جست‌وجوی استاد و پژوهشگر

• Water Resources Systems Management
• Groundwater and Surface Water Quality
• Saltwater Intrusion Control
• +۷ مورد دیگر

Bithin Datta is a Senior Lecturer in the Discipline of Civil Engineering at James Cook University (Australia), part of the College of Science and Engineering and the Division of Tropical Environments and Societies. He is affiliated with TropWATER (Centre for Tropical Waters and Aquatic Ecosystem Research), the Economic Geology Research Unit (EGRU) at JCU, and the CRC-for Contamination Assessment and Remediation of the Environment (CRC-CARE) at the University of Newcastle. Previously, he held Professor and Senior Professor positions at IIT Kanpur, India (1995-2009), and served as Head of the Civil Engineering Department and Head of the Postgraduate Environmental Engineering and Management Program. He has held Visiting Professorships at Dalhousie University (Canada), Denmark Technical University (Copenhagen), and the Asian Institute of Technology (Bangkok). Education: B.Tech (Hons) in Civil Engineering from IIT Kharagpur (India), Master’s degree in Civil Engineering (first rank in specialization), and a PhD in Civil Engineering from Purdue University (USA), specializing in Hydraulics and Systems Engineering. He is a Fellow of Engineers Australia (FIEAust). Research interests include water resources systems management, groundwater and surface water modeling, reservoir operation optimization, saltwater intrusion control, AI-driven environmental predictions, and ecological flow assessment. His work integrates simulation-optimization frameworks, machine learning (e.g., ANFIS, SVM), and hydraulic engineering principles to address contamination, climate change, and infrastructure challenges in tropical and coastal regions. His publications (178+ entries) focus on computational tools for groundwater contamination source identification, sustainable aquifer management, and reservoir environmental impacts. Notable projects include a $629,000 CRC-CARE-funded initiative for contamination monitoring networks and AI-based drought prediction models in tropical Queensland. Advising: Coordinated the Master of Engineering (Water Resources Management) at JCU and supervised over 30+ Master’s and 19 Ph.D. students across IIT Kanpur, JCU, and the University of South Australia. His research has been ranked #1 globally by ScholarGPS in Groundwater Pollution, Surrogate Models (AI/ML), and Saltwater Intrusion management. Labs/Teams: Core member of TropWATER, EGRU, and CRC-CARE, leading interdisciplinary projects on tropical water systems and geochemical contamination modeling in mine sites.

• Machine Learning
• Environmental Science
• Climate Science
• +۳ مورد دیگر

Professor Stephen Roberts holds the Royal Academy of Engineering / Man Group Chair in Machine Learning at the University of Oxford. He is affiliated with the Oxford-Man Institute and Somerville College. With a DPhil in machine learning and a physics background, his research spans environmental science, financial systems, and geophysics. He co-leads the Machine Learning Research Group and directs the EPSRC Centre for Doctoral Training in Autonomous, Intelligent Machines and Systems (AIMS). His academic journey includes prior faculty roles at Imperial College London before joining Oxford in 1999. Key research interests include tidal analysis using AI, climate modeling, geospatial data interpretation, and financial algorithm design. He has pioneered tools like RTide for coastal flooding prediction and developed machine learning frameworks for environmental and economic applications. Education: DPhil in Machine Learning, Physics undergraduate degree Affiliations: Oxford-Man Institute, Somerville College, EPSRC AIMS CDT Key Projects: SWOT mission data corrections, Antarctic bedrock mapping, carbon footprint reduction in ML His work bridges disciplines, applying ML to solve complex problems in climate science, finance, and geology. Awards include Fellowship of the Royal Academy of Engineering and IET. Current focus areas include improving climate model accuracy and fostering interdisciplinary training through the AIMS program.

Aida Haghighiمشاهده پروفایل

استادیار

• Occupational Health and Safety (OHS) Management
• Industrial Safety
• OHS Risk Management
• +۳ مورد دیگر

Dr. Aida Haghighi is an Assistant Professor at the School of Occupational and Public Health, Toronto Metropolitan University. She holds a PhD from Polytechnique Montréal, with prior industrial experience at the National Gas Company. Her research focuses on occupational health and safety (OHS) management systems, Industry 4.0 integration, and risk mitigation strategies. She teaches courses like OHS 718: Systems Management and OHS 811: OHSE Management Systems. Education: BSc, MSc, PhD in Industrial Engineering from Iran and Canada. Professional Engineer (PEng) designation. Research highlights include developing assessment tools to prevent machine safeguard bypassing and analyzing Industry 4.0's impact on worker safety. Research Interests: Industrial safety systems, OHS risk management, integrated management systems, and continual improvement. Current projects explore emerging technologies in OHS frameworks and gender disparities in workplace health. Awards include the Best Doctoral Thesis (2020, Polytechnique Montréal) and NSERC-funded research abroad. She advises MSc students in Occupational and Public Health starting 2025-2026. Teaching and Grants: Active in curriculum development for OHS education. Collaborates on projects funded by industry partnerships and governmental grants focusing on safety innovation.

Christopher Brooksمشاهده پروفایل

استادیار

• Educational Technology
• Data Science Education
• Learning Analytics
• +۳ مورد دیگر

Christopher Brooks is an Assistant Professor at the University of Michigan's School of Information, specializing in educational technologies and data science education. He directs the Educational Technology Collective (etc), a multidisciplinary research group focused on learning analytics, educational data mining, and collaborative learning systems. His work bridges computer science and education, with a focus on improving teaching methods through AI-driven tools and platforms. Research Interests: Development and impact assessment of educational technologies Predictive modeling for student success Data science pedagogy Privacy in smart home technologies Publications reflect a focus on learning analytics, MOOC design, and educational AI, with notable contributions to conferences like CHI, LAK, and AIED. Awards include multiple best paper recognitions. Teaching includes applied data science courses at UMich and Coursera. He leads the Master of Applied Data Science (MADS) program and collaborates with institutions like Microsoft to build AI-driven educational tools.

Jingbang Chenمشاهده پروفایل

عضو هیئت علمی

• Algorithms
• Graph Theory
• Data Structures
• +۳ مورد دیگر

Jingbang Chen is a Research Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) and holds a joint faculty position at Shenzhen Loop Area Institute (SLAI) starting September 2025. His academic journey includes a Ph.D. from the University of Waterloo, an M.S. from Georgia Institute of Technology, and a B.Eng (Honors) from Zhejiang University under the supervision of Can Wang. Education: Ph.D., Computer Science, University of Waterloo (2023-2025) M.S., Computer Science, Georgia Institute of Technology (2020-2022) B.Eng. (Honors), Pursuit Science Class, Chu Kochen Honors College (Joint Program with College of Computer Science and Technology), Zhejiang University (2016-2020) High School, Guangzhou No.2 High School (2010-2016) Dr. Chen's research focuses on the design, analysis, and implementation of provably efficient algorithms and data structures, with a particular emphasis on graph theory. He is also exploring intersections between traditional algorithm design and artificial intelligence. His work bridges theoretical computer science with practical applications in network analysis, temporal data processing, and optimization. The publication record shows a strong trajectory with papers in top venues including ICML, VLDB, KDD, and theoretical computer science conferences. Scientific Contributions: Published in premier venues including ICML 2025, VLDB 2025, KDD 2024, and multiple theoretical conferences Research spans graph algorithms, optimization techniques, network analysis, and the emerging field of learning-augmented algorithms Active contributor to the competitive programming community as both researcher and practitioner Dr. Chen is deeply involved in Competitive Programming activities, having competed in ICPC World Finals 2018 (Beijing) and 2022 (Egypt), winning regional champion titles and several gold medals. He serves as chief judge for multiple ICPC Asia regionals and coaches training camps including the North American Programming Camp (NAPC). He is also the founder and co-president of the Universal Cup, an international competitive programming contest platform. Currently, he is recruiting highly motivated PhD students with strong backgrounds in competitive programming and interest in research, collaborating with Prof. Chenhao Ma on algorithm design projects.

Hanan Sametمشاهده پروفایل

استاد

• Spatial Databases
• Data Structures
• Geographic Information Systems (GIS)
• +۵ مورد دیگر

Hanan Samet is a Distinguished University Professor at the University of Maryland's Computer Science Department, affiliated with the Institute for Advanced Computer Studies (UMIACS) and the Center for Automation Research. He holds a Ph.D. from Stanford University (1975) and specializes in spatial databases, data structures, and geographic information systems. His research bridges computer science and geospatial analytics, with applications in image databases, computer vision, and spatio-temporal data management. Education: Ph.D., Computer Science, Stanford University, 1975 Research Interests: Focuses on spatial data structures, GIS, spatio-textual systems like NewsStand and CoronaViz, trajectory analysis, and metric indexing. His work emphasizes scalable algorithms for spatial networks and multimedia databases. Notable Projects: CoronaViz : Tracks disease spread via spatio-temporal data visualization NewsStand : Maps news articles geospatially SAND: Spatial browser for digital government Awards: ACM Paris Kanellakis Award (2014), IEEE McDowell Award (2015), UCGIS Research Award, and Fellowships in ACM/IEEE/AAAS. Recognized for advancing spatial database theory and practice. Grants/Advising: Leads NSF-funded projects on spatio-textual extraction and similarity search. Advises graduate students (e.g., Nicole Schneider, Montana Hoover) and undergraduate researchers. Labs/Teams: Active in UMIACS and the Center for Automation Research, collaborating on projects like VASCO (spatial visualization tools) and MARCO (image database systems).

Paul Ralphمشاهده پروفایل

استاد

• Software Engineering
• Empirical Software Engineering
• Human-Computer Interaction
• +۱۷ مورد دیگر

Dr. Paul Ralph is a Professor in the Faculty of Computer Science at Dalhousie University , where he leads the Dalhousie Software Engineering Lab (DalSEL) . His work bridges software engineering, human-computer interaction, and project management, with a focus on empirical research and social sustainability in software development. Education: PhD in Computer Science, University of British Columbia BSc in Computer Science, Memorial University BComm in Business, Memorial University Dr. Ralph's research centers on the sociotechnical aspects of software engineering , particularly how team dynamics, ethics, and human factors influence software success. He rejects pseudoscientific models like Waterfall and SDLC, and avoids AI/ML/data science, instead emphasizing rigorous qualitative and quantitative human-participant studies. His lab is known for its work on socially sustainable software engineering and evidence standards in computing research. His recent publications reflect a strong trend toward methodological rigor, ethical computing, and human-centered practices . Themes include empirical standards, agile methods, requirements engineering, and the social impact of technology. He publishes in top venues like IEEE TSE and ICSE , and has authored over 80 scholarly works. Scientific Awards and Recognition: Award-winning scientist (multiple unspecified awards) Editor-in-Chief, SIGSOFT Empirical Standards for Software Engineering Research Dr. Ralph is actively involved in mentoring and funding graduate students , particularly through external scholarships like NSERC, Killam, and Banting. He prioritizes applicants from underrepresented groups and emphasizes original, non-AI-generated work. His lab offers strong industry connections, professional development, and support for tenure-track aspirations among postdocs. Labs and Research Groups: Dalhousie Software Engineering Lab (DalSEL) : Focuses on empirical, human-centered software engineering research, with active projects in social sustainability and evidence standards.

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +۵ مورد دیگر

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

• Astrophysics
• Early Universe Studies
• Gamma-Ray Bursts
• +۳ مورد دیگر

Nathaniel Butler is an Associate Professor at Arizona State University's School of Earth and Space Exploration. His research focuses on astrophysical explosions and their role in probing the early universe, with emphasis on gamma-ray bursts (GRBs) and their optical counterparts. He holds a Ph.D. in Physics from MIT (2003) and a B.A. in Physics (magna cum laude) from Princeton University (1998). Education: Ph.D. Physics, Massachusetts Institute of Technology, 2003 B.A. Physics (magna cum laude), Princeton University, 1998 Research Interests: Dr. Butler's work centers on understanding the properties and evolution of the early universe through observations of GRBs and associated phenomena. His projects involve collaborations with advanced facilities like the COLIBRÍ robotic telescope and the SVOM mission, focusing on optical counterpart detection, afterglow analysis, and multi-messenger astronomy. His expertise in data mining and transient event analysis supports large-scale sky surveys and rapid-response observation protocols. Grants & Advising: No student advisees or grant details listed. His research is supported through institutional affiliations and collaborative projects. Labs/Teams: Active in the COLIBRÍ telescope initiative and projects like the Exoplanet Climate Infrared Telescope (EXCITE) and Star-Planet Activity Research CubeSat (SPARCS).

Marco Lazzariمشاهده پروفایل

استاد

• Educational Technology
• Digital Storytelling
• Inclusive Education
• +۳ مورد دیگر

Marco Lazzari is a Full Professor at the University of Bergamo in the Department of Human and Social Sciences. His research focuses on educational technology integration, digital storytelling applications in teaching, and inclusive education methodologies. Previously he served as Department Head (2018-2024) and coordinates doctoral studies in Human Sciences and Welfare Innovation. Education Background: Ph.D. in Computer Science M.S. in Information Sciences B.S. in Information Sciences Research Interests: Digital storytelling for inclusive pedagogy Educational robotics applications Technology-mediated distance learning Teacher training in digital literacy Social media's educational impact His recent publications demonstrate strong focus on educational technology in post-pandemic contexts, inclusive robotics programs, and digital storytelling frameworks. Research trends include addressing educational disparities through technology, evaluating pandemic learning impacts, and developing sustainable digital pedagogies across educational levels. Scientific Awards: No major awards listed in provided materials As Director of the Educational Technologies Center (2005-2018), he established technology-enhanced learning initiatives. Currently supervises doctoral students investigating educational technology efficacy, digital inclusion, and robot-assisted learning. Research collaborations span computer science, pedagogy, and special education disciplines. Laboratory Focus: Educational robotics implementation Digital storytelling frameworks Teacher technology training programs Accessibility in educational technology

Latifur Khanمشاهده پروفایل

استاد

• Data Mining
• Cybersecurity
• Machine Learning
• +۲ مورد دیگر

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

Kevin W. Hamlenمشاهده پروفایل

استاد

• Cybersecurity
• Software Security
• Formal Methods
• +۶ مورد دیگر

Kevin W. Hamlen is the Louis A. Beecherl, Jr. Distinguished Professor in the Department of Computer Science at the University of Texas at Dallas. He serves as Executive Director of UT Dallas' Cyber Security Research and Education Institute. His research focuses on language-based security , binary software hardening , cyberdeception , and formal program verification . He has received multiple grants from agencies like AFOSR, NSF, DARPA, and industry partners including Lockheed Martin and Intel. PhD and MS from Cornell University BS from Carnegie Mellon University His research explores automated approaches to software security through techniques like binary disassembly , control-flow integrity , and honey-patching . He has pioneered methods for malware defense and cloud/web/mobile security . Recent work examines adaptive cyberdeception and GPU-based security frameworks . His publications span binary code manipulation , malware mitigation , and blockchain security . Key awards include the NSF IUCRC Technology Breakthrough Award and two CSAW Best Paper 2nd Prizes . He advises numerous PhD students, many of whom now work at Google, IBM, and Microsoft. His book Autonomous Cyber Deception (Springer, 2019) with Ehab Al-Shaer and Cliff Wang provides comprehensive coverage of adaptive cyberdeception strategies.

Girija Chettyمشاهده پروفایل

استاد

• Multimodal Systems
• Medical Image Computing
• Computer Vision
• +۷ مورد دیگر

Girija Chetty is a Full Professor in Computing and Information Technology at the University of Canberra's School of Information Technology and Systems. She holds a PhD in Information Sciences and Engineering and has over 35 years of experience in academia and research leadership roles, including Head of Software Engineering and Program Director of ITS courses. Her research focuses on multimodal systems, medical image computing, AI, and data science. She leads a dynamic research group comprising PhD students, postdocs, and international collaborators. Education: PhD in Information Sciences (Australia, 2007), MSc and BSc in Electrical Engineering/Computer Science (India). She has held visiting roles at Deakin University and CSIRO. Research interests span computer vision, pattern recognition, and medical diagnostics, with 200+ publications in top journals/conferences. Her work addresses global challenges via AI-driven solutions in healthcare (e.g., pain assessment systems, malaria diagnostics) and sustainability (SDG impact frameworks). Projects include AI for remote ultrasound imaging and smart farming systems. She actively collaborates with industry and global research institutions. Grants/Projects: 12 funded initiatives including AI for extreme environment healthcare, malaria pathogen detection, and big data-driven population health. Awards: Senior IEEE/Australian Computer Society membership, editorial roles in IEEE/Elsevier journals. Labs/Teams: Leads a multidisciplinary research group focused on medical AI and multimodal systems.

Iris Baharمشاهده پروفایل

استاد

• Energy-Efficient Computing
• Reliable Computing
• Electronic Design Automation
• +۶ مورد دیگر

Iris Bahar is the Department Head and Professor in the Department of Computer Science at the Colorado School of Mines since January 2022. Previously, she held a faculty position at Brown University for 26 years with a joint appointment in the School of Engineering and Department of Computer Science. She earned her B.S. in Computer Engineering (1986) and M.S. in Electrical Engineering (1987) from the University of Illinois, Urbana, and her Ph.D. in Electrical and Computer Engineering from the University of Colorado, Boulder (1995). Her research focuses on energy-efficient and reliable computing systems, including computer architecture design, low-power computing, and interdisciplinary areas such as robotics, machine learning, and security. Her work has been continuously funded by NSF, DARPA, DoD, Intel, IBM, and others since 1997. Education: B.S., Computer Engineering, University of Illinois, 1986 M.S., Electrical Engineering, University of Illinois, 1987 Ph.D., Electrical and Computer Engineering, University of Colorado Boulder, 1995 Her research interests span electronic design automation, near-data-processing architectures, and robust scene perception for robotics. She has published over 200 articles and is recognized for her contributions to teaching and engineering excellence, including the 2019 Marie R. Pistilli Award. She holds IEEE Fellow and ACM Distinguished Scientist distinctions.

Seth Polsleyمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Intelligent Systems
• Educational Technology
• +۴ مورد دیگر

Seth Polsley is an Assistant Professor in the Jeffrey S. Raikes School of Computer Science and Management at the University of Nebraska-Lincoln. His academic home resides in the School of Computing, where he bridges intelligent systems design with human-computer interaction to enhance educational and universal computing experiences. BS in Computer Engineering (2014) - University of Kansas MS (2017) & PhD (2023) in Computer Engineering - Texas A&M University His research explores: Intelligent tutoring systems Brain-computer interfaces Accessible educational technologies Machine learning for child development assessment Sketch recognition in STEM learning Recent publications demonstrate expertise in tactile learning interfaces, sketch-based developmental assessment, and equitable AI systems. Key disciplines span Human-Computer Interaction, Machine Learning, and Educational Technology. Scientific recognition includes: James Blackiston Memorial Graduate Fellowship Sigma Xi Research Award With professional experience at Lexmark International and MIT Lincoln Lab, Polsley combines practical engineering with educational innovation. His work on sketch-based tools and wearable systems addresses both technical and societal challenges in computing.