Aparna Suvrathan is an Assistant Professor at the Department of Neurology and Neurosurgery within the Faculty of Medicine and Health Sciences at McGill University . She holds a secondary appointment as a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) , specifically at the Montreal General Hospital site. Her research program is integrated into the broader Brain Repair and Integrative Neuroscience Program at the Center for Translational Biology. Her research focuses on synaptic plasticity mechanisms in the cerebellum, particularly how neural circuits encode learning through Electrophysiological recordings Mouse genetic models Behavioral analysis Microscopic imaging Her work bridges motor learning research with neurodevelopmental disorder studies, especially autism spectrum disorders . Recent publications highlight her contributions to understanding Cerebellar synaptic timing heterogeneity Experience-dependent plasticity rules Amygdala microstructural changes Translational regulation in neural learning Her laboratory investigates both healthy brain function and disease states , with particular emphasis on Fragile X syndrome models Stress-induced neural modifications Cerebellar circuit mechanisms Synaptic receptor trafficking at the Research Institute of the McGill University Health Centre in Montreal, Canada. Current work continues to explore the relationship between synaptic plasticity rules and behavioral outcomes through advanced mouse genetic approaches.
Bahar Shahsavarani is a Full-Time Assistant Professor in the Department of Audiology at San Jose State University , affiliated with the College of Health and Human Sciences . She holds a Ph.D. in Communication Disorders from the University of Nebraska-Lincoln and completed postdoctoral training at Columbia University, NIMH, and the University of Illinois at Urbana-Champaign. Education: Ph.D. in Communication Disorders, University of Nebraska-Lincoln Postdoctoral Training: Columbia University National Institute of Mental Health (NIMH) University of Illinois at Urbana-Champaign Research Focus Her research explores how hearing disorders (tinnitus, hearing loss, vertigo) alter brain structure and cognitive function. Key themes include: Neuroimaging of auditory and cognitive networks Functional connectivity dynamics in tinnitus Neuroplasticity in hearing loss Computational methods for analyzing brain data Impact of auditory interventions on brain states Publication Trends Recent work emphasizes cyclicity analysis for mapping cortical dynamics, multi-modal neuroimaging of tinnitus, and cross-species comparisons of auditory processing. Studies frequently link structural/functional brain changes to behavioral outcomes like speech-in-noise performance.
Hobart H. Cleveland, III is a Professor in the Department of Human Development and Family Studies at Pennsylvania State University's College of Health and Human Development. He serves as a researcher within the Social Science Research Institute (SSRI), focusing on adolescent substance use, behavioral genetics, and recovery identity dynamics. Current research emphasizes recovery-focused social contexts Specializes in genetic influences on addiction vulnerability Active in residential treatment evaluation Recent publications examine the interplay between chronic pain and opioid cravings, recovery community center efficacy, and temporal patterns in addiction relapse. His work contributes to UN Sustainable Development Goals related to health and well-being.
Somali Ghosh is an Associate Professor at the Weatherhead School of Management, Case Western Reserve University , where she teaches Marketing Management, Strategic Internet Marketing, and Brand Management . Prior academic appointments include The University of Michigan, SUNY Binghamton, and University of Nebraska-Lincoln . Education: PhD in Marketing from State University of New York Binghamton (2011) Master of Science in University of Nebraska Lincoln (2005) Master of Science in University of Calcutta (1995) Her research focuses on the intersection of economics and marketing management , with particular emphasis on marketing research methodology, consumer behavior in cultural contexts , and applying sensory marketing to digital platforms . Recent work explores website optimization through behavioral insights and price perception psychology . From 2021-2024, she served as Director of Marketing Programs (Undergraduate) Faculty Advisor to CWRU American Marketing Association Chapter Committee Member on Curriculum Development Her scientific awards include the Golden Apple Award (2013) and multiple Weatherhead School of Management teaching honors (2019-2021).
PD Dr. Michael Josef Burtscher serves as Co-Leader of the Center for Organizational Behavior at Zurich University of Applied Sciences (ZHAW) within the Department of Applied Psychology. His academic roles include coordinating the doctoral program in "Applied Psychological and Communication Sciences" and teaching methodology, diagnostics, and team psychology across degree and continuing education programs. His educational background features a Psychology Diploma from University of Trier (2006), PhD in Work and Organizational Psychology from ETH Zurich (2010), and Habilitation (Venia Legendi) from University of Zurich (2019). Additional qualifications include CAS Higher Education Didactics (2018) and upcoming CAS Leadership & Governance in Higher Education (2025). Burtscher's research centers on team dynamics in high-stakes environments, examining leadership effects on team innovation, occupational stress in surgical settings, and voice behavior in healthcare. His work bridges psychological theory with practical applications through digital diagnostics and coaching tools, particularly focusing on resource-oriented interventions for stress reduction. Publication trends (2020-2025) reveal concentrated investigation into healthcare team performance (especially robot-assisted surgery), systematic analysis of coaching behaviors, and incentive structures in scientific collaboration. His work consistently applies observational and multi-modal assessment methods to real-world organizational challenges. No scientific awards were documented in the source material. As coordinator of ZHAW's doctoral program, Burtscher supervises PhD candidates in applied psychological sciences. His externally funded projects include the ongoing "Participatory Design of Resource-Oriented Self-Help Application" and "Multi-Modal Online Assessment for Co-Leadership Development," alongside completed initiatives like the Zurich Team Diagnostics Inventory and digital HR tools for SMEs. Burtscher operates within ZHAW's Center for Organizational Behavior, collaborating with healthcare institutions on surgical team research and industry partners on digital assessment solutions. His network includes INGRoup, Swiss Psychological Society, and editorial roles at Swiss Psychology Open.
Dr. Yang Liu is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science. He also serves as the Associate Director of the Health Informatics Center. His academic career spans prestigious institutions including Yale University and Carnegie Mellon University, demonstrating his expertise in both theoretical and applied aspects of computer science. Dr. Liu received his B.Eng. and M.Eng. degrees in Automation from National University of Defense Technology in 2004 and 2007, respectively. He earned his Ph.D. in Computing from The Hong Kong Polytechnic University in 2011. His academic journey included a Visiting Scholar position at Carnegie Mellon University's Robotics Institute (Feb.-Aug. 2010) and a Postdoctoral Research Associate position in the Department of Statistics at Yale University (2011-2012). Dr. Liu's research spans the intersection of artificial intelligence, machine learning, and practical applications in health and complex systems. His work focuses on artificial intelligence , machine learning , pattern recognition , dimensionality reduction , and subspace learning , with particular emphasis on multi-way/multi-view/multi-label/multi-task learning approaches. His research extends to modeling complex dynamical systems with applications in computational epidemiology and infectious disease modeling , addressing critical public health challenges through data-driven approaches. Analysis of Dr. Liu's recent publications reveals a strong focus on applying machine learning techniques to epidemiological challenges, particularly in modeling infectious disease transmission patterns. His work bridges theoretical advances in graph neural networks, subspace learning, and multi-view analysis with practical applications in public health. A significant portion of his research addresses the challenges of high-dimensional and heterogeneous data analytics, with applications ranging from malaria transmission modeling in Cambodia to uncovering COVID-19 transmission patterns in Hong Kong. Dr. Liu is an IEEE Senior Member and ACM Member, reflecting recognition of his contributions to the field. His paper "What are the underlying transmission patterns of COVID-19 outbreak? – An age-specific social contact characterization" was recognized as one of the most cited articles in EClinicalMedicine, Lancet Discovery Science, during 2020-2021, highlighting the impact of his work on pandemic response research. Dr. Liu actively mentors research students and regularly has research student and RA positions available. His professional service includes serving on editorial boards for SPJ Health Data Science and as a journal guest editor for special issues on cross-media analysis. He has extensive experience as a journal reviewer for top publications including IEEE TNNLS/TNN, IEEE T-Cyber/TSMC-B, IEEE TAC, IEEE TKDE, IEEE TMM, IEEE TCSVT, ACM TIST, ACM TOMM, and others, and serves on program committees for major conferences including WWW, IJCAI, and AAAI. Dr. Liu is affiliated with the Centre for Health Informatics and the Artificial Intelligence and Machine Learning Laboratory (AIML) at Hong Kong Baptist University. These research centers provide the infrastructure and collaborative environment necessary for his work in health informatics and machine learning applications. His role as Associate Director of the Health Informatics Center positions him at the forefront of interdisciplinary research connecting computing with public health challenges.
Mehmed Özkan serves as a Professor at the Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey, where he leads research at the intersection of engineering and clinical medicine with a focus on surgical robotics and biomedical instrumentation. His academic foundation includes: Bachelor of Science in Electrical Engineering from Bogazici University, Istanbul (1986) Master of Science and Ph.D. in Electrical Engineering from Vanderbilt University, Nashville, TN (1988-1991) Professor Özkan's research program centers on robotics and mechatronics for surgical applications, particularly stereotactic neurosurgery and biomechanical simulation in orthopedics. His work extends to medical image segmentation using neural networks, functional electric stimulation systems, and implantable medical devices . This multidisciplinary approach bridges computational engineering with clinical neurosurgery and rehabilitation medicine to develop innovative surgical planning tools and therapeutic devices. Analysis of his publication record (1993-2008) reveals consistent innovation in applying artificial intelligence to biomedical challenges. His research spans neurosurgical robotics , biomechanical modeling for orthopedic applications, medical imaging analysis, and bioelectromagnetics , with notable contributions to carpal tunnel syndrome treatment through neuro-orthosis development and knee biomechanics via 3D modeling. The integration of neural networks across diverse medical contexts represents a unifying thread in his scholarly output. He has secured significant research funding including: Virtual Orthopedic Surgery project (TUBITAK, 2005-2007) for preoperative simulation in cerebral palsy Biological Effects of Electromagnetic Fields study (BAP, 2001-2002) Target Detection in Parkinson's Imaging (BAP, 1999-2001) Computer Aided Surgical Planning (BAP, 1998-1999) At the Robotics Laboratory, Professor Özkan directs development of surgical robotics systems and neuro-orthotic devices, with current emphasis on personalized surgical planning tools and rehabilitation engineering solutions for neurological disorders.
Kadir GÖKGÖZ is an Associate Professor in the Department of Linguistics at Boğaziçi University, specializing in sign language linguistics with primary focus on Turkish Sign Language (TİD) and cross-modal typological studies. His research bridges theoretical syntax, phonology, and language acquisition within deaf communities. Education: PhD in Linguistics, Purdue University (2013) MA in Linguistics, Boğaziçi University (2009) BA in Linguistics (Minor), Boğaziçi University (2006) BA in Foreign Language Education, Boğaziçi University (2006) Research Focus: Professor Gökgöz investigates age effects in language acquisition, bimodal bilingualism, classifier systems, and syntax-phonology interfaces in sign languages. His work emphasizes how modality (visual-gestural vs. spoken) shapes linguistic structure, particularly examining negation, verb morphology, and discourse strategies in TİD. Recent studies demonstrate how age of acquisition impacts morphosyntactic processing and verbal fluency in deaf signers. Publication Trends: His 15 most recent articles (2020-2024) reveal consistent focus on age-of-acquisition effects across morphosyntax, discourse, and phonology domains. Key patterns include: (1) systematic investigation of classifier predicates and verb agreement; (2) cross-modal comparisons of syntactic phenomena; (3) computational approaches to sign language annotation; and (4) experimental analyses of reference tracking and telicity marking. Scientific Recognition: Winner of Best Article Contributing to Linguistic Diversity (2022) Impact Award for Established Investigators (2022) Research Leadership: As Principal Investigator for Boğaziçi University Research Fund #14458 (2018-2020), he developed linguistics-supported preschool story interventions for deaf children with hearing parents. He served as Turkish contact for the €5M Horizon 2020 Sign-Hub project (2016-2020), preserving European sign language heritage. His lab work includes directing the Sign Language Linguistics Lab at Boğaziçi University and previously managing the Sign Language Acquisition Lab at University of Connecticut. Academic Service: Teaches undergraduate courses including Sign Language Linguistics (LING 414) and graduate seminars on Sign Language Morpho-Syntax (LING 582), mentoring numerous students in TİD research.
Dr. Alex A. Nagelschneider is a radiologist specializing in neuroradiology at Mayo Clinic in Rochester, Minnesota. He is affiliated with the Mayo Clinic Comprehensive Cancer Center and the Oral Cavity Cancer and Reconstruction Clinic, with clinical expertise in head and neck imaging and brain MRI procedures. Dr. Nagelschneider's educational background includes: MD from University of Oklahoma College of Medicine (2011) Internship in Internal Medicine at University of Oklahoma, Tulsa (2012) Residency in Diagnostic Radiology at Mayo Clinic College of Medicine (2016) Fellowship in Neuroradiology at Mayo Clinic College of Medicine (2017) His research primarily focuses on head and neck imaging, particularly using PET/MRI techniques for cancer diagnosis and treatment monitoring. Dr. Nagelschneider has made significant contributions to the field of photon-counting detector CT applications in pituitary and neurovascular imaging. His work bridges clinical practice with technological innovation to improve diagnostic accuracy in complex cancer cases and neurological disorders. Analysis of Dr. Nagelschneider's publication record reveals a strong emphasis on head and neck cancer imaging, advanced CT techniques, and radiation therapy complications. His research increasingly focuses on photon-counting CT technology and its clinical applications, with numerous 2024-2025 publications demonstrating leadership in this emerging field. Dr. Nagelschneider has received numerous scientific awards throughout his career: 2021 RSNA Trainee Research Prize recipient from the Radiological Society of North America 2014 Distinguished poster presentation award at the Mayo Clinic Young Investigator Research Symposium 2012 Intern of the Year Award from the University of Oklahoma Department of Internal Medicine 2012 Crimson Apple Teaching Award from the University of Oklahoma As an active educator, Dr. Nagelschneider serves on multiple education and clinical committees at Mayo Clinic, including the Neuroradiology Education Committee since 2018. His research activity is reflected in his extensive collaborative publications, particularly in head and neck cancer imaging and advanced CT technologies, indicating likely support from various research initiatives. Dr. Nagelschneider is actively involved in several clinical and research teams at Mayo Clinic, including the Neuroradiology CT Committee and the Head and Neck Clinical Council. His collaborative approach is evident in his numerous multi-institutional publications addressing challenges in head and neck cancer imaging and advanced neuroimaging techniques.
Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.
Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.
Prof. Dr. Christoph van Thriel serves as Head of the Neurotoxicology and Chemosensory Research Group at the Leibniz Research Centre for Working Environment and Human Factors (IfADo), affiliated with TU Dortmund University. His research program investigates the complex interactions between chemical exposures and neurological function, with particular focus on sensory processing, neurocognitive effects, and individual differences in toxic responses. Dr. van Thriel completed his doctorate in Natural Sciences from Ruhr University Bochum in 1999. His academic trajectory includes progression from research associate in the 'Toluene Project' (1996-1999), to post-doctoral work (1999-2003), leadership of a junior research group 'Neurological Behavioral Toxicology' (2003-2011), and ultimately heading the Neurotoxicology and Chemosensory Research Group since 2011. He completed his habilitation in 2011, solidifying his academic credentials. His research spans physiological and psychological mechanisms of chemical-nervous system interactions, neurocognitive functions, chemosensory perceptions, sensory neurotoxicology, and interindividual differences in toxic responses. He employs both in vitro and human subject approaches to understand how environmental chemicals affect neurological function, with particular attention to real-world exposure scenarios. Analysis of his recent publications (2023-2025) reveals a strong interdisciplinary focus bridging toxicology, neuroscience, and environmental health. His work examines how chemical exposures affect cognitive function, olfactory processing, and neural responses across multiple domains including occupational settings, environmental exposures, and complex mixtures. Key themes include multisensory integration in odor perception, effects of metals exposure on sensory function, neurocognitive impacts of environmental chemicals, and methodological advances in toxicological assessment. His research group maintains active collaborations with international institutions and contributes significantly to both fundamental understanding and applied aspects of neurotoxicology. Current projects investigate topics ranging from odor-visual interactions to the neurological effects of dietary patterns and environmental contaminants, demonstrating the breadth and relevance of his scientific program.
Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.
Jesper Løve Hinrich serves as a Guest Researcher in the Design and Consumer Behavior section at the University of Copenhagen's Department of Food Science, focusing on advanced computational methodologies for complex data analysis. His research spans interdisciplinary domains including: Machine Learning for predictive modeling Tensor Decomposition techniques for multiway data Chemometrics applications in spectroscopy Health Informatics for clinical decision support Signal Processing of time-resolved measurements Data Science for multi-modal integration Analysis of his 2019-2024 publications reveals a cohesive research trajectory centered on probabilistic tensor frameworks. His work progresses from foundational tensor train decomposition (2019) to sophisticated coupled decompositions handling data drift (2023) and higher-order arrays (2024), while bridging theoretical advances with practical implementations in fluorescence spectroscopy and obstetric care analytics. This demonstrates consistent innovation in mathematical frameworks for real-world data challenges across scientific domains.
Dr. Cui Yu serves as an Associate Professor in the Department of Computer Science and Software Engineering at Monmouth University, where she maintains an active research program focused on database systems and information retrieval. With over two decades of scholarly contributions, her work bridges theoretical foundations and practical applications in data management. Her academic foundation includes a Ph.D. from the National University of Singapore, establishing her expertise in advanced database methodologies. This background enabled her pioneering work in high-dimensional indexing techniques that address the 'curse of dimensionality' in modern data systems. Dr. Yu's research program centers on innovative solutions for database indexing, query optimization, and information retrieval. She developed the influential iDistance method and FB+-tree indexing structures that transform high-dimensional data into manageable single-dimensional spaces. Her recent work demonstrates significant evolution from core database theory toward applied domains including privacy-preserving social networks, smart city infrastructure (notably CNN-based parking systems), and mobile applications for skill development. This trajectory reflects her ability to adapt database principles to emerging technological challenges while maintaining rigorous theoretical grounding. Analysis of her publication history reveals consistent leadership in database indexing innovation, with foundational work in the early 2000s maturing into diverse applications by the late 2010s. Her research maintains strong connections between theoretical indexing advances and real-world implementations across domains including multimedia retrieval, geospatial systems, and security applications. Her scientific recognition includes two significant U.S. patents: Apparatus and method for performing transformation-based indexing of high-dimensional data (US patent: 7117217, 2006) Transformation-based method for indexing high-dimensional data for nearest neighbor queries (US patent: 6,834,278, 2004) As an educator, Dr. Yu actively mentors students through research projects and application development initiatives. She teaches core curriculum courses including Computer Architecture, Database Systems at multiple levels (undergraduate CS 432, graduate CS 533), and Senior Capstone Projects. Her teaching load demonstrates substantial commitment to both foundational computer science education and advanced specialized topics, with consistent course offerings through 2025. She maintains active professional engagement through IEEE membership and continuous scholarly output.
