Youssef M. A. Hashash is the Grainger Distinguished Chair in Engineering and a Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds a B.S., M.S., and Ph.D. in Civil Engineering from MIT (1987–1992). His expertise spans geotechnical engineering, earthquake engineering, and computational geomechanics, with a focus on deep excavations, tunneling, and soil-structure interaction. He co-developed DEEPSOIL, widely used for seismic soil response analysis. Education: B.S. Civil Engineering, MIT (1987) M.S. Civil (Geotechnical) Engineering, MIT (1988) Ph.D. Civil (Geotechnical) Engineering, MIT (1992) Research Interests: Dr. Hashash's work integrates geotechnical engineering with advanced technologies like AI, visualization, and discrete element modeling. Key areas include: Seismic site response and amplification models for Central/Eastern North America Tunneling and underground infrastructure resilience Geotechnical applications of machine learning and augmented reality Soil-structure interaction and liquefaction analysis Professional Roles: Geotechnical co-leader, NIST investigation of the Champlain Towers South collapse (2022–present) Chair, National Academies' Committee on Geological and Geotechnical Engineering (2024–present) Past President, Geo-Institute of ASCE Awards: Presidential Early Career Award for Scientists and Engineers ASCE 2014 Peck Medal Elected to National Academy of Engineering (2022) Labs/Teams: Leads research groups at UIUC focused on computational geomechanics and geotechnical earthquake engineering. Collaborates with federal agencies like NIST and NSF on large-scale projects.
Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.
Dr. Ian Stavness is a Professor and Department Head in the Department of Computer Science at the University of Saskatchewan. His research focuses on interdisciplinary applications of computer science, including deep learning in agriculture, biomedical computation, and 3D display technologies. He leads the Biological Imaging & Graphics (BIGLAB) laboratory, which develops tools for plant phenotyping and musculoskeletal modeling. Education: Ph.D. in Computer Engineering, University of British Columbia, 2010 M.A.Sc. in Computer Engineering, University of British Columbia, 2006 B.Sc. in Computer Science & B.Eng. in Electrical Engineering, University of Saskatchewan, 2004 Research Interests: Deep Learning for Plant Phenomics & Agriculture 3D Displays and VR/AR Technologies Musculoskeletal Biomechanical Simulation (OpenSim, ArtiSynth) Computer Vision and Image Analysis Awards: ACM CHI 2019 Honourable Mention ACM VRST 2018 Polyphony Digital Award Key Projects: Deep Plant Phenomics Platform Parametric Human Project (Digital Human Modeling) P2IRC (Plant Phenotyping & Imaging Research Center)
Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.
Raj Rajkumar is the George Westinghouse Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He holds leadership roles as Director of the USDOT Mobility21 and Safety21 National University Transportation Centers, and Director of the Metro21: Smart Cities Institute. His work focuses on cyber-physical systems, autonomous driving, and vehicular networking. Education: Ph.D. in Electrical and Computer Engineering from CMU (1989). Research interests include connected/autonomous vehicles, cyber-physical systems, embedded systems, and vehicular networking. Key projects include the General Motors-CMU Collaborative Research Lab and the development of autonomous vehicle protocols like Ballroom Intersection Protocol. Notable awards include the 2016 National Academy of Inventors Fellowship and the 2017 Pittsburgh Business Times Innovation Award. His publications span real-time systems, sensor networks, and automotive cyber-physical frameworks. Rajkumar advises on autonomous vehicle safety, energy efficiency, and societal impacts, with frequent media engagements on self-driving technologies.
Klaus Mølmer is a Professor at the Niels Bohr Institute, University of Copenhagen, specializing in Quantum Optics and Photonics. His research spans quantum information, entanglement, and cavity QED, leveraging machine learning and Grover's algorithm for quantum state engineering. His recent work focuses on spin squeezing, Rydberg atom interactions, and mechanical resonator cooling. A leader in quantum simulation and superradiance, he collaborates on cavity-mediated emission and quantum network design. The 15 most recent articles highlight advancements in quantum state manipulation, entanglement protocols, and robust differential phase sensing. These studies bridge theoretical frameworks with experimental applications in cavity QED, Rydberg arrays, and zero-photon detection.
Jiwoong Park is Professor of Chemistry and Chair of the Department of Chemistry at the University of Chicago, and simultaneously Professor of Molecular Engineering in the Pritzker School of Molecular Engineering. His interdisciplinary research group, the Park Group, is jointly affiliated with the James Franck Institute and the Materials Research Science and Engineering Center (MRSEC) at UChicago, and operates from the Gordon Center for Integrative Science. Education & Training Ph.D., University of California, Berkeley (2003) B.S., Seoul National University (1996) Junior Fellow, Rowland Institute, Harvard University (2003–2006) Assistant → Associate Professor, Department of Chemistry and Chemical Biology, Cornell University (2006–2016) Research Interests Park’s research centers on the science and technology of precisely engineered nanomaterials, particularly atomically-thin two-dimensional (2D) crystals and van der Waals solids. Spanning chemistry, physics, materials science and electrical engineering, his group develops novel synthetic, imaging and characterization techniques to uncover new physical phenomena and translate them into scalable device technologies. Key thrusts include growth of wafer-scale molecular crystals, optical and transport spectroscopy of 2D semiconductors, mechanical behavior of polycrystalline nanomembranes, and integration of these materials into photonic, electronic and energy-harvesting devices. Scientific Awards Elected Fellow of the American Physical Society (2022) – “for the development of synthetic, imaging, and characterization techniques of atomically thin materials and the discovery of novel properties of van der Waals solids.” Clarivate Highly Cited Researcher (2023) – recognition for multiple papers ranking in the global top 1% by citations in Materials Science and Chemistry. Group & Collaborations The Park Group is an interdisciplinary team of postdocs, graduate researchers and undergraduates housed in the Gordon Center for Integrative Science. The group actively collaborates with colleagues across the Department of Chemistry, Department of Physics, and the Pritzker School of Molecular Engineering, leveraging shared facilities at the James Franck Institute and MRSEC to push the frontiers of 2D material science.
Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).
Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .
Professor Behzad Fatahi is a distinguished academic in Civil and Environmental Engineering at the University of Technology Sydney (UTS), specializing in geotechnical engineering, railway infrastructure, and sustainable construction technologies. With a career spanning over 16 years at UTS, he has served as Deputy Head of School - Teaching and Learning (2024-present), Head of Discipline (2020-2024), and School Research Coordinator (2012-2017). His research focuses on unsaturated soil mechanics , dynamic soil-structure interaction , and green infrastructure solutions . Academic Appointments : Professor (2024-present), Associate Professor (2017-2024), Senior Lecturer (2011-2017), Lecturer (2008-2011) Research Leadership : Supervised 21 PhD students to completion, developed groundbreaking techniques for landfill waste reuse and tyre-derived aggregates in railway construction His work on seismic resilience of LNG tanks and bioengineered soil stabilization has received international recognition, including the 2023 Best Research Paper Award at the Australasian Association for Engineering Education conference. Professor Fatahi's industry experience includes geotechnical engineering roles at Coffey International and SES Engineering prior to academia. Key Research Contributions : Developed green corridor models for railway lines using coupled flow-deformation equations Pioneered AI-integrated teaching frameworks for civil engineering education Advanced machine learning techniques for intelligent compaction and structural buckling analysis As a Category 1 supervisor , he mentors graduate researchers in Civil Engineering , Geomechanics , and Earthquake Engineering . His peer-reviewed work (>240 publications) demonstrates technical excellence and innovation across multiple geotechnical domains.
N. Rich Nguyen is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), where he joined in August 2018. He's part of the School of Engineering and Applied Science and is on a teaching track , focusing on making machine learning accessible and engaging for all students. Research and Innovation: Rich Nguyen's research interests include biomedical image analysis , machine learning , and computer science education . He aims to reinvent instructional activities to make them adaptive and engaging by incorporating art and music elements to help everyone learn coding. Notable research contributions include: Floodwatch : A system for flood monitoring using crowdsourced images TuneScope : A digital music creation tool combining SoundScope and Snaps! technology CAD Library : Open-source design tools for educators AI for early sepsis detection : Highlighted in UVA Today Teaching Accomplishments: Before UVA, Rich taught computer science courses at UNC Charlotte for four years to a total of 1,458 students. At UVA, he teaches several courses including: CS 4774: Machine Learning (multiple semesters) CS 2501: Machine Learning for All (launched in Fall 2021) SYS 6016 / SDS 6050: Deep Learning CS 2150: Data and Program Representation (multiple semesters) CS 6316: Machine Learning (Graduate Level) CS 2910: CS Education Practicum (for Teaching Assistants) He previously taught at UNC Charlotte: ITCS 1600: Computing Professionals ITCS 2600: Computing Professionals for Transfer Students ITCS 4156: Introduction to Machine Learning ITCS 2215: Design and Analysis of Algorithms Academic Achievements: Rich Nguyen has received several notable awards and grants: Google Faculty Award for Machine Learning Education with TensorFlow (2019) Best Paper Award at IEEE BigDataSE (2022) Best Poster Award at SITE Conference (2022) CCI Faculty Innovation Award (2018) NSF grants for Smart and Connected Communities (2022) and Computational Thinking (2021) 3 Cavaliers Grant on Coding and Music (2021) Student Mentorship: Rich has mentored numerous students and teaching assistants who have achieved recognition. Notable students include: Joy Qiu - Published in Clinical Infectious Diseases Louisa Edwards and Zach Boner - Invited to Ken Ono Podcast Mike Ferguson - Winner of CS Louis T. Rader Undergraduate Teaching Award He has also served as faculty advisor for HooHacks (UVA's hackathon) and co-founded CharlotteHack at UNC Charlotte. Labs and Collaborations: Rich Nguyen leads the ML4VA (Machine Learning for Virginia) initiative, engaging students in project-based learning to apply machine learning to real-world problems affecting Virginia communities. He collaborates with institutions for symposiums on smart cities, particularly with ASEAN universities, and has partnered with Premier Healthcare for hackathons and with Glen Bull on educational technology projects.
Andres Arrieta is an Associate Professor in the School of Mechanical Engineering at Purdue University. His research focuses on adaptive structures, mechanical metamaterials, and programmable systems. He holds a PhD from the University of Bristol and conducted postdoctoral research at ETH Zurich. Education: Mechanical Engineer, Universidad de los Andes, 2006 PhD in Mechanical Engineering, University of Bristol, 2010 Postdoctoral Research Fellow, ETH Zurich, 2012 Research Interests: Adaptive Structures Multistable Systems Structural Nonlinearity Robotics & Mechanosensing Origami Engineering Awards: 2019 ASME Best Paper Award 2018 Gary Anderson Early Achievement Award 2012 ETH Postdoctoral Fellowship Labs: Directs the Programmable Structures Lab , exploring smart materials and morphing systems.
Guglielmo Scovazzi is a Professor at Duke University with appointments across multiple departments including the Department of Civil and Environmental Engineering, the Thomas Lord Department of Mechanical Engineering and Materials Science, and as Professor of Mathematics. His interdisciplinary research bridges computational mechanics, scientific computing, and engineering applications. Dr. Scovazzi earned his B.S/M.S. in aerospace engineering (summa cum laude) from Politecnico di Torino (Italy), followed by an M.S. and Ph.D. in mechanical engineering from Stanford University. Prior to joining Duke, he was a Senior Member of the Technical Staff at Sandia National Laboratories' Computer Science Research Institute. His research focuses on developing advanced numerical methods for computational mechanics, particularly finite element methods for fluid and solid mechanics. Key areas include multiphase porous media flows, computational methods for materials under extreme conditions, turbulent flow computations, and instability phenomena. His work emphasizes creating accurate computational approaches that reduce design/analysis costs for complex engineering problems involving fluid-structure interactions and transient phenomena in complex geometries. Dr. Scovazzi's most significant recent contribution is the development of the Shifted Boundary Method, an innovative computational framework that enables efficient simulations on complex geometries without requiring boundary-fitted meshes. This method has found applications in geomechanics, energy systems, and resilient infrastructure design. Kavli Fellow, National Academy of Sciences & Kavli Foundation (2018) Presidential Early Career Award for Scientists and Engineers (PECASE), White House (2017) Early Career Award, U.S. Department of Energy, Advanced Scientific Computing Research Program (2014) Dr. Scovazzi teaches multiple courses in computational mechanics including Nonlinear Finite Element Analysis and Introduction to the Finite Element Method. His research has been supported by substantial federal funding, and he actively collaborates across disciplines to address challenging problems in energy, environment, and infrastructure resilience through advanced computational methods.
Dr. Zara Ersozlu is a Senior Lecturer in Mathematics Education within the School of Education at the University of Newcastle, Australia. With a distinguished international career spanning multiple continents, she has held academic positions at prestigious institutions including North Carolina State University (USA), Gazi and Gaziosmanpasa Universities (Turkey), National Taiwan Normal University (Taiwan), The University of Western Australia, Murdoch University, and Deakin University. Her academic journey includes tenured positions as an Associate Professor and leadership roles as Department Head and Chair in teacher education disciplines. Currently, she teaches undergraduate and postgraduate courses in mathematics education, including Effective Pedagogies in Primary Mathematics, K-6 Mathematics, K-6 Numeracy, and Digitally Supported Learning. Dr. Ersozlu earned her Doctor of Philosophy from Firat University in Turkey and her Master of Art from Sakarya University. Her extensive academic preparation is complemented by five years of practical teaching experience in public schools prior to entering academia. This blend of theoretical knowledge and practical classroom experience informs her approach to teacher education and educational research. At the broadest level, Dr. Ersozlu's research investigates solutions to real-life problems impacting people's well-being, success, and capacity to achieve. Her scholarly work spans primary and secondary mathematics education, the psychology of mathematics (including metacognition, self-regulation, and anxiety), cross-cultural educational studies, teacher education, virtual simulated learning environments, and educational assessment. She has increasingly focused on the transformative potential of AI and machine learning in education, exploring how these technologies alter teaching, learning, and research processes. Her methodological expertise encompasses both quantitative and qualitative approaches, allowing her to effectively analyze both small and large educational datasets. Analysis of Dr. Ersozlu's recent publications reveals a strong emphasis on mathematics anxiety, teacher education, and the integration of technology in learning environments. Her work demonstrates a consistent focus on practical applications of educational research to address real-world challenges in mathematics education. The interdisciplinary nature of her research connects educational psychology, technology integration, and cross-cultural perspectives, with particular attention to how these elements intersect in teacher preparation and student learning outcomes. 2023 ATEA Research Recognition Award from the Australian Teacher Education Association 2021 Fellow of the Higher Education Academy (Advance HE, UK) 2010 Fellowship Program for Postdoctoral Researchers from the Council of Higher Education of Turkey Dr. Ersozlu is deeply committed to mentoring the next generation of scholars, currently supervising four PhD students and having successfully guided ten students to completion. Her grant portfolio includes significant funding for projects such as Best Practice Guidelines for RPL in Initial Teacher Education Programs ($60,000), Exploring the Reciprocal Relationship Between Mathematics Anxiety and Mathematical Resilience ($2,599), and multiple conference travel awards. She serves as an Associate Editor for several prominent journals including the International Electronic Journal of Mathematics Education and as Editor for Interdisciplinary STEM Education. Her editorial work reflects her standing as a respected voice in mathematics education research. Dr. Ersozlu's academic leadership extends to her role in developing innovative teaching approaches that integrate virtual simulation technology and learning analytics. Her work with TeachLivE™, a mixed-reality classroom simulation platform, demonstrates her commitment to creating authentic learning experiences for teacher education students. Through these initiatives, she bridges the gap between educational theory and classroom practice, preparing future educators to effectively implement evidence-based teaching strategies in diverse learning environments.
Du Changwen is a Researcher (Professor) at the Nanjing Institute of Soil Science, Chinese Academy of Sciences, serving as Deputy Director of the National Engineering Laboratory for Soil Nutrient Management. He supervises doctoral and master's students in soil science and agricultural technology development. His academic journey includes: Bachelor's degree from Huazhong Agricultural University's College of Resources and Environmental Science (1997) Master's degree from Huazhong Agricultural University's Trace Element Laboratory (2000) PhD from Nanjing Institute of Soil Science, Chinese Academy of Sciences (joint program with Technion - Israel Institute of Technology) (2003) Dr. Du's pioneering research focuses on precision fertilization technologies, particularly polymer-coated controlled-release fertilizers developed through model membrane and water-based reaction film-forming techniques. His work integrates Fourier Transform Infrared spectroscopy (ATR and PAS modes) with engineering mathematics to monitor nutrient release dynamics, soil chemistry processes, and plant nutrition in real-time. This interdisciplinary approach bridges agricultural chemistry, materials science, and environmental engineering to optimize fertilizer efficiency while minimizing ecological impact. Analysis of his 2015-2017 publications reveals a consistent emphasis on spectroscopic methods for soil-plant system analysis, with dominant themes in controlled-release fertilizer development, soil organic matter characterization, and in-situ nutrient monitoring. His work demonstrates strong cross-disciplinary integration between agricultural technology, analytical chemistry, and environmental science. His scientific recognition includes: Special Award of the First China Agricultural Science and Technology Innovation and Entrepreneurship Competition First Prize of Jiangsu Science and Technology Award First Jiangsu Youth Entrepreneurship Award Second Prize of Chinese Academy of Sciences Science and Technology Contribution Award First Prize of China Agricultural Science and Technology Award Dr. Du has secured major research funding including National Natural Science Foundation projects (key, general, youth), National '973' Basic Research Program, '13th Five-Year' R&D Plan sub-projects, '863' High-tech Program sub-projects, and Jiangsu Provincial Science and Technology Support Plan initiatives. His leadership in the National Engineering Laboratory for Soil Nutrient Management drives innovation in fertilizer technology, with significant outputs including 198 academic papers (89 SCI, 35 EI), 6 monographs, 1 international patent, 8 national patents, and 2 software copyrights. His laboratory specializes in advanced spectral analysis of soil-plant systems, utilizing FTIR-ATR and FTIR-PAS technologies for real-time monitoring of nutrient dynamics and polymer membrane reactions. Current research focuses on next-generation controlled-release fertilizers, machine learning-enhanced spectral analysis, and precision nutrient management systems for sustainable agriculture.
