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Sallie Ann KellerView profile

Professor

• Data Science
• Social and Decision Informatics
• Statistical Foundations of Data Science
• +4 more

Sallie Ann Keller is a Professor of Data Science by Courtesy at the University of Virginia's School of Data Science, with additional faculty appointments in the School of Medicine (Department of Public Health Sciences) and the School of Engineering and Applied Science (Department of Engineering Systems and Environment). She also holds the title of Distinguished Professor and Director of the Social & Decision Analytics Division at UVA's Biocomplexity Institute. Concurrently, she serves as Chief Scientist and Associate Director of the Research and Methodology Directorate at the U.S. Census Bureau, where she leads national research initiatives in social and economic statistics. Her education includes: Ph.D. in Statistics, Iowa State University of Science and Technology M.S. in Mathematics, University of South Florida B.S. in Mathematics, University of South Florida Sallie Keller's research focuses on the development of the science of all data, with core expertise in social and decision informatics, statistical underpinnings of data science, and data access and confidentiality. She is a leading advocate for interdisciplinary data science to address societal challenges, particularly in public health, policy, and national statistics. Her work bridges engineering, public health, and computational social science, emphasizing ethical data use and methodological innovation. No articles were provided in the scraped text, so no publication trends can be analyzed at this time. Her scientific honors include: Elected Member, U.S. National Academy of Engineering Fellow, American Association for the Advancement of Science (AAAS) Elected Member, International Statistics Institute Fellow and Past President, American Statistical Association Keller has held significant leadership roles in academia and government, including Provost at the University of Waterloo, Dean of Engineering at Rice University, Director of the Social and Decision Analytics Laboratory at Virginia Tech, and Statistics Program Director at the National Science Foundation. While no specific grants or student advisees are mentioned in the text, her leadership in major research directorates and academic institutions indicates extensive grant funding and mentorship. She has served on key national committees, including the National Academy of Sciences Board on Mathematical Sciences and the Committee on National Statistics. She leads the Social & Decision Analytics Division at the Biocomplexity Institute, a multidisciplinary research team focused on data-driven decision-making for societal benefit. Her work integrates advanced analytics, policy modeling, and public health applications, fostering collaboration across UVA and federal agencies like the U.S. Census Bureau.

Aniruddha AdigaView profile

Assistant Professor

• Signal Processing
• Machine Learning
• Infectious Disease Modeling
• +3 more

Aniruddha Adiga serves as a Research Assistant Professor at the Biocomplexity Institute, University of Virginia, where he co-leads infectious disease forecasting initiatives. His work develops statistical and deep-learning models for real-time disease prediction, bridging signal processing and machine learning to address public health challenges. Education: Ph.D. in Electrical Engineering from the Indian Institute of Science (2018), focusing on sparse signal representation and blind deconvolution algorithms Research Interests: Dr. Adiga's work centers on applying machine learning and signal processing to infectious disease dynamics. He pioneers real-time forecasting systems using deep neural networks and statistical methods to model outbreak trajectories. His research integrates epidemiological data with computational techniques to enhance prediction accuracy for diseases like influenza and COVID-19, directly supporting public health decision-making through interdisciplinary approaches spanning computer science, data analytics, and global health security. Scientific Awards: No awards specified in source material Advising and Funding: While specific students and grants aren't detailed in the text, his leadership in infectious disease forecasting implies active mentorship within the Biocomplexity Institute's research ecosystem. His work likely involves collaborative funding from public health agencies focused on epidemic prediction infrastructure. Labs and Teams: Dr. Adiga operates within the Biocomplexity Institute's infectious disease forecasting team, utilizing high-performance computing resources at their Charlottesville facility (Town Center Four). This group specializes in operational prediction systems that process real-world surveillance data for near-term outbreak projections.

Baltazar EspinozaView profile

Assistant Professor

• Mathematical Modeling
• Computational Modeling
• Infectious Diseases
• +3 more

Baltazar Espinoza serves as a Research Assistant Professor at the University of Virginia's Biocomplexity Institute, focusing on interdisciplinary research bridging mathematics, computation, and public health. Education: Ph.D. in Applied Mathematics for Life and Social Sciences, Arizona State University (2018) Postdoctoral Research Scholar, Arizona State University Visiting Research Scholar, Brown University His research centers on mathematical and computational modeling of infectious disease dynamics and population behavior , with emphasis on how individual risk-response decisions during epidemics form complex adaptive systems . This work provides critical insights for epidemic control strategies in heterogeneous communities. As former co-director and five-year mentor of Arizona State University's Mathematical and Theoretical Biology Institute (MTBI), he has guided numerous undergraduate researchers from Latin America and the USA, demonstrating strong commitment to mentoring emerging scientists. Based at UVA's Biocomplexity Institute in Charlottesville, he leverages institutional resources for cross-disciplinary collaboration on biological and social complexity.

Diksha GuptaView profile

Research Fellow

• Artificial Intelligence
• Network Science
• Distributed Systems
• +4 more

Diksha Gupta is a Postdoctoral Research Associate at the Biocomplexity Institute, University of Virginia, where she applies artificial intelligence (AI) to study complex real-world networks such as social, cyber, and supply chain systems. She also explores robust federated learning within distributed systems. Prior positions include research roles at INRIA France, IBM Research, and the National University of Singapore’s School of Computing. She earned her Ph.D. with Distinction from the University of New Mexico. Education: Ph.D. with Distinction, University of New Mexico. Her research focuses on designing provably secure, scalable, and efficient protocols for distributed systems. Key interests include randomized algorithms, distributed graph algorithms, algorithmic game theory, and biologically-inspired systems. Recent publications highlight trends in AI applications for network systems, theoretical foundations of Sybil defense mechanisms, and distributed algorithms for secure systems. Notable work spans AI-driven network science, decentralized cybersecurity, and swarm robotics. Scientific Awards: PODC Student Travel Grant for PODC’19, TCPP Student Travel Grant for IPDPS’19, Ph.D. with Distinction from the University of New Mexico. No formal advising roles or students are currently documented. She is affiliated with the Biocomplexity Institute at the University of Virginia, collaborating on interdisciplinary research projects.

• Biological Oscillators
• Systems Biology
• Biophysics
• +4 more

Alessandra Lucchetti is a Postdoctoral Fellow at the Biocomplexity research group within the Niels Bohr Institute at the University of Copenhagen. Her interdisciplinary work bridges physics, biology, and computational modeling to understand complex biological systems with implications for cancer research, neuroscience, and medical applications. Dr. Lucchetti earned her PhD in 2023 from the Faculty of Science at the University of Copenhagen with her dissertation titled "Exploring the complex dynamics of biological oscillators and their downstream effects." Her educational background combines expertise in theoretical physics with biomedical applications. Her primary research focuses on biological oscillators, particularly the p53 network that plays a critical role in cancer prevention and DNA repair. Dr. Lucchetti applies advanced mathematical and computational approaches to model complex biological phenomena across multiple scales - from molecular interactions to tissue-level effects. Her work spans several key areas including the dynamics of cellular oscillators, biophysical modeling of neurodegenerative diseases like Parkinson's, and application of machine learning to clinical outcomes prediction. Analysis of Dr. Lucchetti's publication record reveals a consistent theme of applying physics-based modeling to biomedical problems. Her recent work demonstrates particular expertise in oscillatory systems, with publications examining p53 dynamics, chimera states in neuronal networks, and therapeutic strategies derived from biophysical modeling. The research shows strong translational potential, connecting fundamental biophysical principles with clinical applications in oncology and neuroscience. Dr. Lucchetti maintains active collaborations with both experimental and clinical researchers, as evidenced by her co-authorship on studies involving wet-lab validation of computational models. Her work has been published in high-impact journals including Cell, Cell Systems, and eNeuro, with several papers generating significant attention in both academic and social media spheres. She is part of the vibrant research ecosystem at the Niels Bohr Institute, contributing to the Biocomplexity group's mission of applying physics approaches to complex biological systems.

• Biocomplexity
• Biophysics
• Theoretical Physics
• +2 more

Adrien Sarlet is a postdoctoral researcher at the Niels Bohr Institute , University of Copenhagen , specializing in the Biocomplexity section. His work intersects physics, biology, and computational modeling, focusing on complex systems and biophysics. Research Interests: Biocomplexity, biophysics, theoretical physics, and computational biology, with applications to complex systems. His affiliation with the Niels Bohr Institute underscores his engagement with interdisciplinary research at the intersection of physical and life sciences.

• Biophysics
• Developmental Biology
• Wound Healing
• +3 more

Jakob Hallundbæk Schauser is a PhD Fellow at the Biocomplexity research group within the Niels Bohr Institute at the University of Copenhagen. His research focuses on the intersection of biophysics and developmental biology, particularly examining how mechanical cues influence protein complexes in vertebrate morphogenesis and wound healing processes. His work combines experimental approaches with theoretical modeling to understand the fundamental mechanisms driving cellular organization and tissue formation. Dr. Schauser's research contributes to our understanding of developmental processes and has potential applications in regenerative medicine and tissue engineering. His notable work includes research on ADIP protein complexes and their role in controlling vertebrate morphogenesis and wound healing. Dr. Schauser can be reached at jakob.schauser@nbi.ku.dk or by phone at +4535329993.

• Bioinformatics
• Immunology
• Computational Immunology
• +6 more

Dr. C. (Can) Kesmir is an Associate Professor in the Bioinformatics group at the Faculty of Science, Utrecht University, where he leads research at the intersection of computational methods and immunological discovery. His work focuses on developing bioinformatics approaches to understand antigen presentation, T-cell recognition, and immune responses in health and disease. Dr. Kesmir studied Computer Science at Bosphorus University in Istanbul and Chemical Engineering at Danish Technical University, earning his Ph.D. in 1999 from the same institution. He completed postdoctoral training in the laboratory of Prof. Soeren Brunak at the Center for Biological Sequence Analysis in Copenhagen, Denmark, before establishing his independent research group at Utrecht University in 2006. His research interests center on computational immunology, with particular focus on: Development of bioinformatics methods to identify antigenic regions in genomes Evolution of antigen presentation and processing pathways Host-pathogen co-evolution, particularly using HIV-1 as a model system Genetic associations of infectious diseases Identification of neo-antigens in cancer immunotherapy Improving organ transplantation through HLA mismatch analysis Analysis of Dr. Kesmir's publication record reveals a consistent trajectory in computational immunology, with increasing focus on translational applications in infectious disease and cancer. His work demonstrates strong interdisciplinary integration between theoretical modeling, bioinformatics tool development, and experimental immunology validation. Recent publications show particular emphasis on T-cell receptor specificity, MHC-peptide interactions, and applications to emerging pathogens like SARS-CoV-2. Among his notable achievements is receiving the High Potential award from Utrecht University in 2007. He serves on the editorial board of the Immunomics journal and was a member of the Danish CensorNet for engineering students. Dr. Kesmir teaches courses including Animal Biology, Host-Microbe Interactions, Immunobiology, and Thesis Projects. He leads research within the Biodynamics and Biocomplexity group at Utrecht University and is associated with the Utrecht Molecular Immunology Hub, where his team develops computational approaches to address fundamental questions in immune recognition and response.

Stephen EubankView profile

Adjunct Professor

• Computational Epidemiology
• Network Science
• Complex Systems
• +3 more

Stephen Eubank is an Adjunct Professor in the Department of Population Health Sciences at the Virginia-Maryland College of Veterinary Medicine, Virginia Tech. He holds a PhD in Physics from the University of Texas and postdoctoral experience at Los Alamos National Lab and the La Jolla Institute. His research focuses on computational epidemiology, network science, and socio-technical systems simulation. He has led major projects like the Epidemiological Simulation System (EpiSims) and contributed to the NIH MIDAS network for infectious disease modeling. Professional roles include Deputy Director of the Network Systems Science and Advanced Computing division at the University of Virginia’s Biocomplexity Institute. His work spans infrastructure resilience, pandemic response strategies, and agent-based modeling of complex systems. He has developed tools for evaluating vaccine allocation, analyzing cascading failures in power grids, and assessing economic impacts of public health policies. Research interests include network structure analysis, scaling in complex systems, and diffusive processes on networks. His articles prioritize understanding disease spread dynamics, network reliability, and policy implications of pandemic interventions. Collaborative efforts involve interdisciplinary teams addressing global health challenges such as Ebola and influenza outbreaks, as well as socio-technical system vulnerabilities.

Simon LevinView profile

Professor

• Ecology
• Mathematical epidemiology
• Theoretical Biology
• +3 more

Simon Levin is the James S. McDonnell Distinguished University Professor in Ecology and Evolutionary Biology at Princeton University. He holds associated faculty appointments at the Andlinger Center for Energy and the Environment (since 2019), Princeton University Center for Human Values (since 2017), and the Program in Global Health and Health Policy at the Princeton School of Public and International Affairs (since 2016). Since 2001, he has directed the Center for BioComplexity within the High Meadows Environmental Institute. Education: B.A. in Mathematics, The Johns Hopkins University, 1961 Ph.D. in Mathematics, The University of Maryland, 1964 Research Interests: Levin's interdisciplinary work integrates theoretical biology, ecology, and mathematics to address complex systems across scales—from microbial interactions to global ecosystems. His research focuses on mathematical epidemiology, evolutionary dynamics, ecological economics, and environmental stochasticity, particularly in savanna-forest transitions and pandemic modeling. Honors and Awards: MacArthur Award (1988) National Medal of Science (2014) Kyoto Prize (2005) BBVA Frontiers of Knowledge Award (2022) 20+ additional major international prizes and academy fellowships Grants and Advising: Levin leads multiple active NSF grants on pandemic preparedness ($125,606), ecological resilience ($981,900), and computational epidemiology ($413,568). He has supervised 73 PhD students (with 10 ongoing) and 112 postdoctoral researchers through the Center for BioComplexity.

Hossein KhodamoradiView profile

Researcher

• Fluid Dynamics
• Atmospheric Sciences
• Aerosol Physics
• +3 more

Dr. Hossein Khodamoradi is a Postdoctoral Researcher at the Max Planck Institute for Dynamics and Self-Organization, specializing in Fluid Physics and interdisciplinary domains. He earned his academic foundation in Condensed-Matter Physics at Bu-Ali Sina University in Iran, advancing his expertise in numerical simulations and experimental studies. Laboratory for Fluid Physics, Pattern Formation and Biocomplexity Research in aerosol dynamics, atmospheric science, and semiconductor crystal growth His research spans Fluid Dynamics , Thermal Stress Analysis , and Atmospheric Sciences , with a focus on computational modeling using COMSOL, OpenFoam, and Ansys. Key projects include WindArts (wind-atmosphere interactions), Crystal Growth Optimization for silicon carbide and germanium, and Aerosol Transport & Infection Risk assessments for airborne diseases. Recent publications emphasize crystal growth methodologies , thermal stress in materials , and surface force dynamics . Notable work includes refining Czochralski and Vertical Bridgman processes, analyzing coil-crucible geometries, and applying AFM to nanocomposite surface studies. He actively engages in interdisciplinary collaborations, leveraging advanced computational tools to address challenges in fluid mechanics, atmospheric research, and semiconductor manufacturing. Contact details include phone numbers (+49 551 5176-456, +49 551 5176-302) and affiliation with the Turbulence and Particles in Fluids research group.

Nicholas LandryView profile

Associate Professor

• Complex Systems
• Network Science
• Dynamical Systems
• +5 more

Nicholas Landry is an Associate Professor of Biology at the University of Virginia with a courtesy appointment in the School of Data Science. His research examines contagion dynamics on complex systems, focusing on how higher-order network structures influence disease and information spread through interdisciplinary approaches combining network science, dynamical systems, and open-source development. His educational background includes a B.S. in Mechanical Engineering from the University of New Hampshire (2014), M.S. and Ph.D. in Applied Mathematics from the University of Colorado Boulder (2020, 2022), and postdoctoral training at the Vermont Complex Systems Center (2022-2024). Landry's core research investigates dynamical processes on higher-order networks, Bayesian inference for network structure reconstruction, and open-source software development. He maintains the XGI and HyperContagion libraries to advance cross-disciplinary research in network science, with applications spanning epidemiology, social contagion, and governance systems. His work emphasizes the critical role of group interactions in altering contagion dynamics compared to traditional pairwise models. Analysis of recent publications reveals accelerating focus on hypergraph structures, real-world dataset integration (particularly Bluesky social media), and computational frameworks for governance modeling. The research trajectory shows increasing methodological sophistication in handling higher-order interactions while maintaining strong connections to empirical social and biological systems. Scientific recognition includes: Small Data Analytics Resource Award from UVA's Data Analytics Center for "Analyzing large-scale social networks through the Bluesky social media platform" Landry actively mentors graduate researchers including Kuankuan Hu, Abhay Gupta, and Ahmed Ahmed through the Computing for Global Challenges program. His lab secured the Small Data Analytics Resource Award and participates in EXPAND fellowship collaborations with Professors Kasimatis and Brodie, integrating network methods into sexual conflict research and behavioral phenotyping. The Landry Lab, established August 2025, collaborates extensively with UVA's Biocomplexity Institute, Quantitative Collaborative, and School of Data Science. Current expansion includes postdoc Daniel Kaiser and multiple PhD students, with active development of open-source tools and participation in international conferences like NetSci 2025 where Landry organized the "Software and Data for Supporting Network Science" workshop.

• Statistical Physics
• Non-equilibrium Thermodynamics
• Theoretical Physics
• +3 more

Prashant Singh is a Postdoctoral Researcher (Researcher) in the Biocomplexity group at the Niels Bohr Institute, University of Copenhagen. His work focuses on theoretical and statistical physics with applications in biological systems, leveraging analytical methods to solve complex non-equilibrium problems. His research spans statistical physics, non-equilibrium thermodynamics, and theoretical biophysics, investigating phenomena like active matter phase transitions, positional information in boundary-driven systems, and stochastic thermodynamics. Key interests include diffusion with resetting, entropy production inference, and reaction-diffusion modeling, often revealing fundamental trade-offs in biological information processing and thermodynamic efficiency. Analysis of his 2024-2025 publications shows a cohesive focus on exact solutions for non-equilibrium systems, particularly single-file diffusion under resetting and active matter with long-range interactions. Recurring themes include cost-time optimization, boundary effects on information transmission, and the thermodynamic constraints of biological computation. No information is available regarding student advising or research grants. He is embedded within the Biocomplexity research group at the Niels Bohr Institute, which employs theoretical frameworks to study collective dynamics in biological systems, including pattern formation, cellular information processing, and emergent behavior in active matter.

• Fluid Dynamics
• Turbulence
• Atmospheric Physics
• +3 more

Dr. Florencia Falkinhoff serves as Group Leader of the Small-scale Dynamics in Turbulence research group and Ombudsperson at the Laboratory for Fluid Physics, Pattern Formation and Biocomplexity (LFPB) within the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany. Her academic background includes: Ph.D. in Physics from École Normale Supérieure de Lyon (France), funded by IFP Energies Nouvelles, focusing on porous media and hydrodynamics of fixed particle beds M.Sc. in Physics from University of Buenos Aires (Argentina), specializing in direct numerical simulations of particle-laden turbulent flows Her research centers on fundamental turbulence mechanisms at microscopic scales, utilizing the Göttingen Variable Density Turbulence Tunnel (VDTT) to investigate stochasticity in turbulent flows and energy dissipation dynamics. She pioneers experimental approaches to separate deterministic and random velocity field components through repeated turbulence generation (up to 30,000 iterations), advancing predictability models for technological and natural flow systems. Concurrently, she conducts atmospheric boundary layer research through instrumented balloon field experiments, analyzing Lagrangian dispersion of aerosols and gases at scales from 100 meters to 50 kilometers. The Small-scale Dynamics in Turbulence group operates within LFPB's experimental infrastructure, collaborating with Dr. Bagheri and Prof. Bodenschatz to bridge theoretical fluid dynamics with real-world atmospheric transport phenomena, addressing critical gaps in understanding extreme events at turbulent dissipation ranges.

• Computational Biology
• Biophysics
• Image Analysis
• +6 more

Albert Alonso is a Research Fellow at the Department of Computer Science within the Faculty of Science at the University of Copenhagen . He is affiliated with the Niels Bohr Institute and holds the position of Guest PhD Student in addition to his postdoctoral role. Postdoctoral Fellow in Biocomplexity and Biophysics Guest PhD Student Image Analysis and Computational Modeling Albert’s research focuses on computational biology , biophysics , and image analysis , with particular emphasis on gradient sensing and biological transport networks . His work bridges theoretical physics and computer science to develop differentiable computational methods for understanding microorganism behavior and cell motility . Recent publications highlight advancements in optimal node positioning , non-reciprocal chaotic systems , and chemotaxis strategies . His studies often integrate computational modeling with biological systems , including applications in high-density microscopy data analysis and receptor dynamics for spatial gradient sensing. Albert’s work has been published in high-impact journals such as Physical Review Letters , PNAS , and Communications Biology . He employs open-access platforms and collaborates with institutions globally, with a focus on Denmark and international networks .