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Jacob DaarView profile

Associate Professor

• Behavioral Analysis
• Autism Spectrum Disorders
• Developmental Disabilities
• +5 more

Jacob Daar is an Associate Professor in the Department of Psychological Science at Northern Michigan University's College of Arts and Sciences. Holding a Ph.D. from Southern Illinois University, he is a Board Certified Behavior Analyst at the Doctorate Level. Education: B.A./M.A. from University of South Florida, Ph.D. from Southern Illinois University Dr. Daar's research focuses on promoting emergent language repertoires, improving behavior assessment and treatment, and understanding gambling behavior through behavioral analysis frameworks. He directs the Behavior Education Assessment and Research (BEAR) Center, overseeing multiple service and practicum training programs for children with developmental disabilities. His recent work trends include: PEAK relational training system applications for autism intervention Stimulus equivalence and deictic framing in language acquisition Gambling behavior analysis through contingency confusion studies Relational Frame Theory in implicit social stereotyping Leadership & Advocacy Roles: Director of BEAR Center President of UP Association for Behavior Analysis Core Faculty, Michigan Leadership in Neurodevelopmental Disabilities (Mi-LEND) Member, Michigan Autism Council

John LachinView profile

Professor

• Diabetes Research
• Biostatistics
• Clinical Trials
• +4 more

John Lachin is a Professor and leading biostatistician at George Washington University, renowned for his pivotal role in diabetes research. He joined the Biostatistics Center in 1973 as an Assistant Research Professor and later served as Assistant Director, Co-Director, Director (1988-2000), and Interim Director (2010-2012). His career has been marked by leadership in landmark clinical trials, including the Diabetes Control and Complications Trial (DCCT) and its extension, the EDIC Study. These studies transformed Type 1 diabetes care by demonstrating the benefits of tight glycemic control. His research has generated over $471 million in grants, produced 397 peer-reviewed papers, and 5 books, with over 96,000 citations. Key contributions include advancing statistical methodologies in clinical trials, analyzing biomarkers for cardiovascular and renal complications, and mentoring generations of biostatisticians. In 2024, he received the Tulane Science and Engineering Outstanding Alumni Award for Professional Excellence, recognizing his unparalleled impact on diabetes research and public health. Key Projects: DCCT/EDIC (Type 1 diabetes), GRADE Study (Type 2 diabetes therapies), DPP/DPPOS (diabetes prevention). Awards: 2024 Tulane Alumni Award, cited as one of the most influential figures in diabetes science. Grants: Over $471 million secured as PI/Co-PI since 1985, supporting multi-center trials and coordinated research initiatives. His work bridges biostatistics and clinical practice, emphasizing rigorous data analysis to improve patient outcomes. Ongoing research focuses on glycemic variability, epigenetic mediation of complications, and cardiovascular risk prediction in diabetes.

Nikos PantazisView profile

Assistant Professor

• HIV Epidemiology
• Longitudinal Data Analysis
• Survival Analysis
• +3 more

Nikos Pantazis serves as Assistant Professor of Epidemiology and Medical Statistics at the Department of Hygiene, Epidemiology and Medical Statistics within the Medical School of the National and Kapodistrian University of Athens (NKUA). He delivers core instruction in Medical Statistics and Epidemiology across undergraduate programs in Medicine, Dentistry, and Biology, while advancing specialized postgraduate coursework in Generalized Linear Models, Longitudinal Data Analysis, and Meta-analysis at NKUA and the University of Patras. His academic foundation includes a Physics degree from NKUA, followed by Master's and PhD credentials from the university's joint Medical School/Department of Mathematics Biostatistics program. This interdisciplinary background fuels his dual expertise in theoretical statistics and clinical epidemiology. Dr. Pantazis's research centers on HIV epidemiology and advanced longitudinal data methodologies , with particular focus on statistical solutions for non-random missing data scenarios in survival analysis. His work bridges theoretical biostatistics with urgent public health applications, especially in infectious disease surveillance systems. Recent publications reveal consistent engagement with HIV transmission dynamics, cohort study design, and methodological innovations for complex missing data. His output spans clinical domains (HIV, HCV, COVID-19) and statistical theory, demonstrating significant cross-disciplinary impact through high-impact journals like Lancet HIV and Journal of the Royal Statistical Society . He leads as local Principal Investigator for an ECDC initiative enhancing European HIV surveillance data quality and has secured funding from the European Union, NIH-USA, and national agencies. To date, he has mentored over 30 Master's students through thesis supervision and committee roles, shaping the next generation of epidemiological researchers.

Tzahi CathView profile

Professor

• Water Reuse Technologies
• Membrane Filtration
• Desalination Processes
• +5 more

Professor Tzahi Cath is a leading academic at the Colorado School of Mines , holding the Director role at the Advanced Water Technology Center (AQWATEC) and Co-Director of the Colorado Center for a Sustainable WE2ST. His research spans water reclamation, industrial wastewater treatment, and integrated energy-water systems, with a focus on membrane technologies and data science applications. He maintains a joint appointment at the National Renewable Energy Laboratory (NREL) Water and the Environment. PhD in Environmental Engineering, University of Nevada, Reno (2003) MS in Environmental Engineering, University of Nevada, Reno (2001) BSc in Mechanical Engineering, Tel Aviv University (1992) Tzahi Cath's research explores advanced water treatment technologies, including membrane distillation , reverse osmosis , and biological treatment systems . His work integrates data science for optimizing treatment processes and addressing challenges in direct potable reuse and PFAS removal . Key areas include electrophoretic mixing , buoyancy-driven convection , and decision support tools . His publications reveal a strong focus on membrane technologies (15+ papers), resource recovery (lithium, ammonia), machine learning applications (6 papers), and regional produced water treatment (Oklahoma, Texas, Colorado). Recent work emphasizes low-salt rejection RO and vacuum membrane distillation for hypersaline systems. WateReuse Award for Education and Outreach (2022) Professor Cath leads the Direct Potable Reuse (DPR) mobile lab , recognized for innovative solutions in water sustainability and drought resilience. His work involves collaborations across the National Renewable Energy Laboratory , Metro Wastewater Reclamation District , and Colorado Springs Utilities .

• Gestational Diabetes
• Maternal-Fetal Medicine
• Pregnancy Complications
• +3 more

Dorothy Graham is a Clinical Senior Lecturer at the University of Western Australia (UWA), affiliated with the UWA Medical School's Department of Obstetrics and Gynaecology. Her research focuses on maternal-fetal medicine, gestational diabetes, and pregnancy-related complications. She contributes to the UN Sustainable Development Goals (SDGs) through studies addressing health challenges in pregnancy. Her work spans clinical trials, case studies, and observational analyses of conditions like diabetic retinopathy and Cushing's syndrome during pregnancy. Key research interests include gestational diabetes management, preterm birth prevention, and rare genetic disorders in pregnancy. She has collaborated on projects involving probiotics' effects on infants and the role of continuous glucose monitoring in improving pregnancy outcomes. Her research outputs highlight interdisciplinary approaches, integrating endocrinology, obstetrics, and pediatrics. Award information is not explicitly stated. Advising roles and grants are not detailed in the provided text. She contributed to a dataset on avian locomotion biomechanics, though this appears unrelated to her primary clinical research. No lab or team affiliations are specified beyond her departmental affiliation.

• Pulmonary Medicine
• Critical Care Medicine
• Pulmonary Hypertension
• +3 more

Fernando Torres, M.D., is a Professor in the Department of Internal Medicine at UT Southwestern Medical Center. He serves as Clinical Program Development Chief in the Division of Pulmonary and Critical Care Medicine, Medical Director of Lung Transplantation, and Head of the Pulmonary Hypertension Program. His roles include leadership in clinical development, transplant oversight, and specialty program direction. Education: Bachelor's degrees in Biology and Chemistry from Cornell University M.D. from Cornell University (Weill Cornell Medical College) Internal Medicine Residency at UT Southwestern Medical Center Fellowship in Pulmonary Disease & Critical Care Medicine at University of Colorado Denver, focusing on lung transplantation and lung volume reduction Research Interests: Dr. Torres' work focuses on critical areas impacting lung transplant recipients and pulmonary hypertension patients. Key topics include: Infectious complications following transplantation Cardiopulmonary interaction disorders Mechanisms of pulmonary arterial hypertension Genetic risk factors in PAH Novel therapeutic strategies for advanced lung disease His research has driven over 34 peer-reviewed publications and multiple clinical trials as Principal Investigator. Professional Engagement: Associate Editor of Advances in Pulmonary Hypertension Member of Texas Medical Association and International Society for Heart & Lung Transplantation Leadership roles in committees like the Solid Organ Transplant Programs Steering Committee Grants & Advising: Dr. Torres has led numerous studies funded by NIH and industry partners, with a focus on translational research bridging laboratory findings to clinical practice. His mentorship has contributed to training future generations of pulmonary critical care specialists. Labs/Teams: Oversees UT Southwestern's Pulmonary Hypertension Program and collaborates with multidisciplinary teams in lung transplantation, critical care medicine, and translational research initiatives.

• Statistics
• Machine Learning
• Data Science
• +3 more

Diego Nascimento is an Assistant Professor in Statistics at NEOMA Business School. His research focuses on statistical modeling, machine learning, spatio-temporal dependencies, and data visualization, with applications in supply chain management, neurosciences, and industrial projects. He has published in top-tier journals like the Journal of the Royal Statistical Society Series C and Pattern Recognition , collaborating with companies in South America and healthcare institutions. His work bridges theoretical statistics with practical industry challenges, emphasizing Bayesian methods and dynamic models. Key research areas include spatio-temporal modeling, neural data analysis, and optimization techniques. Recent studies involve clinical trials on transcranial stimulation effects and predictive analytics for supply chains during the pandemic. He actively contributes to interdisciplinary projects, combining statistical rigor with real-world applications in healthcare and environmental monitoring. Award-winning research includes advancements in imbalanced classification, safety-stock prediction, and entropy analysis in clinical trials. His collaborative projects reflect a commitment to both academic innovation and industrial relevance, with ongoing work in neuroimaging and supply chain resilience.

Jérôme ROCHETEAUView profile

Researcher

• IoT Engineering
• Industry 5.0
• Digital Twin Technology
• +4 more

Jérôme ROCHETEAU is a Researcher at ICAM (École d'Ingénieurs) in Nantes, France, specializing in IoT Engineering for Industry 5.0. He is affiliated with the LS2N laboratory and focuses on digital twin technology, software engineering, and machine learning operations. His research integrates applied projects like Recyplast-DEMO, emphasizing sustainable manufacturing and industrial IoT solutions. He collaborates closely with industry partners to develop model-driven engineering frameworks for sensor networks and green computing metrics. His work bridges formal methods with practical applications in cyber-physical systems and industrial automation. Education and academic background details are not explicitly provided in the text. His projects emphasize real-time systems, sensor data analysis, and sustainable software practices. He contributes to international academic partnerships through ICAM's global campuses, fostering applied research with industrial relevance. His publications span digital twin development, IoT system correctness, and energy-efficient software engineering. Key collaborations include the MEASURE project for green software metrics and the SMART REIFIER framework for SCADA applications. His expertise in model-driven engineering and formal verification underscores his contributions to reliable IoT architectures and industrial automation systems.

Aiping XuView profile

Assistant Professor

• Functional Data Analysis
• Statistical Learning
• Mathematics Education
• +3 more

Aiping Xu serves as Assistant Professor Academic at Coventry University's Mathematics Support Centre (LIB), actively supervising PhD students while leading institutional mathematics education initiatives. Her research bridges advanced statistical methodology with practical educational applications across engineering, data science, and higher education contexts. Her academic foundation includes: Doctorate in Signal Processing and Telecommunication from Research Institute of Computer Science and Random Systems (awarded 2002) MSc in Operational Research and Control Theory from Shandong University (awarded 1998) Bachelor's degree in Mathematics from Shandong University (awarded 1995) Dr. Xu's research program demonstrates significant evolution from early work on nonlinear system fault diagnosis using adaptive observers (2001-2004) to contemporary expertise in functional data analysis, where she develops principal curve clustering methods and Gaussian process regression frameworks. Her fingerprint analysis confirms dominant specialization in Functional Data Mathematics (100%), Principal Curve Mathematics (100%), and Forecasting (100%), with strong contributions to mathematics education through support centre evaluations and mathematical modeling competitions. Recent publications (2017-2022) reveal a strategic pivot toward applying statistical learning in educational contexts, particularly through her leadership in Coventry University's Mathematics Support Centre. This work integrates machine learning techniques with pedagogical innovation to address student challenges in mathematical comprehension and application. As an active PhD supervisor, Dr. Xu contributes to academic development through the Mathematical Contest in Modelling and institutional support services. Her research outputs show consistent international collaboration, particularly with Newcastle University, spanning statistical methodology, educational practice, and interdisciplinary applications. The Mathematics Support Centre under her involvement operates as a critical academic infrastructure unit, providing cross-disciplinary mathematical assistance to students across Coventry University through structured tutoring, resource development, and pedagogical research initiatives.

• Laser Physics
• Non-Linear Optics
• Optical Materials
• +3 more

Professor Stylianos Kouris holds a position at the Department of Physics, University of Patras, with affiliations to IEXH/FORTH and visiting roles at French universities. His research focuses on Laser Physics, Non-Linear Optics, and nanomaterials like graphene derivatives. He earned his B.A. in Physics from the University of Athens and advanced degrees (DEA/PhD) in Plasma Physics from the University of Toulouse. Key roles include Professor of Laser Physics since 2004, Principal Investigator at IHDL/FORTH, and visiting professorships at Angers, Bourgogne, and Cergy-Pontoise universities. Education: B.A. Physics (University of Athens, 1980), DEA/PhD in Plasma Physics (University of Toulouse, 1984). His research explores nonlinear optical materials, including graphene-based systems, nanocomposites, and plasmonic nanoparticles. Over 150+ publications span topics like optical limiting, femtosecond dynamics, and LIBS for material analysis. Awards include NATO and French Government scholarships. His work extends to environmental diagnostics via LIBS and machine learning-based material classification. Publications emphasize nanomaterials' nonlinear responses, with recent trends focusing on graphene derivatives, functionalized 2D materials, and broadband optical limiting. Collaborations include FORTH, University of Crete, and international teams. Awards: NATO Postdoctoral Fellowship (1988), French Government Scholarship (1981), IKY Scholarship (1976). Advising and grants highlight interdisciplinary research, including EU-funded projects on nanomaterials. His lab (http://nam.wpnet.upatras.gr/) focuses on experimental and theoretical studies in laser physics and nonlinear optics.

FRANCESCO FERRACUTIView profile

Researcher

• Control Systems
• Robotics
• Brain-Computer Interfaces
• +6 more

Prof. Francesco Ferracuti is a Researcher at the Department of Information Engineering (Università Politecnica delle Marche, Ancona, Italy). His work focuses on advanced control systems, robotics, brain-computer interfaces, and energy management. Key research themes include fault-tolerant control, autonomous navigation, and human-machine interaction through wearable sensors. Research interests span across: Control Engineering (MIMO systems, PID control, Model Predictive Control) Robotics (mobile robotics, UAVs, smart wheelchairs) Neuroengineering (EEG-based BCI, cognitive workload assessment) Energy Systems (renewable integration, home energy management) Data-Driven Methods (machine learning, system identification) Publications emphasize innovative solutions like EEG-driven obstacle avoidance and thermal stress monitoring for smart wheelchairs, demonstrating interdisciplinary approaches combining robotics with biometrics. His work on fault detection in multirotors and energy optimization algorithms highlights practical applications in both industrial and healthcare domains. Current projects include the E-MOTIVE initiative for mobility solutions via affective computing and BCI. His lab develops cutting-edge algorithms for cyber-physical systems security and predictive maintenance in industrial equipment.

Ali CinarView profile

Professor

• Complex Adaptive Systems
• Biomedical Engineering
• Control Systems
• +4 more

Ali Cinar is a Professor of Chemical Engineering and the Hyosung S. R. Cho Endowed Chair in Engineering at Illinois Institute of Technology (IIT), within the Armour College of Engineering. He directs the Engineering Center for Diabetes Research and Education (ECDRE), the first U.S. engineering center focused on diabetes treatment. His research spans complex adaptive systems, biomedical system modeling, and manufacturing process control. Cinar holds a Ph.D. from Texas A&M University (1976), with earlier degrees from Texas A&M and Robert College, Istanbul. His research focuses on developing algorithms for artificial pancreas systems, integrating wearable sensor data, and improving glucose control in diabetes. Notable projects include multivariable control systems that address meal intake, physical activity, and psychological stress. He has authored over 100 publications and co-authored books on chemical process control and biopharmaceutical manufacturing. Cinar is a Fellow of the American Institute of Chemical Engineers (AIChE) and has received awards such as the Ernst W. Thiele Award (2003) and the W. M. Langdon Award for Excellence in Teaching (1987, 1996). His work is supported by grants from NIH, JDRF, and NSF, advancing technologies like adaptive model predictive control and AI-driven diabetes management systems. His lab, ECDRE, collaborates with universities and institutions to develop next-generation diabetes care solutions. Current efforts include machine learning for stress detection, multivariable insulin delivery systems, and open-source simulation tools like py-mgipsim.

Ann SmithView profile

Senior Lecturer

• Statistical Inference
• Condition Monitoring
• Predictive Maintenance
• +7 more

Dr. Ann Smith is a Senior Lecturer in Data Science at the Department of Computer Science, University of Huddersfield. She is affiliated with the Centre for Efficiency and Performance Engineering and the Centre for Autonomous and Intelligent Systems. Her research focuses on statistical inference applied to industrial contexts, condition monitoring for predictive maintenance, and the reduction of input parameters in predictive classifiers. She is also engaged in promoting e-learning in mathematics and contributes to evidence-based healthcare diagnostics. Dr. Smith holds a PhD in Engineering, MSc in Applied Statistics, BSc in Mathematics, and PGCE(FE). She is a Knowledge Exchange Champion at the Isaac Newton Institute (University of Cambridge) and a Fellow of both the Institute of Mathematics and its Applications and the Higher Education Academy. Her educational background includes advanced degrees in mathematics and engineering, complemented by a PGCE in further education. Her international collaborations include visiting lecturer roles at Fuzhou Normal University (China) and Universität Greiswald (Germany). Key research areas include fault detection in mechanical systems, non-linear systems analysis, and autonomous abnormality assessment techniques. She actively participates in industry-focused initiatives like the Analysis for Innovators scheme and European Study Groups for Industry (ESGI). Dr. Smith’s scientific awards include Advanced Data Science Professional status and prestigious fellowships. Her work contributes to the UN Sustainable Development Goals, particularly in advancing quality education and industry innovation. She supervises PhD students and has published widely on topics ranging from coffee brewing optimization to genetic disorders linked to nasal polyps and bronchiectasis. Her recent articles highlight advancements in medical imaging techniques (e.g., PET/CT), fault detection in renewable energy systems, and mathematical modeling of industrial processes. She has delivered invited talks on predictive maintenance and participated in numerous international conferences, showcasing her interdisciplinary impact.

Edith Alice KovácsView profile

Associate Professor

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +8 more

Edith Alice Kovács is an Associate Professor at the Department of Differential Equations, Budapest University of Technology and Economics (BME). She holds a PhD from the West University of Timisoara (2005) and has taught at multiple institutions, including the General College of Entrepreneurship (ÁVF) and universities in Arad. Her research focuses on multivariate statistics, probability graph models, information theory, AI/ML, network reliability, and mathematical modeling in finance/medicine. She has received the BME Innovation Award 2020 for anomaly detection work. Kovács has presented at conferences worldwide, including the European Conference on Operational Research and the IEEE SACI conference. Her teaching spans subjects like economic modeling, operations research, and data science across BME and ÁVF programs. Education: BSc and MSc in mathematics from the West University of Timisoara (1994), PhD in Mathematics (2005). Professional roles include full-time employment at BME since 2015 and prior experience at Aurel Vlaicu and Vasile Goldis Universities (1994–2000). Research interests include decision-making under uncertainty, metaheuristic algorithms, neural halos, and social network modeling. Recent work explores copula-based anomaly detection and vine copula structures. Kovács has contributed to projects like stochastic inventory modeling for TESCO and VNF fingerprinting for Nokia.

• Digital Engineering
• Through-life Engineering Services
• Augmented Reality Applications
• +6 more

John Ahmet Erkoyuncu is a Professor of Digital Engineering and Head of the Centre for Digital Engineering and Manufacturing at Cranfield University. His research focuses on digitalization of manufacturing processes, augmented reality (AR) applications in maintenance, and uncertainty management in industrial service contracts. He holds roles such as co-chair of the Through-life Engineering Services Council and is an Associate Member of CIRP. Education: PhD in maintenance cost estimation (collaboration with BAE Systems, Rolls-Royce, and MoD). Current Activities: Leads initiatives in digital twins, AI integration, and AR-driven maintenance solutions. Supervises 8 industry-cofunded PhD projects across aerospace, automotive, and healthcare sectors. Awards: Not explicitly listed in provided texts, though his contributions to digital twin frameworks and AR applications are internationally recognized. Research Interests: Erkoyuncu’s work bridges engineering and data science, emphasizing: - Digital transformation of manufacturing systems, - Human-centric AR tools for maintenance, - Sustainable product-service systems (IPS²), - Uncertainty quantification in industrial contracts. Publications: Over 100 peer-reviewed articles in journals like CIRP Annals, IEEE Access, and Robotics and Computer-Integrated Manufacturing. Recent work includes cognitive digital twin architectures and AI-driven knowledge extraction for degradation analysis. Grants: Actively involved with Innovate UK and EPSRC projects, focusing on autonomous maintenance systems and digital twin applications. Labs/Teams: Oversees the Centre for Digital Engineering, collaborating with industry partners like Airbus, Rolls-Royce, and NHS. Leads the Digital Twin Hub’s Community Council.