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Rebecca Bromley-TrujilloView profile

Associate Professor

• American Environmental Policy
• Public Opinion and Policy
• Climate Change Policy
• +3 more

Dr. Rebecca Bromley-Trujillo is an Associate Professor in the Department of Political Science at Christopher Newport University (CNU) and serves as Research Director of the Wason Center for Civic Leadership. She holds a Ph.D. and M.A. in Political Science from Michigan State University and a B.A. in Political Science from the University of Texas, San Antonio. Her research focuses on state and local climate policy, the interplay between public opinion and policy, and the role of science in policymaking. Dr. Bromley-Trujillo previously taught as an Assistant Professor at the University of Kentucky before joining CNU. Research Interests: American environmental policy State and local climate change policy efforts Public opinion and policy relationships Scientific influence on policy processes Her work appears in journals such as Review of Policy Research , Climatic Change , and Journal of Public Policy . She co-authored Climate Policy in the American States (2024) and developed the CHORUS dataset analyzing state interest group policy positions. Her recent research explores experimental climate policy designs and the judicialization of federalism in U.S. politics. Professional Affiliations: Center for Sustainability in Education (CNU) Wason Center for Civic Leadership (CNU) Dr. Bromley-Trujillo's teaching spans American politics, environmental policy, public administration, and political behavior. She emphasizes applied research and policy analysis in her academic work.

Luca CarloniView profile

Professor

• Computer Science
• Embedded Systems
• Hardware Design
• +6 more

Luca Carloni is a Professor of Computer Science and Department Chair at Columbia University's Columbia Engineering. He leads the System-Level Design Group, focusing on heterogeneous system-on-chip (SoC) architectures, networks-on-chip (NoC), and embedded systems. Carloni holds a Laurea Summa Cum Laude in Electronics Engineering from the University of Bologna and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His work emphasizes specialized hardware design, energy-efficient computing, and FPGA-based prototyping. Research interests include system-level design methodologies for SoCs, embedded accelerators, and quantum computing hardware. He has pioneered frameworks like Embedded Scalable Platforms (ESP) and tools like MosaicSim for rapid SoC prototyping. Carloni has received numerous awards, including the NSF CAREER Award (2006), IEEE Fellow (2017), and multiple best paper awards at DATE and CloudCom conferences. He has served on editorial boards of IEEE Transactions on CAD and ACM Transactions on Embedded Computing , and chaired key conferences like EMSOFT and ESWeek. His research addresses challenges in heterogeneous architectures, power management, and the intersection of machine learning with embedded systems. Current projects explore quantum control systems, brain-computer interfaces, and energy-efficient datacenter computing.

Felix KrahmerView profile

Assistant Professor

• Compressed Sensing
• Signal Processing
• Optimization
• +4 more

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

Chutima Boonthum-DeneckeView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +6 more

Dr. Chutima Boonthum-Denecke is a Professor in the Department of Computer Science at Hampton University's School of Science. She joined Hampton University in 2006 as an Assistant Professor and now serves as Director of the Information Assurance and Cyber Security Center (IAC@HU). She leads the NSF CyberCorps Scholarship for Service program and has contributed to NSF initiatives like ARTSI and STARS Alliances. Her educational background includes a Ph.D. in Computer Science from Old Dominion University (2007), an MS in Applied Computer Science from Illinois State University (2000), and a BS in Computer Science from Srinakharinwirot University (1997). Dr. Boonthum-Denecke's research integrates artificial intelligence, natural language processing, and cybersecurity. Key interests include: Developing intelligent tutoring systems and educational games Secure coding practices for software engineering NLP applications in information retrieval and assessment tools Cyber-physical security for IoT and robotics Her recent publications (2016-2021) focus on machine learning applications in cybersecurity, including sentiment analysis for threat detection, blockchain-enhanced IoT security, and vulnerability assessments of emerging technologies. Collaborative work with students frequently addresses privacy ethics in AI assistants, RFID implants, and cloud systems. She mentors students through the IAC@HU lab, resulting in award-winning conference presentations on cybersecurity topics. As Principal Investigator of NSF CyberCorps, she oversees scholarship programs that bridge academic research with national security needs.

Prashant MaliView profile

Professor

• Genome Engineering
• RNA Editing
• Stem Cell Research
• +4 more

Prashant Mali is a Professor in the Department of Bioengineering at the University of California, San Diego . His research bridges genome engineering, RNA biology, and biomedical applications, with a focus on CRISPR-Cas systems and ADAR-mediated RNA editing. Education : Ph.D. in Bioengineering Key Affiliations : UC San Diego, Altman Clinical and Translational Research Institute Dr. Mali's work centers on CRISPR-Cas9 technology , RNA editing , and human pluripotent stem cells . His lab develops tools for programmable gene regulation, synthetic lethal screens, and metabolic pathway analysis in disease contexts. Recent publications highlight innovations in circular RNA engineering , ADAR activity mapping , and metabolic reprogramming in cancer . His team employs multi-omics approaches and in vivo models to translate genome editing into clinical applications. Students and Collaborators Current Lab Members : Sami Nourreddine (Postdoc), Amir Dailamy (Graduate), Andrew Portell (Graduate), Michael Tong (Graduate) Alumni : Kyle Ford (PhD 2022), Nathan Palmer (PhD 2022), Udit Parekh (PhD 2021) Research Themes CRISPR Screens : Synthetic lethal interactions, oncogenic pathways, metabolic vulnerabilities RNA Editing : ADAR engineering, circular guide RNAs, clinical translation Tissue Engineering : Vascularized organoids, cardiac maturation, ex vivo models

Abraham SilberschatzView profile

Professor

• Database Systems
• Operating Systems
• Storage Systems
• +3 more

Abraham Silberschatz is the Sidney J. Weinberg Professor of Computer Science at Yale University. He previously served as Vice President of the Information Sciences Research Center at Bell Laboratories and held a chaired professorship at the University of Texas at Austin. His research focuses on database systems, operating systems, and network management. Silberschatz has advised over a dozen PhD students, many now in academia and industry. Education: Ph.D., Computer Science, Stony Brook University (SUNY) Research Interests: His work spans database systems, operating systems, storage systems, and network management. Notable contributions include foundational textbooks like Operating System Concepts and Database System Concepts , which have become industry standards. He has also developed innovative systems like DataPlay and contributed to projects such as NetInventory. Publications: His 15+ years of research include influential papers on database architecture, network routing, and distributed systems. Recent work explores leveraging non-volatile memory technologies in systems design. Awards: ACM Karl V. Karlstrom Outstanding Educator Award (1998) IEEE Taylor L. Booth Education Award (2002) VLDB Test of Time Award (2019) Multiple Bell Laboratories President's Awards for innovation Grants & Patents: Recipient of over two dozen grants and over four dozen patents, including foundational IP in multimedia storage and distributed systems. His team's HadoopDB project merged MapReduce and DBMS technologies. Labs/Teams: Collaborates with Prof. Robert Soulé on projects in database systems and networking, focusing on next-gen memory technologies. Active in mentoring graduate students and postdocs in their research group.

Masuma ShahidView profile

Assistant Professor

• LGBTQ+ Rights
• Artificial Intelligence and Law
• Queer Data
• +12 more

Masuma Shahid is an Assistant Professor and Lecturer at the Erasmus School of Law, specializing in International and European Union Law, with a dedicated research focus on LGBTQ+ rights, AI and human rights, and privacy law. She is affiliated with the International and European Union Law (IEUR) department and actively contributes to academic and public discourse on equality and digital governance. Assistant Professor, Erasmus School of Law Lecturer, International and European Union Law (IEUR) Co-Coordinator, LGBTQI+ Working Group, Berkeley Center on Comparative Equality and Anti-Discrimination Law Member, Diversity Platform, Erasmus School of Law Her research centers on the intersection of LGBTQ+ rights and artificial intelligence, queer data, equal marriage rights, European internal market law, competition law, and international human rights law. She employs critical legal theory to examine how legal systems address marginalization and digital discrimination. Her recent scholarly work includes a doctoral thesis on equal marriage rights litigation in major courts, publications on AI’s impact on LGBTQ+ communities, and authoritative textbooks on EU law. The articles reflect a strong trend in using comparative legal analysis to advocate for inclusive legal frameworks, particularly in digital and human rights contexts. She has been recognized through invitations to speak at academic forums and contribute to public debates on LGBTQ+ rights and AI ethics. Her media contributions highlight her role as a public intellectual in advancing legal inclusivity. Invited speaker on Queer Legal Theory and AI governance Expert commentator on LGBTQ+ rights in Dutch media Contributor to discussions on constitutional reforms for LGBTQIA+ and disabled persons Masuma Shahid advises on legal research and contributes to collaborative academic initiatives, particularly through her role at the Berkeley Center. While no formal grants are listed, her extensive publication and speaking record indicate active research funding and institutional support. She is involved in shaping policy discourse through academic and public engagement. She is a key figure in the LGBTQI+ Working Group at the Berkeley Center on Comparative Equality and Anti-Discrimination Law, contributing to an international network focused on anti-discrimination law and equality. Her work bridges academia, policy, and social justice advocacy.

James A. EvansView profile

Professor

• Computational Social Science
• Collective Intelligence
• Science of Science
• +12 more

James A. Evans is the Max Palevsky Professor of Sociology and Data Science at the University of Chicago, where he is a faculty member in the Department of Sociology within the Division of the Social Sciences. He is the director of Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science . He holds additional affiliations as an External Professor at the Santa Fe Institute , External Faculty at the Complexity Science Hub, Vienna , and Visiting Faculty Researcher at Google . Education: B.A. in Anthropology, Brigham Young University (1994) M.A. in Sociology, Stanford University (1999) Ph.D. in Sociology, Stanford University (2004) His research centers on the collective system of thinking and knowing , exploring how ideas emerge, spread, and evolve through social and technical systems. He investigates innovation, collective intelligence, and the science of science , using large-scale data modeling, machine learning, generative AI, and network analysis to study knowledge creation. His work spans domains including science, technology, law, and religion, with a focus on how AI is reshaping discovery processes. The most recent publications highlight trends in AI and scientific discovery , with a strong emphasis on innovation, knowledge systems, and human-machine intelligence . His research increasingly explores AI as a transformative agent in science , including the concept of 'alien intelligence' and the development of complementary AI to augment human capacity. Projects like the $20M NSF-funded APTO initiative aim to build language models that predict technological outcomes by analyzing historical data. Scientific Recognition and Funding: Research supported by the National Science Foundation (NSF) , National Institutes of Health (NIH) , Air Force Office of Scientific Research (AFOSR) , and philanthropic sources Work published in Nature, Science, PNAS , and leading social science journals Featured in The New York Times, The Economist, The Atlantic, Wired, NPR, BBC, Le Monde , and others James Evans advises on science policy and funding strategies, emphasizing the importance of diversity, interdisciplinary collaboration, and demographic balance in fostering innovation. He critiques current academic incentives and proposes alternative discovery regimes. He leads Knowledge Lab , a collaborative research environment that conducts seminars, grants, and employment opportunities in computational social science and AI.

• Macroeconomic modeling of health disorders
• Infectious disease economics
• Computable General Equilibrium (CGE) Modeling
• +7 more

Dr Marcus Keogh-Brown is an Associate Professor in the Department of Global Health and Development within the Faculty of Public Health and Policy at the London School of Hygiene & Tropical Medicine (LSHTM). His position is split between research focusing on macroeconomic modeling of health and serving as Deputy Programme Director for the school's public health distance learning program. Education: BSc in Mathematics and Statistics, Queen Mary University of London (1995-1998) MSc in Computer Studies, University of Essex (1998-1999) PhD in "A Statistical Model of Internet Traffic," Queen Mary University of London (1999-2004) PGCILT Modules 1 and 2, London School of Hygiene and Tropical Medicine (2008-2012) Dr Keogh-Brown's research focuses on analyzing the macro-economic impact of health disorders and developing macro-economic models in health contexts. His areas of interest include infectious diseases (SARS, influenza, COVID-19, tuberculosis, malaria) and non-communicable diseases (Alzheimer's Disease, Dementia). He specializes in health-related applications of Computable General Equilibrium (CGE) Modeling with GAMS, with current work on health and macroeconomic modeling of COVID-19, tuberculosis, child labor, and health-related food policies. His publication record shows a strong trend toward integrated macroeconomic-epidemiological modeling, particularly for infectious disease outbreaks and public health interventions. Recent work focuses on tuberculosis in India, Covid-19 impacts in Pakistan, and food policy interventions like the UK Soft Drinks Industry Levy. His research consistently applies economic modeling frameworks to evaluate the health and economic impacts of disease and health policies across multiple countries including Ghana, India, Thailand, Myanmar, UK, and China. Grants: Current: "Modelling the health social care and macroeconomic impacts of dementia policies in the UK" (NIHR, 2025-2026) Current: "Co-designing food system fiscal policy for healthy people and planet" (University of Oxford, 2022-2025) Completed: "COVID-19 vaccine scenario analysis for health economic and social impacts" (WHO, 2022) Completed: "Evaluation of the impact of the UK industry levy of sugar-sweetened beverages" (University of Cambridge, 2017-2023) Completed: "Macroeconomic Burden of Alzheimers in China" (Jansen Global Services LLC, 2014-2015) Dr Keogh-Brown is affiliated with several research centers at LSHTM including the Malaria Centre, Global Health Economics Centre, and Centre for Mathematical Modelling of Infectious Diseases. His collaborative work spans multiple countries and addresses critical intersections between health, economics, and policy.

Francesca GrisoniView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Drug Discovery
• +8 more

Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.

Nelly V. LitvakView profile

Professor

• Algorithms for Complex Networks
• Applied Probability
• Stochastic Operations Research
• +5 more

Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.

ChanMin KimView profile

Professor

• Educational Technology
• Computer Science Education
• Science Writing
• +3 more

ChanMin Kim is a Professor in the Learning and Performance Systems department at Penn State College of Education. With over 80 publications and 2553 citations, their work focuses on integrating artificial intelligence , robotics , and educational technology into science and computer science education. Research interests: Science writing, AI-human partnerships, robotics in education, equity-focused technology design Key methodologies: Natural Language Processing, learning analytics, scaffolding strategies Recent work explores large language models in education, debugging processes in pre-service teacher training, and automated assessment systems for science explanations. While the provided data doesn't show specific scientific awards or student advisees, their publications in venues like British Journal of Educational Technology and Journal of Science Education and Technology demonstrate significant contributions to learning sciences. Kim collaborates extensively with researchers in AI, educational technology, and equity-focused domains.

Yuki MiuraView profile

Assistant Professor

• Climate Risk Assessment and Management
• Urban Resilience and Adaptation
• Engineering, Social Science, and Economics
• +3 more

Dr. Yuki Miura serves as Assistant Professor at New York University's Tandon School of Engineering in the Department of Mechanical and Aerospace Engineering and Center for Urban Science and Progress (CUSP), with additional affiliations at NYU Stern's Volatility and Risk Institute and the New York City Panel on Climate Change (NPCC5). Her academic credentials include a Ph.D. (2022), M.Phil. (2021), and M.S. (2017) in Civil Engineering and Engineering Mechanics from Columbia University, complemented by a B.Eng. in System Design Engineering from Keio University (2015). Dr. Miura's research integrates engineering, climate science, finance, and social sciences to develop actionable resilience solutions. Her work focuses on climate risk quantification , urban adaptation strategies , and socioeconomic impact modeling through advanced data analytics. She pioneers methodologies that couple physical climate modeling with socioeconomic vulnerability assessments to deliver implementable risk mitigation frameworks for public and private institutions. Her publication portfolio demonstrates consistent advancement in climate risk analytics, with recent work addressing urban flooding dynamics, precipitation extremes, and infrastructure protection. These studies reveal a distinct trajectory toward integrated risk modeling that bridges climate physics with financial and social dimensions, increasingly leveraging AI-driven approaches for compound hazard assessment. Recognition for her contributions includes the Mindlin Scholar award from Columbia University (2022), with research featured in The New York Times , The New Yorker , and The Nikkei . As director of the Climate, Energy, and Risk Analytics Lab (CERA), Dr. Miura mentors graduate students in developing data-driven solutions for climate resilience. Her industry experience at Morgan Stanley (2021-2024) in climate risk management directly informs her applied research approach, fostering strong connections between academic innovation and real-world implementation. CERA operates as an interdisciplinary hub developing AI-driven methodologies for urban flood modeling, compound climate risk assessment, and socioeconomic impact analysis. The lab maintains active collaborations with the National Center for Atmospheric Research and New York City/State governments to translate research into actionable climate adaptation policies.

Seth BlumsackView profile

Professor

• Electricity and Natural Gas Industries
• Energy Infrastructure Resilience
• Network Science
• +4 more

Seth Blumsack is a Professor at the Pennsylvania State University in the Department of Energy and Mineral Engineering and serves as Director of the Center for Energy Law and Policy . He holds an Adjunct Research Professor position at the Carnegie Mellon Electricity Industry Center and is affiliated with the Santa Fe Institute as an External Faculty member. His research spans energy economics , power grid reliability , and complex infrastructure networks . Key projects include: Interdependent natural gas and electricity systems analysis Governance of regional transmission organizations Smart grid consumer behavior studies Power grid reliability tools development He has secured funding from the U.S. National Science Foundation , Department of Energy , Environmental Protection Agency , and private industry. His Best paper award at Hawai’i International Conference on System Sciences (2011) and John T. Ryan, Jr. Fellowship (2011-17) highlight his scientific recognition. Publications emphasize electricity market deregulation , energy infrastructure resilience , and consumer response to smart grid technologies . His work has been cited in major media outlets like The New York Times and The Los Angeles Times , and he has consulted for National Renewable Energy Laboratory , U.S. Department of Energy , and other industry stakeholders.

Andrew HeadView profile

Assistant Professor

• Human-computer interaction
• Programming
• Reading
• +5 more

Andrew Head is an Assistant Professor at the University of Pennsylvania's Department of Computer Science, specializing in Human-Computer Interaction (HCI) and Programming. His work bridges interactive reading , math notation accessibility , and AI-assisted code comprehension . Affiliated with Penn HCI, PLClub, and MindCORE, he co-leads research with Danaé Metaxa and Benjamin Pierce. University of Pennsylvania Assistant Professor, Computer Science Affiliations: Penn HCI, PLClub, MindCORE His research focuses on interactive reading interfaces , AI-powered programming tools , and math notation analysis . Recent projects include: FreeForm : Interactive math notation editor Tyche : Property-based testing tools Explainable Notes : Medical note interpretation systems Publications in CHI , UIST , and ICSE demonstrate his systems-centric approach combining user studies with working prototypes. Notable awards include Best Paper at UIST 2024 and CHI 2022. Advising: Ph.D. Students: Alyssa Hwang, Litao Yan, Hita Kambhamettu, Jeff Tao, Jessica Shi Grants: $1M NSF grant for Property-based Testing Tools (2024) Teaching: Spring 2025: CIS 4120/5120 - Human-Computer Interaction Fall 2024: CIS 7000 - Interactive Reading