Anton Ehrmanntraut is a researcher at the University of Würzburg, affiliated with the Chair of Computational Philology and Modern German Literary History. His work bridges computational methods with literary and linguistic analysis. Institution: University of Würzburg Role: Researcher Location: Emil-Hilb-Weg 23, Campus Hubland Nord Contact: anton.ehrmanntraut@uni-wuerzburg.de Research Focus: Computational Linguistics Digital Humanities German Literary History Natural Language Processing Computer Science Publishing Trends: Recent publications demonstrate a dual focus: (1) advancing NLP techniques for German texts (e.g., ModernGBERT, text normalization, literary pipelines) and (2) theoretical computer science contributions to complexity classes like UP, DisjNP, and DisjCoNP.
Professor Seda Kundak at Istanbul Technical University's Faculty of Architecture is a leading expert in Disaster Risk Management , Urban Resilience , and Multi-hazard Impact Chains . She has served as Program Head, Deputy Head of Department, and Commission President at Istanbul Technical University's Urban and Regional Planning Department since 2010. PhD in Urban Planning (Istanbul Technical University) Research focus: Seismic Risk, Climate Change Adaptation, Disaster Logistics Her scientific work includes 21 research outputs (Scopus h-index: 94) analyzing earthquake scenarios, compound disasters, and systemic risk assessment. Recent articles explore Impact Chains frameworks and Istanbul's seismic risk dynamics. Awards : SRA Fellow (2022) SRA/Sigma Xi Distinguished Lecturer (2017) She leads the PARATUS Project (€2,064,375 budget) developing open-source tools for multi-hazard risk assessment and has completed 5 major research projects on disaster logistics since 2005.
Simon J. A. Mason is a Senior Researcher and Head of the Mediation Support Team at the Center for Security Studies (CSS) at ETH Zurich. He has been working with the Mediation Support Project (MSP), a joint initiative between CSS and swisspeace supported by the Swiss Federal Department of Foreign Affairs (FDFA), since 2005. Additionally, he has been involved in the Culture and Religion in Mediation project (CARIM), also supported by the Swiss FDFA, since 2011. Mason is a member of the UN and OSCE mediation rosters and serves as a senior advisor in the Master of Advanced Studies ETH Mediation in Peace Processes program. Mason holds a doctorate in environmental science from ETH Zurich and is a trained mediator accredited by the Swiss Mediation Association SDM. His educational background combines scientific training with practical mediation expertise, allowing him to bridge technical and diplomatic aspects of conflict resolution. Mason's research focuses on the application of mediation in complex peace processes, with particular attention to contexts where religious and cultural factors significantly influence conflicts. His work explores how mediation can effectively address security arrangements and navigate the complex relationship between environmental factors, natural resources, and conflict dynamics. Through extensive field experience in regions including Afghanistan, Egypt, Ethiopia, Indonesia, Israel, Kenya, Libya, North Korea, Palestine, Sudan, and Zimbabwe, Mason has developed practical insights that inform his academic contributions to the field of peace mediation. Mason has been actively involved in designing and delivering specialized training programs, including co-organizing the Peace Mediation Course for the FDFA and serving as a trainer in the annual UN Ceasefire Mediation Course and the Religion and Mediation Course. His practical experience spans conflict contexts worldwide, where he has facilitated workshops on conflict analysis, dialogue, negotiation, and mediation for diverse stakeholders including UN, OSCE, IGAD, and EU institutions. As head of the Mediation Support Team at CSS, Mason leads a group dedicated to advancing the theory and practice of peace mediation. The team produces research, develops practical tools for mediators, and provides direct support to mediation processes worldwide. Under Mason's leadership, the team has established itself as a leading center for mediation expertise, particularly in the areas of ceasefire negotiation, religious dimensions of conflict, and the intersection of environmental factors with peace processes.
Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.
Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.
Geneive Henry is a Jamaican-American chemist and the Charles B. Degenstein Professor of Chemistry at Susquehanna University since 2017. She received her Ph.D. in Organic Chemistry (1998) and B.Sc. in Chemistry (First Class Honors, 1993) from the University of the West Indies (Mona). Her academic journey includes postdoctoral fellowships at Harvard University and the University of Rhode Island, and visiting appointments at Lincoln University. Her research focuses on natural product and medicinal chemistry , particularly investigating antioxidant mechanisms, enzyme inhibition (tyrosinase, SARS-CoV-2 Mpro), and anticancer properties of chromene, quinoline, and thymol derivatives. She has secured multiple NSF and Cottrell grants for NMR instrumentation and chemical investigations of Pennsylvania Hypericum species. 2024 : CUR Fellows Award for Undergraduate Research Leadership 2020 : CUR Outstanding Mentorship Award 1998 : IODE Fellowship (Canada), Best Ph.D. Poster & Thesis (UW-I) She has mentored over 40 undergraduate researchers and co-authored 15+ peer-reviewed publications with students since 2015. Her work spans bioinorganic chemistry , pharmacognosy , and computational drug design , with recurring themes in antioxidant activity , DNA interaction studies , and metal complexation .
Stuart Long is an associate dean of undergraduate research and faculty member at the Honors College of the University of Houston, where he serves as the academic adviser for all honors students majoring in Electrical and Computer Engineering. He teaches courses on electromagnetic waves and conducts research in antenna design and applied electromagnetics. Education: Received his doctorate from Harvard University. Stuart Long's research focuses on biomedical applications of electromagnetics, particularly MRI safety testing for implantable medical devices. His work addresses RF-induced heating, electromagnetic compatibility, and safety protocols for devices such as orthopaedic implants and active implantable systems. Recent publications emphasize computational modeling, machine learning, and historical advancements in antenna design. His scholarly contributions include the 2024 Distinguished Achievement Award, the 2018 Chen-To Tai Distinguished Educator Award, and the 2014 John Kraus Antenna Award. These honors reflect his leadership in electromagnetic safety research and engineering education. Stuart Long has actively contributed to improving pedagogy in engineering education, particularly through collaborative learning and retention workshops for diverse student populations. His academic advising role supports the integration of rigorous technical training and interdisciplinary research opportunities for honors students.
Saikat Dutta is an Assistant Professor in the Department of Computer Science at Cornell University. He is affiliated with the Software Engineering Group and focuses on the intersection of Software Engineering and Machine Learning . His work aims to enhance the reliability of ML-based systems while applying ML techniques to solve software engineering challenges. PhD in Computer Science from University of Illinois Urbana-Champaign (Summer 2023) Postdoctoral Researcher at University of Pennsylvania Bachelor's in Computer Science and Engineering from Jadavpur University Research Interests: Dr. Dutta's research spans several key areas: Automated test generation and debugging for ML/DL libraries Using AI/ML for automated software engineering tasks Improving performance of regression tests in ML libraries Static and dynamic analysis for probabilistic programming Article Trends: His recent publications emphasize: Automated testing of ML systems Security vulnerability detection using LLMs Probabilistic program analysis Neurosymbolic learning frameworks Flaky test management in stochastic environments Stochastic regression test optimization Scientific Awards: Meta AI LLM Evaluation Research Grant (2025) Mavis Future Faculty Fellowship (2022-23) Facebook PhD Fellowship (2020-22) 3M Foundation Fellowship (2019-2020) Advising & Grants: Dr. Dutta actively recruits PhD students and postdocs. He leads research projects supported by grants from Meta AI and participates in program committees for top conferences like ICSE and ISSTA. His lab focuses on neurosymbolic systems and ML-based software verification.
Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.
Dr. Jennifer Volk is an Assistant Professor at the College of Engineering, University of Wisconsin-Madison, specializing in Electrical & Computer Engineering. Her research focuses on leveraging novel technologies like superconductor electronics and photonics to create efficient systems for datacenters, neuromorphic computing, quantum computing, and space/sensing applications. She employs a holistic approach spanning circuit design, materials science, and computer microarchitecture. PhD (2024), University of California, Santa Barbara BS (2016), University of California, Santa Cruz Her research interests include superconducting logic , bio-based architectures , and novel computing mediums , emphasizing co-optimization of logic and circuit blocks. Her work develops design abstractions to simplify adoption of unconventional technologies. Dr. Volk's publications demonstrate expertise in superconducting circuit design, radiation-hardened CMOS for particle physics, and photonic materials. She has received numerous awards including the 2025 John D. Wiley Assistant Professorship and IEEE fellowships in applied superconductivity. 2025 John D. Wiley Assistant Professorship 2024 UC Santa Barbara President's Dissertation Year Fellowship 2023 IEEE CSC Graduate Study Fellowship in Applied Superconductivity 2022 IEEE Micro Top Picks Honorable Mention 2021 IEEE Micro Top Picks She teaches E C E 340 - Electronic Circuits I (Spring 2025). Her work bridges materials science, circuit design, and system architecture to enable next-generation computing platforms.
Louisa Foss-Kelly is a Professor and Coordinator of the Counselor Education and Supervision Program at Southern Connecticut State University. Her work bridges clinical practice, counselor education, and legislative advocacy, with a focus on poverty-related counseling and professional identity development. Education: Ph.D. in Counseling from Kent State University Research Interests : Counseling people living in poverty via the I-CARE Model Legislative advocacy for professional counseling Substance abuse prevention in school counseling Graduate counseling student experiences Multicultural and socioeconomic competence Article Trends emphasize poverty interventions (I-CARE Model), counselor education innovations, and legislative advocacy. Her work spans qualitative research, clinical tools, and policy-focused studies. Leadership Roles include: President, Connecticut Counseling Association 2023-2024 President, Chi Sigma Iota Honor Society Grants highlight SBIRT training, evidence-based practices, and addictions counseling program development. She collaborates with researchers like Margaret Generali on poverty and counselor training initiatives.
David Steurer is an Associate Professor in the Department of Computer Science at ETH Zurich, where he leads the Institute for Theoretical Computer Science. His research bridges theoretical computer science, mathematics, and practical applications in machine learning and statistics. Steurer has made significant contributions to the understanding of sum-of-squares methods, semidefinite programming, and high-dimensional estimation problems. Steurer earned his B.Sc. & M.Sc. in Computer Science from Saarland University in 2006, followed by a Ph.D. in Computer Science from Princeton University in 2010 under the supervision of Sanjeev Arora. His doctoral dissertation, "On the Complexity of Unique Games and Graph Expansion," received an Honorable Mention for the ACM Doctoral Dissertation Award in 2011. Steurer's research focuses on algorithm design through mathematical programming relaxations, particularly semidefinite programming and the sum-of-squares method. His work addresses computational complexity of high-dimensional estimation problems including tensor decomposition, clustering, Gaussian mixture models, and stochastic block models. He also investigates algorithmic aspects of robustness and differential privacy, especially for estimation tasks. His theoretical contributions have practical implications for machine learning and data analysis in the presence of noise and adversarial conditions. Analysis of Steurer's recent publications reveals a consistent trajectory in advancing the sum-of-squares framework for high-dimensional estimation and robust statistics. His work demonstrates how sophisticated mathematical programming techniques can provide certifiable guarantees for challenging problems in machine learning. The research spans theoretical foundations while maintaining relevance to practical applications, particularly in scenarios involving corrupted or noisy data. A notable trend is the extension of sum-of-squares methods to handle increasingly complex statistical models while providing rigorous performance guarantees. ERC Consolidator Grant (2019) Michael and Sheila Held prize (2018, with Raghavendra) Invited speaker at International Congress of Mathematicians (2018) STOC Best Paper Award (2015) Alfred P. Sloan Research Fellowship (2014) NSF CAREER Award (2014) Microsoft Research Faculty Fellowship (2014) FOCS Best Paper Award (2010) Steurer has advised numerous PhD students and postdocs who have gone on to prominent positions, including Sam Hopkins (now faculty at MIT), Jonathan Shi, and Aaron Potechin. His research is supported by multiple prestigious grants, including an ERC Consolidator Grant, an NSF CAREER Award, and a Microsoft Research Faculty Fellowship. He has served on numerous program committees for top theoretical computer science conferences and has been recognized for his service to the community through roles such as internal member of the Scientific Advisory Committee of the Institute for Theoretical Studies at ETH Zurich. As head of the Institute for Theoretical Computer Science at ETH Zurich, Steurer leads a research group focused on advancing the theoretical foundations of computer science with particular emphasis on mathematical optimization methods. His team explores the boundaries of what can be efficiently computed in high-dimensional settings, with applications spanning machine learning, statistics, and data science. The group maintains strong connections with both theoretical and applied researchers across ETH and the broader academic community.
Jiefeng Sun serves as Assistant Professor in the Department of Aerospace and Mechanical Engineering within Arizona State University's School for Engineering of Matter, Transport and Energy. His research program centers on designing artificial-muscle-driven robots that replicate biological adaptivity through advanced modeling and control systems. His academic credentials include: Ph.D. in Robotics and Control from Colorado State University (2022) M.S. in Mechanical Engineering from Dalian University of Technology (2017) B.S. in Mechanical Engineering from Lanzhou University of Technology (2014) Dr. Sun's research integrates soft robotics, artificial muscles, and adaptive control to create morphologically intelligent systems. His work spans aerial robotics, wearable exoskeletons, and biomimetic locomotion, with emphasis on shape-changing mechanisms and energy-efficient actuation that enables robots to operate in unstructured environments. Analysis of his recent publications reveals dominant themes in twisted-and-coiled actuators, tensegrity structures, and physics-informed control methods. Key trends include variable-stiffness systems for wearable devices, data-efficient simulation techniques using Koopman operators, and bistable mechanisms for aerial grasping applications. His research excellence has been recognized through: Finalist for Best Student Paper Award at IEEE/RSJ IROS 2018 Reviewer of the Year 2021 for Smart Materials and Structures Journal 2022 DARPA Riser designation Dr. Sun actively recruits graduate students for robotics research and has secured significant funding including DARPA support. He teaches core courses including System Dynamics and Control I (MAE 318) while supervising thesis research and applied projects through MAE 599 and MAE 792. He directs the Sun Robotics Lab (https://sunroboticslab.github.io), which collaborates across biomechanics, materials science, and control theory to develop next-generation adaptive robotic systems with applications in healthcare, exploration, and human augmentation.
Graham Ormondroyd is a Professor at the School of Environmental and Natural Sciences , Bangor University , specializing in the Biocompounds department. His research focuses on innovative wood modification techniques, sustainable biomaterials, and environmental impact assessments. Current projects include scaling waste-based composites for infrastructure and developing Welsh wool applications. Collaborations span academia-industry partnerships like Bangor University and Zentia Ltd KTP. Research Trends: His 2025 publications emphasize multi-scale resin diffusion analysis and UK wood recycling frameworks, while 2024 work explores laser incising and phenolic resin-NMR interactions. Recent studies also address VOC emissions from formaldehyde-free composites. Professional Activities: He chairs academic panels (e.g., International Panel Products Symposium) and reviews publications. Projects focus on net-zero construction and climate resilience.
Marco Panesi is a Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign and Director of the Center for Hypersonics and Entry Systems Studies (CHESS). His research focuses on non-equilibrium phenomena in high-enthalpy flows, plasma dynamics, and uncertainty quantification. He holds a Ph.D. from the von Kármán Institute for Fluid Dynamics (2009) and M.S. degrees from Università di Pisa (2003) and VKI (2005). Roles: Faculty Member, Research Director, Principal Investigator Key Affiliations: CHESS, University of Illinois, VKI Research Interests: Hypersonic flow modeling, non-equilibrium plasmas, radiation effects, machine learning applications in aerothermodynamics, ablation processes, and state-to-state chemistry. His work bridges computational fluid dynamics with experimental validation in facilities like the Plasmatron X wind tunnel. Publications: Over 100 peer-reviewed articles on topics ranging from plasma kinetics to thermal protection systems. Recent work emphasizes adaptive neural operator models and Bayesian uncertainty quantification. Awards: Includes the Vannevar Bush Faculty Fellowship (2021), NASA Groundbreaker Award (2021), and multiple early-career recognitions from AFOSR, NASA, and ESA. Grants & Leadership: Secured funding from NSF, NASA, and DOD. Leads multidisciplinary teams on projects like the CHyPS material response solver and hypersonic entry modeling. Labs & Facilities: Principal investigator for the UIUC Plasmatron X facility, a key resource for studying high-enthalpy plasma flows.
