Sheelagh Carpendale is a Professor and Canada Research Chair in Information Visualization at Simon Fraser University's School of Computing Science. Her research focuses on Information Visualization, Interaction Design, and Human-Computer Interaction, with a strong emphasis on large display interaction, visual analytics, and personal visualization. She leads the Innovations in Visualization Interactive Experiences (ixLab) and has contributed to over 200 publications. Education: PhD (Computing Science, Simon Fraser University, 1999); BSc (Computing Science, Simon Fraser University, 1992). Research Interests: Dr. Carpendale's work bridges theory and practice, emphasizing user-centered design and interdisciplinary collaboration. Key areas include data physicalization (e.g., Kirigami-inspired visualizations), interactive technologies for healthcare, and educational tools like TangiBooks for programming concepts. Her lab explores novel interaction paradigms for large displays and mobile devices. Recognition: Recipient of the 2018 IEEE Visualization Career Award, numerous best paper awards, and leadership roles in conferences like IEEE VIS. Her contributions span academic, industrial, and public engagement contexts, including projects on clinical decision support and public data literacy. Grants & Labs: Active in securing research grants for projects like the Arctic Movement visualization and Energy Data initiatives. The ixLab collaborates with artists, scientists, and healthcare professionals to create impactful visualizations.
Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.
Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.
Wang Jianmin serves as Professor and Doctoral Supervisor at Tongji University's School of Art and Media, concurrently holding the position of Vice Dean since 2014. With a computer science PhD from Sun Yat-sen University, he bridges engineering and media arts through pioneering research in intelligent communication systems and digital media interfaces. His work focuses on human-centered design for emerging technologies, particularly in automotive and virtual environments. His academic foundation includes: PhD in Engineering (Computer Software and Theory), Sun Yat-sen University (2003) Master's in Computational Mathematics, Sun Yat-sen University (1999) Bachelor's in Computational Mathematics, Nankai University (1996) Professor Wang's research centers on intelligent communication systems and digital media art, with significant contributions to automotive human-machine interfaces (HMI), virtual reality applications, and user experience methodologies. His investigations into driver-robot transparency, augmented reality navigation, and mixed-reality educational platforms demonstrate interdisciplinary innovation connecting computer science, cognitive psychology, and design theory. Current projects explore AI-driven media systems for urban environments and safety-critical interaction frameworks. Analysis of his recent publications reveals a cohesive research trajectory focusing on automotive HMI (40% of output), human-robot interaction (30%), and mixed reality applications (30%). His work consistently emphasizes experimental validation through driving simulators and user studies, yielding practical design guidelines for industry implementation. The interdisciplinary nature spans computer science, cognitive ergonomics, and media studies, with increasing emphasis on AI integration in communication systems. His scientific recognition includes national and provincial awards for innovation in human-computer interaction and educational technology: 2019 China Industry-University-Research Innovation Award for automotive HMI systems 2020 China User Experience Alliance Excellence Award 2012 Guangdong Dingying Science and Technology Award Multiple national/provincial science progress awards (2001-2009) 2020 Tongji University Teaching Achievement Award for curriculum development As an educator, Professor Wang mentors graduate students in national design competitions including the 'Core Cup' Future Automotive HMI Challenge and International User Experience Innovation Competition. His research program is supported by substantial funding from diverse sources: National Grants: National Natural Science Foundation projects on driver behavior modeling and cognitive testing Ministry of Education: 15+产学合作 projects for virtual simulation labs and curriculum development Shanghai Municipal: Publicity Department funding for smart city media research Industry Partnerships: Huawei (intelligent vehicle HMI), SAIC Motor (AR-HUD design), and automotive electronics firms He directs Tongji's Media Experiment and Practice Teaching Center and the All-Media Research Institute, leading teams developing virtual simulation platforms for emergency news reporting, intelligent vehicle interaction testing systems, and mixed reality educational tools. Current initiatives focus on AI-enhanced media art for urban applications and next-generation HMI frameworks for autonomous mobility solutions.
William A. Goddard, III is the Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics at the California Institute of Technology. With a career spanning over five decades, he has held positions from Noyes Research Fellow (1964–66) to his current professorship since 2001. His educational background includes a B.S. from UCLA (1960) and a Ph.D. from Caltech (1965). Quantum chemistry and first-principles simulations Multiscale modeling (QM→MD→mesoscale) Catalysis and protein structure prediction Nanotechnology and bionanotechnology Energy storage (batteries, supercapacitors) Recent publications emphasize applications in metal-organic frameworks , electrocatalysis , and space manufacturing , reflecting his interdisciplinary approach. His work on G-protein coupled receptors and Li-S batteries demonstrates methodological innovation through quantum mechanics and machine learning . Horizon Prize , Royal Society of Chemistry Over 1548 total publications (1967–2022) As Director of Caltech's Material and Process Simulation Center , he leads development of software like ReaxFF for reactive dynamics. He teaches Ch 120 ab (Nature of the Chemical Bond) and Ch 121 ab (Atomic-Level Simulations), emphasizing hands-on computational applications for experimentalists and theorists.
Prof. Dr. Soeren Lienkamp is an Assistant Professor at the Institute of Anatomy , Faculty of Medicine , University of Zurich . His work bridges digital education and genetic research , focusing on enhancing medical teaching through innovative formats. Research Interests : Genetics, developmental biology, kidney disease modeling, CRISPR applications, digital medical education, and advanced microscopy. Methodologies : Combines Xenopus tropicalis models, deep learning , and bioengineering to study genetic kidney disorders and improve diagnostic tools. Publication Trends : His recent articles highlight predictable genome editing , 3D imaging technologies , and mechanistic insights into kidney and eye development. Earlier works focus on ciliary function , Wnt signaling , and metabolic stress in renal cells.
Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.
Ronald G. Larson serves as the George Granger Brown Professor of Chemical Engineering and A. H. White Distinguished University Professor at the University of Michigan's College of Engineering, with additional appointments in Mechanical Engineering and Macromolecular Science & Engineering. His research leadership spans multiple departments within the Chemical Engineering Division, where he directs the Larson Lab focused on fundamental and applied soft matter physics. His research program investigates complex fluids through computational and theoretical frameworks, emphasizing polymer physics, rheology, and molecular simulations. Key thrusts include polymer melt processing, biomembrane dynamics, colloidal systems, and polyelectrolyte coacervation. The group employs advanced techniques like Brownian dynamics, coarse-grained modeling, and multiscale simulation to address challenges ranging from industrial polymer processing to biomedical applications. Recent publications (2023-2025) reveal strong momentum in rheological modeling of complex fluids, with particular emphasis on self-healing materials, wax deposition in pipelines, and crystallization mechanisms. The work bridges fundamental molecular insights with industrial applications, demonstrating consistent high-impact output across polymer science, soft matter physics, and chemical engineering domains. The Larson Lab operates as a collaborative hub within the Chemical Engineering Department, leveraging computational resources to advance understanding of fluid mechanics and material properties. Current projects integrate machine learning with traditional modeling approaches, reflecting the group's commitment to methodological innovation while maintaining strong connections to experimental validation and real-world engineering problems.
Associate Professor LAM Yulin is affiliated with the National University of Singapore (NUS), specializing in bioorganic and medicinal chemistry with a focus on green synthesis methodologies. His research interests include developing anti-cancer, anti-inflammatory, and neurological agents, alongside creating recyclable catalysts for sustainable organic transformations. He holds a Ph.D. (1992) and B.Sc. (1987) from NUS, with prior research fellowships at the Institute of Molecular and Cell Biology (1994–1996) and The Scripps Research Institute (1992–1994). Teaching contributions include courses such as CM2122 Organic Chemistry, CM3225 Biomolecules, and CM5224 Emerging Concepts in Drug Discovery. His research highlights include synthesizing chondroitin sulfate analogs for molecular recognition via surface-enhanced Raman scattering (SERS) and developing fluorous boronic acid catalysts for amide bond formation under eco-friendly conditions. Research focuses on glycosaminoglycan structure-function relationships, novel mycobacterial inhibitors, and autophagy-inducing agents. His work bridges organic synthesis with biomedical applications, emphasizing sustainability and translational potential.
Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.
Lan Guan is a Professor at Texas Tech University Health Sciences Center in the Department of Cell Physiology and Molecular Biophysics within the School of Medicine. He also serves as Co-Director of the Center for Membrane Protein Research. His research focuses on membrane proteins, which constitute approximately 30% of all eukaryotic proteins and play crucial roles in many aspects of cell function. Dr. Guan's research seeks to understand the mechanisms of solute transport and lay the foundation for advances in disease treatment and human health. He employs an integrated approach including cryo-EM single-particle analysis, X-ray crystallography, ligand binding, molecular dynamics simulations, thermodynamics, genetic engineering, novel amphiphiles, and many other biochemical & biophysical analyses. His current research focuses on cation-coupled bacterial and human transporters. Dr. Guan is currently supported by an NIGMS MIRA R35 Award (2024). His publication record demonstrates expertise in membrane protein structure and function, particularly with melibiose transporters (MelB) and their mechanisms. His work spans structural biology, biochemistry, and biophysics, with significant contributions to understanding membrane transport mechanisms. His research has led to important insights into membrane protein structure-function relationships, particularly in sugar transporters. Dr. Guan's work has implications for understanding fundamental biological processes and potential therapeutic applications related to membrane transport. NIGMS MIRA R35 Award 2024 Dr. Guan actively collaborates with researchers across disciplines and institutions, as evidenced by his extensive publication record with numerous co-authors. His work bridges structural biology, biochemistry, and biophysics to advance our understanding of membrane protein function. He is affiliated with the Center for Membrane Protein Research, where he contributes to advancing methodologies for studying these challenging but critically important biological molecules. His work on novel amphiphiles and detergent design has helped overcome technical barriers in membrane protein research.
Zechuan Lin is a Lecturer at the Department of Neurology , Yale School of Medicine, and a member of the Adams Center for Parkinson's Disease Research . He previously held a postdoctoral research fellowship at Harvard Medical School/Brigham and Women's Hospital in 2023 and earned his PhD from Peking University, College of Life Sciences in 2019. His research bridges computational biology , genomics , and plant genetics , focusing on genetic improvement in crops like rice and maize. Key methodologies include heterosis analysis , transcriptomic profiling , and QTL mapping to dissect agronomic traits and environmental adaptation. Lin's publications highlight advancements in hybrid rice breeding , genome-wide selection , and computational tools for allelic imbalance and daylength-sensing models. His work integrates bioinformatics and genetic networks to address both fundamental and applied biological questions. He is affiliated with Scherzer's Lab , which employs interdisciplinary approaches to neurogenomics and personalized medicine for neurological disorders like Parkinson's disease. His contributions to big data analysis and cross-species genetic studies reflect a unique intersection of plant and neurogenomics.
Pia Vogel is a Professor in the Department of Biological Sciences at Southern Methodist University (SMU), where she leads research on nucleotide-binding proteins using Electron Spin Resonance spectroscopy and molecular modeling. Her work focuses on elucidating structural mechanisms in ATP synthase, multidrug resistance transporters, and calcium channels with biomedical applications in cancer therapy and neurodegenerative diseases. Education: Ph.D., University of Kaiserlautern Dr. Vogel's research program investigates three interconnected domains: the rotary mechanics of FoF1-ATP synthase (particularly the external stalk subunit b-dimer), the structural basis of multidrug resistance in P-glycoprotein and MRPs, and ATP-regulated calcium release via ryanodine receptors. Her laboratory employs site-specific spin labeling, ESR spectroscopy, and computational modeling to resolve protein dynamics and interactions at molecular resolution, contributing to understanding energy transduction in ATP synthase and mechanisms of drug resistance. Analysis of her 15 most recent publications (2020-2025) reveals a dominant focus on developing and characterizing P-glycoprotein and BCRP inhibitors to overcome chemotherapy resistance in cancer. These studies integrate computational screening, ATPase assays, and cell-based models to evaluate inhibitor efficacy, with emerging applications in Alzheimer's research through amyloid-β transport studies. The work demonstrates consistent methodological synergy between biophysical characterization and therapeutic development. Dr. Vogel maintains an active research group supported by sustained funding, evidenced by continuous publication output and laboratory infrastructure. Her team employs multidisciplinary approaches spanning biophysics, biochemistry, and computational biology to address fundamental questions in membrane protein function. Her laboratory facilities in DLSB 221 include specialized Electron Spin Resonance instrumentation and dual Linux computing clusters for molecular dynamics simulations. The research environment supports collaborative projects extending her work into cancer therapeutics and neurodegenerative disease mechanisms through partnerships with clinical and computational researchers.
Dr. Imad El Haddad serves as Group Head of the Molecular Cluster and Particle Processes group at the Laboratory of Atmospheric Chemistry (LAC), part of the Center for Energy and Environmental Sciences at Paul Scherrer Institute (PSI), Switzerland, since 2018. Previously, he held positions as Tenured Scientist and Deputy Head (2018-2019), Senior Scientist in the Smog Chamber group (2015-2018), and Postdoctoral Fellow (2011-2015) at PSI. His research aims to quantify how anthropogenic emissions alter atmospheric pollutant composition and impact Earth's climate and public health through molecular-level analysis using advanced mass spectrometry techniques. His academic background includes: Ph.D. in Atmospheric Chemistry, University of Provence, Marseille (2007-2011) Master's in Environmental Sciences (with distinction, rank 1/9), University of Provence (2006-2007) Master's in General Chemistry (with distinction, rank 1/10), Saint-Joseph University of Beirut (2005-2006) Bachelor of Science in Chemistry (with distinction, rank 1/14), Saint-Joseph University of Beirut (2002-2005) El Haddad's work centers on molecular fingerprinting of atmospheric aerosols , utilizing mass spectrometry (GC/MS, HPLC/APCI-MS2, HPLC/ESI-MS2) to identify primary and secondary molecular markers. He conducts smog chamber experiments to characterize emissions from wood burning, traffic, and cooking processes, determining secondary organic aerosol potential and oxidation state evolution. His group also studies in-cloud aqueous-phase aging and collaborates with global modelers to link aerosol composition to climate forcing and health outcomes like oxidative stress. Recent publications (2025-2024) reveal three dominant trends: (1) rigorous molecular-scale analysis of secondary aerosol formation under varying humidity/temperature, (2) source apportionment breakthroughs in diverse regions (India, Europe, Arctic) using 14C and AMS data, and (3) quantification of health-relevant aerosol properties such as oxidative potential through DTT assays. High-resolution mass spectrometry is a consistent methodological thread across these studies. His scientific awards include: MENRT research fellowship from French ministry of research (2007-2010) Excellence Scholarship (top 1% student, University of Saint Joseph, 2005) Distinction Prize (best student, University of Saint Joseph, 2005) As Group Head, El Haddad oversees the Molecular Cluster and Particle Processes group's research direction and mentorship of junior scientists. While specific grant details are absent from the text, his leadership in multi-institutional publications (e.g., CERN CLOUD, iCUPE) implies active grant management and international collaboration. The group's work bridges laboratory simulations, field deployments, and health/climate modeling to address air pollution complexities. The Molecular Cluster and Particle Processes group develops cutting-edge online/offline mass spectrometers for 1 Hz-resolution atmospheric analysis. They deploy instruments in laboratory smog chamber experiments and global field studies, focusing on molecular marker identification, emission source characterization, and aging process quantification. Collaborations with biochemists and climate modelers extend their impact beyond pure aerosol physics into health risk assessment and policy-relevant climate science.
Suzanne Estaphan is a Senior Lecturer in Medical Science (Physiology and Clinical Pharmacology) at the Australian National University (ANU) School of Medicine and Psychology . She holds qualifications including a Doctorate of Physiology (2015) , MSc in Physiology (2012) , MBBCh (2005) , and Basic Certificate in Medical Education (2018) . Her academic roles span curriculum development, admissions, and assessment committees in the ANU Medical School. Senior Lecturer, ANU (2023–present) Academic Editor, PLOS ONE (2024–present) Review Editor, Frontiers in Physiology (2023–present) Dr. Estaphan's research bridges oxidative stress and inflammation in disease pathology with medical education innovation . Her work explores antioxidant interventions for organ damage and investigates AI/virtual reality applications in education. Recent projects include studying sleep dysfunction in eating disorders and diabetes medication effects on exercise capacity . Between 2023–2025, her 15 most recent publications span oxidative stress , medical education , and molecular pathophysiology . Themes include homeostasis , clinical reasoning , and metabolic disorders , with subfields like MMP-9 biology , pancreatic injury , and VR-based hypoxia training . Scientific accolades include the American Physiological Society Travel Award (2017) and recognition as Senior Fellow of the Higher Education Academy (2023) . She mentors medical students, contributes to national physiology curriculum consensus projects, and has secured approximately $200,000 in research funding .
