analysis. Recent publications demonstrate applications in wearables for ADHD monitoring, critical event prediction in pediatrics, and AI ethics in mental health. His research leverages large-scale datasets to develop innovative tools for child well-being.

• Health Information Technology
• EHR and mHealth
• Patient Engagement
• +۸ مورد دیگر

Bryan Smith Gibson serves as an Assistant Professor in the Department of Biomedical Informatics at the University of Utah, holding an additional adjunct appointment in Physical Therapy & Athletic Training. His academic career builds upon extensive clinical experience including management of the Salt Lake City VA Cardiac Rehabilitation Program (2002-2008) and service on the LDS Hospital Inpatient wound care team. Dr. Gibson's educational trajectory demonstrates deep institutional commitment to the University of Utah: BS in Undergraduate Studies MPT in Graduate Training DPT in Doctoral Training (2006) PhD in Doctoral Training (2012) Postdoctoral Fellowship at Veterans Affairs Medical Center His research program centers on the sociotechnical dimensions of health informatics, with particular emphasis on translating psychological science into health technology design. Core methodologies include Workflow Analysis, Cognitive Task Analysis, and Human Centered Design and Evaluation. Primary application domains encompass Type 2 Diabetes management through technology-based behavioral interventions, EHR optimization, and mHealth solutions for patient engagement. Analysis of Dr. Gibson's publication record reveals consistent thematic focus across 15 recent works: chronic disease management (particularly diabetes) through informatics solutions, EHR redesign for improved clinical workflows, and patient engagement strategies using simulation and behavioral science principles. His work bridges technical health IT development with human factors considerations, frequently employing mixed-methods approaches to evaluate sociotechnical system impacts. Professional activities include significant contributions to tobacco cessation implementation science (QuitSMART Utah) and community-engaged diabetes research with Hispanic populations. While no formal advisees are documented in the provided materials, his collaborative publication pattern indicates active mentorship within multidisciplinary research teams.

Nizar Bouguilaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +۷ مورد دیگر

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۵ مورد دیگر

Eugene Belilovsky is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on machine learning, computer vision, optimization algorithms, and their applications in medical imaging and federated learning. He explores topics such as continual learning, model merging, and bias mitigation in deep learning systems. His work bridges theoretical advancements with practical applications, including healthcare diagnostics and efficient training strategies for large models. Belilovsky's recent publications emphasize innovative methods like MuLoCo for inner optimizer design, FairDropout for enhancing minority group generalization, and techniques to scale multi-task learning with sparse masks. His studies on federated learning explore incentive mechanisms for decentralized systems and robust pre-training of large language models. His contributions to 3D reconstruction and motion prediction highlight interdisciplinary applications in computer graphics and generative models. His research trends reflect a strong emphasis on improving model efficiency, fairness, and adaptability across diverse domains. While no scientific awards are explicitly listed, his prolific publication record underscores his impactful contributions to AI and machine learning.

• Geoinformatics
• Intelligence Informatics
• Geospatial Data Mining
• +۷ مورد دیگر

Ickjai Lee is an Associate Professor at the School of IT, James Cook University, specializing in geoinformatics and intelligence informatics. He leads the Information Technology department and has held roles including Senior Lecturer and Lecturer since 2003. He obtained his PhD in 2002 from the University of Newcastle, Australia. Research Interests: Geospatial data mining, trajectory analysis, Voronoi tessellations, mobile AR, and IoT applications. Projects: Includes VR healthcare trials, AI-driven vehicle damage assessment, and environmental monitoring systems. His work focuses on applying machine learning and geospatial techniques to solve real-world problems in health, environmental, and urban domains. Notable achievements include best paper awards at international conferences and faculty citations for teaching excellence. He collaborates on interdisciplinary projects, such as using AR for marine growth monitoring and developing tools for crime pattern analysis. His research also involves large-scale ecoacoustic data analysis and VR-based education platforms. Lee has supervised numerous PhD students exploring topics like differential privacy in biokinetics, spatio-temporal anomaly detection, and VR in cultural heritage preservation.

Nils Murrugarra-Llerenaمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Nils Murrugarra-Llerena is a Teaching Assistant Professor at the University of Pittsburgh's School of Computing and Information, joining in Fall 2024. He holds a PhD from the same institution. His research focuses on computer vision, machine learning, and natural language processing, with emphasis on attribute recognition, deep learning, gaze prediction, transfer learning, reinforcement learning, and metric learning. Education: PhD, University of Pittsburgh His research interests span advanced topics like visual embeddings, multimodal systems, and NLP applications in social media analysis. Recent work includes improving visual embeddings for e-commerce search engines, probabilistic models for feature selection, and analyzing weather impacts on social sentiment. Publications highlight interdisciplinary approaches, blending computer vision with applications in education technology and sketch-based retrieval. No scientific awards are mentioned in the provided information. No advisees or grant details are listed. While no specific labs or teams are noted, his work intersects with computational models and data-driven methods.

Thienkhai H. Vuمشاهده پروفایل

دانشیار بالینی

• Molecular imaging
• Thoracic imaging
• MRI
• +۹ مورد دیگر

Thienkhai H. Vu, MD, PhD, is an Associate Clinical Professor in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco (UCSF). He specializes in chest imaging at SFGH and contributes to ultrasound, abdominal, and musculoskeletal imaging. Dr. Vu holds a PhD in Pathology from the University of Oklahoma HSC (1997) and an MD from the same institution (1999). His training includes residencies in Ophthalmology (University of Wisconsin, 2001), Diagnostic Radiology (King/Drew Medical Center, 2004; UCSF, 2005), and a fellowship in Cross-Sectional Imaging at SFGH (2006). He previously served as Program Director for the Cross-Sectional Training Program and Co-Director of Breast/Body Imaging at SFGH. Currently, he participates in the UCSF Abdominal Imaging Fellowship Selection Committee. Education: PhD in Pathology: University of Oklahoma HSC, 1997 MD: University of Oklahoma HSC, 1999 Residencies: Ophthalmology (UW Madison), Diagnostic Radiology (King/Drew, UCSF) Fellowship: Cross-Sectional Imaging (SFGH) His research focuses on molecular imaging, thoracic imaging, and advanced modalities like MRI, MDCT, microimaging techniques, and quantum dots. He has authored 20 peer-reviewed publications and 12 abstracts. His work bridges clinical practice and technical innovation in radiology. Awards: Nominated for the Robert Lull, MD Award (SFGH, 2009) Labs/Teams: Involved in UCSF’s radiology training programs and imaging research initiatives.

Sheela Ramannaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Soft Computing
• +۷ مورد دیگر

Dr. Sheela Ramanna is a Professor and Chair of the Applied Computer Science Graduate Program at the University of Winnipeg , with an adjunct appointment in the Department of Computer Science at the University of Manitoba. She holds a Ph.D. in Computer Science from Kansas State University (2003), an M.S. in Computer Science (1998), and a B.S. in Electrical Engineering (1996) from Osmania University , India. Adjunct Professor, University of Manitoba Professor & Chair, University of Winnipeg Graduate Program Her research focuses on Artificial Intelligence , Machine Learning , and Soft Computing (Rough Sets, Fuzzy-Rough Sets, Tolerance-based Methods) with applications in Multimodal Information Processing , Natural Language Processing , and Topological Data Analysis . She has developed novel tolerance near-set algorithms for sentiment classification, named entity recognition, and community detection in social networks. Recent publications include 2025 work on speech emotion recognition, 2024 studies on diabetic retinopathy detection and plant species recognition, and 2023 research on text summarization and NLP applications. Her work spans 15+ peer-reviewed articles in journals like Scientific Reports , Information Fusion , and Frontiers in Artificial Intelligence . Scientific Awards include the UW Merit Award for Exceptional Performance (multiple years), MITACS Globalink Research Intern (2024), and 3MT People's Choice Award (2018). She has served as Editor for EAAI Journal , Associate Editor for KES Journal , and Organizing Co-Chair for ISCMI 2025 . She supervises 22+ graduate students and postdocs, including Vrushang Patel (President's Scholarship), Habib Ben Abdallah (MITACS Fellow), and Anil Rahate (collaborative PhD with SIT Pune). Her NSERC-funded projects include precipitation forecasting with WeatherLogics Inc., road condition classification, and LULC mapping using satellite imagery.

Sarath Chandar Anbil Parthipanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Sarath Chandar Anbil Parthipan is an Associate Professor at Polytechnique Montréal and leads the Chandar Research Lab (CRL) . He holds the Canada Research Chair Tier 2 in Lifelong Machine Learning and the CIFAR Chair in Artificial Intelligence . As a core faculty member at Mila - the Quebec AI Institute , he also serves as an adjunct professor at the University of Montreal and IIT Madras . PhD in Computer Science (University of Montreal, 2015) MS by Research in Computer Science (IIT Madras, 2015) B.Eng in Computer Science (Anna University, 2009) His research focuses on lifelong/continual learning , deep learning , reinforcement learning , and natural language processing . He created the Conference on Lifelong Learning Agents (CoLLAs) in 2022 and serves as its organizer. Recent works explore molecular design with BindGPT , multi-agent RL coordination, and model interpretability. Scientific recognition includes: Canada Research Chair Tier 2 CIFAR Chair in AI He supervises students in topics spanning AI for science , medical image segmentation , and reset-free reinforcement learning . The CRL Annual Symposium 2023 featured collaborations with institutions like Princeton University and IBM.

Diptesh Kanojiaمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Machine Learning
• Low-resource Languages
• +۴ مورد دیگر

Affiliations Senior Lecturer in People-Centred AI at the Surrey Institute for People-Centred Artificial Intelligence (PAI) and School of Computer Science and Electronic Engineering, University of Surrey. Visiting Faculty at IIIT Lucknow. Former Postdoctoral Research Fellow at the Centre for Translation Studies (CTS), University of Surrey. Education PhD, IIT Bombay & Monash University (2021) - Dissertation on distributional similarity for cognate detection and computational phylogenetics. BTech in CSE, Uttar Pradesh Technical University (2013). Research Interests Focused on NLP and ML, particularly in low-resource languages, machine translation quality estimation, cognitive NLP, and multimodal information processing. Leads research in Human-Machine Interaction at PAI and the NLP subgroup in the Nature Inspired Computing and Engineering (NICE) group. Publications & Awards Over 30 publications in top conferences (ACL, EMNLP, EACL) including Best Paper Honourable Mention at EACL 2021. Awards include CISCO Fellowship (2016-2020) and Teaching Assistant of the Semester (2017, 2019). Teaching Leads NLP module for undergrad and postgrad students at Surrey. Co-teaches NLP at IIIT Lucknow. Integrates cutting-edge topics like multimodal processing into coursework.

Harish Tayyar Madabushiمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Harish Tayyar Madabushi is a Lecturer in the Department of Computer Science at the University of Bath, affiliated with the Bath Institute for the Augmented Human and the Artificial Intelligence and Machine Learning group. His research focuses on Large Language Models (LLMs), their mechanisms, and applications in bias mitigation, speech-to-text systems, and construction grammar integration. He previously held an Honorary Research Fellow position at the University of Birmingham (2021–2024). Madabushi's work bridges computational linguistics and AI ethics, addressing challenges like regional dialect adaptation in public services and healthcare applications. He leads the Wyser project, funded by Innovate UK, aiming to reduce bias in Automatic Speech Recognition (ASR) systems. His research has been featured in foundational discussions at the UK AI Safety Summit and contributes to UN SDGs related to innovation and sustainable development. He supervises doctoral students in areas such as neuro-symbolic AI and explainable NLP, offering projects funded by ART-AI. His publications span conferences like ACL, COLING, and LREC-COLING, with over 45 peer-reviewed outputs. He actively collaborates internationally, addressing multilingual, code-switched, and specialized lexical learning challenges in LLMs.

Hao Zhuمشاهده پروفایل

پژوهشگر ارشد

• Fusion Plasma Physics
• Nonlinear Dynamics
• Turbulence Modeling
• +۷ مورد دیگر

Hao Zhu is a CFSA Postdoctoral Visiting Fellow at the University of Warwick, supervised by Prof. Sandra Chapman and Prof. Richard Dendy. His research focuses on modeling nonlinear structures in fusion plasmas using Lotka-Volterra equations, bridging plasma physics with computational methodologies. He holds a BSc in Physics from Dalian University of Technology (DLUT, 2006–2010) and is affiliated with the Physics Centre for Fusion, Space and Astrophysics (CFSA). His interdisciplinary work spans plasma turbulence, machine learning accelerators, computer vision, and sustainable materials engineering. Key contributions include FPGAs for Tsetlin Machine training, dense captioning algorithms, and multispectral pedestrian detection systems. Notably, he explores applications of large language-vision models and circular economy strategies in steel production. Publications highlight advancements in edge computing, 3D model retrieval, and bias-mitigation in AI systems. Despite no listed awards, his research demonstrates cross-disciplinary innovation across physics, computer science, and environmental engineering. Labs and collaborations are centered at CFSA, with potential affiliations in computer vision labs given his dual focus areas. No advising roles or grants are explicitly stated in the provided texts.