جست‌وجوی استاد و پژوهشگر

• Applied Mathematics
• Theoretical Mechanics
• Computational Mechanics
• +۷ مورد دیگر

Dr. Luis Espath is an Assistant Professor of Applied Mathematics at the University of Nottingham, affiliated with the School of Mathematical Sciences. His research spans theoretical and computational mechanics, uncertainty quantification, and machine learning. He has held postdoctoral and research scientist roles at KAUST and RWTH Aachen, respectively, and authored the Springer Nature book Mechanics & Geometry of Enriched Continua . Education : Engineering Diploma (2007) at PUCRS, Brazil; Master’s and Ph.D. (2013) at UFRGS, Brazil; Habilitation in Mathematics (2021) at RWTH Aachen, Germany. Dr. Espath’s research focuses on the application of phase-field and continuum mechanics to model complex systems, with an emphasis on thermodynamic consistency and computational efficiency. He integrates stochastic optimization and machine learning into fluid dynamics and structural modeling. His recent publications highlight expertise in phase-field gradient theory, fluid-structure interaction, and Bayesian experimental design. The 2021 articles reflect his work on Navier-Stokes equations, reactive Cahn-Hilliard systems, and divergence-free fluid flow approximations.

• Computational Mechanics
• Fluid Dynamics
• Biomedical Engineering
• +۴ مورد دیگر

George E Karniadakis is the Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Professor of Engineering at Brown University . He holds a joint appointment in the Department of Applied Mathematics and has extensive affiliations in Engineering and Computational Biology . His career spans prestigious institutions including MIT, Stanford/NASA Ames, Princeton, Caltech, and Peking University as a Visiting Professor. Karniadakis is a triple Fellow of AAAS, SIAM, and APS, and a distinguished awardee of the SIAM/ACM Prize and Oden Medal . His research focuses on fractional PDEs , uncertainty quantification , mesoscale simulations of biological flows, and fluid-structure interaction problems. He directs the DOE Center on Physics-Informed Learning Machines and leads a MURI project on Physics-Informed Neural Networks . Key Research Areas: Computational fluid dynamics (CFD) and turbulence modeling Stochastic differential equations and polynomial chaos Biomechanics of red blood cells and sickle cell disease Machine learning for physics-informed models Fractional calculus applications in viscoelasticity Multiscale and multi-resolution modeling Scientific Awards: SIAM/ACM Prize in Computational Science & Engineering (2021) SIAM Ralf Kleinman Award (2015) CFD Award (2007) Inaugural J Tinsley Oden Medal (2013) Labs & Teams: Director of DOE Center on Physics-Informed Learning Machines Lead PI of OSD/AFOSR MURI on Physics-Informed Neural Networks Collaborator in multiscale simulations of brain blood flow and arterial networks

Sébastien Breteauxمشاهده پروفایل

مدرس

• Mathematical analysis of physical systems with large numbers of particles
• Mathematical analysis of many-body physical systems
• Partial differential equations
• +۲ مورد دیگر

Sébastien Breteaux is a Lecturer at the University of Lorraine, where he is affiliated with the Faculty of Mathematics, Computer Science and Mechanics (UFR MIM) in Metz. He conducts research at the Élie Cartan Institute of Lorraine (IECL), a joint research unit between the University of Lorraine and CNRS with sites in both Metz and Nancy. His primary research focuses on the mathematical analysis of physical systems with large numbers of particles and many-body quantum systems. Dr. Breteaux's research spans partial differential equations, infinite-dimensional pseudodifferential calculus, and mathematical physics, with particular emphasis on developing effective equations for quantum systems. His work combines rigorous mathematical analysis with applications to quantum mechanics, investigating topics such as fractional Schrödinger operators, Hartree-Fock-Bogoliubov equations for Bosons, and quasi-classical approximations in quantum electrodynamics. His recent publications demonstrate a consistent focus on asymptotic methods and mean-field approximations for complex quantum systems. His research program includes significant funding as coordinator for France of the "Effective Approximation and Dynamics of Many-Body Quantum Systems" project, supported by the Agence Nationale pour la Recherche and the Deutsche Forschungsgemeinschaft with over half a million euros. He has supervised PhD students including Jimmy Payet (2019-2023) and currently co-supervises Tommaso Pistillo with Jérémy Faupin and Michele Correggi. His service activities include membership on the IECL laboratory council, participation in the AM2I pole council (Automatics, Mathematics, Computer Science, and their Interactions), and serving on personnel committees evaluating academic positions. He has organized numerous scientific events, including the ICMP Satellite Summer School on Effective Approximation and Dynamics of Many-Body Quantum Systems at the University of Lorraine. As an educator, Dr. Breteaux teaches integrated mathematics courses at ISFATES (Franco-German Higher Institute of Technology, Economics and Sciences), probability tutorials for mathematics students, and courses for the MEÉF Master's degree specializing in mathematics. He has developed educational initiatives including the Development of Automation in Mathematics (DAM) project and the Online Mathematics Bridge Course at ISFATES.

Somdatta Goswamiمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Artificial Intelligence for Science
• Computational Mechanics
• +۳ مورد دیگر

Somdatta Goswami serves as Assistant Professor in Civil and Systems Engineering and Applied Mathematics and Statistics at Johns Hopkins University, with dual affiliations at the Institute for Data Intensive Engineering and Science (IDIES) and Hopkins Extreme Materials Institute (HEMI). She leads the Centrum IntelliPhysics research group developing AI-driven methodologies for scientific discovery. Her educational trajectory includes: Bachelor's in Civil Engineering from Birla Institute of Technology, Mesra (2011) Master's in Structural Engineering from Indian Institute of Engineering Science and Technology (2013) PhD in Civil Engineering and Structural Mechanics from Bauhaus University-Weimar, Germany (2020) funded by DAAD Dr. Goswami's research pioneers Scientific Machine Learning at the intersection of computational mechanics and AI, focusing on neural operator architectures that accelerate physics-based simulations. Her group develops methods for long-time horizon prediction, multiscale multiphysics modeling, and real-time inference in complex systems through latent space representations and physics-informed learning. Current emphases include cardiac digital twins, structural response under natural hazards, and RNA electrophoresis modeling. Analysis of her 2024-2025 publications reveals dominant trends in latent operator learning, physics-informed neural networks, and hybrid solvers combining traditional numerical methods with deep learning. These innovations enable breakthroughs in computational efficiency across engineering and biological domains, particularly in multiscale modeling and uncertainty-aware simulation. Her scientific recognition includes: National Science Foundation’s National Artificial Intelligence Research Resource (NAIRR) Pilot Johns Hopkins University Discovery Award 2024 Dr. Goswami mentors PhD candidates including Dibakar Roy Sarkar (Creel Family Engineering Fellow), Sharmila, and Maryam. Major research funding comprises: NSF grant for "Cardiac Digital Twins" with Kevrekidis, Trayanova, and Maggioni NSF grant for exascale AI-integrated simulations with UT Austin DOE grant for uncertainty-informed latent operators with Shields, Graham-Brady, and Kevrekidis Johns Hopkins Discovery Award for biological systems modeling The Centrum IntelliPhysics group operates within JHU's Latrobe Hall, collaborating with IDIES and HEMI on interdisciplinary projects spanning computational mechanics, materials science, and biological systems. Their work integrates high-performance computing with novel neural architectures to solve previously intractable scientific problems.

Jean-Baptiste Colliatمشاهده پروفایل

استاد

• Numerical Simulation of Materials and Structures
• Civil Engineering
• Mechanical Behavior of Heterogeneous Materials
• +۷ مورد دیگر

Jean-Baptiste Colliat is a Full Professor at Polytech Lille , Lille University, specialized in Numerical Simulation of Materials and Structures within Civil Engineering. His work bridges computational mechanics with practical applications in concrete durability, geothermal systems, and biomedical simulations. Develops Enriched Finite Element Methods for heterogeneous materials Focuses on Uncertainty Quantification in multi-scale systems Key applications: Nuclear Waste Containment , Rockfill Stability , and Obstetric Biomechanics Recent research trends include 3D fracture network modeling, stress-permeability coupling in porous media, and stochastic analysis of material heterogeneity. His work integrates X-ray Micro-CT , DEM , and Multiscale Homogenization techniques. Professor Colliat leads computational frameworks for Embedded Finite Element Methods and Excursion Set Theory applications. He actively collaborates with LaMcube (Laboratoire de Mécanique Multi-physique Multiéchelle) on problems ranging from microstructural evolution to large-scale infrastructure failure.

Zaher Haniمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Dispersive Wave Equations
• Turbulence Theory
• +۴ مورد دیگر

Zaher Hani is a Professor of Mathematics at the University of Michigan, holding the Frederick W. and Lois B. Gehring Professorship. He previously served as an assistant professor at Georgia Tech (2014-2018) and as a Courant Instructor/Simons Fellow at NYU's Courant Institute (2011-2014). He earned his Ph.D. (2011) and M.A. (2008) in Mathematics from UCLA under Terence Tao. Research Focus: Nonlinear partial differential equations (PDE), particularly dispersive wave equations, turbulence theory, and connections to harmonic analysis, dynamical systems, probability, and mathematical physics. Editorial Roles: Editor for Archive for Rational Mechanics and Analysis and Ars Inveniendi Analytica . His work explores the behavior of solutions to nonlinear dispersive PDEs in deterministic and probabilistic frameworks, with applications in quantum mechanics, nonlinear optics, plasma physics, and general relativity. Recent publications focus on wave kinetic equations, turbulence derivation, and Sobolev norm growth. He has collaborated extensively with Yu Deng, Pierre Germain, Jalal Shatah, and others. Scientific Awards: Courant Instructor/Simons Fellow at NYU Frederick W. and Lois B. Gehring Professorship He contributes to expository works and curriculum development, including a Ph.D. thesis on nonlinear Schrödinger equations. His teaching and administrative contact details are listed at the University of Michigan's Mathematics Department.

Mauro Salazarمشاهده پروفایل

استادیار

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +۷ مورد دیگر

Mauro Salazar is an Assistant Professor in the Control Systems Technology section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), with co-affiliations at the Eindhoven AI Systems Institute (EAISI) in both Health and Mobility domains. He leads his own research group focused on optimization models and methods for systems and control. His educational background includes: B.Sc. and M.Sc. in Mechanical Engineering from ETH Zürich (2012, 2015) Master thesis conducted at EPFL's Automatic Control Lab Ph.D. in Mechanical Engineering from ETH Zürich (2019) in collaboration with Ferrari Formula 1 Postdoctoral Scholar at Stanford University's Autonomous Systems Lab (2019-2020) Salazar's research centers on optimization models for multi-scale cyber-socio-technical systems with applications spanning mobility systems, pandemic response, and material design. His work bridges theoretical optimization with practical implementation, particularly in sustainable mobility solutions where he develops methods for optimal design and operation of transportation systems from single vehicles to entire networks. He investigates mesoscopic user behavior modeling and designs incentive schemes to align individual and system-level objectives in transportation networks. His recent publications (2025) demonstrate a strong focus on sustainable mobility systems, with recurring themes in electric vehicle fleet management, battery degradation modeling, ride-pooling algorithms, and energy management for transportation electrification. His work spans multiple disciplines including automotive engineering, control systems, energy management, and public health, characterized by rigorous mathematical optimization approaches applied to real-world challenges. His scientific recognitions include: Two ETH Medals (for Master's and PhD theses) Best Student Paper awards at ITS 2018 and ECC 2022 Best Teacher Award (2021) Nomination for TU/e Young Researcher Award (2022) Salazar actively advises research projects and secured funding through the Dutch Research Council's NEON project (Crossover research program, Grant 17628). His teaching portfolio includes Optimal Control and Reinforcement Learning, Engineering Optimization, and Advanced Full-Electric and Hybrid Powertrain Design. He leads the 'Group Salazar' within the Control Systems Technology section and contributes to EAISI Health and Mobility initiatives.

• Mathematical Modeling
• Numerical Methods
• Mass Transport in Soft Porous Materials
• +۴ مورد دیگر

Tobias Gebäck is an Associate Professor at Chalmers University of Technology's Applied Mathematics and Statistics department, affiliated with the SuMo Biomaterials research center. His work focuses on mathematical modeling and numerical methods for mass transport in soft porous materials , with applications in drug delivery systems and absorbent product development . He also investigates charged particle transport mechanisms relevant to cancer radiation therapy . Key research areas: mathematical modeling, numerical methods, transport in porous media, radiation therapy applications Active collaborations: SuMo Biomaterials research center Recent publications emphasize stochastic modeling of 3D structures , Lattice Boltzmann simulations for transport phenomena in complex materials, and multi-scale diffusion analysis of bio-based systems. This work connects fundamental mathematics with industrial applications in pharmaceutical and consumer product development.

Hans van Dommelenمشاهده پروفایل

دانشیار

• Micromechanics
• Multi-scale mechanics
• Structure–property relations
• +۷ مورد دیگر

Hans van Dommelen is Associate Professor of Micromechanics at Eindhoven University of Technology (TU/e), Department of Mechanical Engineering, where he leads the Group Van Dommelen . His research couples microstructure to mechanical and functional behaviour of materials spanning nuclear fusion, additive manufacturing, polymers, and biomechanics. Education PhD in Mechanical Engineering, TU/e (2003) – Micromechanics of particle-modified semicrystalline polymers Visiting researcher, MIT (1999–2000), University of Virginia (2003–2004), and Cambridge University (2010–2012) Research Interests Van Dommelen’s work focuses on multi-scale mechanics and structure–property relationships . Using microstructural modelling and homogenization techniques, he links phenomena at the microscale to macroscopic response in: Crystalline and heterogeneous materials Nuclear fusion reactor materials (tungsten, liquid-metal shields) Additive manufacturing (wire-arc, selective laser sintering, vat photopolymerization) Semi-crystalline polymers and short-fiber composites Traumatic brain injury biomechanics Scientific Output He has authored over 230 peer-reviewed publications (h-index > 40) in leading journals such as Journal of the Mechanics and Physics of Solids , Biomechanics and Modeling in Mechanobiology , Nuclear Fusion , and Additive Manufacturing . Recent trends include viscoelastic-viscoplastic metamaterials, anisotropic food printing, recrystallization kinetics of tungsten under fusion loads, and multiscale fracture of additively manufactured metals. Teaching & Supervision Van Dommelen coordinates and lectures in: Structure and Properties of Materials Computational and Experimental Micro-mechanics Fusion Reactor Materials and Plasma-Wall Interaction He has supervised >85 MSc and PhD theses to date. Laboratory & Collaborations He heads the Group Van Dommelen within the Mechanics of Materials section, maintaining strong collaborations with DIFFER, ITER, and international partners on liquid-metal technologies for fusion blankets and advanced additive manufacturing processes.

Ondrej Rokosمشاهده پروفایل

استادیار

• Mathematical Modelling
• Homogenization
• Computational Mechanics
• +۷ مورد دیگر

Ondrej Rokos is an Assistant Professor in the Mechanics of Materials section at Eindhoven University of Technology (TU/e), Department of Mechanical Engineering. He is actively involved in research and teaching related to multiscale materials modeling, with a focus on computational mechanics, metamaterials, and homogenization techniques. He leads Group Rokos and is affiliated with the Institute for Complex Molecular Systems (ICMS). PhD in Civil Engineering (2014) from Czech Technical University in Prague Postdoctoral positions at CTU Prague and TU/e Visiting researcher at TU/e (2015) and University of Luxembourg (2016) His research centers on understanding multiscale physical phenomena in materials engineering to enable optimal material design. Key areas include: Homogenization and quasicontinuum methods for discrete microstructures Machine learning integration in surrogate modeling Stochastic structural dynamics and experimental-computational frameworks Mechanical metamaterials with pattern transformation capabilities Recent publications emphasize the application of machine learning (e.g., symmetric positive definite convolutional networks) and advanced homogenization techniques (e.g., similarity-equivariant graph neural networks) to optimize metamaterials. Notable trends include: Development of data-driven models for modular structures Geometrical parameterization of elastomeric metamaterials Active stiffness control in pneumatic systems Extended quasicontinuum methodologies for heterogeneous systems He teaches courses in: Computer Aided Engineering Solid Mechanics Machine Learning for Multi-Physics Modeling and Design Research is conducted within the Mechanics of Materials group, with affiliations to ICMS and collaborations across institutions.

Vrushali A. Bokilمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۹ مورد دیگر

Dr. Vrushali A. Bokil is a Professor of Mathematics and currently serves as Executive Associate Dean of the College of Science at Oregon State University (October 2024 - Present). Previously, she served as Interim Dean of the College of Science (August 1, 2022 - October 30, 2023) and Associate Dean for Research & Graduate Studies (October 2020 - July 2022; November 2023 - September 2024). She earned her Ph.D. in Mathematics from the University of Houston in 2003 under Professor Roland Glowinski and completed postdoctoral research at North Carolina State University under Professor H.T. Banks. Her research spans applied mathematics with focus on numerical methods for wave propagation problems, particularly Maxwell's equations using finite difference and finite element methods, and mathematical ecology involving deterministic and stochastic models for population dynamics, epidemiology, and spatial ecology. She has secured significant funding including NSF grants for computational mathematics and mathematical biology projects. Dr. Bokil's recent publications demonstrate expertise in virtual element methods for magnetohydrodynamics, convergence analysis of numerical schemes for Maxwell's equations, and optimal control of plant disease epidemics. Her work bridges computational mathematics with biological applications, particularly in plant virus modeling and control strategies. Scientific Awards: Champion of Science award (2022) Inclusive Excellence Award (2019) ELATES Fellow (2021-2022) ADVANCE Faculty Fellowship Thomas Jefferson Fund award recipient As an advisor, Dr. Bokil has mentored several Ph.D. students including Sebastian Naranjo Alvarez, Brady Bowen, and Puttha Sakkaplangkul. She has served as PI or co-PI on multiple major grants including NSF DMS #1720116 (Collaborative Research: Compatible Discretizations for Maxwell Models in Nonlinear Optics), NSF DMS #2012882 (Computational and Multi-Scale Methods for Nonlinear Electromagnetic Models), and FACE Foundation funding for mathematical epidemiology of plant viruses. Dr. Bokil leads initiatives in diversity, equity, and inclusion as Chair of the SIAM Career Opportunities Committee, member of the AMS-MAA-SIAM Committee on Employment Opportunities, and past member of OSU's President's Commission on the Status of Women. She co-organized the AWM Aligning Actions at Crossroads Workshop to improve culture in mathematical sciences.

Stefan Heinzمشاهده پروفایل

استاد

• Mathematical Modeling
• Multiscale Processes
• Stochastic Analysis
• +۵ مورد دیگر

Professor Stefan Heinz is an academic at the University of Wyoming , affiliated with the School of Computing as an Adjunct Professor. He holds a Ph.D. in Physics from the Heinrich-Hertz Institute, Berlin (1990) and an M.Sc. in Physics from Humboldt-Universitaet zu Berlin (1986). Research Interests: Mathematical Modeling Multiscale Processes Stochastic Analysis Monte Carlo Simulations Computational Fluid Dynamics Turbulence, Combustion, and Multiphase Flows His recent publications focus on hybrid turbulence modeling , machine learning applications for fluid dynamics , and physically consistent simulation methods , particularly for separated flows and atmospheric systems . Key keywords from his work include: Hybrid RANS-LES , Continuous Eddy Simulation , Probability Density Functions , Dynamic LES , von Kármán Constant , and Stochastic Micro-Mixing . Scientific Awards include: 2022 Adjunct Professor, University of Wyoming School of Computing 2022 Finalist for Provost Term Professorship 2015 Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA) 2011 Outstanding Dissertation Supervision Award 2008 Extraordinary Merit in Research He has taught advanced courses in Stochastic Modeling (MATH 5490) and Mathematical Modeling (MATH 4300) since 2004. He organized the 2014 RMMC Summer School and led workshops on the 100 Years of Fokker-Planck Equation (2017) and Turbulence Benchmarking (2019).

Alexander W. Dowlingمشاهده پروفایل

دانشیار

• Chemical Engineering
• Computational Optimization
• Machine Learning
• +۷ مورد دیگر

Prof. Alexander W. Dowling is an Associate Professor at the Department of Chemical and Biomolecular Engineering within the College of Engineering at the University of Notre Dame. He holds joint appointments with the Department of Applied & Computational Mathematics & Statistics and is affiliated with the Center for Sustainable Energy at Notre Dame (ND Energy) and the Center for Nano Science and Technology (ND nano). His research combines chemical engineering, computational optimization, machine learning, and data science across three core themes: (1) molecular-to-systems (multiscale) modeling and optimization, (2) optimal design of experiments and statistical inference, and (3) machine learning for bridging timescales. Dowling earned his Ph.D. in Chemical Engineering from Carnegie Mellon University (2015) and a B.S.E. from the University of Michigan-Ann Arbor (2010). His research applications span energy markets and infrastructure, integrated energy systems, carbon capture and sequestration, circular economies for refrigerants, sustainable hydrogen production, critical minerals, and advanced separation technologies like membranes and ionic liquids. Education Ph.D., Carnegie Mellon University, 2015 B.S.E., University of Michigan-Ann Arbor, 2010 His recent publications focus on multiscale modeling frameworks, Bayesian hybrid models for uncertainty quantification, and machine learning applications in energy systems and materials design. These works emphasize techno-economic analysis, dynamic process optimization, and uncertainty mitigation strategies. Scientific awards include the 2025 James A. Burns, C.S.C., Award and the 2020 NSF CAREER Award (CBET-1941596). He actively mentors graduate students and postdoctoral researchers, with lab alumni including Dr. Bridgette Befort (2022), Dr. Xian Gao (2022), Dr. Ke Wang (2025), and Dr. Jialu Wang (2024).

Suri Vaikuntanathanمشاهده پروفایل

استاد

• Biophysics
• Theoretical Chemistry
• Physical Chemistry
• +۴ مورد دیگر

Suri Vaikuntanathan is a Professor of Chemistry at the University of Chicago with dual affiliations in the Pritzker School of Molecular Engineering and the James Franck Institute. His research focuses on developing and applying tools from equilibrium and nonequilibrium statistical mechanics to understand complex systems in physical chemistry, soft condensed matter physics, and biophysics. His research interests span statistical mechanics of driven systems and self-assembly out of equilibrium, information processing and control in biology, and studies of aqueous fluctuations in heterogeneous environments. His group develops coarse-grained models for efficient multiscale chemical and biophysical simulations. His most recent publication introduces TopicVelo, a novel method combining probabilistic topic modeling with stochastic transcriptional models to analyze single-cell RNA sequencing data, revealing how cells process information without a central 'brain'. His research has been recognized with prestigious awards including the Sloan Research Fellowship and the JFI Director's Postdoctoral Fellowship Award. Sloan Research Fellowship (2017) JFI Director's Postdoctoral Fellowship Award (2022) Professor Vaikuntanathan advises PhD students including Cheng Frank Gao and maintains an active research group focused on the intersection of statistical physics, machine learning, and biological systems. His work has significant implications for understanding cellular decision-making processes, which could advance cancer research and immunology studies. The Vaikuntanathan Group operates at the intersection of physics, chemistry, and biology, developing theoretical frameworks to understand how cells run complex computations without centralized control. Their recent work on TopicVelo demonstrates how cells process information through stochastic mechanisms, challenging traditional engineering principles while providing new tools for biomedical research.

Vince Lyzinskiمشاهده پروفایل

دانشیار

• Statistical Network Inference
• Graph Matching
• Vertex Nomination
• +۶ مورد دیگر

Vince Lyzinski is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, with additional affiliations in the Applied Mathematics, Statistics, and Scientific Computation (AMSC) program. His work spans statistical network inference, graph matching algorithms, and machine learning applications to complex networks. Ph.D. in Applied Mathematics and Statistics (2013), M.S.E. (2011), and M.A. (2007) from Johns Hopkins University B.S. in Mathematics from University of Notre Dame (2006) His research focuses on statistical network inference , particularly graph matching and vertex nomination , with applications to connectomics and adversarial activity analytics. Supported by DARPA MAA , AFOSR , and JHU HLTCOE , he develops algorithms like iGraphMatch for graph alignment and analysis. Recent publications (2025-2023) explore vertex label error impacts, network trimming for robustness, and stochastic blockmodel extensions. Key papers include "Asymptotically Perfect Seeded Graph Matching" and "ACRONYM: Augmented Degree-Corrected Network Models" . Scientific Awards : Best Paper Award at GTA3 2018 Workshop He has advised 13 Ph.D. students and 1 postdoc across UMD , JHU , and Texas A&M , including Keith Levin (now at UW-Madison) and Jesus Arroyo (now at Texas A&M). His DARPA-funded team includes researchers from UMass, UMD, BU, and JHU.