Dr. Elisenda Bueichekú serves as a Research Scientist at the Yale School of Medicine within the Radiology & Biomedical Imaging Department . Her work focuses on neuroimaging applications in neurodegenerative disorders, particularly Alzheimer’s disease progression and memory systems analysis. She collaborates with leading researchers in the Mental Health PET Radioligand Development (MHPRD) Program and the PET Core facility. Primary Affiliation: Yale School of Medicine Department: Radiology & Biomedical Imaging Research Programs: MHPRD Program, PET Core Her research integrates multimodal neuroimaging techniques to investigate: Tau pathology propagation patterns Connectome-based disease modeling Cortical hub analysis in memory systems Cognitive resilience mechanisms Neuroimaging of anosognosia Functional network contributions to creativity Dr. Bueichekú contributes to translational research through: Advanced PET/MRI methodologies Spatiotemporal disease progression mapping Development of imaging-based biomarkers Recent publications demonstrate expertise in: Alzheimer’s disease neuroimaging Tau and amyloid co-accumulation Functional connectivity analysis Memory system characterization Scientific contributions include: 2025 Center for Brain & Mind Health Pilot Grant Key publications in top-tier journals (Nature Aging, Alzheimer's & Dementia, etc.)
Nikolaos Koutsouleris serves as a Research Professor leading the Precision Psychiatry team at the Max Planck Institute of Psychiatry in Munich, Germany. He directs the KOUTSOULERIS LAB, which focuses on developing advanced machine learning methodologies for clinical psychiatry applications. His work bridges computational neuroscience, clinical psychology, and precision medicine approaches to transform psychiatric diagnosis and treatment. Dr. Koutsouleris's research program centers on precision psychiatry, with particular emphasis on multimodal data integration for predicting psychiatric outcomes. His laboratory develops sophisticated machine learning workflows that combine neuroimaging, clinical assessments, genetic information, and biomarker data to create personalized prediction models. This approach enables more accurate identification of individuals at risk for psychosis and other psychiatric disorders, facilitating earlier intervention and tailored treatment strategies. Analysis of Dr. Koutsouleris's publication record reveals a consistent trajectory toward increasingly sophisticated applications of artificial intelligence in psychiatry. His recent work demonstrates a shift from single-modality prediction to complex multimodal frameworks that capture the heterogeneous nature of psychiatric conditions. Key themes include addressing methodological challenges in clinical prediction generalizability, exploring neurobiological underpinnings of mental illness through advanced analytics, and translating computational findings into clinically actionable tools. As leader of the KOUTSOULERIS LAB at the Max Planck Institute of Psychiatry, Dr. Koutsouleris oversees a multidisciplinary research environment that brings together computational scientists, clinicians, neuroscientists, and data analysts. His team collaborates extensively with international consortia to validate prediction models across diverse populations, ensuring robustness and clinical applicability of their findings. The laboratory serves as a nexus for innovation in computational psychiatry, driving methodological advances while maintaining strong connections to clinical practice.
Peter A. Tass is a Professor of Neurosurgery at Stanford University's School of Medicine, where he leads the Tass Lab within the Department of Neurosurgery. His research focuses on developing groundbreaking neuromodulation techniques designed to impact the course of neurological diseases including Parkinson's disease, stroke, epilepsy, and tinnitus. The Tass Lab is part of several prestigious Stanford initiatives including Bio-X, the Wu Tsai Human Performance Alliance, the Maternal & Child Health Research Institute (MCHRI), and the Wu Tsai Neurosciences Institute. MD from Universities of Ulm and Heidelberg, Germany (1989) PhD in Physics from University of Stuttgart, Germany (1993) Diploma (master's degree) in Mathematics from University of Stuttgart, Germany (1993) Habilitation thesis in Physiology from RWTH Aachen University, Aachen, Germany (2001) Dr. Tass's primary research interests center around computational neuroscience approaches to understanding and treating neurological disorders. His lab pioneers neuromodulation techniques based on thorough computational modeling that employs dynamic self-organization, plasticity, and other neuromodulation principles to produce sustained therapeutic effects after stimulation. He specifically focuses on developing stimulation methods that cause sustained neural desynchronization by unlearning abnormal synaptic interactions. His work spans both invasive techniques like deep brain stimulation and non-invasive approaches such as vibrotactile and acoustic stimulation. Current projects involve developing novel therapies for Parkinson's disease, epilepsy, tinnitus, and other neurological conditions using comprehensive computational neuroscience methods derived from non-linear dynamics, statistical physics, and numerics. Analysis of Dr. Tass's recent publications reveals a strong focus on coordinated reset stimulation techniques, neural network modeling with plasticity mechanisms, and computational approaches to brain stimulation. His work consistently bridges theoretical computational neuroscience with clinical applications, particularly for Parkinson's disease treatment. A significant portion of his recent research examines how stimulation parameters, sequences, and timing affect long-lasting desynchronization effects in neural networks. His publications demonstrate an interdisciplinary approach combining physics, mathematics, neuroscience, and clinical medicine to develop novel therapeutic interventions. Member of the European Academy of Sciences and Arts (2012) Nicolaus August Otto Innovation Prize (2011) German Innovation Award in Medicine (2011) Rapid Response Innovation Awards from The Michael J. Fox Foundation (2009, 2010) Runner-up for the German future prize (2006) Erwin Schrödinger prize (2005) Fritz Winter prize (2000) Dr. Tass actively mentors a diverse team of researchers including staff scientists, postdoctoral fellows, clinician-scientists, and students. His lab currently includes researchers with backgrounds in physics, computational neuroscience, biomedical engineering, and clinical neurology. The lab is involved in multiple clinical trials, including studies on coordinated reset spinal cord stimulation and vibrotactile coordinated reset stimulation for Parkinson's disease. His research is supported by various funding sources including foundations focused on neurological disorders and innovation in medical technology. Dr. Tass collaborates extensively with both internal Stanford researchers and external collaborators worldwide. The Tass Lab at Stanford is a multidisciplinary research group comprising physicists, neuroscientists, engineers, and clinicians working together to develop novel neuromodulation therapies. The lab team includes staff scientists like Justus Kromer (theoretical physicist), postdocs like Daniel Ehrens and Kanishk Chauhan, clinician-scientists like Tina Munjal, and clinical research coordinators. The lab maintains active collaborations with Stanford colleagues across departments including Kwabena Boahen, Vivek P. Buch, and Jaimie Henderson, as well as external collaborators like Alexander Neiman and Kęstutis Pyragas. Current research directions include developing non-invasive vibrotactile treatments for Parkinson's disease, acoustic coordinated reset therapy for tinnitus, and responsive deep brain stimulation for conditions like loss-of-control eating.
Ju Lu serves as an Assistant Professor at Lehigh University with office location in Iacocca Hall (room 0111), contactable via phone (610.758-3687) and email (jul724@lehigh.edu). Her academic position reflects active engagement in neuroscience research and education within the university's life sciences framework. Education Background: Ph.D. in Neurobiology from Harvard University (2008) B.Eng. in Microelectronics from Tsinghua University (2002) Research Focus: Dr. Lu's work pioneers investigations into neural circuit dynamics and synaptic plasticity mechanisms using advanced optical imaging technologies. Her research spans: Cortical circuit reorganization during motor skill acquisition across species Stress-induced synaptic alterations mediated by microglia in prefrontal circuits Therapeutic applications of psychedelic compounds for neural circuit restoration Development of three-photon microscopy for deep-brain imaging Genetically-encoded neurotransmitter sensors for in vivo studies This multidisciplinary approach bridges molecular neuroscience, systems-level circuit analysis, and translational mental health applications. Publication Trends: Analysis of Dr. Lu's 15 most recent publications (2016-2023) reveals an evolving trajectory from foundational studies on dendritic spine plasticity toward translational neuroscience. Early work emphasized optical imaging methodology and basic plasticity mechanisms, while her 2021-2023 publications increasingly focus on stress-related circuit disruptions and psychedelic therapeutics. A consistent thread involves combining high-resolution in vivo imaging with behavioral models to establish causal links between neural circuit dynamics and cognitive functions. Honors and Awards: No scientific awards or fellowships were documented in the provided materials. Mentorship and Funding: While specific student mentees and grant funding details are not specified in the source text, her extensive collaborative publication record indicates active supervision of research personnel and successful acquisition of research support. Research Infrastructure: Her methodological expertise in advanced microscopy suggests utilization of specialized imaging facilities, though no dedicated laboratory or research team is explicitly identified in the available documentation.
David J. Moore, Ph.D., is a licensed clinical psychologist and Professor of Psychiatry at UC San Diego Health. He co-directs the SDSU/UCSD Joint Doctoral Program (JDP) in Clinical Psychology and leads research at the HIV Neurobehavioral Research Program (HNRP). His work focuses on neurocognitive impairments in individuals with HIV, comorbid mental illness, and substance use disorders, with a special emphasis on technological interventions for medication adherence. Doctorate: Ph.D. in Clinical Psychology (SDSU/UCSD JDP, neuropsychology specialization) Postdoctoral Fellowship: UCSD (serious mental illness) Clinical Internship: West Los Angeles VA Dr. Moore’s research addresses: Neurocognitive complications of HIV infection Intersection of HIV and aging Technological interventions for medication adherence Impact of comorbid psychiatric/substance use disorders on functioning His publications span topics including HIV neurocognitive profiles, syndemic factors in sexual risk behavior, and aging with HIV. Current studies integrate metabolomics, neuroimaging, and behavioral data through NIH-funded grants.
Gerald Quon is an Associate Professor in the Department of Molecular and Cellular Biology at the University of California, Davis. He is affiliated with the Genome Center and participates in multiple graduate programs, including Integrative Genetics and Genomics, Neuroscience, Computer Science, Biostatistics, and Biomedical Engineering. Education: PhD in Computer Science from the University of Toronto (2012) MSc in Biochemistry from the University of Toronto (2006) Research Interests: Dr. Quon applies computational approaches to genetics and genomics problems, focusing on the genetics of human disease , models of cell population dynamics , and neurogenomics . His lab builds neural network models to understand how genetic variation affects disease risk through molecular and cellular phenotypes, with applications to obesity, Alzheimer’s disease, psychiatric disorders, and Rett syndrome. Recent Research Trends: Recent publications highlight work in neuroplasticity , single-cell multimodal analysis , brain evolution , morphological variation modeling , and microbiome-based classification . His team combines sequencing and imaging technologies to model cellular interactions and gene expression dynamics. Scientific Awards: NIH New Innovator Award (2021) Grants & Collaborations: He received NSF funding (2019) for computational tools in single-cell analysis and collaborates across disciplines, including neuroscience, biomedical engineering, and computational biology. His lab develops software like scProjection , siVAE , and scAlign .
Professor Jun Huang is a faculty member in the School of Chemical and Biomolecular Engineering at the University of Sydney, where he holds the rank of Professor and is Director of the Laboratory for Catalysis Engineering. He is also a Domain Leader for Materials at the nanoscale at Sydney Nano Institute and a member of several interdisciplinary institutes, including the China Studies Centre and Sydney Institute of Agriculture. His research focuses on catalysis engineering, with an emphasis on developing sustainable processes for renewable fuels, pollutant treatment, and greenhouse gas mitigation. Huang has held prestigious awards such as the Australia Research Council Future Fellowship (2022) and the Sydney Accelerator Fellowship (2018). Education: Huang earned his PhD from the University of Stuttgart (2008) and completed postdoctoral research at Georgia Institute of Technology and ETH Zurich. He joined the University of Sydney in 2010 as a Lecturer, advancing to Senior Lecturer, Associate Professor, and Professor. Research Interests: Huang's work centers on catalyst design for green chemical processes, including biomass conversion to biofuels, wastewater treatment, and CO2 utilization. He emphasizes sustainable manufacturing and environmental impact reduction through innovative catalytic systems. Current Projects: These include catalytic transformation of hydrocarbons/CO2/biomass, nano-catalysts for renewable energy, and advanced NMR spectroscopy for catalysis analysis. Collaborative projects involve anti-cancer therapies and drug pharmacology studies. Awards: Over 15 awards, including the 2021 ACS Sustainable Chemistry & Engineering Lectureship and 2017 Vice-Chancellor’s Research Excellence Award. Teaching: Huang instructs courses such as CHNG2801 (Conservation Processes), CHNG3802 (Industrial Systems), and advanced chemical engineering topics. He supervises PhD/Master students in catalysis and sustainable engineering. Labs/Teams: Leads the Catalysis Engineering Lab and collaborates with Sydney Nano Institute on nanomaterials research.
Danilo Bzdok is an Associate Professor in the Department of Biomedical Engineering at McGill University’s Faculty of Medicine and a Canada CIFAR AI Chair at Mila – Quebec Artificial Intelligence Institute. He holds dual expertise in systems neuroscience and machine learning, with two doctoral degrees: one in neuroscience from Forschungszentrum Jülich (Germany) and another in computer science (machine learning statistics) from INRIA–Saclay and Neurospin (France). His research bridges computational neuroscience and AI, focusing on understanding human intelligence through neuroimaging and biomedical data. Education: PhDs in Neuroscience (Jülich) and Computer Science (INRIA/Neurospin). Postdoctoral training at Harvard Medical School. Current affiliations include McGill University and Mila. Research interests span computational biology, deep learning, LLMs, and their applications in neuroimaging, precision medicine, and neurodegenerative diseases. Over 150+ peer-reviewed publications, with recent work on LLMs in autism diagnostics, brain network modeling, and social neuroscience. Key Awards: Canada CIFAR AI Chair. Lab focuses on interdisciplinary projects like AI4Science, neuroimaging analysis, and AI ethics. Supervises a dynamic team of PhD/Master’s students and postdocs. Collaborations include clinical institutions and industry partners through Mila’s Applied Research programs.
Professor Thierry Langer is a Full Professor of Pharmaceutical Chemistry at the University of Vienna’s Faculty of Life Sciences (Department of Pharmaceutical Sciences). He leads research in computational drug design, with a focus on pharmacophore modeling, 3D-QSAR analysis, and AI-driven molecular design. His work bridges theoretical and experimental chemistry, addressing targets like viral proteases (e.g., SARS-CoV-2), GABA receptors, and dopamine transporters. Research interests include: Pharmacophore-guided drug discovery for anti-viral and CNS therapies Development of next-generation computational tools (e.g., PharmacoMatch, QPhAR) Protein-ligand interaction modeling using neural networks and graph-based algorithms Recent studies focus on: Inhibitors for herpesvirus nuclear egress complexes, AI-optimized antivirals, and dopamine transporter inhibitors for cognitive enhancement. His lab collaborates on projects like the NeuroDeRisk initiative to de-risk neurotoxic compounds. Publications emphasize drug repurposing, metabolic pathway analysis, and scalable synthesis methods for promising drug candidates.
Dr. Rani Moran is a Lecturer in Psychology at Queen Mary University of London's School of Biological and Behavioural Sciences. She is affiliated with the Centre for Brain and Behaviour. Her research focuses on decision-making, memory, and learning mechanisms, employing methods like computational modeling, neuroimaging, and pharmacological interventions. Key interests include reinforcement learning, cognitive maps, and exploration-exploitation dilemmas. Her work integrates experimental designs with advanced statistical analysis to understand flexible behavioral control. Recent studies explore model-based vs. model-free learning, credit assignment mechanisms, and disinformation's impact on learning biases. She has published in top journals such as Psychological Review and Nature Communications . Dr. Moran collaborates on projects involving neurocomputational models of confidence, meta-cognition, and social learning. Her research aims to develop interventions for optimizing cognitive processes, with applications in mental health and decision-making contexts.
Dr. Anna De Simoni is a Clinical Associate Professor in Primary Care Research at Queen Mary University of London. She specializes in improving self-management and adherence to medications for patients with long-term conditions, particularly asthma and stroke. She co-leads the Centre for Primary Care and leads the AD-HOC NIHR Programme Grant, focusing on digital social interventions and online peer support systems. Her research integrates network science, big data analytics, and computational social science to enhance primary care outcomes. Education: MBBS (Medicine), PhD in Neurophysiology (University of Milan), followed by postdoctoral fellowships at UCL and the University of Cambridge. She holds NIHR Academic Clinical Fellowship and Lectureship awards, and is a Fellow of the Higher Education Academy (FHEA) and a Member of the Royal College of General Practitioners (MRCGP). Research Interests: Digital health interventions, medication adherence strategies, primary care after stroke/TIA, domestic violence in healthcare settings, and patient engagement in online communities. She employs mixed-methods approaches including systematic reviews, qualitative studies, and computational analyses. Key Awards: EU Marie Curie Individual Fellowship (2010-2012), NIHR Academic Clinical Lectureship (2013-2016). Current roles include co-leadership of the Asthma UK Centre for Applied Research (AUKCAR) and supervision of postgraduate students in MSc/PhD programs. Grants and Projects: AD-HOC NIHR Programme Grant, PAM Programme on Adherence to Medication, TEAM-care project on asthma management. Collaborations include industry partners and charities to develop scalable digital solutions for chronic disease management. Labs/Teams: Clinical Effectiveness Group at Queen Mary, Asthma UK Centre for Applied Research (AUKCAR).
Matthew Price is the George W. Albee Green & Gold Professor of Psychological Science and Director of the Clinical Psychology Training Program at the University of Vermont's College of Arts and Sciences. He holds a B.A. from SUNY Binghamton (2004), an M.A. (2006), and Ph.D. (2011) from Georgia State University. His research focuses on expanding clinical care access for trauma survivors and anxiety disorder patients via technology-driven interventions. Key areas include mobile health applications, wearable sensors, and acute trauma care in Emergency Departments. His interdisciplinary approach involves collaborations with computer science, bioinformatics, and medicine. Current projects explore digital biomarkers (e.g., heart rate variability), technology adoption barriers, and culturally adapted therapies. He leads the Center for Research on Emotion, Stress, and Technology, emphasizing translational frameworks bridging basic research and clinical practice. Recent work includes randomized controlled trials evaluating mobile apps like 'Bounce Back Now' for disaster-related PTSD, and sleep-monitoring studies using wearable devices. Over 150 peer-reviewed articles highlight his focus on trauma mechanisms, symptom networks in veterans, and tech-enabled mental health innovations. His lab actively addresses global mental health disparities through mHealth solutions.
Ivan Selesnick is a Professor of Electrical and Computer Engineering at the NYU Tandon School of Engineering, with joint appointments in Biomedical Engineering and Radiology. He holds affiliations with the Center for Advanced Technology in Telecommunications (CATT) and leads the Selesnick Lab. His research focuses on signal and image processing, sparse signal models, wavelet analysis, and biomedical applications. He received his degrees from Rice University (BS, MEE, PhD in EE) and has been recognized with prestigious awards including the Alexander von Humboldt Fellowship (1997), NSF Career Award (1999), and IEEE Fellow (2016). Education: BS, MEE, and PhD in Electrical Engineering from Rice University (1990, 1991, 1996). He joined NYU Tandon in 1997 and served as a visiting professor at the University of Erlangen-Nuremberg in 1997. Research Interests: Signal Processing, Sparse Signal Models, Wavelet Analysis, Biomedical Signal Processing, and Optimization Techniques. His work emphasizes applications in medicine, imaging, and engineering systems. Awards: In addition to his fellowships, he received the Jacobs Excellence in Education Award (2003) and the Budd Award for Best Engineering Thesis (1996). He has held editorial roles at IEEE Transactions on Image Processing, Signal Processing Letters, and Computational Imaging. Teaching: Courses include Signals, Systems, and Transforms (EE 3054), Digital Signal Processing I/II (EL 6113/EL 7133), Wavelets and Filter Banks (EL 7163), and Biomedical Signal Processing (EL 9133). Labs and Affiliations: Director of the Selesnick Lab, involved in NYU Tandon Future Labs (business incubators) and CATT (telecommunications research). His research spans biomedical sensing, radar signal processing, and algorithm development for medical diagnostics.
Drexler James is an Assistant Professor of Psychology at the University of Minnesota, Twin Cities, and an affiliate faculty member of the Robert J. Jones Urban Research and Outreach-Engagement Center. His research focuses on the social and psychological determinants of health among minoritized groups, particularly African American adults and men of color who have sex with men. He also examines the health consequences of biological race essentialism, using intersectionality as a framework to understand health inequities. James holds a B.Sc. in Psychology from the Illinois Institute of Technology and a Ph.D. in Social & Personality Psychology from the University of Illinois at Chicago, with a minor in Community Health Research. His specialties include racism and health, psychological essentialism, and internalized oppression. As the primary investigator of the Health, Intersectionality, Essentialism, & Stigma (HIES) Research Team, he investigates how stigma operates dynamically across perceivers and targets to reinforce health disparities. Recent work explores barriers to healthcare access, internalized racism’s mental health impacts, and the socio-cultural implications of racialized stereotypes. His academic contributions span over 20 peer-reviewed articles addressing race-related health disparities, internalized oppression, and the psychological impacts of systemic racism. While no formal awards are listed, his work demonstrates significant engagement with marginalized communities’ health challenges.
Lief Fenno, MD, PhD, is an Assistant Professor at The University of Texas at Austin, affiliated with Dell Medical School’s Department of Psychiatry and Behavioral Sciences and the College of Natural Sciences’ Department of Neurology. He is a board-certified psychiatrist specializing in addiction medicine, particularly medication-assisted treatment (MAT) for opioid use disorder. His research focuses on molecular and viral tools to study neuron circuitry and behavior, with applications in precision medicine for neurological and psychiatric conditions. Fenno earned his MD and PhD from Stanford University and a BA in neurobiology from Harvard University. His research integrates neuroscience, bioengineering, and clinical medicine to develop novel tools for understanding neural circuits. Key interests include optogenetics, chemogenetics, and optical imaging of neuronal activity in awake subjects. The Fenno Lab explores mechanisms of neurological diseases, particularly addiction, and aims to translate findings into clinical treatments. Recent work emphasizes brain-wide mapping of neural circuits, including studies on glutamate neuron subtypes and VTA-lateral habenula interactions. His lab also develops sono-optogenetic technologies and nanotransducers for deep brain stimulation. Fenno’s educational background includes residency in psychiatry and a bioengineering fellowship at Stanford, underscoring his interdisciplinary approach to neuroscientific challenges.
