Mark Y. Liberman is the Christopher H. Browne Distinguished Professor of Linguistics and Trustee Professor at the University of Pennsylvania. He holds a joint appointment in the Department of Linguistics and the Department of Computer and Information Science. His roles include Director of the Linguistic Data Consortium (LDC), Faculty Director of Ware College House, and former Director of the Institute for Research in Cognitive Science. Education: A.B. in Linguistics and Applied Mathematics from Harvard University (1965–1969), M.S. (1972) and Ph.D. (1975) in Linguistics from MIT. Research focuses on corpus-based phonetics, clinical linguistics applications, tonal phonology, formal models for linguistic annotation, and computational linguistics. He explores speech production, prosody, and interdisciplinary topics like language evolution and neurobiology of speech. Recent articles highlight advancements in speech biomarkers for neurodegenerative diseases, autism analysis, and computational linguistics. Awards include Fellowships from the AAAS and Linguistic Society of America. He advises graduate students and leads large-scale language resource initiatives like LDC, contributing to open-access linguistic datasets. Labs/Teams: Linguistic Data Consortium (LDC), Institute for Research in Cognitive Science (IRCS), and collaborations in computational linguistics and neuroscience.
Polina Golland is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and a Principal Investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on developing novel techniques for biomedical image analysis and understanding, particularly in medical vision, AI/ML, and health care applications. She leads the Medical Vision Group and collaborates with the Vision Group at CSAIL. Her work emphasizes statistical modeling of medical images, shape modeling, and predictive analytics for biological processes. Current projects include fetal MRI analysis, cardiac MRI segmentation, and quantitative assessment of pulmonary edema in chest X-rays. She has secured grants from NIH, MIT-IBM Watson AI Lab, and other institutions to support her research. Dr. Golland teaches courses on inference, probability, and probabilistic systems. She advises graduate students in MIT's EECS program and has mentored numerous postdocs and researchers. Her lab focuses on translating advanced imaging techniques into clinical workflows, with applications in neuroimaging, fetal health monitoring, and cardiovascular disease analysis. Notable collaborations include work with Harvard Medical School affiliates, Brigham and Women's Hospital, and the MIT Jameel Clinic. Her research aims to bridge computational methods with clinical needs, improving diagnostic tools and treatment planning through machine learning and medical imaging innovation.
Hanjie Chen is an Assistant Professor in the Department of Computer Science at Rice University, affiliated with the Ken Kennedy Institute. She holds a Ph.D. from the University of Virginia and a Master's from the University of Science and Technology of China. Her research focuses on Natural Language Processing, Interpretable Machine Learning, and Trustworthy AI, emphasizing model explainability, alignment with human needs, and applications in healthcare, sports, and medicine. She has advised numerous students and led initiatives in AI ethics and education. Education: Ph.D. (Computer Science, UVA 2023), M.Sc. (USTC 2018), B.Sc. (Nanjing University of Aeronautics and Astronautics 2015). Awards include the Outstanding Doctoral Student Award (UVA 2023) and John A. Stankovic Research Award (UVA 2023). She has organized workshops like BlackboxNLP and served on program committees for ACL, NAACL, and EMNLP. Her recent work includes developing benchmarks like SPORTU for multimodal LLMs, evaluating medical question-answering systems, and advancing methods for robust rationale evaluation (RORA). She teaches courses on Natural Language Processing and Trustworthy NLP, emphasizing pedagogical innovation recognized by teaching awards at UVA. Research collaborations include internships at Microsoft Research, IBM, and the Allen Institute for AI. She mentors students in SURF programs and advocates for diversity in tech, serving as a mentor in UVA's CSGSG Council.
Hang Lu is a Professor and holds the Cecil J. "Pete" Silas Chair of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Dr. Lu also holds a Love Family Professorship and leads the Lµ Fluidics Group, which focuses on engineering microfluidic systems and machine learning tools to address complex questions in neuroscience, developmental biology, and cell biology that are difficult to address with conventional techniques. Dr. Lu's research lies at the intersection of engineering and biology, with primary interests including: Microfluidic systems for high-throughput screens and image-based genetics and genomics Systems biology: large-scale experimentation and data mining Microtechnologies for optical stimulation and optical recording Big data, machine vision, and automation Developmental neurobiology, behavioral neurobiology, and systems neuroscience Cancer biology, immunology, embryonic development, and stem cells Her laboratory engineers microfluidic devices and BioMEMS to study neuroscience, genetics, cancer biology, and biotechnology. These miniaturized Lab-on-a-chip tools operate at scales comparable to biological systems, leveraging unique micro and nano-scale phenomena to gather large-scale quantitative data about complex biological systems. Current projects include Microfluidics for Life Sciences, Optical Neuron Recordings and Manipulations, Machine Learning Tools for Neuroscience, Measuring and Modeling Behavior, and High-throughput, High-content Cell-based Assays. Analysis of Dr. Lu's recent publications (2024-2025) reveals a strong trend toward integrating microfluidics with advanced computational methods: Development of deep learning frameworks for biological image analysis Advanced neuron tracking and functional imaging techniques Non-invasive characterization of 3D organoid cultures Sophisticated neuromechanical modeling of locomotion Microfluidic temperature control systems for in vivo studies Label-free imaging pipelines for neural development Dr. Lu's significant professional honors include: Cecil J. "Pete" Silas Chair of Chemical & Biomolecular Engineering Love Family Professorship The Lµ Fluidics Group actively mentors students and postdocs, currently accepting new postdoctoral researchers. The lab receives substantial funding for interdisciplinary projects at the engineering-biology interface, with research implications spanning fundamental biological understanding to therapeutic development. The group operates within Georgia Tech's School of Chemical & Biomolecular Engineering, with specialized facilities for microfluidic device fabrication, biological experimentation, and advanced imaging, maintaining strong collaborative ties across engineering, neuroscience, and biological disciplines.
Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.
Vivek Shenoy is the Eduardo D. Glandt President's Distinguished Professor at the University of Pennsylvania, with primary appointments in the Department of Materials Science and Engineering and secondary appointments in Bioengineering and Mechanical Engineering and Applied Mechanics. He leads the Multiscale Mechanobiology and Biomaterials Laboratory, which focuses on developing theoretical frameworks and numerical methods to understand complex biological and engineering systems across multiple length scales. Shenoy's research spans mechanobiology, chromatin organization, cell mechanics, and biomaterials. His work addresses the fundamental challenge of modeling how small-scale cellular phenomena couple with long-range tissue-level interactions across micrometers to centimeters. By integrating insights from soft matter physics, solid mechanics, chemistry, and applied mathematics, his group develops multiphysics continuum and mesoscale theories to elucidate mechanisms controlling both biological and engineering systems. His recent publications demonstrate an increasing focus on nuclear mechanics, chromatin organization, and the interplay between mechanical forces and gene regulation. Analysis of Shenoy's publication record reveals a strong interdisciplinary approach, with high-impact papers spanning biophysics, materials science, and cell biology. His work shows consistent evolution from fundamental mechanics of materials to complex biological systems, with recent emphasis on the mechanical regulation of chromatin architecture, cell migration dynamics in 3D environments, and mechanotransduction in development and disease. His publications appear regularly in top journals including Nature, Science, and their affiliated publications, demonstrating significant influence across multiple fields. Eduardo D. Glandt President's Distinguished Professor Multiple publications in Nature, Science, and PNAS Active research program with publications through 2025 Shenoy actively mentors students and postdocs through his laboratory, with numerous co-authored publications indicating strong mentorship. His research program appears to be well-funded through multiple grants supporting his work in mechanobiology and biomaterials. The Multiscale Mechanobiology and Biomaterials Laboratory maintains active collaborations across disciplines and institutions, reflecting the interdisciplinary nature of his research. The Multiscale Mechanobiology and Biomaterials Laboratory, housed within the Department of Materials Science and Engineering at the University of Pennsylvania, serves as the primary research hub for Shenoy's work. The lab maintains an active presence on social media (Twitter: @ShenoyLab) for updates on activities and publications. Their research approach combines theoretical modeling with experimental validation to address fundamental questions at the interface of mechanics, materials science, and biology.
Professor Nancy W. Y. Law is a leading scholar in Learning Sciences and Technology at the University of Hong Kong's Faculty of Education . She has served as Founding Director of the Centre for Information Technology in Education (CITE) (1998-2013) and currently holds the Chair of Learning Sciences and Technology . Her career spans over 25 years of international comparative studies, policy analysis, and large-scale educational innovation. Current roles: Associate Dean (Strategic Development), HKU Key affiliations: Sciences of Learning Strategic Research Theme (co-convener) Research Expertise Her work focuses on technology-enhanced learning , digital literacy , knowledge building communities , and multilevel educational change . She pioneered network models of teacher innovation and socio-technical frameworks for sustainable pedagogical transformation, particularly in STEM education and Cyberbullying prevention . Scientific Leadership With over 25 major research grants (2000-2023) exceeding HK$50 million, she leads projects like: Learning and Assessment for Digital Citizenship Jockey Club Self-directed Learning in STEM Design-Aware Learning Analytics A Fellow of the International Society of Learning Sciences and recipient of the HKSAR Humanities and Social Sciences Prestigious Fellowship , she has supervised numerous international comparative studies including SITES 2006, ICILS 2013, and longitudinal digital citizenship research (2019-2021).
Andre Levchenko is the John C. Malone Professor of Biomedical Engineering at Yale University, with secondary appointments in the Department of Neurosurgery and affiliations with the Cancer Signaling Networks, Immunology, and the Yale Program in Neurodevelopment and Regeneration. His research focuses on systems biology, signal transduction, and cell-cell communication, utilizing microfluidics and computational modeling to study cancer progression, stem cell behavior, and neurological disorders. PhD, Columbia University MEng, Moscow Institute of Physics and Technology Levchenko's work explores how cells process dynamic signals to make critical decisions, particularly in glioblastoma migration, organoid development, and cardiovascular tissue engineering. His lab develops innovative microfluidic platforms and mathematical models to dissect multicellular communication and signaling networks. Recent publications highlight his contributions to understanding YAP-driven cancer invasion , NOTCH signaling in angiogenesis , and metabolic regulation of hypoxia responses . He has pioneered methods for organoid modeling and single-cell analysis , advancing precision in biological signaling studies. Scientific Awards : Computational Molecular Biology Post-Doctoral Fellowship (Burroughs Wellcome Fund) National Academies Keck Futures Conference Invitee Distinguished Guest Lecturer, University of Virginia American Asthma Foundation Early Excellence Award Fellow, American Institute for Medical and Biological Engineering Levchenko leads the Levchenko Lab at the Yale Systems Biology Institute, collaborating with institutions like Mayo Clinic and Yale Cancer Center. His research has received recognition in Faculty of 1000 and multiple journal highlights.
James Briscoe is a Senior Group Leader at The Francis Crick Institute in London, where he leads a research group focused on developmental biology and morphogen signaling. He previously held positions at the Medical Research Council's National Institute for Medical Research, which later became part of the Francis Crick Institute. Education: BSc in Microbiology and Virology from the University of Warwick, UK PhD from Imperial Cancer Research Fund/King's College London Postdoctoral training at Columbia University with Thomas Jessell Dr. Briscoe's research focuses on the molecular and cellular mechanisms of graded signaling by morphogens and the role of transcriptional networks in cell fate specification. His laboratory employs a range of experimental and computational techniques using model systems including mouse and chick embryos and embryonic stem cells. His work has significant implications for understanding developmental processes and their relationship to disease. His recent publications demonstrate a continued focus on morphogen gradients, neural tube development, and computational approaches to understanding cell fate decisions. His research increasingly integrates single-cell technologies and computational modeling to unravel the complexities of developmental patterning. Scientific Awards and Honors: EMBO Young Investigator (2001) EMBO Gold Medal (2008) Elected to EMBO (2009) Fellow of the Academy of Medical Sciences (2019) Fellow of the Royal Society (2019) As Editor-in-Chief of the journal Development since 2018, Dr. Briscoe plays a significant role in shaping the field of developmental biology. His leadership extends to mentoring researchers and contributing to scientific policy discussions, as evidenced by his recent publication 'Science under siege: protecting scientific progress in turbulent times.' Dr. Briscoe's laboratory at the Crick Institute is well-equipped with access to advanced facilities including light microscopy, flow cytometry, genomics, and computational resources, enabling a multidisciplinary approach to developmental biology questions.
David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.
Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Johannes Larsch is a tenure-track Assistant Professor at the Center for Integrative Genomics within the Faculty of Biology and Medicine at Université de Lausanne (UNIL). His research focuses on understanding the neuronal mechanisms underlying social interactions, using larval zebrafish as a model organism. He employs advanced techniques like virtual reality and optical imaging to study how social signals are processed in the brain. Education: Bachelor of Biology, University of Konstanz, Germany PhD in Neurobiology, Rockefeller University, USA (lab of Cori Bargmann) Postdoc at Max Planck Institute of Neurobiology, Germany (with Herwig Baier) Research interests center on neuronal circuits driving group behavior, social recognition, and the interplay between genetics and environment in shaping behavior. His lab investigates how individual brains coordinate collective social behaviors through studies of neuronal activity and circuit dynamics. Current lab openings include fully funded PhD and PostDoc positions focused on social neuroscience and neurogenomics. Contact: johannes.larsch@unil.ch .
Douglas D. Ready is an Associate Professor of Education and Public Policy at Teachers College, Columbia University , where he serves as Director of the Consortium for Policy Research in Education (CPRE-TC). His research focuses on the intersections of education policy, social policy, and educational equity, particularly how policies influence socio-demographic disparities in cognitive development. Research Interests: Ready’s work examines the structural determinants of educational equity, including school choice architecture, personalized learning, early childhood education, and the impacts of technology on instruction. He has published extensively in journals such as Educational Evaluation and Policy Analysis and Sociology of Education . Editorial Roles: Ready contributes to the academic community as a member of the editorial boards of Educational Evaluation and Policy Analysis and American Educational Research Journal . Key Projects: He has led analyses of programs like Teach to One: Math and Pre-K for All , exploring their effects on student outcomes and equity.
Aylwyn Scally is a researcher at the Department of Genetics, University of Cambridge, specializing in human evolutionary genetics and ancestry. His work focuses on computational and mathematical models of genome evolution, leveraging population-scale datasets and archeogenetic evidence to explore ancient human populations and their spatial dynamics. University: University of Cambridge Department: Department of Genetics Fields of Interest: Human Evolutionary Genetics, Population Genetics, Ancestry Analysis, Paleobiology, Environmental Genomics, Genome Evolution Scally's research integrates spatial dynamics into genetic models, offering insights into demographic, social, and cultural factors in ancient populations. His methodologies have broader applications for studying other species as genomic data becomes available. Recent publications highlight his work on mutation rate analysis, historical migration patterns, and ancestral population structures. These studies often employ computational simulations and large-scale genomic datasets to address evolutionary questions. He is associated with the Cambridge NERC Doctoral Landscape Awards (CREATES) and C-CLEAR DTP, contributing to training and collaborative research in genetics and environmental genomics.
