Data Quality in Machine Learning Professors and Supervisors | ApplySharif

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David WanikView profile

Assistant Professor

University of ConnecticutUnited States

David Wanik is an Assistant Professor in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. He serves as Academic Director for Business Data Analytics at the Stamford campus and conducts research in the Eversource Energy Center, focusing on data science, natural hazards, remote sensing, and IoT applications in utility systems. PhD, MS, and BS in Environmental Engineering from University of Connecticut His research bridges natural hazard prediction, power grid resilience, and environmental data science. Key themes include: Machine learning for power outage prediction Climate change impact on energy demand Remote sensing for population and environmental monitoring IoT-enabled infrastructure hardening Recent publications emphasize deep learning for nighttime light imagery analysis, hybrid physics-data-driven models for grid resilience, and climate-integrated demand forecasting. His work integrates satellite data, LiDAR, and utility infrastructure records for predictive analytics. Teaching includes courses in business analytics, Python-based data science, and deep learning for the MS Business Analytics and Project Management program.

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Zoe LiView profile

Associate Professor

McMaster UniversityCanada

Dr. Zoe Li is an Associate Professor in the Department of Civil Engineering at McMaster University, with an additional role as an Associate Member in the Department of Computing and Software. She specializes in developing modeling and decision-support tools to address challenges in water resources management, environmental systems analysis, and climate change impacts. Her research focuses on hydrological modeling, probabilistic forecasting, risk analysis, and environmental systems optimization. She holds a B.Eng. from China and M.A.Sc./Ph.D. degrees from the University of Regina. Her work spans interdisciplinary collaborations, including studies on dam safety under climate change, wastewater treatment plant optimization, and AI-driven environmental monitoring. She has been actively involved in projects such as CityDNA, a digital tool to model urban environments for decision-making during crises like the pandemic. Dr. Li teaches courses in environmental systems engineering and principles of environmental engineering, emphasizing uncertainty quantification and sustainable practices. Her research publications (15+ recent articles) address topics like machine learning applications in environmental systems, climate change impacts on water resources, and infrastructure resilience. She advises graduate students in environmental engineering and systems optimization. Notable grants include funding from Roche Canada for pandemic-related urban modeling. Her work is frequently published in high-impact journals like Journal of Environmental Management and Water Resources Research .

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Stuart PhinnView profile

Professor

University of QueenslandAustralia

Professor Stuart Phinn is a distinguished academic at the University of Queensland, serving as Professor in the School of the Environment and Centre Director of the Remote Sensing Research Centre (Earth Observation Research Centre). He also maintains affiliations with the Centre for Marine Science. With a career spanning over two decades, Professor Phinn has established himself as a leading expert in earth observation and environmental monitoring, with over 559 publications including 295 journal articles. His educational background includes a Bachelor (Honours) of Science (Advanced) from The University of Queensland and a Doctor of Philosophy from San Diego State University. Professor Phinn's leadership extends to founding directorships of Australia's national earth observation coordination body (www.eoa.org.au) and collaborative research infrastructure (www.tern.org.au), as well as a world-leading research-to-operational program supporting government environmental monitoring (www.jrsrp.org.au). He also leads the Earth Observation for Government Network. Professor Phinn's research focuses on monitoring environmental change using earth observation and field data. His work primarily involves using images collected from satellites and aircraft, combined with field measurements, to map and monitor Earth's environments and how they change over time. This research is conducted in collaboration with environmental scientists, government agencies, NGOs, and private companies. A growing aspect of his work focuses on national coordination of earth observation activities and the collection, publishing, and sharing of ecosystem data. His work provides solutions to support sustainable development and resource use for governments, industries, and communities. His recent publications demonstrate a consistent focus on applying earth observation technologies to solve environmental challenges across multiple domains. The 15 most recent articles reveal strong themes in coral reef mapping and monitoring, land cover change detection, fire resilience analysis, and advanced remote sensing techniques including multi-sensor fusion and machine learning applications. His work spans terrestrial, coastal, and marine environments, with significant contributions to understanding environmental change in Australia and internationally, particularly in Indonesia. Professor Phinn has secured substantial research funding from diverse sources including government agencies (Queensland Government, Great Barrier Reef Marine Park Authority), industry partners (SmartSat CRC, Blue Economy CRC), and international organizations (Google Inc, Vulcan Inc). Current projects include evaluating impacts of threats to endangered reptiles, automating tree-scale vegetation structure monitoring, and continuing the Joint Remote Sensing Research Program. As an academic supervisor, Professor Phinn has mentored numerous PhD and Master's students, with current supervision spanning topics from forest disturbance analysis to kelp forest mapping and fire resilience of mine site rehabilitation. His extensive supervision history demonstrates his commitment to training the next generation of earth observation scientists. The Earth Observation Research Centre he directs fosters a collaborative research environment focused on transforming satellite and airborne images with field survey data into meaningful environmental information for decision-making.

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Saleh TaghvaeianView profile

Associate Professor

University of Nebraska-LincolnUnited States

Saleh Taghvaeian is an Associate Professor in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln and a Faculty Fellow at the Daugherty Water for Food Global Institute. His research focuses on precision irrigation systems, water resource management, and climate-resilient agricultural practices. He holds a Ph.D. in Irrigation Engineering from Utah State University and academic degrees from Ferdowsi University of Mashhad, Iran. Education: Ph.D., Irrigation Engineering, Utah State University M.S., Irrigation Engineering, Ferdowsi University of Mashhad, Iran B.S., Irrigation Engineering, Ferdowsi University of Mashhad, Iran Research Interests: His work integrates remote sensing, hydrological modeling, and machine learning to address challenges in irrigation efficiency, drought response, and sustainable water use. Key areas include soil moisture dynamics, crop water productivity, and the application of big data in agricultural systems. Publications Trends: Recent articles emphasize GRACE data downscaled analysis for water storage monitoring, AquaCrop modeling for crop performance under water stress, and machine learning applications in precision agriculture. His work spans transboundary water basins (Indus), U.S. High Plains, and Oklahoma Panhandle regions. Awards: 2021 Oklahoma State University Distinguished Early Career Faculty Award 2019 ASABE Larry W. Turner Young Extension Professional Award 2018 Oklahoma Cooperative Extension Service Honor Award Grants and Advising: Leads research projects on irrigation energy conservation, soil salinity mitigation, and agricultural drought resilience. Collaborates with institutions like USDA, UCOWR, and international partners on transboundary water management. Labs/Teams: Active in Nebraska's Water for Food Institute, leading interdisciplinary teams developing decision-support tools for irrigators and policymakers. Engaged in extension programs promoting smart irrigation technologies and water policy advocacy.

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Kate FuView profile

Associate Professor

University of Wisconsin-MadisonUnited States

Dr. Kate Fu is the Jay and Cynthia Ihlenfeld Associate Professor of Mechanical Engineering at the University of Wisconsin-Madison. She previously held academic positions at Georgia Institute of Technology (2014-2021) and completed postdoctoral fellowships at MIT and Singapore University of Technology and Design. Her education includes a Ph.D. (2012), M.S. (2009) from Carnegie Mellon University, and B.S. (2007) from Brown University. Her research focuses on engineering design cognition, computational design tools, and design innovation. Key areas include creativity enhancement, design-by-analogy methodologies, and integrating artificial intelligence into design processes. She has pioneered work on gender dynamics in engineering teams and equity in education. Notable awards include the NSF CAREER Award (2019), ASME Young Investigator Award (2020), and SREB Mentor of the Year (2022). She actively contributes to engineering education through courses like Geometric Modeling for Design, Experiential Design Projects, and graduate research supervision. Her work bridges disciplinary boundaries with projects on additive manufacturing, design justice frameworks, and social impacts of engineering solutions. Current research explores equity in makerspaces, energy justice metrics, and cognitive processes in design teams.

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Marco ToffolonView profile

Professor

University of TrentoItaly

Marco Toffolon is a Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering, where he leads the Physical Limnology Laboratory. He serves as Deputy Director for International Relations and previously directed the Environmental Engineering programs. His research spans ecohydraulics, sediment transport, lake hydrodynamics, and environmental modeling. He investigates physical limnology, tidal morphodynamics, and stratified flows using analytical and numerical approaches. His work integrates field measurements with machine learning for water quality prediction and climate impact assessment. His publications focus on lake dynamics, river morphodynamics, and sustainable water management, with recent emphasis on climate-driven changes in alpine systems. Research demonstrates strong interdisciplinary linkages between hydraulics, ecology, and climate science. Awards: 2016 Coastal Engineering Journal Award 2013 Enrico Marchi Lecture invitation He leads international collaborations with institutions like EPFL and Sun Yat-sen University, and organizes conferences including the Physical Processes in Natural Waters workshop series.

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Shahram ShiraniView profile

Professor

McMaster UniversityCanada

Dr. Shahram Shirani is a Professor and holds the L.R. Wilson/Bell Canada Chair in Data Communications in the Department of Electrical & Computer Engineering at McMaster University. He also serves as Acting Chair of the department. His research focuses on multimedia communications, image/video processing, medical imaging, and hardware architectures. He teaches courses like Image Processing (COMPENG 4TN4) and 3D Image Processing and Computer Vision (ECE 736). Shirani earned his B.Sc. from Isfahan University of Technology (1989), M.Sc. from Amirkabir University of Technology (1994), and Ph.D. from the University of British Columbia (2000). His achievements include the Faculty of Engineering Leadership Fellowship (2014–15) and leadership roles in editorial boards for IEEE Transactions on Multimedia and Circuits and Systems for Video Technology. Research interests include video quality assessment, biomedical signal processing, and edge computing for traffic monitoring. His lab develops algorithms for multimedia representation, compression, and hardware implementation. Recent work includes AI-driven medical sound datasets, real-time noise removal in MRI, and efficient CNN pruning techniques. He advises over 15 graduate students and collaborates on projects like the HLS-CMDS dataset and cardiac segmentation reviews. His lab’s contributions span biomedical engineering, autonomous systems, and smart sensor technologies.

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Marjan AlaviView profile

Assistant Professor

McMaster UniversityCanada

Dr. Marjan Alavi is an Assistant Professor at McMaster University's W Booth School of Engineering Practice and Technology, affiliated with the Mechanical Engineering department as an Associate Member. She holds a Professional Engineer (P.Eng.) license in Ontario and has over 15 years of academic and industrial experience in electrical engineering. Her research focuses on model-based and data-driven approaches for fault diagnosis, prognosis, and fault-tolerant control in hybrid systems, with applications in power electronics, energy systems, and smart infrastructure. Education: B.Sc. (2004) from K.N. Toosi University of Technology, M.Sc. (2007) from Sharif University of Technology, Ph.D. (2014) from Nanyang Technological University (Singapore), and a Postdoc (2015) at the University of Toronto's Energy Systems Group. Teaching: Instructs courses on Real-Time Systems (SEP 6ES3, SFWRTECH 4ES3), Smart Cities and Communities (SMRTTECH 4SC3), integrating real-world engineering challenges with theoretical frameworks. She emphasizes hands-on learning through remote labs and experiential projects. Professional Contributions: Serves as IEEE Toronto Section Executive Member, Technical Reviewer for IEEE Transactions on Industrial Electronics, and Vice Chair of IEEE Industrial Applications Society (2015). Founded Intelligent Diagnosis Corporations, a Canadian startup focused on research and innovation in diagnostics technologies. Key Projects: Developed fault diagnosis strategies for electro-hydraulic actuators, vehicle-mounted infrastructure monitoring systems, and remote laboratory platforms for emergency traffic control. Research spans predictive maintenance, smart city technologies, and railway systems certification benefits. Awards: Recipient of the Singapore International Graduate Award (SINGA) 2010. Recognized for her work in bridging academic research with industrial applications, particularly in enhancing system reliability through advanced control methodologies.

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Ferreira Joao CarlosView profile

Professor

Molde University CollegeNorway

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

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Chenang LiuView profile

Associate Professor

Oklahoma State UniversityUnited States

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)

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Dewei YiView profile

Senior Lecturer

University of AberdeenUnited Kingdom

Dewei Yi is a Senior Lecturer (Associate Professor) in the Department of Computing Science, School of Natural and Computing Sciences at the University of Aberdeen, UK. He holds a PhD from Loughborough University and is an active researcher in AI, computer vision, and intelligent systems. He serves as Director of the MSc AI and MSc Robotics and AI programmes. Research Interests: His research spans AI-enabled healthcare, medical image processing, intelligent vehicles, robotics, precision agriculture, remote sensing, and applied machine learning. He focuses on hybrid intelligent systems, personalised AI, federated learning, fairness, and explainability. Recent Publication Trends: His latest work includes medical image quality evaluation using contrastive learning, federated learning for diabetic retinopathy, UAV-based solar panel inspection, vascular image analysis, and emotion recognition from ECG data, reflecting a strong trend toward healthcare and intelligent systems with real-world impact. Scientific Awards: Fellow of the Higher Education Academy (FHEA) Outstanding Reviewer, Transportation Research Part C (TRC) Advising and Grants: Dr Yi supervises multiple PhD students in AI, computer vision, and machine learning. His graduated PhDs include Debinal Bakyavathi Rajan, Sami Hamid Al Sulaimani, and Adinath Abhimanyu Ghadage. He has secured significant funding as PI and Co-PI, including a £408K Smartawl 5.0 project and a £794K Cancer Research UK grant (Co-PI). Labs and Teams: He leads research in AI for healthcare and intelligent vehicles, collaborating with institutions like University of Warwick, Loughborough University, and industry partners such as AVL Powertrain Ltd. His work is supported by interdisciplinary teams focusing on embedded AI, medical applications, and sustainable technologies.

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Kenan LiView profile

Associate Professor

Saint Louis UniversityUnited States

Kenan Li, Ph.D., is an Associate Professor in the Department of Epidemiology and Biostatistics at Saint Louis University’s College for Public Health and Social Justice. He joined SLU in August 2022 and teaches courses such as Statistical Learning, R for Spatial Analysis, and Environmental Determinants of Health. His research bridges data science, GIS, and public health, focusing on spatial computation, environmental exposures, and community resilience. Ph.D. in Environmental Sciences, Louisiana State University M.S. in Environmental Sciences, Louisiana State University B.S. in Environmental Sciences and Applied Mathematics, Nankai University, China Dr. Li’s research interests lie at the intersection of spatial computation, environmental health, and community resilience . He develops geo-AI frameworks , integrated geo-cyber-infrastructures , and biostatistics algorithms using big data, deep learning, and sensor data. His work emphasizes understanding human-environment interactions, urban sustainability, and health disparities. His recent publications from 2023 to 2015 reveal a strong trend in spatial modeling of population dynamics , machine learning for environmental exposure analysis , and resilience assessment in vulnerable coastal regions. He has pioneered methods like Dynamic Time Warping Self-Organizing Maps and Wavelet-based Shapelet Discovery to extract meaningful patterns from high-frequency sensor data. His scientific awards include the Taylor Geospatial Institute Seed Grant (2023) , the Saint Louis University 2023 Health Research Grant , and selection for the Scholarly Undergraduate Research Grants and Experiences . He has secured funding from NSF, NIH, USC Keck School of Medicine, and the US Army Corps of Engineers. Dr. Li has advised and collaborated on numerous research projects, particularly in interdisciplinary teams studying the Mississippi River Delta and urban health interventions. He has been involved in NIH/NIBIB-funded projects and led research on emergency management of trail systems in Los Angeles County. He is actively involved in building research labs and teams focused on spatial data science and public health analytics , having previously worked at USC’s Spatial Sciences Institute and Population and Public Health Sciences Department.

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Robin CrockettView profile

Researcher

The University of NorthamptonUnited Kingdom

Dr Robin Crockett is the University Academic Integrity Lead at the University of Northampton, based in the Academic Registry. He is a mathematician-ethicist actively engaged in research and professional development in academic integrity, document forensics, and the detection of contract cheating and AI-generated text. He is a member of the European Network for Academic Integrity (ENAI), co-founder of the Midlands Integrity Group (UK), and has advised UK policymakers on legislation to ban essay mills. He holds Chartered Scientist and Chartered Mathematician status. MPhil, The Management of Electricity Supplies via Storage as Hydrogen, Cranfield University Master, Energy Conservation and the Environment, Cranfield University PhD, Electrostatic Damage to Semiconductor Devices, University of Southampton Master, Natural & Electrical Sciences, University of Cambridge Bachelor, Natural & Electrical Sciences, University of Cambridge Dr Crockett's research centers on document forensics and academic integrity, with core interests in Fourier theory, time-series analysis, and stylometry for identifying contract cheating. His work increasingly addresses the challenges posed by generative artificial intelligence in education. He applies mathematical and statistical methods to analyze linguistic cues, writing styles, and embedded information in student submissions. His recent publications highlight a strong trend toward understanding and mitigating academic misconduct in the AI era. Topics include AI-text detection uncertainties, forensic stylometry, and policy development for generative AI misuse. Earlier work includes environmental research on radon remediation and signal processing applications in telecommunications. Chartered Scientist Chartered Mathematician Dr Crockett has supervised PhD students, including Believe Nwamae in Computing. He has secured internal research funding, such as the Small Grants Scheme for Early Career Researchers at the University of Northampton for a project on AI-synthesized text detection. He has been an Academic Visitor at Loughborough University and served on the Turnitin Advisory Board, indicating active collaboration and external engagement. He frequently presents at academic events and contributes to policy discussions. He is affiliated with research networks including the European Network for Academic Integrity (ENAI) and the European Geosciences Union (as a former Scientific Officer). His work is supported by institutional and collaborative projects focused on advancing machine discernment of academic misconduct.

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Humphrey SouthallView profile

Professor

University of PortsmouthUnited Kingdom

Humphrey Southall is Professor of Historical Geography at the University of Portsmouth, affiliated with the Faculty of Science & Health and the School of the Environment and Life Sciences. He has been a key figure in advancing digital historical geography through large-scale projects such as A Vision of Britain through Time and the Great Britain Historical GIS . University: University of Portsmouth Faculty: Faculty of Science & Health Department: School of the Environment and Life Sciences Academic Rank: Professor Education: Bachelor's Degree, University of Cambridge (1976) Special Student, Harvard University (1976–1977) PhD, University of Cambridge (awarded 1984) Humphrey Southall's research centers on historical geography, with a focus on geospatial data modeling, gazetteers, and historical GIS. His work explores the long-term impacts of locality on health inequalities, the North-South divide in Britain, and the political roles of travellers. He has pioneered methods for disambiguating historical place names and constructing digital gazetteers from old maps and census data. His interdisciplinary approach bridges geography, history, and data science, contributing to digital humanities and sustainable development goals related to health and inequality. His recent publications reveal a strong trend toward integrating historical data with modern GIS and ontologies. Themes include electoral analysis (e.g., Reform UK), climate-land use interactions, and the digitization of historical statistics. His work emphasizes data accessibility, public engagement, and the application of historical insights to contemporary policy challenges. Scientific Awards and Recognition: No specific awards listed in the provided text. Humphrey Southall has secured major research grants from the ESRC, AHRC, JISC, and the European Commission. He leads the DDME: Data Discovery Made Easy project, applying machine learning to social science databases. He has supervised PhD students and actively collaborates with researchers across disciplines. His public engagement includes media commentary on homelessness, air pollution, and elections via The Conversation and national press. Labs and Research Teams: Lead Principal Investigator, A Vision of Britain through Time (VoB) Lead, Great Britain Historical GIS (GBHGIS) Team Member, PastPlace and AOU Resources as a Pelagios GIF Resource Collaborator with Patrick Aucott, Chris Thomas, and others on biodiversity and health projects

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Torgeir WeloView profile

Professor

Norwegian University of Science And TechnologyNorway

Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.

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