Lorenza Brusini is a Temporary Assistant Professor at the Department of Engineering for Innovation Medicine, University of Verona, specializing in Bioengineering (IBIO-01/A). Her work integrates Machine Learning , Neuroimaging , and Computer Vision to develop numerical biomarkers for neurological conditions. Research Focus: Multi-modal neuroimaging (diffusion MRI, EEG, fNIRS), computational neuroscience, and AI-driven medical data analysis. Teaching: Offers courses in Biomedical Image Analysis, Neurohealth Data Modeling, and Intelligent Healthcare Systems for both bachelor's and master's programs. Labs: Active in INTELLIGO Labs and ALTAIR Laboratory, with expertise in Electronic Systems Design and Parallel Computing. Her recent publications emphasize explainable AI in neuroimaging, systems neuroscience , and medical signal processing . She serves on academic committees including the Information Engineering Teaching Committee and Department Council.
Dane Taylor is an Assistant Professor at the University of Wyoming's School of Computing and Department of Mathematics and Statistics, with an adjunct appointment in Electrical Engineering and Computer Science. He joined UW in 2023 after serving as assistant professor at University at Buffalo (2017-2023). His research focuses on network science, complex systems, and data science methodologies. Education includes: Ph.D. Applied Mathematics, University of Colorado Boulder (2013) B.S. Electrical Engineering (bioengineering), University of Wyoming (2008) B.S. Physics, University of Wyoming (2008) Research interests span: network-based modeling, spectral theory, random matrix theory, neurocomputation, and self-organization in complex systems. His work develops mathematical frameworks for temporal/multilayer networks and simplicial complexes. Publication trends show consistent focus on network dynamics, topological data analysis, and applications in neuroscience, epidemiology, and social systems. Recent work explores machine learning integration with network theory and pandemic-related human mobility patterns. Awards and research support: NSF Algorithms for Threat Detection (ATD) grant NSF Mathematical Biology grant Nakatani Foundation (2020-2021) Simons Foundation (2018-2024) Postdoctoral Award for Research Excellence, UNC (2015) Teaching Award, UNC/HHMI (2014) ARCS Foundation Fellowship (2009-2012) NASA Space Grant Fellowship (2006) Organizational leadership includes: 2022 Northeast Regional Conference on Complex Systems, 2020 SIAM Workshop on Network Science, and multiple minisymposia for major conferences. Outside academia, enjoys hiking, snowboarding, and mountain biking.
Richard Flavel is Associate Professor of Crop Science at the University of New England's School of Environmental and Rural Science. He specializes in crop agronomy and plant nutrition, with expertise in root system architecture, soil-plant interactions, and advanced imaging techniques like X-ray computed tomography and X-ray fluorescence. His research investigates the physico-chemical interactions at the root-soil interface and their impact on crop productivity. Flavel holds a BRurSc (Hons) and PhD from UNE. His recent publications demonstrate a focus on optimizing nutrient management strategies, particularly phosphorus fertilization, and understanding plant-pathogen interactions in cereal and legume crops. Research methods frequently employ advanced imaging technologies to visualize root responses to environmental factors. His work extends to agricultural education, developing innovative plant identification tools to enhance student learning outcomes. Field applications include tropical pasture systems, wheat pathology, and soil amelioration strategies.
Dr. Amanda Boyes is a Postdoctoral Research Fellow (Youth Mental Health) and Team Leader (Research) at the Thompson Institute, University of the Sunshine Coast. She leads the 'Youth Neurobiology and Wellbeing' stream within the Longitudinal Adolescent Brain Study (LABS), investigating associations between brain structure, wellbeing, and mental health in adolescents. Her work focuses on neurobiological markers of resilience, particularly subcortical grey matter volume and cortical thickness. Education: Holds a PhD in Neuroscience (2020–2023, USC), two First Class Honours degrees—a Bachelor of Social Science (Psychology) and Bachelor of Criminology/Criminal Justice (Griffith University). She also completed a Bachelor of Psychological Science. Research Interests : Adolescent brain development and mental health Neuroimaging (MRI/EEG) and psychological distress Resilience mechanisms in early adolescence Cyberbullying effects on brain structure Social connectedness and health behaviors Awards : 2023 UniSC SPARK Grant for Research Development 2021 USC 3MT Finalist & People's Choice Award 2020 Australian Government RTP Scholarship 2015 USC Excellence in Service Award Grants & Projects : Leading LABS cohort studies Developing the BRC-PicS research tool Bayesian analysis of cyberbullying neurophysiology Professional memberships: Society for Mental Health Research, Flux Society (Trainee Committee), Biological Psychiatry Australia.
Taliah Prince is a Postdoctoral Research Fellow at the University of the Sunshine Coast's Thompson Institute, specializing in youth mental health and adolescent brain development. Her work focuses on the intersection of cyberbullying, body image, and neurological development in adolescent females. She contributes to teaching within the Graduate Certificate and Graduate Diploma programs in Mental Health and Neuroscience, and serves as a student experience mentor. Dr. Prince's research interests center on understanding how appearance-related cyberbullying impacts adolescent mental health, with particular emphasis on body image disturbances, eating disorders, and associated neural mechanisms. Her work examines how exposure to body shaming online affects brain function, emotional regulation, and social cognition in teenage girls. She investigates the pathways through which cyberbullying leads to increased body dissatisfaction, desire for cosmetic procedures, and disordered eating behaviors. Her publication record reveals a strong focus on the neurobiological underpinnings of body image-related cyberbullying, with multiple 2025 publications utilizing advanced neuroimaging techniques to document how adolescent brains respond to online body shaming. Her research demonstrates how cyberbullying activates brain regions associated with emotional pain and social threat, even when girls are merely witnessing body shaming directed at others. Australian Government Research Training Program (RTP) - University of the Sunshine Coast, Full scholarship 3 Minute Thesis Winner (UniSC School of Health), Second place for UniSC competition Student of the Year Runner-up - local electorate 'Fisher Community Awards' (voted by local community members) As a key member of the Thompson Institute's Longitudinal Adolescent Brain Study, Dr. Prince contributes to this world-leading project examining brain development and mental health during adolescence. Her PhD research investigated the psychological and neurobiological impacts of appearance-related cyberbullying on adolescent girls, with a focus on body image, social stress, and brain development. Her work has received significant media attention, including features on how body image bullying affects teenage girls' brains and how cyberbullying pushes girls toward cosmetic procedures. Dr. Prince works within the Thompson Institute research environment, collaborating with multidisciplinary teams including neuroscientists, psychologists, and clinicians to advance understanding of adolescent mental health. Her research integrates neuroimaging, psychological assessment, and longitudinal tracking to develop comprehensive models of how online experiences shape adolescent brain development and mental wellbeing.
Dr. Carlo Condello is an Associate Professor in the Department of Neurology at the University of California San Francisco (UCSF), affiliated with the Institute for Neurodegenerative Diseases (IND) and the UCSF Weill Institute for Neurosciences. He holds a PhD in Neuroscience from Northwestern University and completed postdoctoral training at Yale University and UCSF under Dr. Stanley Prusiner. His research focuses on neurodegenerative diseases, particularly Alzheimer’s and prion-related disorders, emphasizing microglial biology, protein misfolding, and advanced optical imaging techniques. Dr. Condello leads a lab developing novel mouse models and human cell assays to study neurodegeneration mechanisms and therapeutic interventions. Education: PhD in Neuroscience (Northwestern, 2011), Postdoc (Yale Neurology, 2012), Postdoc (UCSF IND, 2015) Affiliations: IND, UCSF Weill Institute for Neurosciences Grants: NIH R01AG082141, RF1AG061874, and others totaling over $10M in funding Research Interests: Dr. Condello investigates molecular pathways in microglia governing protein aggregation and neurodegeneration, with particular emphasis on how genetic risk factors influence amyloid conformational heterogeneity. His lab employs interdisciplinary approaches including advanced optical imaging, CRISPR screens, and chemical biology to study tauopathies, Aβ prions, and neuroinflammation. Recent breakthroughs include identifying sex-specific microglial responses to tauopathy and discovering shared prion mechanisms in Alzheimer’s/Parkinson’s. Recent Article Trends: His work bridges basic science and translational research, focusing on prion-like propagation of amyloids, strain-specific therapeutics, and microglial modulation. Recent studies highlight sex differences in tauopathy treatment responses and the role of interferon signaling in cortical development. Major Awards: John Watson Faculty Scholar (2021), Thome Alzheimer’s Award (2022) Labs/Teams: Condello Lab (IND), collaborations with UCSF IND drug discovery teams
Dr. Mehdi Maadooliat is an Associate Professor in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on statistical machine learning, bioinformatics, functional data analysis, and skewed distribution modeling. He holds a Ph.D. in Statistics from Texas A&M University (2011) and has held postdoctoral positions at Texas A&M and KAUST. He leads the Machine Learning and Bioinformatics Lab (MLAB) and advises multiple Ph.D. students. His work has led to software packages like Rfssa and GWID, and he has been recognized with the Way Klingler Young Scholar Award (2016). Education: Ph.D., Statistics, Texas A&M University, 2011 M.Sc., Mathematics & Statistics, Marquette University, 2006 B.Sc., Applied Mathematics, Sharif University of Technology, 2003 Research Interests: Dr. Maadooliat specializes in developing statistical methods for bioinformatics, functional data analysis, and spectral density estimation. His work bridges theory and application, with contributions to protein structure modeling, genome-wide analysis, and medical data science. Recent efforts include functional singular spectrum analysis and regularized multivariate functional PCA. Grants & Awards: National Institute of Health (NIH) R01 grants (2020–2027) for fatigability studies Marshfield Clinic Research Institute grant (2018–2020) for genetic analysis NVIDIA GPU Grant (2017) Teaching & Service: Teaches advanced courses in statistical machine learning and computational probability. Served as Graduate Chair and co-director of Bioinformatics programs at Marquette. Editor for Journal of Statistical Theory and Applications and Journal of the Iranian Statistical Society . Labs & Collaborations: His MLAB develops statistical tools for bioinformatics and functional data analysis. Collaborates with biomedical researchers on projects like aortic coarctation modeling and protein loop prediction.
Amanda Rysling is an Associate Professor in the Department of Linguistics at the University of California, Santa Cruz (UCSC). She is affiliated with the Teaching & Learning Center as a 2024-2025 faculty fellow, focusing on improving teaching practices to address inequities faced by transfer students in linguistics coursework. Her research explores how language comprehension and representation are shaped by contextual factors, including prosody, syntax, and pragmatics. Her teaching spans phonology, quantitative statistics in linguistics, and the physics of phonetics, emphasizing interdisciplinary challenges. She advocates for curricular accessibility, particularly regarding knowledge transfer across sub-disciplines like syntax, semantics, and phonetics. Her research interests include prosodic processing, pragmatic representations in discourse, and the cognitive mechanisms underlying language comprehension. Recent work examines the role of prosodic accents in phoneme detection, stress repair in metrical structures, and appositive structures’ semantic effects. Articles highlight methodological innovations such as pupillometry and eye-tracking to study real-time language processing. While no scientific awards are listed, her contributions to pedagogical equity and linguistic inquiry are central to her profile. She actively engages in grant-funded initiatives through UCSC’s Teaching & Learning Center to refine inclusive teaching strategies.
Dr. Carlos Bonilla is a Professor of Crop and Soil Science at Oregon State University and serves as the Director of the Hermiston Agricultural Research and Extension Center (HAREC). He is affiliated with the College of Agricultural Sciences and the Department of Crop and Soil Science at Oregon State University. Dr. Bonilla earned his PhD in Soil Science from the University of Wisconsin-Madison in 2006, an MS in Engineering (Hydraulic and Environmental Engineering) from Pontificia Universidad Católica de Chile in 1996, and a BS in Agronomy from the same institution in 1993. His research focuses on soil physics and hydrology, soil health and carbon sequestration in natural and managed landscapes, irrigation improvement, soil aggregates, soil erosion, and mathematical models to assess the impact of management practices on nutrient fate and transport in soil and water. His work spans both theoretical modeling and practical field applications, with particular emphasis on wildfire-affected soils, soil hydraulic properties, and sustainable land management practices. Recent publications demonstrate a strong focus on soil physical properties, particularly how they're affected by environmental stressors like heating and wetting/drying cycles, with increasing incorporation of machine learning techniques for prediction and analysis. Associate Editor for the Soil Science Society of America Journal Member of the editorial Advisory Board in Geoderma Soil Science Society of America (2003-) American Society of Agronomy (2005-) Crop Science Society of America (2005-) Soil and Water Conservation Society (2012-) American Society of Agricultural and Biological Engineers (2017-) Dr. Bonilla has received numerous teaching awards throughout his career, particularly during his time at Pontificia Universidad Católica de Chile, where he was recognized multiple times as an inspiring professor and for teaching excellence. His research has been supported by various grants and fellowships, including the President of the Republic Graduate Scholarship from the Government of Chile and the Cochrane Fellowship from the USDA-ARS Fellowship Program. His laboratory and research team focus on soil physical properties measurement and modeling, with recent work emphasizing the impacts of wildfires on soil properties and the development of innovative methods for soil hydraulic characterization.
Jing Zhou is an Assistant Professor at the Department of Crop and Soil Science , Oregon State University, specializing in Precision Agriculture through technologies like remote sensing, machine learning, computer vision, and robotics. She is affiliated with the Crop Science Building in Corvallis, Oregon. 2023–Present: Assistant Professor, Department of Crop and Soil Science, Oregon State University 2021–2023: Postdoctoral Research Associate, Department of Biological Systems Engineering, University of Wisconsin-Madison 2021: Postdoctoral Researcher, Department of Plant Science and Technology, University of Missouri-Columbia Her research focuses on integrating UAV imagery with advanced machine learning algorithms to improve crop yield estimation, stress detection, and phenotyping. Key areas include alfalfa quality analysis , soybean lodging assessment , and maize stand counting . Recent publications highlight applications of Bayesian semi-supervised learning , deep learning , and image processing pipelines for precision agriculture. These studies often involve Sentinel-2 imagery , multi-sensor UAV systems , and genomic analysis of herbicide tolerance in soybeans. Scientific Awards: ITSC Paper award, American Society of Agricultural and Biological Engineers (ASABE), 2020 Biological Engineering Outstanding Ph.D. Student, University of Missouri-Columbia, 2020 Jing Zhou accepts graduate students in the Crop and Soil Science Department and has contributed to advancements in automated plant phenotyping platforms, flooding stress quantification, and UAV-based crop monitoring systems.
Dr. Douglas Angus is an Assistant Professor in the Faculty of Society & Design at Bond University, Australia. He holds a PhD in Psychology from the University of New South Wales and previously conducted research at The University of Sydney and UNSW. His academic background includes a Bachelor of Science, Postgraduate Diploma, and Master of Science in Psychology from The University of Auckland. Research Focus: Dr. Angus investigates neural mechanisms of emotion, motivation, and cognition using EEG and psychophysiology. His work targets risk factors for cognitive decline and emotional dysfunction, organized into three core areas: Neural noise/aperiodic activity in cognitive aging Temporal dynamics of reward processing Self-regulation in emotional contexts He also collaborates on projects in personality neuroscience, meta-science, and self-related cognition. Teaching Expertise: He teaches across psychology sub-disciplines including introductory psychology, social psychology, cross-cultural psychology, cognitive psychology, and biological psychology. Recent Research Trends: His 2023-2025 publications demonstrate a strong focus on EEG biomarkers, reward processing mechanisms, and cross-cultural mental health. Key methodological themes include systematic reviews, multi-lab replications, and neurophysiological investigations of emotional regulation. Professional Affiliations: Member of the Society for Psychophysiological Research and Society for Teaching Psychology. Currently accepting PhD students for projects examining aperiodic neural activity in emotion-cognition interactions.
A. Agung Julius is a Professor at the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute and serves as Associate Dean for Research and Graduate Studies in the School of Engineering. He is also a faculty member of the Rensselaer Center for Automation Technologies and Systems, the Center for Mobility with Vertical Lift (MOVE), and the Center for Lighting Enabled Systems and Applications (LESA). Dr. Julius received his Bachelors degree in Electrical Engineering from Institut Teknologi Bandung (ITB) in Indonesia (1998), and MSc and PhD in Applied Mathematics from the University of Twente in the Netherlands (2001 and 2005). Prior to joining Rensselaer, he was a Postdoctoral Researcher at the GRASP Lab at the University of Pennsylvania. His research lies at the intersection of systems and control theory, systems biology, and theoretical computer science. Key research areas include networks in molecular biology, microbiorobotics (using both bacteria and eukaryotic cells as microactuators), human-powered formal controller synthesis, and abstraction of complex hybrid systems. His work often bridges theoretical developments with practical applications in robotics and biological systems. His recent publications (2023-2025) demonstrate a strong focus on multi-agent systems, human-autonomy teaming, circadian rhythm control, time series analysis, and industrial robotics applications. These works reflect his interdisciplinary approach, combining control theory with applications in robotics, biology, and human factors engineering. National Science Foundation CAREER award (2010) School of Engineering Award for Research Excellence (2016) James M. Tien'66 Early Career Award for Faculty (2017) Best Application Paper Award at URAI 2013 Finalist for Best Paper Award at IROS 2013 Dr. Julius has advised numerous graduate students including Sina Afshari, Nick Cooper, Andy Winn, Yan Ou, and Yi Deng. His research has been supported by the National Science Foundation, Army Research Office, and Rensselaer Polytechnic Institute. He serves as Associate Editor for several prestigious journals including the IFAC Journal of Nonlinear Analysis: Hybrid Systems and IEEE Transactions on Automation Science and Engineering.
Bilal Akin is a Professor in the Electrical Engineering Department at the University of Texas at Dallas, working within the Erik Jonsson School of Engineering and Computer Science. With over 30 PhD students and postdocs mentored, his research group has successfully delivered more than 40 industry-sponsored projects that directly contributed to new products and features in the power electronics field. Dr. Akin's educational background includes a Ph.D. from Texas A&M University (2007), an MSc from Middle East Technical University (METU) in Ankara (2003), and a B.S. from METU (2000). Prior to joining UT Dallas in 2012, he worked as a Systems and Applications Engineer at Texas Instruments (2008-2012) and as an R&D Engineer at Toshiba International Corporation (2005-2008). His research focuses on power electronics, digital power control systems, electric motor drives, and fault diagnosis of industrial components. Specifically, his work spans power semiconductor reliability, condition monitoring of power electronics components, control of electric motors and drives, digital power control, and applications of machine learning to energy conversion systems. His research has significant applications in electric and hybrid vehicles, renewable energy systems, and industrial power conversion. Dr. Akin's extensive publication record demonstrates expertise across power electronics reliability, motor drive control, and fault diagnosis techniques. His recent work shows increasing integration of machine learning approaches with traditional power electronics and motor control techniques, particularly for condition monitoring and prognostics applications. The research spans from fundamental semiconductor device characterization to complete system-level implementations. NSF CAREER Award (2015) Multiple IEEE Prize Transaction Paper Awards (2018, 2020, 2023) IEEE PE Magazine cover story (2016) Engineering School Faculty Research Awards (2015, 2020) Excellence in Teaching Award (2019) Co-Editor in Chief of IEEE Transaction on Vehicular Technology IEEE VT Society Distinguished Lecturer (2016-2018) Dr. Akin has successfully advised numerous graduate students, with several receiving prestigious dissertation awards. His former PhD students have secured positions at leading companies including Texas Instruments, Onsemi, Nidec, and Wolfspeed/Cree. His research group collaborates extensively with industry partners on motor drives, power electronics systems, power semiconductors, and AI applications. Current research focuses on wide-bandgap semiconductors (SiC and GaN), advanced motor control techniques, and machine learning applications for condition monitoring. His research team consists of multiple PhD candidates working on various aspects of power electronics and motor drives, along with visiting scholars and research associates from around the world. The group maintains strong industry connections and actively seeks new collaborations for cutting-edge research in power electronics and energy conversion systems.
Kaijian Liu is an Assistant Professor in the Department of Civil, Environmental, and Ocean Engineering at Stevens Institute of Technology. He joined Stevens in October 2019 and holds a Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign. His research focuses on resilient infrastructure systems, machine learning, and cyber-physical systems for sustainability. He has been funded by NSF, USGS, NOAA, and the U.S. Army. Education: Ph.D. (2019), University of Illinois at Urbana-Champaign (Civil Engineering) M.S. (2014), University of Illinois at Urbana-Champaign (Civil Engineering) B.A. (2012), Tongji University (Construction Engineering and Management) Research interests include: Machine learning applications in civil infrastructure Ontology-driven data fusion for bridge analytics Human-behavior modeling for energy efficiency Cyber-physical systems for smart cities His work emphasizes integrating AI with physical systems to enhance infrastructure resilience. Professional contributions include roles as: Associate Editor, ASCE Journal of Computing in Civil Engineering Member of ASCE Computing Division committees (Data Sensing & Analysis, Visualization, Education) Awards include the 2023 ASCE Outstanding Reviewer Award and the 2017 CSE Fellowship. His teaching spans numerical modeling, machine learning in civil engineering, and construction informatics.
J.M. Shawn Hutchinson is a Professor at Kansas State University's Department of Geography and Geospatial Sciences and Director of the Geographic Information Systems Spatial Analysis Laboratory (GISSAL) . He co-leads the interdisciplinary Institute for Digital Agriculture and Advanced Analytics (ID3A) , focusing on geospatial innovation for environmental and agricultural challenges. Education : Not explicitly stated in provided text Research interests center on geospatial analytics, remote sensing, and environmental modeling. His work spans: Monitoring aircraft overflight impacts in national parks Developing automated GIS toolboxes for environmental tracking Analyzing soil moisture dynamics and climate impacts on the Great Plains Hydrologic modeling and ecosystem services assessment Disease ecology and socioeconomic risk factors Recent publications demonstrate a consistent focus on spatial-temporal analysis , remote sensing applications , and GIS tool development across environmental, agricultural, and public health domains. Overflight studies (2023–2025) dominate recent output, while older works (2019–2021) emphasize soil moisture modeling and fire emissions. Scientific awards : Geographic Information Systems Professional (GISP) certification Labs and initiatives : Leads GISSAL, an interdisciplinary lab established in 1991 for geospatial research, and co-directs ID3A, a strategic institute under K-State's Next-Gen plan. Maintains a GIScience Virtual Collaboratory and institutional ArcGIS portals.
