Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.
CHONG Yidong is a Professor in the Division of Physics and Applied Physics at Nanyang Technological University (NTU), Singapore. He leads the Centre for Disruptive Photonic Technologies and holds positions in the School of Physical and Mathematical Sciences. His research focuses on theoretical photonics, topological systems, and non-Hermitian physics, with contributions to photonic crystals, topological insulators, and coherent perfect absorbers. He has been recognized with awards including the President's Science Award (2020) and the National Research Foundation Fellowship (2012). Education: Ph.D. (Physics), Massachusetts Institute of Technology (2005–2008) B.Sc. (Physics) and B.Sc. (Mathematical & Computational Sciences), Stanford University (1999–2003) Research Interests: Topological photonics, non-Hermitian systems, photonic topological insulators, PT-symmetric structures, and applications in quantum optics and acoustics. Recent Articles: Focus on experimental realizations of topological lasers, exceptional points in non-Hermitian systems, and higher-order topological phenomena in acoustic and photonic platforms. His work bridges theory and experiment, with collaborations in materials science and electrical engineering. Awards: Extensive recognition for both research and education, including Nanyang Research and Education Awards. Teaching: Courses in mathematical methods for scientists, quantum mechanics, and computational physics, emphasizing numerical techniques and wave phenomena.
Christiana Mavroyiakoumou is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. She specializes in fluid dynamics and fluid-structure interactions, with a focus on vortex dynamics, membrane flutter, and bio-inspired systems. Her research integrates modeling, numerical simulations, and experimental insights to study phenomena such as bird flock formations and fish swimming hydrodynamics. Mavroyiakoumou holds a Ph.D. from the University of Michigan (2022), an M.Sc. from the University of Oxford (2017), and a B.Sc. from Imperial College London (2016). Education: PhD in Applied & Interdisciplinary Mathematics, University of Michigan (2017–2022) MSc in Mathematical Modeling and Scientific Computing, University of Oxford (2016–2017) BSc in Mathematics, Imperial College London (2013–2016) Her research interests span fluid-structure interactions, vortex dynamics, and collective locomotion. She investigates how fluid flows mediate interactions between bodies, such as the aerodynamics of bird formations and the hydrodynamics of flapping foils. Her work bridges theoretical models with experimental observations, contributing to both fundamental science and bio-inspired engineering. Mavroyiakoumou has received prestigious awards including the Joseph B. Keller Fellowship (NYU), Peter Smereka Award (U-M), and ProQuest Distinguished Dissertation (U-M). She actively engages in academic service, organizing conferences and mentoring students. Her teaching experience includes courses on mathematical modeling, differential equations, and algebra at NYU and the University of Michigan. Key Research Themes: Flow-mediated collective behavior and instability mechanisms Vortex wake interactions and their role in locomotion Membrane dynamics in inviscid and viscous flows She collaborates with experimentalists like Leif Ristroph and Jun Zhang at NYU's Applied Math Lab, focusing on experimental validation of theoretical models. Her recent work explores self-amplifying waves in bird formations and the aerodynamic origins of flight coordination.
Oliver Schmitz is a Professor in the Department of Nuclear Engineering & Engineering Physics at the University of Wisconsin-Madison, where he leads research in plasma edge physics for magnetic confinement fusion and next-generation particle accelerators. His work bridges experimental plasma science, computational modeling, and diagnostic development with applications in both tokamaks and stellarators. Education: PhD (2006), Heinrich-Heine-Universität Diploma (2003), Rheinische Friedrich-Wilhelms-Universität Professor Schmitz's research focuses on 3D plasma edge transport phenomena, plasma-wall interactions, and helicon plasma generation for wakefield accelerators. His group employs advanced computational tools like EMC3-EIRENE for 3D plasma edge modeling and develops active spectroscopic diagnostics to measure plasma parameters through atomic emission analysis. Key themes include resonant magnetic perturbation effects in tokamaks, inherent 3D physics in stellarators, and high-density plasma sustainment for accelerator applications. He actively develops atomic models to interpret spectroscopic data and operates helicon plasma test stands for fundamental process studies. Recent publications reveal strong emphasis on experimental-computational integration for fusion boundary physics, with significant contributions to ITER divertor solutions, stellarator exhaust optimization, and plasma-facing materials. The work shows growing focus on wakefield accelerator diagnostics through helicon plasma sources and advanced spectroscopy, alongside persistent innovation in 3D modeling of plasma-material interfaces. Scientific Awards: 2020 Thomas and Suzanne Werner Chair Professorship 2018 UW Madison Teaching Academy Fellow 2017 ITER Science Fellowship & Vilas Mid-Career Award 2015 DOE Early Career Award & NSF CAREER Award 2011 Torkil Jensen Award (General Atomics) 2007 Günther-Leibfried-Preis (Jülich) Professor Schmitz directs multiple DOE/NSF-funded research programs including his UW Madison laboratory and AWAKE project contributions at CERN. He mentors graduate students through NE 890/990 thesis research courses and has developed nationally recognized K-12 outreach including the "Plasma Show" for elementary schools and "Plasma Academy" for high-school educators developing AP Physics curriculum modules. His leadership extends to university governance through the Kaufman seminar on academic leadership. His research group operates helicon plasma test stands and computational facilities for EMC3-EIRENE simulations, with current efforts focused on high-density plasma sources for accelerators and resilient divertor solutions for stellarators. The group maintains strong international collaborations with ITER, CERN, and major fusion facilities worldwide.
Prof. Paul V. Braun is the Grainger Distinguished Chair in Engineering and Professor of Materials Science and Engineering, Chemistry, Mechanical Sciences and Engineering, Chemical and Biomolecular Engineering at the University of Illinois Urbana-Champaign. He is also a part-time faculty member at the Beckman Institute. His research focuses on synthesizing materials with unique optical, electrochemical, thermal, and mechanical properties through nano/mesoscale architectures. Education: B.S. (Cornell University), Ph.D. (Materials Science and Engineering, University of Illinois). Postdoctoral work at Bell Labs (1999–present at UIUC). Research Interests : Electrochemical energy storage (batteries) Polymers and self-assembly Photonics and advanced optics Self-healing materials Control of heat and matter transport Recent articles explore battery recycling, hydrogel thermal conductivity, additive manufacturing for heat transfer, and silicon anode analysis. His work spans materials synthesis, characterization, and applications in energy and photonics. Awards : AAAS Fellow (2020) NAI Fellow (2022) Grainger Award for Translational Research (2023-24) MRS Fellow (2018) Advising & Grants : Braun has co-authored ~350 publications, holds multiple patents, and co-founded four companies. His labs include the Materials Research Laboratory and Beckman Institute.
Dr Elizabeth Hallam is an Associate Professor in Visual, Material, and Museum Anthropology at the University of Oxford's School of Anthropology & Museum Ethnography. She holds additional roles as a Research Affiliate at the Pitt Rivers Museum, Fellow of St Peter's College, and Director of the Interdisciplinary Studies in Cultural Anthropology (ISCA). Her research focuses on the anthropology of the body, death, material and visual cultures, human anatomy, and three-dimensional models in medical education. She is also the Editor of the Journal of the Royal Anthropological Institute and has curated exhibitions such as 'Designing Bodies: Models of Human Anatomy from 1945 to Now'. Her academic journey includes a BA and PhD from the University of Kent, followed by positions at the University of Sussex and Aberdeen University. She has authored/co-authored influential works like Anatomy Museum: Death and the Body Displayed (2016), which won the Wellcome Medal. Her current projects explore museums of anatomy, 3D modeling in anthropology, and collaborative art practices. Key research themes include death and memorializing processes, the role of material culture in knowledge production, and interdisciplinary studies on creativity and improvisation. She advises four DPhil students and has been involved in ARC-funded projects on cemetery futures and postmortem practices. Awards: Wellcome Medal for Anthropology as Applied to Medical Problems (2016) Grants: Projects funded by the Australian Research Council, Henry Moore Foundation, and Royal College of Surgeons Labs/Teams: Collaborations with University of Melbourne and curatorial networks in Europe/USA
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
Fatih Ecevit is Full Professor of Mathematics at Boğaziçi University, serving as Vice Chair of the Mathematics Department. Former research associate at Max-Planck-Institut für Mathematik in den Naturwissenschaften, Leipzig (2005-2007). Research develops computational methods for high-frequency scattering problems, including: Boundary element methods for wave propagation Asymptotic analysis of scattering phenomena Galerkin formulations for integral equations Lattice sum evaluations in graph theory Principal investigator for TÜBİTAK-funded project: 'Hybrid integral equation methods for high-frequency scattering problems' (2017-2020). Teaches graduate and undergraduate courses in numerical analysis, partial differential equations, and real analysis.
Scott Geyer is an Associate Teaching Professor of Chemistry at Wake Forest University, located in Winston-Salem, NC. He holds a B.S. (2005) from the University of Virginia and a Ph.D. (2010) from the Massachusetts Institute of Technology, followed by postdoctoral research at Stanford University. Research Focus : Dr. Geyer’s research bridges chemical education and materials science. In pedagogy, he emphasizes laboratory course design to enhance student decision-making and scientific communication skills, particularly for graduate program applications. His materials research explores nanocrystal-based catalytic systems for energy applications, including water splitting, CO2 reduction, and photocatalytic processes. Key Contributions : His work includes developing trifunctional electrocatalysts for water splitting, lead-free perovskite alternatives for CO2 reduction, and scalable H2O2 electrosynthesis. His studies often combine computational modeling (e.g., DFT simulations) with experimental synthesis of nanomaterials. Awards & Recognition : No specific awards listed, though his publications reflect sustained contributions to catalysis and nanomaterial research. Advising & Grants : While no advisees are listed, his teaching role likely involves mentoring undergraduate and graduate students in laboratory practices and research methodologies. His work is supported by grants focused on sustainable energy materials. Labs & Teams : Engaged with Wake Forest’s chemistry department labs, contributing to interdisciplinary efforts in nanomaterials and sustainable chemistry.
Ghassan AlRegib is the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He directs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), the Center for Energy and Geo Processing (CeGP), and previously led Georgia Tech's MENA initiatives (2015-2018). His research spans machine learning, image processing, and seismic interpretation with real-world applications in autonomous vehicles, medical imaging, and subsurface analysis. His research focuses on trustworthy AI systems through three pillars: enhancing interpretability, improving robustness/generalizability, and tackling domain-specific challenges. Key interests include human-in-the-loop frameworks, uncertainty quantification, explainable AI, and physics-driven learning. The OLIVES lab pioneered modern machine learning applications in seismic interpretation and developed open-source datasets for geological fault analysis. Dr. AlRegib's scientific contributions include over 270 publications, multiple U.S. patents, and leadership roles as Technical Program co-Chair for ICIP 2020/2024. His work demonstrates significant impact through awards like the IEEE Fellow designation (2022) and multiple best paper awards at premier conferences. IEEE Fellow (2022) 2023 EURASIP Best Paper Award 2019 ICIP Best Paper Award 2017 Denning Faculty Award for Global Engagement CSIP Research & Service Awards (2003) He has advised numerous PhD students including Dr. Ashraf Alattar (now Auburn professor) and Dr. Zhiling Long (Kennesaw State faculty). His lab structure emphasizes collaborative teams comprising postdocs, senior/junior PhD students, and undergraduates working on high-impact problems from autonomous systems to medical diagnostics. Current research thrusts include trustworthy neural networks, human-in-the-loop frameworks, and deployment of machine learning in seismic interpretation and ophthalmology.
Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.
Erin Marie Furtak is a Professor of STEM Education at the University of Colorado Boulder, School of Education. She transitioned from high school teaching to academic research, focusing on formative assessment in science education and equitable classroom practices. Her work emphasizes how teachers design and use assessments to improve student engagement and learning outcomes. Education: PhD in Curriculum and Teacher Education, Stanford University (2006) MA in Education, University of Denver (2001) BA in Environmental, Population, and Organismic Biology, University of Colorado Boulder (1999) Research Interests: Formative assessment design, equity in STEM education, teacher professional development, and learning progressions. She leads the Aspire Partnership (NSF-funded) and Elevate Project , studying teacher-student interactions and equitable assessment practices. Her work bridges educational theory with classroom application, particularly for emergent bilingual learners. Selected Awards: 2011 Presidential Early Career Award for Scientists and Engineers (PECASE) German Chancellor Fellowship (Alexander von Humboldt Foundation, 2006) Teaching & Service: Courses include EDUC 4060/5060: Classroom Interactions and EDUC/MCDB 4811/5811: Teaching & Learning Biology . She serves as Associate Dean of Faculty at the School of Education and advises national initiatives like the Sandra K. Abell Institute and National Academies' Updating America’s Lab Report . Labs/Teams: Aspire Partnership, Elevate Project, and collaborations with school districts nationwide to integrate formative assessment into science curricula.
Minah Oh is a Professor and Chair of the Department of Mathematics & Statistics at James Madison University (JMU), where she has served since 2010. Her research focuses on numerical analysis, scientific computing, finite element methods, and optimal control, with a particular emphasis on axisymmetric problems and multigrid techniques. She holds a Ph.D. in Mathematics/Numerical Analysis from the University of Florida (2010) and degrees from Yonsei University (B.S., 2005). Her work bridges theoretical mathematics and computational applications, addressing challenges in PDE discretization, optimal control problems, and geometric numerical methods. Recent publications explore finite element approaches for state-constrained control problems and the analysis of axisymmetric domains using de Rham complexes and Fourier-based methods. No scientific awards are explicitly listed in the provided materials. Her advising and grants sections remain unspecified in the text. Dr. Oh maintains an academic website at educ.jmu.edu/~ohmx for further details.
François Peeters is a Full Professor of Physics at the University of Antwerp, Belgium, holding the position since 2000 (with Dutch title 'gewoon hoogleraar' since 2003). He previously served as Research Director (FWO-VI) at the University of Antwerp (1996-1999), Research Leader (NFWO) (1992-1996), and Senior Research Assistant (NFWO) (1988-1992), establishing a distinguished academic career spanning over three decades. His educational background includes a Ph.D. in Physics from the University of Antwerp (1982), followed by a Habilitation (Hoger aggregaat) from the same institution (1987), and a postdoctoral fellowship at Bell Laboratories in Murray Hill, New Jersey (1982-1983). His academic journey also featured research periods at prestigious institutions including the High Magnetic Field Laboratory in Grenoble, University of California Berkeley, Oxford University, and several Brazilian and Australian universities. Peeters' research focuses on theoretical condensed matter physics , specializing in the electronic, optical, and magnetic properties of nanostructured systems. His work encompasses semiconductors , superconductors , graphene , and hybrid quantum systems , with particular emphasis on strong correlations in both classical (colloids, dusty plasma) and quantum (quantum dots) environments. His theoretical frameworks bridge fundamental quantum mechanics with practical nanotechnology applications, driving innovations in spintronics and quantum device design. Analysis of his publication record reveals a clear evolution from foundational work on polaron physics and quantum Hall systems in the 1980s-1990s toward contemporary research on graphene, topological materials, and programmable quantum nanodevices. His most cited works demonstrate consistent leadership in mesoscopic physics, with recent publications showing increased focus on spin-dependent transport phenomena and two-dimensional material systems. His scientific recognition includes: Fellowship in the American Physical Society (2005) APS Outstanding Referee award (2008) Doctor Honoris Causa from University of Szeged, Hungary (2009) Peeters has supervised 26 completed PhD theses and currently leads the Condensed Matter Theory research group comprising 3 ZAP researchers, 16 PhD students, and 8 postdocs. His grant portfolio includes coordination of an EU Marie Curie Training site on 'Electrons on helium', participation in multiple EU projects, COST actions, and ESF networks, demonstrating sustained success in securing competitive international funding. The Condensed Matter Theory group maintains extensive international collaborations, evidenced by Peeters' research visits to over 10 institutions worldwide and regular hosting of 3-4 international visitors at postdoc or professorial levels. The group's output of over 770 refereed publications with 12,000+ citations reflects its position at the forefront of theoretical condensed matter physics research.
