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Wing K. LiuView profile

Professor

• Computational Mechanics
• Multiscale Modeling
• Additive Manufacturing
• +5 more

Wing K. Liu is the Walter P. Murphy Professor of Mechanical Engineering, Civil and Environmental Engineering (by courtesy), and Materials Science and Engineering at Northwestern University. He leads the HiDeNN-AI research group, focusing on mechanistic data science, hierarchical deep learning neural networks (HiDeNN), and multiscale modeling for advanced manufacturing and materials innovation. His work integrates physics-based principles with machine learning to address challenges in additive manufacturing, computational mechanics, and materials design. Education : Ph.D., M.S., California Institute of Technology B.S. (Hons) in Engineering Science, University of Illinois at Chicago Research Interests : Liu’s research spans mechanistic data science, AI-driven materials innovation, and multiscale modeling. His HiDeNN framework enables fast design, optimization, and discovery in engineering systems. Key areas include laser powder bed fusion, image-based modeling, and predictive manufacturing processes. He emphasizes combining scientific principles with data to accelerate technology development. Awards and Recognition : Gauss-Newton Medal (2012) John von Neumann Medal (2007) ASME Robert Henry Thurston Lecture Award (2007) ISI Highly Cited Researcher in Engineering (2001) Teaching and Academic Leadership : Liu teaches advanced finite element methods, multiscale modeling, and computational mechanics. He has served as editor for Computational Mechanics , vice president of the International Association for Computational Mechanics, and past president of the U.S. Association for Computational Mechanics. Labs and Teams : His research group, Wing K. Liu Group , develops cutting-edge tools like JAX-FEM and C-HiDeNN, focusing on GPU-accelerated simulations and AI integration in engineering systems.

• Multiphysics Modeling
• Thermal-Hydraulics
• Monte Carlo Transport Methods
• +3 more

April Novak is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) in the Department of Nuclear, Plasma, and Radiological Engineering. She holds a joint appointment at the National Center for Supercomputing Applications (NCSA). Her research focuses on multiphysics modeling, thermal-hydraulics, and advanced reactor design, particularly using the MOOSE computational framework. She earned her PhD in Nuclear Engineering from UC Berkeley (2020) and a BS from UIUC (2015). Dr. Novak’s work emphasizes porous media modeling for pebble bed reactors (PBRs), conjugate heat transfer, and Monte Carlo transport methods. She has validated models against experiments like SANA and contributed to tools like Pronghorn and Cardinal. Her research integrates high-performance computing (HPC) for reactor simulations, with recent projects on lead-cooled fast reactors and sodium fast reactor bypass flows. Recipient of the R&D 100 Award (2023) and the Innovations in Nuclear Technology Award (2018), her teaching accolades include the Students' Award for Excellence in Undergraduate Teaching (2024). She advises on multiphysics coupling between neutronics and thermal-hydraulics and collaborates on the Virtual Test Bed (VTB) for advanced reactor models.

Zachary SlepianView profile

Assistant Professor

• Cosmology
• Dark Energy
• Dark Matter
• +7 more

Zachary Slepian is an Assistant Professor in the Department of Astronomy at the University of Florida, part of the College of Liberal Arts and Sciences. His research focuses on cosmology, particularly dark energy, dark matter, large-scale structure formation, and the development of advanced algorithms for analyzing galaxy surveys. He holds a BA from Princeton (2011), an MSt in Philosophy of Physics from Oxford (2012), and a PhD in Astrophysics from Harvard (2016), advised by Daniel J. Eisenstein. His work combines theoretical modeling with computational innovations, such as fast correlation function algorithms and applications to datasets like BOSS, eBOSS, and DESI. He previously held postdoctoral fellowships at Lawrence Berkeley National Laboratory (Chamberlain Fellow and Einstein Fellow). His research spans three primary areas: theoretical models for large-scale structure, algorithm design for efficient data analysis, and applying these tools to observational surveys. Notable contributions include the first high-significance BAO detection in the 3-point correlation function and a transformative 3PCF algorithm enabling analysis of the entire observable universe. He explores topics like parity violation in cosmology, neutrino effects on structure formation, and magnetohydrodynamic turbulence in the interstellar medium. His recent work emphasizes analytic methods and high-performance computing, with a focus on parity-odd cosmological signals and next-generation surveys like DESI and the proposed MegaMapper. He actively contributes to open-source tools like nbodykit and Galactos, advancing computational efficiency in cosmological data analysis.

Larissa NovelinoView profile

Assistant Professor

• Origami Engineering
• Metamaterials
• Deployable Structures
• +5 more

Larissa Novelino is an Assistant Professor of Civil and Environmental Engineering at Rice University, where she leads the Novelino Research Group. Her work bridges art and engineering through origami-inspired innovations, focusing on deployable structures, metamaterials, and tunable systems. She holds a Ph.D. from Georgia Institute of Technology (2021) and an M.S. from Pontifical Catholic University of Rio de Janeiro (2015). Her research explores origami's potential in creating shape-changing structures for space exploration, emergency infrastructure, and robotics. Key areas include auxetic metamaterials, magnetic control of origami microrobots, and computational modeling of origami mechanics. Recent projects address imperfection effects in metamaterials and untethered robotic actuation. Education: B.S., Federal University of Para, Brazil (Civil Engineering) M.S., Pontifical Catholic University of Rio de Janeiro, Brazil (Civil Engineering) Ph.D., Georgia Institute of Technology, USA (Civil Engineering) Her work spans computational methods, additive manufacturing, and geometric mechanics. Current opportunities include Ph.D. positions in origami engineering, deployable systems, and metamaterial design. Labs and collaborations emphasize interdisciplinary approaches, integrating mechanical analysis, robotic systems, and advanced manufacturing techniques. Her group actively seeks students passionate about applying origami principles to solve real-world engineering challenges.

• Optimization
• Continuous Optimization
• Nonlinear Systems
• +3 more

Quentin Rebjock is a Doctoral Assistant at the Chair of Continuous Optimization (OPTIM) within the Department of Mathematics (MATH) at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on optimization theory and applications, including trust-region methods, nonlinear systems, and numerical analysis. He is affiliated with the School of Basic Sciences (SB) and contributes to cutting-edge research in continuous optimization. Recent publications explore topics such as fast convergence in non-isolated minima, bounded-rank matrix optimization, and synchronization dynamics on geometric manifolds. His research bridges theoretical foundations with practical algorithmic development. Office: MA C2 637, EPFL Station 8, 1015 Lausanne Contact: +41 21 693 87 39 Rebjock’s doctoral studies are within the Doctoral Program in Computer and Communication Sciences (EDIC), supervised by the OPTIM lab. His work emphasizes interdisciplinary approaches, combining mathematical rigor with computational efficiency.

Haizhao YangView profile

Associate Professor

• AI for Science
• Machine Learning
• High-Performance Computing
• +4 more

Haizhao Yang is an Associate Professor of Mathematics and Computer Science at the University of Maryland College Park (UMCP). He holds affiliate appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Applied Mathematics and Scientific Computation (AMSC) program. Previously, he served as an Assistant Professor at Purdue University and the National University of Singapore, and as a Visiting Assistant Professor at Duke University (2015–2017). His education includes a B.Sc. from Shanghai Jiao Tong University (2010), M.Sc. from The University of Texas at Austin (2012), and Ph.D. from Stanford University (2015). Yang's research focuses on machine learning theory, scientific computing, and applied mathematics. Key areas include AI for scientific discovery, high-performance computing, and algorithm development for differential equations. His work bridges mathematical rigor with practical applications, such as developing neural network-based solvers for high-dimensional PDEs and quantum computing integration for uncertainty quantification. He leads a dynamic research group with over 30 students and postdocs across PhD, master's, and undergraduate levels. Notable achievements include the NSF CAREER Award (2020), ONR Young Investigator Award (2022), and DARPA Young Faculty Award (2024). His laboratory collaborates with institutions like Lawrence Berkeley National Lab and Duke University, emphasizing interdisciplinary projects in data science and computational physics. Recent publications highlight innovations in operator learning for PDEs, quantum algorithm integration, and adversarial reinforcement learning. His group actively explores AI-driven approaches to overcome computational bottlenecks in scientific simulations, with techniques like the Finite Expression Method and OptimAI framework for automated problem-solving.

• Radio Astronomy
• Astrophysics
• Astrophysical Observations
• +5 more

Dr. Marcin Sokolowski is a Research Fellow at Curtin University, associated with the Curtin Research Institute and Curtin Institute of Radio Astronomy (CIRA), within the School of Electrical Engineering, Computer, and Mathematical Sciences (EECMS). His primary affiliation is with the Faculty of Science and Engineering. His research focuses on radio astronomy, astrophysical observations, and instrumentation development, particularly involving the Murchison Widefield Array (MWA) and SKA precursor projects. Key research areas include Fast Radio Burst (FRB) detection, pulsar surveys, gamma-ray burst (GRB) follow-up, telescope calibration, and low-frequency radio interferometry. He has contributed to the design and testing of SKA-Low prototype systems, holographic calibration techniques, and real-time data processing pipelines for transient detection. His work also addresses radio frequency interference mitigation and satellite emissions analysis in radio-quiet zones. Publications span over 15 years, emphasizing instrumental advancements, transient phenomena studies, and collaborative projects like the SMART pulsar survey. His work intersects computational methods, such as GPU-accelerated imaging, and interdisciplinary applications like space situational awareness using radio telescopes. Awards: No specific prizes or fellowships are explicitly listed in the provided texts. His contributions are primarily recognized through extensive peer-reviewed publications in journals like the Astrophysical Journal, Monthly Notices of the Royal Astronomical Society, and the Publications of the Astronomical Society of Australia. Advising/Grants: While no explicit grant details are provided, his involvement in major facilities like MWA and SKA prototypes suggests funding from astronomical collaborations and institutions. His research team includes international collaborators, reflecting large-scale project coordination. Labs/Teams: Core affiliations include CIRA, the School of EECMS, and collaborations with institutions like the International Center for Radio Astronomy Research (ICRAR). He contributes to initiatives like the Southern-sky MWA Rapid Two-metre (SMART) pulsar survey and the Engineering Development Array (EDA) projects.

Venkatasubramanian SureshView profile

Professor

• Algorithmic Fairness
• Automated Decision Systems
• Privacy
• +3 more

Suresh Venkatasubramanian is a Professor of Data Science and Computer Science at Brown University, with a secondary appointment in the Humanities. He serves as Interim Director of Data Science and directs the Center for Technological Responsibility, Reimagination, and Redesign (CNTR) within the Data Science Institute. His research focuses on algorithmic fairness, automated decision systems, and their societal impacts. He holds a PhD from Stanford University (1999) and previously served as an Assistant Professor at the University of Utah, where he held the John and Marva Warnock Endowed Chair. Key roles include co-authoring the White House’s Blueprint for an AI Bill of Rights during his tenure in the Biden-Harris administration. He has received prestigious awards like the NSF CAREER Award, ICDE 2017 Test-of-Time Award, and Fast Company’s AI20 List recognition. His work bridges computer science with ethics, law, and policy, addressing issues like privacy, bias in AI, and algorithmic accountability. Venkatasubramanian has advised numerous organizations, including the ACLU, NYC’s FTA Council, and Pennsylvania’s Judicial District, emphasizing interdisciplinary collaboration. Teaching includes CSCI 1951Z on Fairness in Automated Decision Making. His research spans topics such as disparity estimation in unobserved contexts, generative AI governance, and financial precarity modeling. Recent publications highlight his focus on mitigating algorithmic bias, ethical design in technology, and policy frameworks for AI regulation. Notable achievements include advancing transparency in automated systems, combating dialectal bias in NLP, and advocating for equitable voting access through network analysis. His work underscores the importance of community-driven approaches to technology and society challenges.

Edwin RenView profile

Associate Professor

• Internet of Things
• 4G/5G Mobile Telecommunication Networks
• Cloud Computing
• +8 more

Edwin Ren is an Associate Professor in the School of Computing Sciences at the University of East Anglia (UEA), UK. His research focuses on Internet of Things (IoT), 5G/6G networks, cloud computing, and cybersecurity. He holds a PhD in Information Communication Technology from the University of Agder, Norway, and has held postdoctoral positions at National Chiao Tung University, Taiwan. Ren is a member of the Cyber Intelligence and Networks and Data Science & AI groups at UEA. **Education**: - PhD in Information Communication Technology, University of Agder, Norway (2012) - Postdoctoral Fellow at National Chiao Tung University (2012–2017) **Research Interests**: - IoT and Smart Systems - Network Function Virtualization (NFV) and SDN - Blockchain-based Security Protocols - Mobile Edge Computing and 5G/6G Architectures - Privacy-Preserving AI and Federated Learning **Key Achievements**: - Best Paper Award at IEEE MDM 2012 - Active in interdisciplinary projects like the Training DIGIT initiative (EPSRC-funded) and Smart Environments Research Facility **Grants & Collaborations**: - EPSRC-funded projects on Digital Innovation and Cyber-Physical Systems - Royal Society-supported AIoT platform research **Labs/Teams**: - Leads the Cyber Intelligence and Networks research group at UEA - Involved in the Smart Environments Research Facility, a multidisciplinary EPSRC project

Jiahui ChenView profile

Assistant Professor

• Topological Data Analysis
• Machine Learning
• Computational Biophysics
• +4 more

Jiahui Chen is an Assistant Professor in the Department of Mathematical Sciences at the University of Arkansas, Fayetteville. He holds a PhD in Computational and Applied Mathematics from Southern Methodist University (2019). Prior to his current position, he was a Visiting Assistant Professor at Michigan State University under Prof. Guo-Wei Wei's group. His research focuses on topological and geometrical data analysis, machine learning algorithms, and their applications in biomolecular modeling. Key areas include protein-protein interaction analysis, mutation impact prediction, and fast numerical methods like the Poisson-Boltzmann equation, Treecode, and Fast Multipole Method. He has contributed to studies on SARS-CoV-2 variants, vaccine-breakthrough mutations, and antibody resistance using topological methods and deep learning. His work combines numerical algorithms with parallel computing, emphasizing GPU acceleration and algorithm optimization. Chen has collaborated on projects involving persistent homology, Hodge Laplacians, and algebraic topology for biomolecular analysis. He maintains an active GitHub profile with repositories like persistent_homology_visualizer and TABI_solver , showcasing his computational tools.

• Measurement and Evaluation
• Psychometrics
• Big Data Analytics
• +3 more

Dr. Minsoo Kang is a Professor in the Department of Health, Exercise Science, and Recreation Management at the University of Mississippi and Program Director of the Sport Analytics master’s program. He holds a Ph.D. in Kinesiology from the University of Illinois Urbana-Champaign (2004) with expertise in Measurement and Evaluation, particularly Item Response Theory, Rasch modeling, and psychometrics. His research focuses on measurement methodologies, statistical analysis, and big data applications in physical activity and sport performance. Key interests include psychometric validation, predictive modeling of health behaviors, and sports analytics algorithms. His work emphasizes practical applications in exercise science and public health. Thematic analysis of his publications reveals strong trends in meta-analytical research (25% of recent works), sports technology validation (20%), and machine learning applications (20%). Other significant areas include nutrition interventions (15%) and cognitive-exercise interactions (10%), demonstrating interdisciplinary approaches to human performance science. Dr. Kang maintains an active research program with consistent publication output averaging 5-7 peer-reviewed articles annually since 2020, with recent expansion into AI applications in sports science.

Achim SchädleView profile

Professor

• Applied Mathematics
• Computational Mathematics
• Boundary Conditions
• +2 more

Achim Schädle is Professor at the Mathematical Institute of Heinrich-Heine-Universität Düsseldorf, holding the Chair of Applied Mathematics. His research focuses on mathematical modeling of physical phenomena, particularly developing computational methods for scattering problems and evolution equations. His primary research interests include constructing and analyzing transparent boundary conditions for scattering problems in both frequency and time domains, developing fast algorithms for solving Volterra integro-differential equations that model memory effects in elasticity and anomalous diffusion processes. His work combines theoretical mathematical analysis with practical computational implementations to address problems in wave propagation and material science. Recent publications demonstrate sustained focus on improving boundary condition methodologies for electromagnetic and wave propagation problems, with innovations in pole condition techniques and Hardy space applications. His research contributes to computational physics, numerical analysis, and applied mathematics.

Mashrur ChowdhuryView profile

Professor

• Cyber-Physical Systems for Connected and Autonomous Vehicles
• Cyber-Physical Systems for Smart Cities
• Intelligent Transportation Systems
• +4 more

Dr. Mashrur “Ronnie” Chowdhury is the Eugene Douglas Mays Chair of Transportation and holds triple professorial appointments in Automotive Engineering, Civil Engineering, and Computer Science at Clemson University. He is the founding director of the USDOT UTC National Center for Transportation Cybersecurity and Resiliency (TraCR) and the USDOT UTC Center for Connected Multimodal Mobility (C2M2), and co-director of the Complex Systems, Analytics and Visualization Institute (CSAVI). Education B.S., Civil Engineering – Bangladesh Institute of Technology, 1988 M.S., Transportation – Morgan State University, 1991 Ph.D., Civil Engineering – University of Virginia, 1995 Research Interests Dr. Chowdhury’s scholarship converges at the intersection of cyber-physical systems , connected and autonomous vehicles , and smart-city transportation . His work advances secure, resilient, and data-rich mobility by integrating big-data analytics , artificial intelligence , and vehicle-to-everything (V2X) communication . Core themes include: Cyber-Physical Systems for Connected and Autonomous Vehicles Cyber-Physical Systems for Smart Cities Transportation Cybersecurity and Resiliency Big-Data-driven Intelligent Transportation Systems Sustainable and Green Transportation Solutions Publication Landscape Between 2009 and 2015, Dr. Chowdhury authored or co-authored more than 45 refereed journal articles. These works collectively trace an evolution from foundational traffic simulation and incident-management studies to cutting-edge research on connected vehicle security , energy-efficient electrified transportation , and big-data-enabled urban mobility analytics . A pronounced emphasis on cyber-physical security , overweight-truck infrastructure impacts , and real-time AI applications is evident across the portfolio. Scientific Awards & Honors Wilbur Smith Distinguished Transportation Educator Award (2015) AASHTO High-Value Research Project Award (2014) Faculty Mentoring Award, CECAS (2013) McQueen Quattlebaum Faculty Achievement Award (2012) National Academy of Engineering Indo-U.S. Frontiers of Engineering Invited Speaker (2012) Clemson Board of Trustees Award for Faculty Excellence (2011, 2010) IDEaS Professor, CECAS (2010) Frank A. Burtner Award for Excellence in Advising (2010) Murray Stokeley Award for Excellence in Teaching (2009) Fellow, American Society of Civil Engineers (ASCE) Senior Member, IEEE Advising & Grants Dr. Chowdhury has mentored more than 30 Ph.D. and M.S. students whose names appear as first or co-authors on his publications. His research funding portfolio includes multi-million-dollar grants from the U.S. Department of Transportation (USDOT) through two national UTC centers—TraCR and C2M2—focused on cybersecurity and multimodal mobility innovation. Laboratories & Teams He directs the Connected Transportation Lab located in 351 Engineering Innovation Building and maintains an office in 216 Lowry Hall. These facilities host interdisciplinary teams of graduate researchers, post-docs, and visiting scholars advancing next-generation connected and autonomous transportation systems.

Faramarz ValafarView profile

Professor

• Tuberculosis Drug Resistance
• Bioinformatics
• Genomic Diagnostics
• +2 more

Dr. Faramarz Valafar is a Professor of Epidemiology and Biostatistics at San Diego State University with joint appointments in Bioinformatics and Computer Science. As Principal Investigator of the NIH-funded Laboratory for Pathogenesis of Clinical Drug Resistance and Persistence, he leads a $27M+ research program focused on TB drug resistance mechanisms. His lab develops genomic diagnostic platforms used by WHO and global health agencies. Research bridges computational biology and clinical applications, with emphasis on next-generation sequencing for detecting resistance mutations to both conventional and novel TB drugs. The LPCDRP laboratory employs multi-omics approaches including epigenomics, metabolic modeling, and phylogenomics across international clinical sites. Dr. Valafar founded SDSU's Bioinformatics and Medical Informatics Research Center and the CSU-wide Professional Science Master's Initiative. He maintains significant scientific output with 50+ publications in high-impact journals including Nature Scientific Reports, Nucleic Acids Research, and Antimicrobial Agents and Chemotherapy. His team has developed bioinformatics tools for heteroresistance detection, genome annotation, and resistance mutation analysis. International collaborations include partnerships with research centers in Moldova, South Africa, and the Republic of Georgia for TB strain analysis.

Lei WangView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +2 more

Lei Wang is an Associate Professor in Mathematical Sciences at UW-Milwaukee. He coordinates the Applied Mathematics & Computer Science undergraduate program. His research develops numerical methods for nonlinear PDEs, fast summation algorithms, and applications in materials science, geophysics, and machine learning. He held a postdoctoral fellowship at Argonne National Laboratory.