Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Alexander Summers is an Associate Professor at the Department of Computer Science , University of British Columbia . He joined UBC in March 2020 after serving as a Senior Researcher (Oberassistent) at ETH Zurich from 2014-2020. His research bridges Programming Languages , Formal Methods , and Software Engineering , with a focus on automated verification tools for heap-based and concurrent programs. MSc Joint Mathematics and Computer Science, Imperial College London (2004) PhD Computer Science, Imperial College London (2009) Postdoc, ETH Zurich (2009-2014) Summers leads the Prusti Project , developing deductive verification tools for Rust, and contributes to the Viper Project for intermediate verification languages. His work addresses challenges in: Memory safety and concurrency verification Ownership models and aliasing control Automated reasoning with SMT solvers Resource-oriented programming specifications Debugging verification condition quantifiers Formal validation of verification infrastructure His research has been recognized with a Amazon Research Award and ACM SIGPLAN Distinguished Paper Awards . He teaches courses like Advanced Software Engineering and Program Verifiers and Program Verification , and supervises graduate students in formal verification and Rust-related research.
Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.
Stephen Y. Chou is the Joseph C. Elgin Professor of Engineering and Professor of Electrical and Computer Engineering at Princeton University. He is affiliated with the Princeton Materials Institute (PMI) and leads the Nano, Meta, and Bio-Health Laboratory (NMBH Lab), previously known as the Nanostructures Lab. His work spans nanotechnology, bioengineering, and photonics, integrating interdisciplinary approaches to address challenges in health, electronics, and manufacturing. Ph.D., Massachusetts Institute of Technology, 1986 M.A., Physics, State University of New York at Stony Brook, 1982 B.S., Physics, University of Science and Technology of China, 1978 Chou's research focuses on nano-bioengineering for diagnostics and health, nanophotonics (meta-optics and subwavelength elements), and nanofabrication techniques. His work has revolutionized nanoimprint lithography, enabling breakthroughs in semiconductor devices, optical sensors, and biomedical tools. The NMBH Lab's innovations include ultra-sensitive biosensors (D2PA), the iMOST™ diagnostic platform, and foundational contributions to gate-all-around (GAA) transistors for sub-3 nm CMOS technology. His publications reflect advancements in plasmonic biosensors, organic solar cells, nanofluidics, and scalable nanoimprint methods. Key themes include nanoscale light manipulation, low-cost diagnostic systems, and quantum electronic devices. Member, National Academy of Engineering (2007) IEEE Cledo Brunetti Award (2004) IEEE Nanotechnology Pioneer Award (2014) Nanoimprint Pioneer Award (2015) Packard Fellow (1991) Fellow, IEEE (2000) Inductee, New Jersey High Tech Hall of Fame (2004) MIT Technology Review Emerging Technologies (2003, 2007) Chou has founded three companies (Nanonex, NanoOpto, Essenlix) and co-founded BioNano Genomics (NASDAQ: BNGO). His work bridges academic research and industrial impact, with over 700 publications (H-index 97) and 400 patents, influencing global nanotechnology and diagnostics. The NMBH Lab develops transformative technologies in nano-bioengineering, nanophotonics, and nanofabrication, emphasizing practical applications for healthcare and electronics.
Toril Aalberg is a Professor and Head of the Department of Sociology and Political Science at the Norwegian University of Science and Technology (NTNU) in Trondheim. She was previously Head of the same department from 2017-2021 and serves as Guest Professor at Mid Sweden University in 2024-2025. Aalberg is a member of the research networks NEPOCS (Network of European Political Communication Scholars) and EVPOC (Elections, Values and Political Communication). Her research interests include comparative politics , election campaigns , media effects , public opinion , and stereotypes in political contexts. She has led major projects like the COST Action IS1308: Populist Political Communication in Europe (2014-2018). Toril Aalberg has authored or co-authored 9 books, including Populist Political Communication in Europe (2016) and Communicating Populism (2019). Her recent articles focus on misinformation during crises , media’s role in political efficacy , and cross-national disinformation studies , often analyzing data from 17-19 democracies. She has conducted extensive research on media polarization , immigrant attitudes , and news consumption patterns . Aalberg has held visiting appointments at institutions including the University of California, Berkeley , Stanford University , and University of Oslo , with current affiliations at the Centre for Advanced Studies in Oslo and Mid Sweden University . Her work spans political communication theory , media sociology , and democratic processes .
Dr. Theresa Jedd is an Environmental Policy Specialist and Post-Doctoral Research Associate at the Department of Environmental and Climate Policy , Technische Universität München , since 2019. Previously, she worked at the National Drought Mitigation Center (University of Nebraska-Lincoln, 2015-2019) and the Natural Resource Ecology Laboratory (Colorado State University, 2015). Her work spans drought policy analysis, transboundary conservation, and participatory governance methods across diverse regions including Montana, Nebraska, and the Middle East/North Africa. Current Affiliation: Technische Universität München (2019-present) Prior Roles: University of Nebraska-Lincoln (2015-2019), Colorado State University (2015) Research Focus: Combines qualitative methods (interviews, focus groups, workshops) with physical drought indicators to analyze policy frameworks at multiple governance levels (local, national, international). Key themes include: Climate risk governance Water allocation mechanisms Drought early warning systems Transboundary environmental policy Civil society engagement Ethics of environmental crises Publication Trends: Recent work highlights polycentric governance challenges, drought resilience limitations, and participatory monitoring approaches. Her research spans environmental policy, water resource management, and climate adaptation.
Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.
Steffi Haag is a Professor of Digital Innovation and Entrepreneurship at the Institute of Computer Science , Heinrich Heine University Düsseldorf (HHU) . She bridges the Faculty of Mathematics and Natural Sciences and Faculty of Business Administration and Economics , collaborating with the Center for Entrepreneurship Düsseldorf (CEDUS) to inspire tech startups. Her research focuses on sustainable information systems, digital experiences, and business models. Steffi Haag’s research integrates Shadow IT , Usable Cybersecurity , and Digital Idea Management . Her work emphasizes theoretical , quantitative , qualitative , and mixed-methods research , including experimental and survey methodologies. Steffi Haag’s publications (2018–2024) span Information Systems , Cybersecurity , Digital Twins , and Sustainable Design . Common themes include Shadow IT dynamics , user behavior in security , and innovation management . Hermann Gutmann Award for special scientific achievements (2023) Schöller Fellow (2020) HMD Best Paper Award (2018) Research Award in Data Protection and Data Security (2017) Dissertation Prize (2017) Steffi Haag has secured grants such as the TU Darmstadt Postdoctoral Fellowship for Female Researchers (2016–2018) and Deloitte Foundation Fellowship (2009–2011). She actively moderates conferences and serves as Associate Editor of Business & Information Systems Engineering .
William John Moss is a Professor in the Department of Epidemiology at the Johns Hopkins Bloomberg School of Public Health, where he also holds joint appointments in International Health and Molecular Microbiology and Immunology. He serves as Executive Director of the International Vaccine Access Center (IVAC) and Deputy Director at the Johns Hopkins Malaria Research Institute. His work spans multiple centers including the Center for Global Health, Center for Humanitarian Health, and Johns Hopkins Vaccine Initiative. Dr. Moss earned his MD from Columbia University in 1984 and his MPH in 1995. As a pediatrician with subspecialty training in infectious diseases, he has conducted extensive field research across multiple countries including Ethiopia, Kenya, South Africa, Zambia, Zimbabwe and India. His research focuses on the epidemiology and control of childhood infections in resource-poor settings, with particular emphasis on measles-HIV interactions, malaria transmission in southern Africa, serosurveillance for immunization programs, and care of HIV-infected children in rural Zambia. His recent work (2024-2025) demonstrates continued focus on measles epidemiology, malaria transmission dynamics, and vaccine implementation research, with numerous publications examining immunity gaps, human mobility patterns, and malaria vector biology in African settings. Scientific Awards: Wyeth-Lederle Vaccines and Pediatrics Young Investigator Award in Vaccine Development (1999-2001) Gustave J. Martin Innovative Research Fund Fellow (2001) Advising, Mentoring and Teaching Recognition Award (2007-2008) Dr. Moss leads multiple research projects including studies on HIV-infected children's immune reconstitution, malaria transmission dynamics in southern Zambia, and strengthening immunization systems through serosurveillance. His work bridges clinical medicine, epidemiology, and public health policy to address critical challenges in global infectious disease control.
Theo Arentze is a Full Professor at Eindhoven University of Technology (TU/e) in the Department of the Built Environment, leading the Real Estate Management and Development group. He is affiliated with EAISI Health and EAISI Mobility research institutes. Education: MSc in Psychology (Cognitive Psychology & AI) from Groningen University, PhD from TU/e Urban Planning Research: Spatial choice behavior, decision support systems, activity-based modeling, agent-based simulation His research integrates bounded rationality into spatial choice models to enhance behavioral realism, with applications in real-estate management , neighborhood development , healthy cities , and hybrid work environments . Recent work includes child-friendly urban planning and energy-efficient housing impacts . Prominent article themes include: Hybrid work location decisions Urban public space affective experiences Child friendliness in residential choices Sustainable energy preferences Spatial decision support systems Social network modeling Scientific recognition includes: Best Poster Award (2025) - Computational Urban Planning Conference Long Paper of Distinction (2021) - Healthy Buildings Europe EuroFM Best Paper Award (2017) Pyke Johnson Award (2016) As an educator, he teaches courses in Urban Planning , Housing Economics , and Quantitative Research Methods . His multidisciplinary team combines expertise from psychology, sociology, and urban economics to create high-quality built environments through behavioral research.
Miguel Rodrigues is a Professor of Information Theory and Processing at University College London's Department of Electronic & Electrical Engineering. He leads the Information, Inference and Machine Learning Lab at UCL and serves as the founder and director of the master programme in Integrated Machine Learning Systems. Rodrigues is also the UCL Turing University Lead and a Turing Fellow with the Alan Turing Institute, the UK National Institute of Data Science and Artificial Intelligence. His academic background includes an undergraduate degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto, Portugal, and a PhD in Electronic and Electrical Engineering from University College London. He has held appointments at prestigious institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto. Dr. Rodrigues's research spans information theory, information processing, and machine learning. His work has attracted over £5 million in funding from competitive national and international funding bodies and resulted in more than 250 publications with over 8000 citations in leading journals and conferences, including top AI venues like NeurIPS, ICML, and ICLR. His recent publications demonstrate a strong focus on multimodal learning, machine learning security, climate modeling with satellite data, and applications of AI in healthcare and precision medicine. His work shows increasing interdisciplinary collaboration across fields from climate science to pharmaceutical engineering. IEEE Communications and Information Theory Societies Joint Paper Award 2011 Fellow of the Institute of Electronics and Electrical Engineers (IEEE) Prize for Merit from the University of Porto Prize Engenheiro Cristian Spratley Prize Engenheiro Antonio de Almeida Fellowships from the Portuguese Foundation for Science and Technology Fellowships from the Foundation Calouste Gulbenkian Dr. Rodrigues has served as Editor for IEEE BITS – The Information Theory Magazine and IEEE Transactions on Information Theory, among other editorial roles. He consults widely in machine learning and AI with government institutions, funding agencies, industry, and startups, and sits on committees responsible for AI standardization such as the BSI Art/1 working group. His leadership extends to directing research labs and educational programs focused on advancing machine learning systems. He leads the Information, Inference and Machine Learning Lab at UCL, which focuses on fundamental aspects of information theory and their applications to machine learning and data processing. The lab works on both theoretical foundations and practical implementations of learning systems.
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.
Iona Cheng is a Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco (UCSF), where she conducts groundbreaking research in cancer epidemiology. She serves as co-Investigator of the SEER Greater Bay Area Cancer Registry and is Principal Investigator of multiple NIH- and foundation-funded projects examining genetics, lifestyle factors, and neighborhood characteristics in relation to cancer risk. Dr. Cheng has developed an extensive research program focused on racial/ethnic differences in cancer risk and leads population-based cancer surveillance studies that document variations in cancer incidence and mortality patterns across diverse racial and ethnic groups. University of California, Davis, BS, 1990–1994, Physiology Yale University, MPH, 1999–2001, Chronic Disease Epidemiology University of Southern California, PhD, 2001–2005, Epidemiology University of California, San Francisco, Postdoc, 2006–2008, Genetic and Molecular Epidemiology Dr. Cheng's research spans multiple disciplines within cancer epidemiology, with particular emphasis on understanding how environmental exposures, genetic factors, and social determinants interact to influence cancer risk and outcomes across different racial and ethnic populations. Her work frequently examines the impact of air pollution, endocrine-disrupting chemicals, and neighborhood characteristics on cancer development and survival. She has made significant contributions to understanding cancer disparities among Asian American, Native Hawaiian, and Pacific Islander populations, bringing attention to the unique cancer risks and outcomes within these understudied groups. Her research often leverages the Multiethnic Cohort Study, one of the largest prospective studies of cancer incidence and mortality across diverse racial/ethnic populations. Analysis of Dr. Cheng's recent publications reveals a consistent focus on environmental and social determinants of cancer risk across multiple organ sites. Her work demonstrates a sophisticated integration of epidemiological methods with environmental exposure assessment, genetic analysis, and health disparities research. Many of her studies examine the intersection of environmental exposures and racial/ethnic disparities in cancer outcomes, particularly regarding breast cancer, lung cancer, and other malignancies. She has published extensively on the impact of air pollution on cancer risk and survival, as well as the effects of endocrine-disrupting chemicals like bisphenol A, parabens, and phthalates. American Association for Cancer Research Scholar-in-Training Award (2007) National Institutes of Health Loan Repayment Award (2007) National Institutes of Health Loan Repayment Renewal Award (2009) American Association for Cancer Research Faculty Scholar Award (2011) National Institutes of Health Loan Repayment Renewal Award (2011) National Institutes of Health Loan Repayment Renewal Award (2013) American Journal of Epidemiology/Society of Epidemiology Research Top 10 manuscripts (2014) Cancer Prevention Institute of California Mentoring Award (2015) American Society of Human Genetics Top poster As Principal Investigator of multiple NIH-funded projects, Dr. Cheng oversees substantial research grants focused on cancer epidemiology and health disparities. Her work often involves large interdisciplinary collaborations with researchers across multiple institutions, including the Multiethnic Cohort Study which follows over 200,000 participants from diverse racial/ethnic backgrounds. She has demonstrated leadership in mentoring junior researchers, particularly those from underrepresented backgrounds in science, as evidenced by her Cancer Prevention Institute of California Mentoring Award. Her research program integrates data from cancer registries, electronic health records, and geospatial information to provide comprehensive insights into cancer patterns and risk factors. Dr. Cheng's research is closely connected to the UCSF Helen Diller Family Comprehensive Cancer Center and leverages collaborations with Lawrence Berkeley National Laboratory, which provides advanced technological resources for cancer research. Her work benefits from access to extensive cohort data, sophisticated exposure assessment methods, and interdisciplinary expertise in genetics, environmental science, and computational biology available through these institutional partnerships. She frequently collaborates with researchers studying the genetic and environmental determinants of cancer across multiple organ systems, contributing to a more comprehensive understanding of cancer etiology and prevention strategies.
Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.
Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.
