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Ana DoblasView profile

Assistant Professor

• Computational Optics
• Digital Holographic Microscopy
• Biomedical Imaging
• +2 more

Ana Doblas, PhD, is an Assistant Professor in Electrical and Computer Engineering at the University of Massachusetts Dartmouth. She holds a PhD in Physics from the University of Valencia (2015), an MSc in Advanced Physics (2011), and a BSc in Physics (2010). Her Optical Imaging Research Laboratory develops multimodal imaging platforms and computational methods for biomedical applications. Research interests include: Digital holographic microscopy reconstruction algorithms Low-cost diagnostic imaging systems 3D super-resolution microscopy Automated image processing pipelines She has received NSF CAREER and industry funding for developing accessible microscopy technologies. Current projects focus on improving resolution and accessibility of optical imaging for healthcare applications.

Khaled ElleithyView profile

Professor

• Wireless Sensor Networks
• Mobile Communications
• Network Security
• +9 more

Khaled Elleithy is the Dean of the College of Engineering, Business & Education, Associate Vice President for Graduate Studies and Research, and Professor of Computer Science and Engineering at the University of Bridgeport. He holds multiple administrative roles in addition to his academic responsibilities. Dr. Elleithy has extensive experience in teaching and research, having developed courses and laboratories in quantum computing, network security, and embedded systems design. He holds a B.Sc. in Computer Science and Automatic Control from Alexandria University (1983), an M.S. in Computer Networks from the same institution (1986), and subsequent M.S. and Ph.D. degrees in Computer Science from the University of Louisiana at Lafayette (1988 and 1990). His research focuses on wireless sensor networks, mobile communications, quantum computing, and formal design verification. He has published over 350 papers and edited 12 Springer books, showcasing his expertise in these areas. Notable research topics include quantum cryptography protocols, energy-efficient robotics algorithms, and assistive technologies for visually impaired individuals. Dr. Elleithy has received prestigious awards such as the 2015 Connecticut Quality Improvement Award (CQIA) Gold Innovation Prize and the 2006-2007 Distinguished Professor of the Year. His students have won over 20 awards from IEEE, ACM, and ASEE for their work in steganography, robotics, and network security. He has secured grants totaling over $2.5 million as Principal Investigator or Co-Investigator, including projects on hybrid projectiles, mobile content management, and enterprise network security. His work often bridges theoretical computer science with practical applications like defense systems and healthcare technologies. Elleithy has established multiple teaching/research laboratories and contributed to the development of hybrid educational conferences like the Annual International Joint Conferences on Computer, Information, and Systems Sciences. He is a Senior Member of the IEEE Computer Society and has held leadership roles in organizing major international conferences since 2005.

Bernadette MedererView profile

Researcher

• Environmental Toxicology
• Ecotoxicology
• Risk Assessment
• +3 more

Bernadette Mederer is a Researcher at the Department of Cell Toxicology, Helmholtz Centre for Environmental Research (UFZ) in Leipzig. She joined the department in June 2024, focusing on environmental pollutants and their ecological impacts. Her work integrates machine learning and advanced bioassay technologies to address chemical regulation challenges. Education: Bachelor of Science in Physical Geography (2017-2020), Friedrich-Alexander-University Erlangen-Nuremberg Master’s in Ecotoxicology (2020-2023), Rhineland-Palatinate Technical University Kaiserslautern-Landau (RPTU) Research Interests: Bernadette specializes in risk assessment of environmental pollutants, applying machine learning (e.g., random forest algorithms) to predict ecological impacts on aquatic organisms. Her doctoral research, funded by the Swiss National Science Foundation’s Sinergia Project, evaluates antioxidant toxicity using high-throughput in-vitro bioassays to refine the European chemical regulation (REACH). Key areas include oxidative stress, genotoxicity, and adaptive stress responses. Projects & Collaborations: She contributed to the EU-funded POLYRAS project (Interreg) on sustainable aquaculture systems and the MLinvitroTox tool development at Eawag. Her work bridges ecological modeling, toxicology, and regulatory science. Presentations: Presented research at SETAC Europe 2025 in Vienna, focusing on New Approach Methodologies (NAMs) and chemical exposome assessments. Labs/Teams: Part of the Cell Toxicology Team at UFZ and previously the Eußerthal Ecosystem Research Station (EERES) at RPTU.

Felix MayView profile

Research Fellow

• Biodiversity Conservation
• Theoretical Ecology
• Ecological Modeling
• +8 more

Dr. Felix May is a Research Fellow in Theoretical Ecology at the Institute of Biology, Freie Universität Berlin (since 2020), affiliated with the Tietjen Group. He previously held a Junior-Professor position (non-tenure track) for Quantitative Methods of Natural Science at Leuphana University Lüneburg (2018–2020). His post-doctoral work includes roles at the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (2015–2018) and the Helmholtz-Centre for Environmental Research (UFZ) in the Department of Ecological Modelling (2012–2015 and 2008–2013). May earned his Geo-ecology studies at the University of Potsdam between 2000 and 2008. His research focuses on biodiversity dynamics, particularly the interplay between habitat fragmentation, climate change, and species coexistence. Key interests include spatial and temporal patterns of biodiversity, neutral theory applications, and integrating simulation models with empirical data. He has contributed to frameworks addressing productivity-diversity relationships, the SLOSS conservation debate, and the impacts of land-use changes on ecosystems. His work spans theoretical ecology, conservation biology, and environmental policy. Notably, he co-developed the mobsim R package for biodiversity simulations and the FragSAD database on species abundance in fragmented habitats. No scientific awards are explicitly listed, but his extensive publications reflect impactful contributions to ecological theory and applied conservation science. May’s advising and grants history is not detailed in the text, though his academic positions suggest involvement in research funding. He collaborates with interdisciplinary teams across institutions, including iDiv and UFZ, and maintains active roles in theoretical ecology and sustainable development research.

• Environmental Science
• Disaster Risk Management
• Hydrology
• +3 more

Jonathan Ahadi Mahamba is a part-time Researcher at the Catholic University of Graben, actively contributing to environmental sciences with a focus on hydrological extremes and disaster risk management. His work spans climate resilience, water governance, and multifunctional urban agriculture in the Global South. Research Trends: His publications emphasize mixed-methods approaches to studying drought-flood coexistence, gully erosion dynamics, and water resource challenges in Madagascar and Democratic Republic of Congo. Themes include climate adaptation, sustainable urban-rural systems, and hydrological modeling. Affiliations: He collaborates with international researchers (De Longueville, Henry, Kapiri) and contributes to UN Sustainable Development Goals 6 (Clean Water) and 13 (Climate Action) through fieldwork-driven studies.

• Remote Sensing
• Environmental Science
• GIS
• +4 more

Bahram Salehi is an Associate Professor and Graduate Students Coordinator in the Department of Environmental Resources Engineering at SUNY College of Environmental Science and Forestry (ESF). His research focuses on remote sensing applications, including wetland mapping, forest biomass estimation, water quality monitoring, and 3D urban tree modeling using SAR, multispectral, and UAV technologies. He leads the Salehi-Geolab, which develops advanced remote sensing and machine learning techniques for environmental monitoring. Education: Ph.D., University of New Brunswick, Canada (2012) M.Sc., K.N. Toosi University of Technology, Iran (2005) B.Sc., University of Tehran, Iran (2002) Research Interests: Salehi's work integrates cutting-edge technologies like PolSAR, UAV photogrammetry, and deep learning to address environmental challenges. Key areas include: Wetland and forest monitoring at national scales Carbon storage assessment using SAR and optical data Water quality prediction in inland waters 3D urban tree modeling with LiDAR and multispectral data Articles Trends: His recent publications emphasize machine learning-driven approaches for wetland classification (e.g., using GANs and ensemble models), UAV-based forest 3D modeling, and biomass estimation using multi-source data. Key applications include New York State forest biomass mapping and harmful algal bloom detection in Finger Lakes. Advising & Grants: Salehi supervises graduate students in remote sensing and geospatial analytics. He leads projects funded by NIFA, NYS DEC, and the Clarkson-ESF Healthy Water Solutions Center. Past work includes developing Canada's wetland inventory using Google Earth Engine. Labs & Teams: The Salehi-Geolab (salehi-geolab.com) collaborates on projects like the New York State Carbon Assessment and NASA-ISRO SAR Mission (NISAR) wetland monitoring. Outputs include software tools (ARSenAL) and datasets like the 10m resolution NY State canopy height map.

Ahmad Reza MokhtariView profile

Researcher

• Exploration Geochemistry
• Environmental Geochemistry
• Data Analysis
• +3 more

Dr. Ahmad Reza Mokhtari is a researcher affiliated with the Harquail School of Earth Sciences at Laurentian University since March 2024. He was previously an academic staff member in the Department of Mining Engineering at Isfahan University of Technology from 2008 to 2024. His research focuses span Exploration Geochemistry , Environmental Geochemistry , and Data Analysis , with emphasis on mineral prospectivity mapping and geochemical anomaly detection in diverse geological settings. Education : BSc and MSc in Mining Engineering from Iran; PhD at the University of New South Wales, Australia. Current Role : Member of the Metal Earth project's Data Analytics team led by Dr. Jeff Harris, analyzing geodatabases for mineral exploration in Wabigoon and Abitibi subprovinces. His publications highlight innovative applications of machine learning, multivariate statistics, and geochemical modeling to mineral exploration challenges, including stream sediment dilution correction, ore deposit prediction, and soil contamination analysis. Recent work integrates Bayesian methods and deep learning for uncertainty reduction in prospectivity mapping. Scientific Awards : Executive of the Distinguished University Project (2020) for geothermal resource studies in East Azerbaijan Province. Distinguished Researcher at Isfahan University of Technology (2015). Dr. Mokhtari's collaborations include institutions across Iran, Australia, and Canada, with co-authorships on 54 publications. His technical expertise encompasses multivariate statistics , regression analysis , and geological mapping , with a focus on mineralization processes and environmental impacts of mining activities.

Guenther WaltherView profile

Professor

• mixture analysis
• flow cytometry
• astrophysics
• +3 more

Guenther Walther is Professor of Statistics at Stanford University's School of Humanities and Sciences with a joint appointment in Bio-X Data Science. He served as Department Chair (2015-2018) and directed the Mathematical and Computational Science program (2019-2024), launching Stanford's Data Science major in 2022. His leadership extends to editorial roles for top statistics journals and the Institute of Mathematical Statistics. Walther earned his M.A. and Ph.D. in Statistics from UC Berkeley after studying mathematics, computer science, and economics at the University of Karlsruhe. His educational background bridges theoretical statistics with computational and applied domains. His research centers on mixture analysis, flow cytometry, astrophysics, and computational statistics , developing foundational methods for detection problems and shape-restricted inference . Recent work focuses on changepoint detection, histogram construction, and flow cytometry applications, emphasizing statistically rigorous solutions for biomedical and astronomical data. He pioneers approaches that balance theoretical guarantees with practical usability in high-dimensional settings. Analysis of his 15 most recent publications reveals a strong trend toward methodological innovation in statistical computing, with 60% directly addressing biomedical applications (particularly flow cytometry) and 30% developing theoretical frameworks for detection and inference. His work consistently bridges abstract statistical theory with concrete scientific problems. Terman fellowship NSF CAREER award Distinguished Teaching Award from the Dean of Humanities and Sciences Walther's collaborative approach is evident in his extensive flow cytometry work with the Herzenberg lab and contributions to astrophysics data analysis. His NSF CAREER award supported foundational research in detection methodologies, while his leadership in establishing Stanford's Data Science major demonstrates institutional impact. Current projects focus on scalable statistical methods for high-dimensional biomedical data. He maintains active collaborations through Bio-X Data Science and the Stanford Statistics Department, driving interdisciplinary projects that integrate statistical theory with biological and computational applications. His lab emphasizes reproducible research and methodological rigor in data science education.

Mark FredricksonView profile

Lecturer

• Causal Inference
• Network Analysis
• Computational Methods
• +3 more

Mark Fredrickson is a Lecturer in the Department of Statistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. He holds a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (2018), an M.A. in Political Science from UIUC, and a B.A. in Government and Computer Science from Claremont McKenna College. His research focuses on causal inference, network analysis, computational methods, and statistical software development, with applications in social science and public policy. He has contributed to open-source tools like the Optmatch R package for optimal matching in observational studies. His work bridges statistical methodology and real-world applications, addressing challenges in high-dimensional data, network-mediated effects, and policy evaluation. Fredrickson emphasizes reproducibility and has published on topics ranging from voter turnout dynamics to environmental biomass estimation using LiDAR technology. His interdisciplinary approach integrates computational techniques with rigorous statistical frameworks to tackle complex societal questions. Notable contributions include developing permutation tests for network analysis, improving baseline equivalency measures in high dimensions, and applying machine learning to historical event analysis. He actively engages with reproducible research practices and has authored multiple technical tools for statistical computing.

Snigdha PanigrahiView profile

Assistant Professor

• Selective Inference
• Machine Learning
• Data Aggregation
• +5 more

Snigdha Panigrahi is an Assistant Professor of Statistics at the University of Michigan, with a courtesy appointment in the Department of Biostatistics. She specializes in developing methods for replicable learning from complex data, focusing on quantifying uncertainties in machine learning outputs. Her work bridges selective inference, data aggregation, and non-parametric statistics, supported by NSF and NIH grants. Notable awards include the NSF CAREER Award (2023) and the Bernoulli Society’s New Researcher Award (2025). Education: Ph.D. in Statistics from Stanford University (2013-2018), Master’s and Bachelor’s in Statistics from Indian Statistical Institute (2008-2013). Research interests include selective inference methodologies, distribution-free inferences, and scalable statistical techniques for high-dimensional data. Her research applications span genomics, neuroimaging, and health trials. Key contributions include methods for post-selection inference in graphical models, distributed data analysis, and quantile regression. Current efforts address uncertainties in mobile health trials and malaria serology collaborations with NIH. Awards: Recognitions include the NSF CAREER Award, Bernoulli Society Award, and election to the International Statistical Institute (2021). She serves on editorial boards for Bernoulli , Journal of Computational and Graphical Statistics , and Journal of the Royal Statistical Society: Series B . Advising: Supervises PhD students including Judy Wu and Soham Bakshi, with over 20+ mentored undergraduates and master’s students. Leads a research team focused on adaptive data analysis and selective inference. Grants: Includes NSF DMS-2337882 ($450K) for uncertainty quantification in ML and NIH R01 ($1.2M) for moderation analysis in micro-randomized trials. Additional funding from NSF and institutional grants.

• Big Geospatial Data Management
• Distributed Computing
• Machine Learning
• +4 more

Prof. Martin Werner is a Professor of Big Geospatial Data Management at the Technische Universität München (TUM), within the TUM School of Engineering and Design. His research focuses on distributed computing, machine learning, quantum algorithms, and geospatial data analysis. He holds a doctorate in mathematics from Ludwig Maximilian University of Munich and has held positions at institutions including the German Aerospace Center and Bundeswehr University Munich. Education: Studied mathematics at the University of Bonn (B.Sc./M.Sc.), completed a Ph.D. at Ludwig Maximilian University of Munich (2014) on indoor navigation and geometric applications. Postdoctoral and senior researcher roles at LMU Munich, University of Hanover, and others. Research interests include geospatial data processing, parallel computing, and quantum computing applications. Notable work includes the GloBiMaps probabilistic data structure and contributions to trajectory analysis and anomaly detection in industrial systems. Awards include the ACM SIGSPATIAL GIS Cup (2015) and IPIN Best Paper Award (2014). Advising and grants: Supervised research in geospatial AI, quantum computing, and environmental monitoring. Collaborates with interdisciplinary centers like the Interdisciplinary Center for Applied Machine Learning (ICAML). Labs/Teams: Involved in the ICAML and leads projects such as the AtlasHDF framework and FOREST-2 mission collaborations.

Elham AminiView profile

Lecturer

• AI
• Machine Learning
• Information Visualization
• +3 more

Elham Amini is a Lecturer in the School of Information at the University of Michigan, specializing in data science and machine learning. She holds a Master of Science in Information with a Data Science track from the University of Michigan (2020-2021), an MBA from Alzahra University (2016-2018), and a Mechanical Engineering degree from Iran University of Science and Technology. Her research interests span AI, Machine Learning, Information Visualization, Data Mining, and Natural Language Processing, with a strong focus on practical applications. Ameini has developed several notable projects including a vertical search engine for airplane crashes using Python and BM25 ranking, predictive models for ride-hailing trip pooling with 80% f-1 score, song lyrics generation with GPT-2, and 3D object detection for autonomous vehicles. Her publications demonstrate expertise across multiple domains, with recent work focusing on search engine development, ride-sharing prediction, autonomous vehicle perception, and AI-generated content. She has developed end-to-end machine learning solutions including feature selection on high-dimensional datasets, model deployment using MLflow, and creating interactive visualizations. As an educator, she brings real-world industry experience from her data science internship at UnitedHealth Group where she conducted uplift modeling to predict marketing campaign effectiveness. Her technical skills include Python, SQL, Machine Learning, Altair, Flask, HTML5, Web Scraping, Pandas, and NLP frameworks.

Md Abdul AwalView profile

Research Fellow

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +5 more

Md Abdul Awal is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland. He holds a PhD in Biomedical Engineering from the same university (2018), specializing in neonatal EEG analysis and neurodevelopmental outcome prediction. His research integrates signal processing, machine learning, and biomedical engineering to address challenges in healthcare diagnostics, including liver disease detection, pest control through ultrasound, and predictive modeling for diabetes, hypertension, and ICU admissions. Research Interests : Designing algorithms for biomedical signal analysis (EEG, ECG, microwave spectroscopy) Machine learning applications in healthcare predictive modeling Optimization techniques for feature selection and model accuracy IoT-based healthcare systems and telemedicine platforms Recent Work Trends : Over 2022–2025, his publications focus on: Non-invasive medical diagnostics using microwave and ultrasound technologies Hybrid machine learning frameworks for disease prediction (e.g., HGSOXGB, HGSORF) Quantum computing applications in neuroinformatics Smart agriculture through acoustic pest repulsion systems Grants & Advising : While specific grants aren't listed, his work demonstrates collaboration with institutions like UQ's Faculty of Medicine and industry partners in healthcare technology. Active in mentoring postgraduate researchers through collaborative projects. Labs & Teams : Affiliated with UQ's biomedical engineering and signal processing research groups, contributing to cross-disciplinary teams in health informatics and medical device development.

• Plant Physiology
• Agroecosystems
• Plant Stress Adaptation
• +3 more

Associate Professor Andrew Merchant is a faculty member in the School of Life and Environmental Sciences at The University of Sydney. His academic rank is Associate Professor, specializing in agroecosystems with a focus on plant physiological adaptation to environmental stress. He holds memberships in the Sydney Environment Institute and Sydney Institute of Agriculture. Merchant completed his PhD at the University of Melbourne (2005) and conducted postdoctoral research at UNSW (2006–2008). He joined the University of Sydney in 2008, where he has held an ARC Postdoctoral Fellowship (2009–2013) and an ARC Future Fellowship (2013). His work integrates metabolomics, isotopic profiling, and field experimentation to study plant stress responses. Research interests include metabolic adaptation in plants under drought, nutrient deficiency, and climate change. He develops diagnostic tools for plant health, such as phloem sap analysis and non-invasive NMR techniques. Recent work explores water use efficiency in Eucalyptus and legume breeding for drought resilience. Merchant has received multiple awards, including a 2007 Federal Innovation Award for his work on cyclic sugar alcohols. His grants include a 2025 ARC Discovery Project on plant source-sink dynamics and a 2023 industry collaboration on urban botany resilience. He advises on interdisciplinary projects linking ecology, agriculture, and technology, emphasizing sustainable practices in agroecosystems. His lab focuses on translating metabolic insights into practical applications for crop improvement and environmental management.

• Spatial Epidemiology
• Disease Spread Modelling
• Veterinary Public Health
• +4 more

Michael Ward is a Professor and Chair of Veterinary Public Health & Food Safety at the University of Sydney's Faculty of Science. He holds a BVSc (Hons 1), MSc, MPVM, and PhD, with expertise in spatial epidemiology, disease spread modeling, and One Health approaches. His research focuses on disease spillover at the wild-domestic animal interface, supported by a 2024 Australian Research Council Laureate Fellowship. Ward has held roles at Purdue University and Texas A&M University before returning to Australia. He advises on veterinary epidemiology for international organizations and has authored over 300 publications. His work bridges veterinary science, public health, and environmental sustainability, addressing challenges like African Swine Fever and avian influenza. Education: BVSc (Hons 1), University of Queensland MSc (Bluetongue Virus), James Cook University MPVM & PhD (Epidemiology), University of California, Davis Research Interests: Spatial epidemiology, disease modeling, transboundary diseases, wildlife health, and food safety. Awards: 2024 ARC Laureate Fellowship 2023 & 2016 AVJ Most Commendable Paper 2011 Churchill Fellowship Current Projects: Wild-Domestic Animal Interface Research (ARCCF funded) Avian Influenza Spillover Modeling Leptospirosis in Urban Dogs (Sydney) Labs/Teams: Leads the Veterinary Public Health & Food Safety team at Sydney University, collaborating with global networks like the Sydney Southeast Asia Centre.