Aron Culotta is an Associate Professor of Computer Science at Tulane University. His research investigates computational methods to learn about human behavior from online social networks, combining machine learning, natural language processing, and social network analysis.  Examples include tracking diseases, measuring effectiveness of public health campaigns, informing crisis response, preventing online harassment, detecting deceptive marketing, and identifying unsafe products. He obtained his Ph.D. in Computer Science from the University of Massachusetts, Amherst in 2008, was a Microsoft Live Labs Fellow, a Nayar Prize Finalist, and recipient of best paper awards at AAAI and CSCW. His interdisciplinary research is supported by several NSF-funded collaborations with researchers in public health, political science, marketing, and emergency management.

Research Interests

natural language processing; social network analysis; data science; machine learning; domain adaptation; causal inference; public health; crisis informatics

Google Scholar Page

Research in the McSkimming group is focused on synthetic inorganic chemistry. Drawing inspiration from the active sites of metalloenzymes, our primary goal is to design and prepare precise ligand frameworks that support unusual and highly reactive metal complexes. Ultimately, we aim to develop creative and effective catalysts for the conversion of industrially important substrates such as CO and N2.

Disciplines

Inorganic chemistry, Organometallic chemistry

Selected Publications

McSkimming, A.; Cheisson, T.; Carrol, P. J.; Schelter, E. J., Functional Synthetic Model for the Lanthanide-Dependent Quinoid Alcohol Dehydrogenase Active Site, J. Am. Chem. Soc., 2018, 140(4), 1223–1226.

McSkimming, A.; Harman, W. H., A Terminal N2 Complex of High-Spin Iron(I) in a Weak, Trigonal Ligand Field, J. Am. Chem. Soc., 2015, 137, 8940–8943.

McSkimming, A.; Bhadbhade, M. M.; Colbran, S. B., Bio‐Inspired Catalytic Imine Reduction by Rhodium Complexes with Tethered Hantzsch Pyridinium Groups: Evidence for Direct Hydride Transfer from Dihydropyridine to Metal‐Activated Substrate, Angew. Chem. Int. Ed. 2013, 52, 3411–3416.

McSkimming, A.; Colbran S. B., The coordination chemistry of organo-hydride donors: new prospects for efficient multi-electron reduction, Chem. Soc. Rev., 2013, 42, 5439–5488.

My research interests include Fluvial Geomorphology, Tectonic Geomorphology, Stream Incision and Hillslope Response to Climatic and Tectonic Forcing, The application of cosmogenic nuclides, Landscape Evolution.

Publications

Kim, D.E., Seong, Y.B., Weber, J., Yu, B.Y. (2019) Unsteady migration of Taebaek Mountain drainage divide, Cenozoic extensional basin margin, Korean Peninsula. Geomorphology, 107012.

Kim, D.E., Seong, Y.B., Choi, K.H., Yu, B.Y. (2017) Role of debris flow on the change of 10Be concentration in rapidly eroding watersheds: a case study on the Seti River, central Nepal. Journal of Mountain Science 14, 716–730.

Kim, D.E., Seong, Y.B., Byun, J., Weber, J., Min, K. (2016) Geomorphic disequilibrium in the Eastern Korean Peninsula: Possible evidence for reactivation of a rift-flank margin. Geomorphology 254, 130–145

In the past several years, I developed a computer modeling system to predict storm surges (ADCIRC/DELFT3D), hurricane waves (SWAN), and corresponding wetland erosion and sedimentation (DELFT3D) under unstructured meshes and curvilinear grids for gulf-scale and regional applications; improved a parametric hurricane wind model based on the asymmetric Holland-type vortex models; analyzed directional spectra of hurricane-generated waves in the Gulf of Mexico; did numerical study of vegetation impact on reducing storm surge by wetlands; and carried numerical modeling of salt marsh morphological change induced by Hurricane Sandy.

My research experiences in China include 2D/3D simulations of tidal current, salinity and sediment transport in the area of Yangtze estuary and Hangzhou bay; prediction of storm-induced wind-waves in the Yangtze estuary; development of a model system, which includes storm-induced wind model, hydrodynamic model and sediment model, for simulating and predicting water levels, waves and morphological changes during a storm event in the Yangtze estuary;

Selected Publications

Book chapters & reports
Wang, H., Chen, Q., Hu, K., Snedden, G.A., Hartig, E.K., Couvillion, B.R., Johnson, C.L., Orton, P.M., 2017. Numerical modeling of the effects of Hurricane Sandy and potential future hurricanes on spatial patterns of salt marsh morphology in Jamaica Bay, New York City. USGS Open-File Report 2017-1016. https://doi.org/10.3133/ofr20171016.

Tao, J., Benger, W., Hu, K., Mathews, E., Ritter, M., Diener, P., Kaiser, C., Zhao, H., Allen, G., and Chen, Q., 2013. An HPC framework for large scale simulations and visualizations of oil spill trajectories. In: Coastal Hazards, Huang, W., Wang, K., and Chen, Q. (ed.), ASCE, ISBN 978-0-7844-1266-4, PP. 13-23, doi: 10.1061/9780784412664.002.

Hu, K., Q. Chen, and P. J. Fitzpatrick, 2012. Assessment of a parametric surface wind model for tropical cyclones in the Gulf of Mexico (http://dx.doi.org/10.5772/51288). In: Advances in Hurricane Research -Modelling, Meteorology, Preparedness and Impacts, Hickey, K. (ed.), InTech, ISBN 980-953-307-559-9, doi: 10.5772/51288.

Peer-reviewed journal articles
Liu, K., Chen, Q., Hu, K., Xu, K., Twilley, R.R., 2018. Modeling hurricane-induced wetland-bay and bay-shelf sediment fluxes. Coastal Engineering 135, 77-90.

Hu, K., Chen, Q., Wang, H., Hartig, E.K., and Orton, P.M., 2018. Numerical modeling of salt marsh morphological change induced by Hurricane Sandy. Coastal Engineering 132, 63-81.

Wang, H., Chen, Q., LaPeyre, M. K., Hu, K., and LaPeyre, J. F., 2017. Predicting the impacts of Mississippi River diversions and sea-level rise on spatial patterns of eastern oyster growth rate and production. Ecological Modelling 352, 40-53.

Wang, H., Chen, Q., Hu, K., and LaPeyre, M. K., 2017. A modeling study of the impacts of Mississippi River diversion and sea-level rise on water quality of a deltaic estuary. Estuaries and Coasts 40(4), 1028-1054.

Xu, K., Mickey, R.C., Chen, Q., Harris, C.K., Hetland, R.D., Hu, K., Wang, J., 2016. Shelf sediment transport during hurricanes Katrina and Rita. Computers & Geosciences 90, 24-39.

Hu, K., Chen, Q., Wang, H., 2015. A numerical study of vegetation impact on reducing storm surge by wetlands in a semi-enclosed estuary. Coastal Engineering 95, 66-76.

Hu, K., Chen, Q., and Kimball, K.S., 2012. Consistency in hurricane surface wind forecasting: An improved parametric model, Natural Hazards 61, 1029-1050.

Hu, K., and Chen, Q., 2011. Directional spectra of hurricane-generated waves in the Gulf of Mexico. Geophysical Research Letters, 38, L19608, doi:10.1029/2011GL049145.

Du, P., Ding, P., and Hu, K., 2010. Simulation of three-dimensional cohesive sediment transport in Hangzhou Bay, China. Acta Oceanologica Sinica, 29(2): 98-106.

Hu, K., Ding, P., Wang, Z., and Yang, S., 2009. A 2D/3D hydrodynamic and sediment transport model for the Yangtze Estuary, China. Journal of Marine Systems 77, 114-136.

Hu, K., and Ding, P., 2009. The effect of deep waterway constructions on hydrodynamics and salinities in Yangtze estuary, China. Journal of Coastal Research, SI 51, 961-965.

Hu, K., and Ding, P., 2007. Numerical study of wave diffraction effect introduced in the SWAN Model. China Ocean Engineering, 21(3): 495-506.

Hu, K., Ding, P., Ge, J., and Kong, Y., 2007. Modelling of storm surge in the coastal waters of Yangtze estuary and Hangzhou bay, China. Journal of Coastal Research, SI 50, 527-533.

Chen, Q., Gu, H., Zhou, J., Meng, Y., and Hu, K., 2007. Trends of soil organic matter turnover in the salt marsh of the Yangtze River estuary. Journal of Geographical Sciences, 17(1): 101-113.

Chen, Q., Zhao, H., Hu, K., and Douglass, S.L., 2005. Prediction of wind waves in a shallow estuary. Journal of Waterway, Port, Coastal and Ocean Engineering, 131(4): 137-148.

Hu, K., Ding, P., Zhu, S., and Cao, Z., 2000. 2-D current field numerical simulation integrating Yangtze Estuary with Hangzhou Bay. China Ocean Engineering, 14(1): 89-102.

Research Interests

Modeling of storm surge, hurricane waves, sediment transports and morphological developments in coastal and estuarine areas.

Prof. Bondar conducts theoretical and computational research at the boundary of quantum technology and ultrafast nonlinear optics. Of particular interest is the exploration how quantum control can be used to produce on-demand nonlinear optical properties, and how tailored nonlinear optical effects can enhance information processing tasks. The research of the Bondar group was recently featured in Quantamagazine, Nature Materials, Physics, PhysicsWorld, US Army,  Finding Genius Podcast, Tulane News etc.

Prof. Bondar joined the Department of Physics and Engineering Physics, Tulane University in 2018. Previously, he had been an Associate Research Scholar and Lecturer at Princeton University since 2014. He was promoted from a postdoctoral appointment at the Department of Chemistry, Princeton University. He earned his Ph.D. in Physics from the University of Waterloo, Canada in 2011 and M.Sc. with Honors from Uzhhorod National University, Ukraine in 2006.

Awards

• W. M. Keck Foundation Award (2021)
• James MacLaren Early Career Professorship in Physics (2021)
• Young Faculty Award DARPA (2019)
• Humboldt Research Fellowship for Experienced Researchers (2017)
• Air Force Young Investigator Research Program (2016)
• Los Alamos Director’s Fellowship (declined) (2013)
• President’s Graduate Scholarship (University of Waterloo) (2010)
• Ontario Graduate Scholarship (2010)

Recent Publications

Google Scholar Link

Interests

• Quantum technology
• Optics including quantum, ultrafast, nonlinear, and incoherent
• Optical communication and sensing
• Nonequilibrium quantum statistical mechanics
• Many-body quantum physics
• Quantum-classical analogies
• Quantum-classical hybrids
• Tunneling of complex systems (BEC)
• Superoscilations

Brief Research Summary

• High performance computing via nonlinear optics: The dominant paradigm of solid-state digital computers is bound to reach the technological limits with no viable alternative in sight. Thus, it is time to seek novel physical realizations of computing. We evaluate the possibility of utilizing nonlinear optical effects as a computational platform. This may pave the way for the development of new physical realization of computation.
• Quantum reservoir engineering: Realistic models of large quantum systems must include dissipative interactions with an environment, which may be of various natures (e.g., spontaneous emission, fluorescence, collisions, etc). It is widely believed that the dissipative forces destroy quantum features. This opinion is being challenged by reservoir engineering. In particular, it is possible to preserve and even enhance the quantum dynamical features of a system by judiciously coupling the system to a dissipative environment. Moreover, dissipative dynamics opens unique possibilities, not offered by potential forces, such as the violation of Newton’s third law.
• Novel optical technology exploring quantum-classical analogies: Optical analogs of quantum phenomena rely upon the resemblance of the Schrodinger equation to the wave equation with the paraxial approximation, where the wavefunction is replaced by the electric field. Using this analogy, we are developing new optical technologies for sensing and communication by adapting quantum reservoir engineering to the realm of classical optics.
• Quantum-classical hybrids: The interplay between quantum and classical systems is one of the most fascinating open questions of modern science. In particular, classical-quantum hybrid systems, in which both quantum and classical degrees of freedom interact, lies at the heart of several scientific disciplines ranging from chemistry to quantum gravity. Despite its importance, a fully consistent classical-quantum theory has eluded the countless attempts to develop one. In order to describe the hybrid systems, we are utilizing the Koopman-von Neumann (KvN) theory, which provides a quantum-like description of classical mechanics in terms of wave functions and self-adjoint operators. The KvN approach is based on a fundamental observation:  both classical and quantum evolutions are represented by unitary transformations. Although widely used in dynamical system theory, the KvN method remains unknown in other areas. The KvN approach is diametrically opposite to the phase-space representation of dynamics, which has been the basis of all the previous attempts to construct hybrid systems, providing a classical-like description of quantum dynamics in terms of momenta and coordinates.
• Theory of theories: We are living in the age of omnipresent data. A much-needed capability is to convert the collected data, irrespective of its nature, into knowledge characterizing the phenomenon that generated the data. This is a bottom-up approach, when a dynamical model is inferred from observed data. Whereas, the top-to-bottom approach refers to when a model is proposed first and then its predictions are confronted with observations (e.g., the least action principle). We are developing the bottom-up framework of Operational Dynamical Modeling that will allow physical models to be distilled directly from measured data in a systematic way. This approach will not only enable to obtain efficient models for complex systems, but also solve open problems in nonequilibrium quantum dynamics.
• Superoscilations: If over a brief time interval, we combine several light waves of different wavelengths into an almost perfect destructive interference, the combined electric field does not become exactly zero. Instead, the field performs weak and rapid oscillations, the superoscillation, that is faster (with a seemingly shorter wavelength) than the original light waves that are combined. We experimentally and theoretical develop the applications of this phenomena to enhance optical sensing.

Aaron Maus is a Professor of Practice in Computer Science at Tulane University. His research area is in computational structural biology, with a focus on protein folding and structure comparison and analysis. He has worked on protein structure refinement using statistical energy functions and developed a novel technique for the identification of regions of similarity within protein structures. Areas of interest include computational simulations, immersive 3D visualizations, computational tractability, and the pedagogy of computer science and technical education.