Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.
Miklós Szócska is a Professor and Dean of the Faculty of Public Health at Semmelweis University, Budapest. He also serves as the Director of the Healthcare Management Training Center and Head of the Institute of Digital Health Sciences. His career bridges academic leadership with healthcare policy, including roles as Secretary of State for Health (2010–2014), where he focused on evidence-based health policy, public health taxes, and e-health systems. Education: General Practitioner, Semmelweis University (1989) Master of Public Administration, Harvard University's John F. Kennedy School of Government (1998–1999) PhD, Semmelweis University (2008) Research Interests: Szócska's work spans network analysis , leadership and change management , crisis communication , social innovation , and big data/AI applications in healthcare . His recent studies focus on reconstructing 3D histological structures via machine learning, global mortality linked to pathogens, and tobacco control policies. Article Trends: His publications emphasize global health metrics (e.g., mortality decomposition, colorectal cancer risk factors) and digital health innovations (AI, e-health). Systematic analyses from the Global Burden of Disease Study frequently inform his research. Academic Affiliations: He leads the Institute of Digital Health Sciences and chairs the Faculty of Public Health at Semmelweis University. His international roles include Board Membership at the European Health Forum Gastein and Supervisory Board positions at EIT Health.
Bernardo Tellini is a Full Professor of Electrical and Electronic Measurements at the Department of Energy, Systems, Land, and Construction Engineering (DESTEC) at the University of Pisa, where he also serves as Vice-Rector for Doctoral Research. He has held this institutional role since 2020, overseeing doctoral program planning, accreditation, and admission procedures. Previously, he chaired the doctoral program in Energy, Electrical, and Thermal Engineering from 2012 to 2016 and served on the Leonardo da Vinci Doctoral School in Engineering from 2008 to 2016. Education: PhD in Electrical Engineering, University of Pisa (1999) Degree in Electrical Engineering, University of Pisa (1993) Postdoctoral research at Karlsruhe Research Center for Technology and Environment Industry experience at ABB Tellini's research focuses on electrical and magnetic measurement methodologies for high-power pulsed applications, characterization of electrical and magnetic properties of materials, aging processes in battery cells, and electromagnetic emissions from power circuits. His work spans from fundamental measurement theory to practical industrial applications, particularly in railway technologies where he represents the University on the Steering Committee of the District for Railway Technologies, High-Speed, and Network Safety in Tuscany. He has served as president of the European Pulsed Power Laboratories agreement and chaired major IEEE conferences including I2MTC 2015 and MELECON 2020. His recent publications reveal a strong emphasis on RFID-based localization systems , nanoparticle-enhanced optical sensors , and advanced battery characterization techniques . The research trajectory shows increasing integration of measurement science with emerging technologies like plasmonic sensing, microwire-based transducers, and smart systems for industrial monitoring. His team has developed innovative approaches for battery health monitoring under vibration stress, temperature sensing using magnetic materials, and precise localization methods using phase-based RFID systems. Professional Service: President of Italian Section of IEEE (2019-2021) Scientific director of Pisa research unit in Association of Electrical and Electronic Measurements (GMEE) Member of Certification Committee of Italcertifer SpA (since 2019) Representative on District for Railway Technologies Steering Committee (since 2013) Tellini has authored approximately 200 publications in international journals and conference proceedings. His leadership extends to academic governance through roles on the DESTEC Department Human Resources Committee and various university committees overseeing scientific qualifications and doctoral programs. His research bridges theoretical measurement principles with practical engineering solutions for energy systems, transportation infrastructure, and industrial monitoring applications.
Kirk D. Dolan is a Professor at Michigan State University (MSU), holding joint appointments in the Department of Food Science and Human Nutrition (lead) and the Department of Biosystems & Agricultural Engineering within the College of Agriculture & Natural Resources. His research focuses on thermal processing modeling of foods and inverse problems in food science, utilizing advanced computational tools like MATLAB and COMSOL for parameter estimation. He actively contributes to food safety through extension work, including co-teaching the FDA-mandated Better Process Control School and HACCP courses. PhD, Agricultural Engineering, Michigan State University, 1989 MS, Agricultural Engineering, University of California, Davis, 1985 BS, Agricultural Engineering, University of Florida, 1983 Dolan’s research spans thermal processing technologies (canning, drying, aseptic processing), inverse problem solving, and statistical methods for food researchers. His recent publications emphasize antioxidant analysis, kinetic modeling, and computational approaches in food systems. Recent publications highlight advancements in parameter estimation for food processing, including studies on thermal conductivity in cherry pomace, starch viscosity models, and microbial inactivation dynamics. His work bridges computational methods with practical applications in food safety and quality preservation. 2019 CANR Camden Endowed Teacher/Scholar Award Dolan chairs the triennial Inverse Problems Symposia at MSU and co-teaches graduate courses like BE 835 (Modeling Methods in Biosystems Engineering). His extension work supports Michigan food entrepreneurs through FDA product registration assistance and industry training programs.
Dr. Andrea L. Wirtz is an Associate Professor in the Department of Epidemiology at Johns Hopkins Bloomberg School of Public Health, with primary division in Infectious Disease Epidemiology and joint division in Social and Behavioral Interventions. She is affiliated with the Center for Global Health, Center for Humanitarian Health, and Center for Public Health and Human Rights. Her educational background includes: PhD, Johns Hopkins Bloomberg School of Public Health (2015) MHS, Johns Hopkins Bloomberg School of Public Health (2007) Dr. Wirtz advances epidemiologic methods to measure associations between human rights and health outcomes, with particular focus on evidence-based interventions and translation to policy. Her research centers on community-partnered international and domestic epidemiologic research addressing intersections between human rights and HIV and other health outcomes, with specific attention to populations traditionally excluded from research or underserved in health services. Internationally, she serves as joint Principal Investigator for estimating HIV prevalence among Venezuelan migrants and refugees in Colombia. In the U.S., she leads a nationwide cohort study focused on health and HIV risks in transgender women. Her work integrates epidemiologic expertise into complex humanitarian interventions and human rights investigations across diverse settings. Analysis of her recent publications reveals a strong focus on transgender health, HIV prevention and treatment, migrant and refugee health, and the application of epidemiologic methods to human rights investigations. Her research increasingly addresses long-term health conditions in marginalized populations, including studies on Long COVID, cardiovascular disease in transgender women with HIV, and pandemic impacts on healthcare access for vulnerable groups. Her scientific recognition includes: Robert Carr Memorial Research Award (2022) for community-academic partnerships advancing human rights-based policies Global Leadership in HIV Prevention Research award from US Department of State (2021) Multiple teaching awards from Johns Hopkins Bloomberg School of Public Health (2014-2022) Advising, Mentoring, & Teaching Recognition Award (2015-16) At Johns Hopkins, Dr. Wirtz instructs graduate-level courses on health survey research methods and using epidemiologic methods to investigate human rights violations. She has secured significant grant funding for studies on HIV prevention, transgender health, and migrant health interventions. Her research portfolio includes community-academic-policy collaborations that address complex health challenges through rigorous epidemiologic approaches. Dr. Wirtz leads several major research initiatives including the American Cohort to Study HIV Acquisition among Transgender Women (LITE study), development of screening tools for gender-based violence in humanitarian settings, and effectiveness studies of HIV preventive interventions for vulnerable populations globally.
Professor Geoffrey Morgan is an epidemiologist and environmental health expert at the University of Sydney , affiliated with the School of Public Health and University Centre for Rural Health . With over 30 years of experience, he investigates environmental risk factors for health, particularly air pollution, heat, and climate-related hazards. He leads the NHMRC Centre for Clean Air Research Excellence and co-leads the Bushfires, Air Pollution and Extreme Events Theme in the HEAL network. His research has directly informed Australian ambient air quality standards and guided state-level air pollution control policies. Key areas include Environmental epidemiology Exposure assessment Climate change health impacts Health burden projections Recent publications (2025–2024) focus on wildfire smoke effects, temperature-related cardiovascular and mental health risks, PM2.5 mortality, and climate-driven infectious disease patterns. These studies span Australia, Singapore, and global contexts, emphasizing policy translation through health impact assessments. Professor Morgan teaches Environmental Health (PUBH5121) and Environmental Epidemiology (PH5125) in the University of Sydney's Master of Public Health program. He supervises PhD and MPhil students in environmental epidemiology.
Dr. Jared G. Carlberg is a Professor in the Department of Agribusiness and Agricultural Economics at the University of Manitoba , with an adjunct appointment in the Department of Economics. He focuses on applied agricultural economics research and teaching in food systems analysis. PhD in Agricultural Economics, Oklahoma State University MSc in Agricultural Economics, University of Saskatchewan BA (Honors) in Economics, University of Saskatchewan B Commerce in Finance, University of Saskatchewan His research spans agricultural marketing (cattle/beef sectors, grain/pulses/oilseeds), nutrition economics (healthcare costs of food-related chronic diseases), and sustainable agrifood systems (conservation easements, pea coproducts, durum wheat supply response). Key projects examine beef quality grade pricing, grain supply dynamics, and health-economic policy linkages. Dr. Carlberg’s 15 most recent publications demonstrate interdisciplinary expertise bridging agricultural markets, food policy, and health economics. Recurring themes include value chain optimization, sustainable practices, policy frameworks, and quantitative analysis of food-health linkages. Professor of the Year Award, Faculty of Agricultural and Food Sciences Student Organization Outstanding Teacher in Agricultural and Food Sciences (twice awarded) He has advised 13 graduate students since 2001, covering topics from flax/corn/oat supply responses to growth-enhancing technologies in cattle feeding. His teaching portfolio includes econometrics (ABIZ 3080) and agricultural marketing research (ABIZ 7240).
Michael Hyland is an Associate Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on the modeling, analysis, and optimization of smart urban transportation systems, with particular emphasis on shared autonomous vehicles, microtransit integration with fixed-route transit, and sustainable mobility solutions. He employs methodologies from operations research (optimization, Markov decision processes), statistical modeling (discrete choice, regression), and economic analysis to address challenges in urban mobility. Education: Ph.D., Civil and Environmental Engineering (Transportation), Northwestern University, 2018 M.Eng., Civil and Environmental Engineering (Transportation), Cornell University, 2013 B.S. Civil and Environmental Engineering, Cornell University, Magna Cum Laude, 2013 His recent research explores emerging mobility paradigms through topics such as dynamic fleet management, vehicle miles traveled (VMT) impacts, equity in job accessibility, electricity demand implications of e-bikes, and human-machine collaborative planning frameworks. The work often combines large-scale simulation with interpretable modeling techniques. Hyland leads the Hyland Lab , which develops computational tools for evaluating integrated transportation systems. The lab's work spans theoretical modeling (e.g., state-space representations, decomposition heuristics) and applied policy analysis (e.g., assessing Senate Bill 1 infrastructure projects, AV-era parking reforms, and micromobility deployment strategies).
Dr. Saidi Siuhi serves as an Associate Professor of Civil Engineering at South Carolina State University, where he teaches undergraduate and graduate courses while conducting research and providing institutional service across departmental and university levels. His academic credentials include: Ph.D. in Civil Engineering from the University of Nevada, Las Vegas (2009) M.Sc. in Civil Engineering from Florida State University (2006) B.Sc. in Civil Engineering from the University of Dar-es-Salaam (2003) Specializing in transportation engineering, Dr. Siuhi's research focuses on traffic safety, transportation planning, and microscopic traffic simulation. His work addresses critical transportation challenges including distracted driving/walking behaviors, traffic management during special events (notably the 2017 solar eclipse), and the application of advanced computational methods to transportation networks. He integrates emerging technologies like virtual reality, machine learning, and deep learning to develop innovative safety solutions for complex transportation systems. Analysis of his recent publications (2021-2025) reveals a strong trajectory toward computational transportation safety, with increasing emphasis on AI-driven solutions for pedestrian safety, driver behavior analysis, and infrastructure monitoring. His work consistently bridges theoretical transportation models with practical safety applications, particularly in distracted behavior analysis and event-based traffic management. Dr. Siuhi actively mentors students through senior design projects (CE 459/460) and graduate coursework, though specific advisee names aren't documented. His service contributions span departmental, college, and university committees, supporting academic operations and strategic initiatives within the engineering program.
David Macii is Associate Professor at the Department of Industrial Engineering, University of Trento, Italy, where he teaches "Digital Signal Processing for Mechatronics" and co-leads the "Laboratory of Internet of Things." His core expertise lies in digital signal processing, measurement science, smart-grid instrumentation, indoor positioning and industrial IoT applications. Research interests revolve around four pillars: (i) advanced estimation algorithms for frequency, ROCOF and synchrophasors to enhance power-quality monitoring in future smart-grids with high PV and EV penetration; (ii) design and metrological characterisation of low-cost PMU and smart-meter solutions; (iii) radar- and RFID-based indoor localisation and tracking for robotics and assisted-living scenarios; and (iv) embedded, IoT-enabled measurement systems bridging DSP, mechatronics and industrial electronics. Recent publications (2023-2025) reveal a clear methodological trend: development of fast, uncertainty-aware DSP algorithms (interpolated DFT, Kalman filtering, harmonic whitening) validated against real-world noise, interference and contingency conditions, followed by their embedding into resource-constrained hardware platforms for EV charging coordination, grid-support converters and robotic navigation. Although the supplied text does not list specific grants or doctoral students, the steady stream of joint publications with European colleagues and his leading teaching role in two inter-departmental master courses indicate an active, well-integrated research and educational profile within the University of Trento.
Dr. Thangavel Thevar is a Senior Lecturer in the School of Engineering at the University of Aberdeen, where he has been teaching since 2005. He completed both his undergraduate degree (First Class Honours in Electrical Engineering) and PhD (in Laser Engineering) at the University of Aberdeen in 1989 and 1993 respectively. Prior to his academic career, he accumulated approximately 10 years of industrial R&D experience in the USA, working on solid-state laser development and holographic applications. Dr. Thevar's research focuses on several key areas: Digital holography for imaging of marine plankton and micro-particles Laser Induced Breakdown Spectroscopy (LIBS) for subsea applications Laser-based instrumentation development Development of solid-state lasers for scientific, industrial, and medical applications Engineering applications of holography His most notable recent achievement is leading a team that developed the weeHoloCam, a state-of-the-art ultracompact underwater holographic camera for imaging microorganisms. Weighing just 3.5 kg, this system is the lightest and most compact of its kind, capable of imaging 240 ml/s and continuously recording up to 200,000 holograms. The system incorporates a rapid hologram processor and an AI-based image classifier. This technology has significant applications in marine studies including spatial and temporal monitoring of plankton species, monitoring harmful plankton & micro-jellyfish, study of vertical transport of floc, and monitoring microplastic pollution in the ocean. Dr. Thevar has secured numerous research grants as Principal Investigator, including projects funded by Sustainable Aquaculture Innovation Centre (SAIC), BBSRC, DEFRA, and Defence & Security Accelerator (DSTL). His current research portfolio demonstrates strong interdisciplinary connections between optical engineering, marine science, and environmental monitoring. His scientific contributions include: Royal Academy of Engineering Visiting Teaching Fellow Award (2010-2013) US patent 8,494,012 B2 for Raman converters Development of alexandrite lasers and ruby holographic lasers during his industrial R&D period Work on US government contracts for non-destructive inspection methods for military aircraft and the space shuttle Sabbatical work at NASA Langley Research Centre developing diode pumped Thulium YALO lasers As an educator, Dr. Thevar has served as Coordinator of MSc Oil & Gas Engineering (2007-2020), Undergraduate Level 1 Coordinator, and has contributed to various committees including Quality Assurance and Students' Progression. He currently teaches courses including Principles of Electronics, Electrical & Mechanical Systems, Control Systems, and supervises individual projects at both undergraduate and postgraduate levels. He is accepting PhD students interested in Engineering research. Dr. Thevar is actively involved in professional organizations, serving as Technical Programme Chair for IEEE/OES Oceans Conference 2007, on organizing committees for various conferences, as a committee member of the Instrument Science and Technology Group (Institute of Physics), and as a member of both IET and IEEE. He also serves as a reviewer for optics-based journals.
Dr. Ann Kennedy is an Assistant Professor of Neuroscience at Northwestern University's Feinberg School of Medicine, where she leads the Kennedy Lab. Her research focuses on theoretical neuroscience, using mathematical modeling and computational approaches to understand how neural circuits produce adaptive behaviors. She investigates how the brain integrates sensory information with survival needs to drive behavior across multiple scales, from single neurons to social groups. Dr. Kennedy received her BS and BA in Biomedical Engineering from Johns Hopkins University in 2008, followed by a PhD in Neuroscience from Columbia University in 2014. She conducted postdoctoral research at the California Institute of Technology before establishing her independent laboratory at Northwestern in 2020. Her research interests center on understanding the computational principles of neural circuits, particularly in subcortical brain regions involved in survival behaviors. She develops theories for distributed behavior control by multiple neural populations, studies computation in heterogeneous neural populations, and constructs richer descriptions of animal behavior using pose estimation and machine learning tools. Her work blends dynamical systems, control theory, reinforcement learning, and machine learning to investigate neural dynamics across multiple scales and model organisms. Dr. Kennedy's recent publications reveal trends in understanding neural heterogeneity, early nervous system evolution, and the theoretical foundations of neuroscience. Her work spans from fundamental computational principles to applications in pain research and behavioral neuroscience, with increasing emphasis on translating theoretical insights to understand neurological conditions. Sloan Research Fellowship, Sloan Foundation (2023) Eppendorf and Science Prize in Neurobiology (2022) McKnight Scholars Award (2024) Pew Scholar in the Biomedical Sciences (2024) Thom Young Researcher Award (2025) Dr. Kennedy actively mentors postdoctoral researchers and collaborates with experimentalists both locally and internationally. Her lab has received significant grant support including the $75,000 Sloan Fellowship and the prestigious McKnight Scholars Award. She co-chairs the CAJAL summer school on quantitative approaches to behavior and serves as Associate Editor for Science Advances. The Kennedy Lab operates at the intersection of theory and experiment, developing community resources like the Mouse Action Recognition System (MARS) for behavioral analysis and contributing to major initiatives like the 2025 Multi-Agent Behavior Challenge on Kaggle. The Kennedy Lab maintains active collaborations with researchers across San Diego and beyond, focusing on theoretical approaches to behavior and neural circuit function. Current projects analyze data from multiple brain regions and organisms, including hypothalamus studies of social and defensive behavior, basal ganglia research on behavior-neural activity correlations, hippocampal studies of social bond formation, and whole-organism neural imaging in cnidarians. The lab values open science, clear communication, and community resource development.
Dr. Jonathan Frame is an Assistant Professor of Artificial Intelligence/Machine Learning in Geological Sciences at the University of Alabama (2024–present) and a Faculty Fellow at the Alabama Water Institute (2024–2027). He holds a PhD in Geological Sciences from the University of Alabama (2022), an MS in Civil Engineering from the University of California, Irvine (2011), and a BS in Earth Systems Science, Technology, and Policy from California State University, Monterey Bay (2010). His research focuses on advancing hydrologic modeling through machine learning, including deep learning for streamflow forecasting, geospatial modeling, and flood prediction systems. Notable projects include improving the National Water Model with LSTM networks and developing rapid inundation mapping techniques using satellite data. He has contributed to over 30 peer-reviewed publications and actively participates in conferences like AGU and NeurIPS. His engineering experience spans flood risk mitigation, groundwater analysis, and pipeline transient modeling across California, Texas, and Washington. Research Interests Machine learning integration in hydrological systems Operational flood forecasting and inundation mapping Data-driven approaches for ungauged basins Climate nonstationarity and model adaptability Hydraulic transient analysis in water infrastructure Recent Contributions Frame’s recent work emphasizes NextGen water modeling frameworks, combining physics-based models with AI to enhance predictive accuracy. His 2025 paper on heterogeneous water modeling frameworks and 2024 studies on rapid inundation mapping highlight innovations in integrating satellite observations with hydrologic models. He also explores topics like mass conservation constraints in rainfall-runoff models and evapotranspiration prediction using deep learning. Grants & Projects FEMA partnership for near-real-time flood damage prediction systems NOAA-funded research on AI in environmental sciences NASA snowpack analysis for water resources forecasting Development of the Tarsier environmental modeling framework Labs & Collaborations Frame collaborates with the Alabama Water Institute and contributes to interdisciplinary teams advancing hydrologic AI. His work intersects with climate science, environmental engineering, and computational hydrology to address global water challenges.
Dr. Ali Yousefi is an Associate Professor in the Department of Biomedical Engineering at the University of Houston's Cullen College of Engineering. His research focuses on developing statistical and computational methods for analyzing neuroscience data, particularly in linking neural activity to biological/behavioral signals. Key areas include model identification, Bayesian analysis, and real-time neural decoding for applications like brain-computer interfaces and closed-loop stimulation systems. Education: B.S. (Electrical Engineering, Iran University of Science & Technology, 1998), M.S. (Electrical Engineering, Sharif University of Technology, 2000), Ph.D. (Electrical Engineering, University of Southern California, 2014). Postdoctoral training at Harvard Medical School (2019) and Boston University (2019). Research Interests: Neural data analysis frameworks Dynamic neural ensemble modeling Closed-loop brain stimulation systems Bayesian statistical inference High-dimensional data decoding Labs/Teams: Principal Investigator of Yousefilab, focused on neurotechnology and BCI development. Active in interdisciplinary collaborations combining engineering, neuroscience, and machine learning. Key Contributions: Developed methodologies for neural signal decoding, including Bayesian Gaussian process models and latent variable techniques. Pioneered real-time cognitive state prediction and closed-loop systems for enhancing cognitive control in humans.
Andres Soler is a Lecturer at NTNU's Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering. His research focuses on EEG signal processing for applications in brain-computer interfaces (BCI), stress/health monitoring, and low-density electrode systems. He has published extensively on topics including EEG source imaging, artifact removal, and optimized channel selection techniques. His work bridges biomedical engineering and machine learning, with notable contributions to driver alcohol detection systems and motor imagery classification for neurorehabilitation. Teaching roles include serving as Guest Lecturer for Biomedical Instrumentation and Control (TTK4270) and Adaptive Data Analysis (TTK7), while acting as main lecturer for Industrial Electrotechnics (TTK4240). His research group collaborates internationally on projects like FlexEEG and has presented at conferences such as IEEE EMBC and Brain Informatics. Key research directions include advancing EEG-based systems for clinical and automotive applications, developing algorithms for real-time brain activity decoding, and optimizing EEG hardware configurations for cost-effective implementations. Current trends show focus on enhancing signal quality through artifact mitigation strategies and improving BCI communication systems for locked-in patients.
