Tae-Ho Lee is an Associate Professor in the Department of Psychology at Virginia Tech, with affiliated appointments in the School of Neuroscience and Translational Biology, Medicine, and Health. His research focuses on affective and cognitive neural development across the lifespan, neurodegeneration, and family-based neural dynamics. PhD in Brain and Cognitive Science, University of Southern California M.A. in Clinical Psychology, Korea University B.A. in Psychology, Korea University Dr. Lee’s work explores brain connectome dynamics, dyadic neural concordance in families, and age-related attentional control. He employs neuroimaging techniques to study how familial and environmental factors shape emotional and cognitive outcomes in adolescents and older adults. Recent publications highlight trends in longitudinal studies , parent-child neural similarity , and functional connectivity in emotion regulation . Key areas include autism spectrum disorder, substance misuse risk, and the role of socioeconomic factors in brain development. Rising Star , Association for Psychological Science (2020) Dr. Lee is not currently accepting students and leads the Affective Neurodynamics and Development (AND) Lab at Virginia Tech.
Michele Zorzi is a Professor of Telecommunications at the School of Engineering, University of Padova, Italy, where he has held a faculty position since 2003. He leads the SIGNET (Signal processing and Networking) Research Group, focusing on cutting-edge wireless networking challenges including mmWave communications and underwater networks. His extensive publication record exceeds 600 papers in top-tier journals and conferences, reflecting significant contributions to the field through both theoretical and experimental work. He received his Laurea Degree (1990) and Ph.D. (1994) in Electrical Engineering from the University of Padova. Prior academic appointments include Politecnico di Milano (1993-1996), University of California San Diego (1995-1998), and University of Ferrara (1998-2003), where he progressed from Associate Professor to full Professor. His educational trajectory demonstrates deep roots in Italian academia with international exposure. Professor Zorzi's research spans wireless communications and networking, with current emphases on mmWave networking for vehicular systems, underwater acoustic/optical communications, non-terrestrial networks, and AI-driven networking solutions. His group conducts experimental validations including at-sea trials for underwater systems and testbeds for vehicular networks. Key projects include PRATA for predictive QoS in autonomous driving and IoT-based environmental monitoring of the Venice Lagoon, demonstrating practical applications of theoretical work. Analysis of his 2022-2025 publications reveals strong trends in applying artificial intelligence to networking challenges across diverse environments. There is significant emphasis on vehicular networks (predictive QoS, teleoperated driving), underwater systems (acoustic/optical communications, AUV swarms), and satellite networks (Starlink integration, NTN security). Experimental validation in real-world scenarios like the Venice Lagoon monitoring project and underwater sea trials characterizes his applied research approach. His scientific accolades include: IEEE Fellow (2007) IEEE Communications Society Best Tutorial Paper Award (2008, 2019) Stephen O. Rice Best Paper Award (2018) Multiple best paper awards at IEEE conferences (2005-2020) As principal investigator for numerous European and US research projects plus 20+ industry-funded initiatives, Professor Zorzi has mentored over 35 PhD students and post-docs. Graduates now hold prominent positions at institutions including Stanford, UCSD, CTTC, and Huawei. His SIGNET group maintains active international collaborations and contributes to open-source networking tools via GitHub, demonstrating commitment to community engagement. The SIGNET Research Group, housed within the Department of Information Engineering, operates specialized experimental facilities for mmWave and underwater communications. Current initiatives include AI-based predictive QoS frameworks for vehicular networks, underwater optical communication systems using ultraviolet light, and large-scale IoT deployments for environmental monitoring. The group's GitHub presence indicates strong open-science practices, while recent sea trials confirm hands-on experimental capabilities beyond theoretical work.
Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Paul R Moorcroft is a Professor of Organismic and Evolutionary Biology at Harvard University and an Affiliate in the School of Engineering and Applied Sciences (SEAS). His research focuses on ecological dynamics of terrestrial plant communities, biosphere-atmosphere interactions, and mechanistic models of animal movement. He collaborates with institutions like NASA JPL on projects such as the ECOSTRESS mission, which investigates environmental processes using satellite data. His lab, the Moorcroft Lab, emphasizes fieldwork and interdisciplinary approaches, as seen in recent collaborations in Italy and Southeast Asia. Key research areas include tropical forest ecology, carbon cycling in peatlands, and the application of remote sensing technologies to environmental monitoring. His work bridges theoretical models with empirical data, addressing global challenges like climate change and biodiversity loss. Recent publications highlight contributions to quantifying carbon emissions from peatland drainage and improving satellite-based mapping of forest functional traits. His advising activities include mentoring graduate students like Rhemi Toth. Collaborations with NASA and academic partners underscore his role in advancing Earth systems science through interdisciplinary research.
Olindo Isabella serves as a Full Professor within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. She heads the Photovoltaic Materials and Devices (PVMD) research group, driving innovation in solar energy conversion technologies. Her academic role encompasses teaching, research leadership, and extensive collaboration with industry and international institutions to advance photovoltaic science and engineering. Professor Isabella's research spans multiple domains of photovoltaics, including silicon and perovskite solar cells, thin-film technologies, offshore floating systems, and agrivoltaics. She investigates material properties, device physics, and system performance to enhance efficiency, reliability, and environmental sustainability of solar energy solutions. Her work integrates experimental and computational approaches for comprehensive analysis. Analysis of her recent publications indicates a strategic focus on machine learning for PV-climate classification, impedance spectroscopy of silicon solar cells, offshore floating platform engineering, and perovskite crystallization processes. These studies collectively address key barriers to large-scale solar deployment, such as performance prediction, structural integrity in marine environments, and novel material synthesis. Scientific Awards: The available information does not mention any specific awards or honors for Professor Isabella. She has guided the research of 20 students and secured competitive funding for impactful projects. Currently, she leads SYMBIOSYST (2023-2026), which explores symbiotic relationships between solar PV and agriculture, and recently completed TRUST-PV (2020-2024), aimed at improving PV plant integration across market segments through machine learning and monitoring technologies. The PVMD group under her direction operates state-of-the-art laboratories for solar cell fabrication and characterization. The team collaborates with global partners on field trials, data analysis, and technology development, contributing to both fundamental knowledge and practical applications in renewable energy.
Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.
Anthony Rollett is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University , where he has been a faculty member since 1995. He serves as the Principal Investigator and Co-Director of the NASA-supported Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM) and co-director of the Next Manufacturing Center . Prior to CMU, he held leadership roles at Los Alamos National Laboratory (1991-1995). Education: Ph.D., Materials Engineering, Drexel University (1987) MA, Metallurgy and Materials Science, Cambridge University (1977) Research Interests: Rollett’s work focuses on microstructural evolution and microstructure-property relationships in 3D using experiments and simulations. His expertise spans additive manufacturing , metal 3D printing , materials for energy systems , grain growth , recrystallization , and stereology , with techniques like high-energy diffraction microscopy (HEDM) and dynamic x-ray radiography (DXR) . Scientific Contributions: He has over 320 peer-reviewed publications and an h-index >80 . His recent articles highlight machine learning for laser processing , fatigue analysis of additively manufactured alloys, and design optimization for heat exchangers in supercritical CO2 and solar thermal applications . Scientific Awards: Fellow of ASM International (1996) Fellow of the Institute of Physics (UK) (2004) Fellow of The Minerals, Metals & Materials Society (TMS) (2011) Cyril Stanley Smith Award (TMS, 2014) Member of Honor, French Metallurgical Society (2015) US Steel Professor (2017) Francqui International Professor (2020-2021) International FAME Award (2023) Leadership & Impact: Rollett co-led the development of a NASA Space Technology Research Institute for additive manufacturing and established a new master’s program in additive manufacturing (2018). His research group is funded by industry , federal agencies , and Pennsylvania state grants . He also serves on the Basic Energy Science Advisory Committee and Defense Programs Advisory Committee for the Department of Energy.
Min Hu is an Assistant Professor in the Department of Economics, Philosophy, and Political Science at the University of British Columbia, Okanagan Campus. He holds an adjunct position at Dalhousie University's School of Health Administration. His primary research focuses on Health Economics, Applied Microeconomics, and Labour Economics, with an emphasis on vulnerable populations in Canada, Indigenous health, income inequalities, and cancer economics. He teaches courses in Microeconomics, Health Economics, and Statistics. Education: PhD in Economics (2019), Dalhousie University; Postdoctoral Fellowship in Health Economics (2019–2020), Dalhousie University. Previous degrees include an M.A. (2014–2016) and B.A. in Economics from Dalhousie University. Research interests include the socio-economic determinants of health disparities, particularly among Indigenous peoples and immigrants, and the economic impacts of cancer-related policies. He employs applied econometric methods to analyze labor market outcomes and social policy effectiveness. His recent articles explore sustainable biomass utilization, energy policies, and environmental engineering innovations. Key themes include biomass supply chain optimization, CO₂ reduction strategies, and waste-to-energy technologies. Scientific Awards: Recipient of SSHRC New Frontiers Grant (2023–2026), CIHR Team Grant (2024–2026), and multiple UBC startup grants. His work is supported by SSHRC, CRDCN, and Indigenous Services Canada. He advises students in Economics and Global Studies (IGS) programs. Grants and funding span health economics, Indigenous well-being, and sustainable energy systems. Collaborations include interdisciplinary teams in health administration and environmental policy. Labs/Teams: Engaged with UBC's Okanagan Sustainability Initiatives and Dalhousie's Health Economics Research Group. Affiliated with professional organizations like the American Economic Association and Canadian Health Economics Study Group.
Dr. Iason Sideris is affiliated with ETH Zürich's Department of Neue Fertigungstechnologien (New Manufacturing Technologies), holding a Researcher position within the Professorship for Advanced Manufacturing. His work focuses on advancing additive manufacturing techniques, particularly in path planning optimization, temperature control, and material processing. He contributes to fields like Direct Energy Deposition, Wire-Arc Additive Manufacturing (WAAM), and data-driven finite volume methods. Key Research Areas: Additive Manufacturing, Thermal Modeling, Process Optimization, Materials Science Recent research emphasizes scalable path planning for temperature uniformity in AM processes, with publications addressing challenges in WAAM thermal management and real-time simulation methods. His work combines computational modeling with experimental validation to enhance manufacturing efficiency and material properties.
Marie Violay is an Associate Professor at the Laboratory of Experimental Rock Mechanics (LEMR) within the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL). She also contributes to teaching and PhD program committees across multiple EPFL divisions. Specializes in rock mechanics, earthquake dynamics, and hydro-mechanical couplings Leads experimental studies on fluid-induced seismicity and reservoir deformation Research Focus : Investigates how pore fluid pressure variations affect rock strength, fault behavior, and permeability evolution. Her work spans from brittle fracture mechanics to ductile deformation processes under geothermal conditions, with applications to carbon storage, earthquake mitigation, and volcanic hazard assessment. Article Trends : Her recent publications examine stress biaxiality effects on fracture energy (2025), alteration impacts on geothermal reservoirs (2025), permeability changes in volcanic rocks (2024), and fundamental studies of water weakening mechanisms in sedimentary rocks (2024-2021). The work combines laboratory experiments, microstructural analysis, and numerical modeling. Labs & Collaborations : Coordinates the LEMR laboratory at EPFL. Collaborates with European Synchrotron Radiation Facility, Freie Universität Berlin, and industry partners like Emch&Berger AG. Leads PhD committee work in the EDME Doctoral School .
Dr. Birgit Frauscher is the Lincoln Financial Group Distinguished Professor in Neurobiology at Duke University School of Medicine, where she serves as Professor of Neurology and holds a secondary appointment in the Department of Biomedical Engineering at the Duke Pratt School of Engineering. She is currently the Director of the Duke Comprehensive Epilepsy Center and leads the Analytical Neurophysiology (ANPHY) Lab. Her clinical and research work focuses on epilepsy and sleep medicine, utilizing both invasive and non-invasive electrical recordings to study brain activity in humans. Dr. Frauscher completed her medical training, neurology residency, and subspecialty training in electroencephalography, epilepsy, and sleep medicine at the Medical University of Innsbruck in Austria. After completing her clinical training in 2008, she earned her habilitation degree in 2011. She further specialized in intracranial EEG and signal analysis during a visiting professorship at the Montreal Neurological Institute and Hospital, McGill University (2013-2015), where she later served as an Attending Epileptologist and Group Leader of Epilepsy. Her research interests focus on developing novel seizure-independent EEG markers for the epileptogenic zone, investigating sleep-epilepsy interactions, and using intracranial EEG to study brain physiology during wakefulness and sleep. Her work aims to improve epilepsy diagnosis, prognosis, and treatment outcomes by better localizing the epileptic focus. Dr. Frauscher's recent publications demonstrate her continued leadership in epilepsy research, with over 170 peer-reviewed papers and an H-index of 62. Dr. Frauscher has received several prestigious awards including the Clinician-Scientist awards of the FRSQ (2018-2023), the Michael Prize of the International League against Epilepsy (2019), and the Ernst Niedermeyer Prize from the Austrian Epilepsy Society (2015). Her scholarly work has significantly advanced clinical knowledge in epilepsy and sleep medicine, establishing her as a leading figure in the field. As Director of the Duke Comprehensive Epilepsy Center and head of the ANPHY lab, Dr. Frauscher oversees a research program dedicated to advancing neuroscience through innovative approaches to studying brain activity. Her lab employs quantifiable tools to investigate neurophysiological and pathological processes related to epilepsy and sleep, with the ultimate goal of improving patient outcomes through better understanding of brain function.
Jonathan Vance is a Lecturer in the School of Computing at the University of Georgia. He holds a Ph.D. and B.S. in Computer Science from the University of Georgia (2023 and 2009, respectively). His research focuses on applying artificial intelligence techniques to precision agriculture, particularly machine learning for biomass yield prediction and audio processing. He explores machine learning applications in agriculture, climate science, and image/audio processing. His educational background includes a strong foundation in computer science from UGA. His work emphasizes interdisciplinary approaches combining machine learning with agricultural challenges. Recent publications highlight advancements in data synthesis, domain adaptation, and feature selection for alfalfa biomass prediction. These studies contribute to sustainable agriculture through AI-driven solutions. No scientific awards or grants are explicitly listed in the provided information. He advises no listed students and maintains a professional website at jonathanvance.online .
Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.
Jeffrey Schall is a Full Professor of Biology and Program Director of the Visual Neurophysiology Centre at York University. He holds the Canada Research Chair in Translating Neuroscience. His research focuses on neural mechanisms underlying behavior, integrating neurophysiological and computational approaches across multiple scales. Schall is a core member of the Centre for Vision Research and the Canada First Research Excellence Fund Connected Minds initiative. Education: PhD in Anatomy (University of Utah School of Medicine, 1986), postdoctoral training at MIT. Awards include the Troland Research Award, Sloan Foundation Fellowship, and AAAS Fellowship. He served as Vision Science Society President in 2019. Research interests include visual attention, executive control, error monitoring, and translational neuroscience applications in law. His work bridges basic science with applied studies in clinical populations like schizophrenia patients. Collaborative projects involve EEG/MEG analysis, cortical microcircuitry modeling, and neuromodulation techniques. Teaching: YU_NRSC 2100 Systems, Behavioral, and Cognitive Neuroscience. Active in interdisciplinary initiatives linking neuroscience with legal systems through scholarship and policy engagement.
