Dr. Zhigang Peng is a Professor in the School of Earth & Atmospheric Sciences at Georgia Institute of Technology, part of the College of Sciences. His research focuses on seismicity dynamics, fault zone imaging, and data science applications in geophysics. He holds a Ph.D. in Geological Sciences from the University of Southern California (2004), an M.S. in Electrical Engineering (2002), and a B.S. in Geophysics from the University of Science and Technology of China (1998). Dr. Peng’s work spans seismological studies of earthquake triggering mechanisms, fault zone structures, and deep-focus earthquakes. He has pioneered dense seismic array techniques to image fault systems and employs machine learning for event detection and phase picking. His recent projects include analyzing the 2023 Kahramanmaraş earthquake sequence in Türkiye and the 2024 Noto earthquake in Japan. He leads initiatives like the Center for Collective Impact in Earthquake Science (C-CIES), promoting inclusive scientific collaboration. Research Highlights: Fault zone imaging, dynamic triggering, AI-driven seismology Labs: ES&T 2235 (Seismology Lab), ES&T 2256 (Office) His awards include the 2002 AGU Outstanding Student Paper Award. He actively contributes to earthquake hazard assessment, nuclear explosion monitoring, and volcano-seismic interactions, with over 150 peer-reviewed publications.
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Antti Poso is a Professor of Drug Design at the University of Eastern Finland (Kuopio), affiliated with the School of Pharmacy under the Faculty of Health Sciences. His research focuses on computer-aided molecular design, particularly targeting anti-cancer drugs and anti-microbials. Key projects include the EDCMET project (2019–2024) and the GeneCellNano Flagship (2020–2028). He leads the Molecular Modeling and Drug Design Research Group, specializing in QSAR analysis, kinase inhibition profiling, and systems-level drug response modeling. Recent work includes studies on SARS-CoV-2 inhibitors, endocrine disruptors, and bacterial pathogenesis. His findings bridge chemical structure with biological outcomes, leveraging computational tools like CCA and molecular dynamics simulations. Collaborations span medicinal chemistry, pharmacology, and systems biology, contributing to both academic and applied drug discovery efforts. Education: Not explicitly stated in texts; assumed to hold advanced degrees in pharmacy or chemistry. Research Themes: Drug design, molecular modeling, QSAR, computational biology, and anti-infective agents. Key Contributions: Over 150+ publications, including influential works on chemoinformatics-driven drug response analysis and structure-based inhibitor design. Publications highlight advancements in kinase inhibitors, anti-microbial strategies, and viral hijacking mechanisms. His work emphasizes translating computational insights into therapeutic solutions for cancer, infectious diseases, and metabolic disorders.
Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.
Dr. Mortaza Saeidi-Javash is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at California State University, Long Beach (CSULB). He joined in Fall 2022 following his Ph.D. in Mechanical Engineering from the University of Notre Dame, where he received the Prince Engineering Fellowship and Dehner Graduate Fellowship. His research focuses on developing next-generation flexible electronics using advanced materials and 3D printing technologies, particularly thermoelectric devices for wearable applications and multifunctional sensors for structural health monitoring. Dr. Saeidi-Javash's academic background includes interdisciplinary work combining materials science, additive manufacturing, and machine learning. His Ph.D. research emphasized aerosol jet printing and ultrafast flash sintering to create high-performance, low-cost thermoelectric devices. He has published extensively in journals like Advanced Materials and Nano Energy , with a focus on flexible electronics, energy harvesting, and sensor integration. His recent publications highlight innovations in machine learning-aided materials discovery, plasma sintering processes, and hybrid printing methods. These contributions address challenges in scalable manufacturing, energy efficiency, and wearable technology applications. Dr. Saeidi-Javash’s work bridges gaps between fundamental materials research and practical engineering solutions for sustainable energy systems and smart devices. Awards: Prince Engineering Fellowship (University of Notre Dame) Dehner Graduate Fellowship in Engineering (University of Notre Dame) Advising & Office Hours: Office: ECS-647 Office Hours: Wednesday 12:00-2:00 PM Advising Hours: Thursday 12:30-1:30 PM His research lab focuses on additive manufacturing of functional materials, with ongoing projects in thermoelectric energy conversion, wearable sensors, and biomaterials for cardiac tissue engineering.
Professor Kaspar Althoefer is a Professor of Robotics Engineering at the School of Engineering and Materials Science , Queen Mary University of London. He leads the Centre for Advanced Robotics @ Queen Mary (ARQ) and serves as Programme Director for BEng/MEng Robotics Engineering. His research focuses on robot autonomy, soft robotics , and tactile sensing with applications in healthcare, nuclear engineering, and manufacturing. Research Highlights: Soft Robotics, Tactile Sensing, Haptic Perception, Minimally Invasive Surgery, Human-Robot Interaction Key Collaborations: St Thomas Hospital London, EU Horizon Europe, EPSRC, Innovate UK Accomplishments: £10M+ as Principal Investigator, 500+ peer-reviewed papers, 7 patent applications His work spans robot autonomy , soft robotics , and machine learning , particularly in modeling tool-environment interactions and developing tactile sensors for surgical and nuclear applications. Recent projects include intelligent endoscopic microsurgery robots and eversion robots for radiation mapping . Scientific Awards: Senior Member, IEEE He supervises a team of 6 PhD students and postdoctoral researchers, with completed projects in soft robotic gloves , pneumatic actuators , and eversion robot navigation .
Oskari Ville Pakari is a Lecturer at the School of Basic Sciences, École polytechnique fédérale de Lausanne (EPFL), affiliated with both the Institute of Physics (IPHYS) and the Swiss Plasma Center (SPH-ENS). He contributes to teaching and research, particularly in reactor physics and radiation detection. His research focuses on nuclear reactor diagnostics , gamma noise analysis , and neutron spectroscopy . He actively develops mixed reality visualization tools for radiation detection data and participates in the European CORTEX project for reactor monitoring. Selected publications highlight his work in gamma-ray imaging , neutron noise simulations , and detector system validation using advanced statistical methods like bootstrapping and Welch's technique. Teaching activities include courses on Radiation biology, protection, and applications Radiation and reactor experiments He advises PhD student Saliba Michel and collaborates with international institutions such as CEA, KIT, and LRS (Laboratory of Reactor Physics and Systems Behaviour) at EPFL.
Paul Wilson serves as the Grainger Professor of Nuclear Engineering and Chair of the Department of Nuclear Engineering & Engineering Physics at the University of Wisconsin-Madison. His research develops computational tools for modeling nuclear energy systems with applications in radiation shielding, waste management, non-proliferation, and energy policy. Education: PhD in Nuclear Engineering, University of Wisconsin-Madison (1999) Dr.-Ing in Mechanical Engineering, Technical University of Karlsruhe (1998) MS in Nuclear Engineering, University of Wisconsin-Madison (1995) B.A.Sc. in Engineering Science (Nuclear Power option), University of Toronto (1992) Wilson's research spans computational nuclear engineering with emphasis on Monte Carlo methods, nuclear fuel cycles, and proliferation analysis. His Computational Nuclear Engineering Research Group (CNERG) develops simulation tools for radiation transport, waste transmutation, and fusion systems. Key projects include the Infinity Two fusion pilot plant design and Cyclus nuclear fuel cycle simulator. Recent publications reveal strong focus on fusion energy systems (particularly stellarator-based designs like Infinity Two), machine learning applications in nuclear security, and advanced neutronics modeling. His work bridges computational methods with real-world nuclear challenges including waste management and non-proliferation. Scientific awards: Fellow of the American Nuclear Society (2023) American Nuclear Society Young Member Advancement Award (2019) American Nuclear Society Arthur Holly Compton Award (2018) Grainger Professor of Nuclear Engineering (2016) American Nuclear Society Presidential Citation (1996) Wilson advises graduate students through thesis research courses (N E 790/890/990) and has secured significant funding from the U.S. Department of Energy. His consultancy roles include work with CEA Saclay, Karlsruhe Institute of Technology, and the Blue Ribbon Commission on America’s Nuclear Energy Future. He previously served on the Generation IV Technology Roadmap Committee (2001-2003). He leads the Computational Nuclear Engineering Research Group (CNERG), which develops open-source tools including PyNE and Cyclus. The group's work spans fusion pilot plant design, nuclear security applications, and fuel cycle simulation for next-generation nuclear systems.
Dr. Richard Y. Zhao is a tenured Professor in the Department of Pathology and Microbiology-Immunology at the University of Maryland School of Medicine. His research combines molecular biology, fission yeast genetics, mammalian biology, and virology to study virus-host interactions, particularly for HIV and Zika virus. He previously held academic positions at Northwestern University and Columbia University and has contributed to over 120 peer-reviewed articles. B.S., China Oceanography University (1981) M.S., Oregon State University (1995) Ph.D., Oregon State University (1991) Postdoctoral Training, Columbia University (1991-1992) Dr. Zhao's research focuses on: Virus-host interactions and pathogenicity High-throughput drug screening for antivirals Role of viral proteins in neuroinflammation and cancer Translational genomics in precision medicine His recent publications highlight SARS-CoV-2 ORF3a, Zika envelope proteins, and HIV protease inhibitors, emphasizing host-pathogen mechanisms across species. He has served on NIH panels and editorial boards for journals like Cell Research and Retrovirology . Scientific awards include: Fellow, American Academy of Microbiology (2019) Bernard L Mirkin Endowed Chair (2001-2004) Honorary Director, Shandong Gallo Institute (2009) Distinguished Service from SCBA (2015) Outstanding Service from CBA-USA (2016) Dr. Zhao also contributes to clinical diagnostics and personalized medicine through molecular testing and pharmacogenetics programs.
Professor Sebastian Hiller is a Full Professor at the Biozentrum of the University of Basel, Switzerland, where he leads a research group focused on structural biology and biophysics. His laboratory specializes in using nuclear magnetic resonance (NMR) spectroscopy to elucidate the structures and functions of proteins and their interactions at the atomic level. His research spans several key areas including molecular chaperones and protein folding mechanisms, outer membrane protein biogenesis in bacteria, and kinase signaling pathways. Notably, his group has made significant contributions to understanding how chaperones like trigger factor function, the mechanisms of outer membrane protein assembly through the Bam complex, and dynamic kinase interactions. Their work has direct implications for neurodegenerative diseases and antibiotic development. The Hiller lab's recent publications demonstrate a strong focus on NMR methodology development, protein folding dynamics, and structural mechanisms of antibiotic action. Their research on darobactin's mechanism of action against Gram-negative bacteria represents a significant advance in antibiotic discovery. The group frequently publishes in high-impact journals including Nature, Science, and Nature Communications. ICMRBS Founder's Medal (2018) EMBO Young Investigator (2014) ERC starting grant (2011) SNSF professorship (2010) SNSF scholarship for young researchers (2008) Professor Hiller supervises numerous PhD students and postdoctoral researchers, with many alumni having secured prestigious positions in academia and industry. His laboratory maintains strong collaborations across multiple institutions and has received significant funding through ERC grants and other competitive mechanisms. The Hiller group also operates advanced NMR facilities that serve the broader research community at the University of Basel.
Professor Jerome Liang is a distinguished faculty member at Stony Brook University's Renaissance School of Medicine, holding professorships in Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science. He serves as Co-Director of Radiology Research and has established himself as a leading expert in medical imaging reconstruction techniques. Dr. Liang's educational background includes a Ph.D. in Physics from City University of New York, postdoctoral training at Duke University, and fellowship at Albert Einstein College of Medicine. His undergraduate degree in Modern Physics was obtained from Lanzhou University in China. His primary research interests focus on advanced medical imaging techniques, particularly low-dose computed tomography image reconstruction, quantitative SPECT reconstruction, high-resolution PET imaging, tissue segmentation from multi-spectral images, computer-aided diagnosis systems, and virtual colonoscopy development. His work bridges engineering principles with clinical applications to improve diagnostic imaging capabilities while reducing radiation exposure. Analysis of his recent publications reveals a strong focus on machine learning applications in medical imaging, particularly in polyp classification, dual-energy CT spectral analysis, and virtual endoscopy. His research consistently aims to enhance diagnostic accuracy while optimizing radiation dose and improving visualization techniques for various medical conditions. 1981 China-US Physics Examination and Application Program (CUSPEA) Winner (Top 25 among 250,000 candidates) 1990 NIH First Investigator Award 1996 American Heart Association Established Investigator Award 1996 Radiological Society of North America Certificate of Merit Award 2002 SUNY Chancellor's Entrepreneur Award 2007 IEEE Society Fellow 2011-2013 SBU, BNL and CSHL Certificates of Excellence in Research and Invention 2013 Stony Brook School of Medicine Award for Excellence in Translational Research Dr. Liang has secured significant research funding including NIH/NCI R01 grants for "Advanced Virtual Colonoscopy for Early Cancer Screening" and "Radiogenomics of Colorectal Polyps." He currently leads active protocols including IRB 93995-MODCR005 focused on integrating virtual and optical colonoscopies with pathological analysis. His laboratory (IRIS - Imaging Research and Informatics) continues to advance medical imaging technology while mentoring the next generation of researchers in this critical field.
Shabaz Mohammed is an Associate Professor of Proteomics at the University of Oxford, holding joint appointments in the Departments of Chemistry and Biochemistry. Since 2020, he has served as Head of the Mechanistic Proteomics research programme at the Rosalind Franklin Institute. His research focuses on advancing proteomics technologies to study protein post-translational modifications and their roles in cellular processes, with applications in viral infections and disease mechanisms. Education: BSc in Chemistry, UMIST (now The University of Manchester), 1999 PhD in Biological Mass Spectrometry, University of Manchester, 2003 Postdoctoral Research, University of Southern Denmark (with Ole Jensen), 2005-2008 Postdoctoral Research, Utrecht University (with Albert Heck), 2008 Professor Mohammed's research centers on developing novel mass spectrometry approaches for large-scale characterization of protein post-translational modifications (PTMs). His group innovates in chromatographic techniques for single-cell proteomics, creates materials for PTM enrichment (glycosylation/phosphorylation), and applies these tools to study viral infections (SARS-CoV-2), cell cycle regulation, and signaling pathways. His work bridges chemistry, biochemistry, and cell biology to understand dynamic protein functions in health and disease. His recent publications (2023-2025) demonstrate strong emphasis on viral proteomics, particularly virus-host RNA-binding protein interactions, and innovations in mass spectrometry fragmentation techniques and chromatography. Key themes include viral remodeling of host cells, new labeling strategies for PTMs, and advancements in single-cell proteomics, with significant implications for understanding viral pathogenesis. Scientific Awards: No specific awards or fellowships were detailed in the source material. Advising and Grants: Information regarding graduate students supervised or specific research grants was not provided in the available text. As an active research group leader, Professor Mohammed likely mentors PhD students and secures competitive funding for proteomics research. Laboratories and Collaborations: Professor Mohammed leads a research group at Oxford focused on proteomics technology development. He collaborates extensively with the Ben Davis group on PTM detection materials and across the university on biochemical applications. At the Rosalind Franklin Institute, he heads the Mechanistic Proteomics programme to unravel protein functions through advanced proteomic methods.
Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.
Keith D. Koper is a Professor in the Department of Geology & Geophysics at the University of Utah and serves as Director of the University of Utah Seismograph Stations (UUSS). He is also the editor-in-chief of The Seismic Record . His work integrates academic research with operational seismic monitoring and public safety initiatives across Utah and the Intermountain West. Education: PhD in Geophysics, Washington University, 1998 BA in Math, Geology, and ISP, Northwestern University, 1993 Dr. Koper's research focuses on array seismology, forensic seismology, deep Earth structure (especially the inner core), earthquake rupture imaging, ambient seismic noise, and seismic hazards in the Intermountain West, including mining-induced and urban earthquakes. His work combines observational seismology with advanced signal processing and machine learning techniques to improve detection, discrimination, and imaging capabilities. He has led or contributed to major projects involving the Wasatch Front, Yellowstone, and regional seismic networks. His recent research emphasizes machine learning for earthquake detection, high-resolution relocation of aftershock sequences (e.g., Magna 2020, Bluffdale 2019), microseism generation in lakes, and fine-scale imaging of the Earth's inner core using seismic reflections. His studies often involve interdisciplinary collaboration, particularly with mining engineering and geodesy. Dr. Koper's research has been consistently funded by federal and state agencies, including the National Science Foundation (NSF), U.S. Geological Survey (USGS), Department of Energy (DOE), Air Force Research Laboratory (AFRL), and the Utah Department of Public Safety. His publications reflect a strong trend toward integrating computational methods with traditional seismological analysis to tackle complex problems in both natural and induced seismicity. Scientific Service and Leadership: Editor-in-Chief, The Seismic Record Director, University of Utah Seismograph Stations Secretary, U.S. Air Force Seismic Review Panel Former Chair and Vice-Chair, Utah Seismic Safety Commission Dr. Koper mentors graduate students in seismology and geophysics, including recent advisees Sean Hutchings and Alysha Armstrong. His research group actively engages in both fundamental and applied seismological research, with strong ties to national labs such as Sandia. The group is involved in deploying portable seismic arrays, analyzing large datasets, and developing new algorithms for event detection and classification. The University of Utah Seismograph Stations, under his leadership, plays a critical role in monitoring seismicity in Utah and Yellowstone, producing real-time earthquake information, ShakeMaps, and public outreach materials. The station also contributes to national and international efforts in nuclear test monitoring and volcanic hazard assessment.
Dr. Bin Zhu is a Research Fellow in the School of Mechanical Engineering Sciences at the University of Surrey, affiliated with the Centre for Engineering Materials. He obtained his PhD from the same institution, focusing on multiscale residual stress evaluation and mechanical property characterization using microscopy and large-scale facilities. His research develops techniques for harsh environments to enhance material longevity by managing manufacturing-induced residual stress, with applications in nuclear fusion components. Education PhD, University of Surrey (Research focus: Multiscale residual stress evaluation and mechanical property characterization) Research Focus Dr. Zhu's research centers on three interconnected areas: 1) Multiscale residual stress evaluation using advanced techniques like plasma-focused ion beam and neutron diffraction; 2) In situ mechanical testing under extreme conditions; and 3) Computational modeling for predicting stress distributions and material behavior. His work primarily addresses nuclear fusion reactor challenges, particularly laser-welded Eurofer97 steel components, where residual stress critically impacts structural integrity. Publication Trends Dr. Zhu's recent publications (2021-2025) demonstrate three key themes: 1) Advanced residual stress analysis in nuclear materials using machine learning, neutron imaging, and synchrotron techniques; 2) High-temperature mechanical performance of welded joints for fusion reactors; and 3) Biomimetic material characterization, including bioinspired composites and biological light-diffraction mechanisms. His methodologies consistently integrate multiscale experimental approaches with computational modeling.
