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Eugenia RhoView profile

Assistant Professor

• Data Analytics
• Machine Learning
• Natural Language Processing
• +5 more

Eugenia Rho is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. Her research focuses on data analytics, machine learning, natural language processing, and human-computer interaction, with particular emphasis on social media discourse, online identity dynamics, and ethical AI applications. Education includes a Ph.D. in Information and Computer Sciences from the University of California, Irvine (2020), and a B.A. in Political Science from Columbia University (2011). Her interdisciplinary background bridges computer science and social sciences. Research interests span AI-assisted communication tools, counterspeech strategies for online hate mitigation, and neurodivergent perspectives in technology design. Her work often integrates computational methods with social science theories to address real-world challenges such as bias detection, mental health support, and ethical AI deployment. Recent publications highlight themes like AI collaboration in writing, identity-driven online interactions, and the efficacy of counterspeech. Her projects frequently involve designing human-centered technologies that prioritize accessibility and ethical considerations. No scientific awards are explicitly mentioned in the provided text. She maintains an active Google Scholar profile and a personal homepage (URLs not provided in the text).

Stelios TimotheouView profile

Associate Professor

• Optimization
• Machine Learning
• Critical Infrastructure Systems
• +2 more

Stelios Timotheou is an Associate Professor at the Department of Electrical and Computer Engineering and faculty member at the KIOS Research and Innovation Center of Excellence, University of Cyprus. He holds a Dipl-Ing from National Technical University of Athens, and MSc/PhD from Imperial College London. His research focuses on developing real-time distributed methodologies using mathematical optimization, machine learning, and computational intelligence to enhance efficiency in critical infrastructure systems. Research interests center on data-driven decision making for urban mobility management, traffic control systems, and energy optimization. Key areas include: Intelligent UAV-based sensing for traffic monitoring Cooperative control algorithms for multi-agent systems Optimization of renewable energy integration Robust infrastructure protection strategies Recent publications demonstrate strong focus on traffic state estimation (76%), energy system optimization (16%), and UAV applications (8%). Awarded the Cyprus Research Award (2017) and ERC Consolidator Grant for URANUS project. Secured multiple research grants focusing on real-time control systems.

• Software Engineering
• Deep Learning
• Autonomous Systems
• +3 more

Prof. Dr. Andrea Stocco is a Professor at the Technische Universität München (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on the intersection of software engineering and deep learning, particularly addressing the robustness and reliability of data-intensive systems. Key areas include autonomous vehicles, web application testing, and automated functional oracles for deep learning systems. He leads initiatives such as the Lehrstuhl für Software und Systems Engineering , collaborating on projects like CrESt and SUPPRA – Algorand Center of Excellence . Research interests encompass monitoring techniques for AI-driven systems, test suite maintainability, and scenario-based testing of cyber-physical systems (CPS). His work emphasizes practical applications, such as improving testing frameworks for evolving web applications and enhancing interoperability in autonomous driving systems (ADS). Recent efforts include leveraging large language models (LLMs) for secure code assessment and benchmarking generative AI for test input generation. No scientific awards are explicitly mentioned in the provided texts. His publications reflect a strong focus on testing methodologies, with over 40 articles since 2013, covering domains like web test automation, dependency-aware testing, and safety-critical failure prediction in autonomous systems. Advising and grants details are not detailed in the current data, but his lab contributes to TUM's broader efforts in software engineering and systems reliability.

Craig H. MeyerView profile

Professor

• Medical and Molecular Imaging
• Signal and Image Processing
• Biomedical Data Sciences
• +2 more

Craig H. Meyer is a Professor in Biomedical Engineering and Radiology & Medical Imaging at the University of Virginia. He holds a Ph.D. from Stanford University and leads the Rapid MRI Research Group, focusing on developing advanced MRI techniques for cardiovascular disease, neural disorders, and pediatrics. His work integrates physics, signal processing, and machine learning to improve MRI acquisition and processing speed. Education: Ph.D. in Biomedical Engineering, Stanford University. Research Interests: Medical and Molecular Imaging, Signal and Image Processing, Biomedical Data Sciences, Biomechanics, and Cardiovascular Engineering. His innovations include fast spiral imaging, conjugate phase reconstruction, and machine learning-enhanced MRI denoising. Awards: Notably includes the Dean’s Award for Excellence in Team Science (2014), Fellowships from NAI (2021), AIMBE (2015), and ISMRM (2013). He also authored two landmark MRI papers recognized as pivotal in the field. Teaching: Courses include BME 6310 (Computation and Modeling in Biomedical Engineering) and BME 8782 (Magnetic Resonance Imaging). He emphasizes translational research, with applications in clinical MRI advancements and collaborative interdisciplinary projects. Labs/Groups: Rapid MRI Research Group focuses on cutting-edge MRI technologies, including real-time cardiac imaging and artifact reduction through deep learning.

Hyun Kyoung RoView profile

Associate Professor

• Gender and Racial Equity in STEM Education
• Higher Education
• Learning Experiences and Outcomes among Marginalized Students
• +7 more

Dr. Hyun Kyoung Ro is an Associate Professor in the Department of Counseling and Higher Education at the University of North Texas, affiliated with the College of Education. She previously served as a faculty member at Bowling Green State University and worked as a Research Designer and Analyst at Carnegie Mellon University’s Office of Institutional Research and Analysis. Ph.D. in Higher Education, The Pennsylvania State University (minor in Educational Psychology—Applied Measurement) M.A. and B.A., Korea University Her research centers on gender and racial equity in STEM education , learning experiences of marginalized students , and critical quantitative research and assessment . She employs rigorous methodologies to examine systemic inequities and institutional practices affecting underrepresented populations in higher education. Dr. Ro's recent publications span top-tier journals such as Journal of Engineering Education , The Review of Higher Education , and Research in Higher Education , reflecting a strong focus on equity, policy, and assessment in STEM and higher education contexts. Her work consistently emphasizes intersectionality, data justice, and institutional reform. She has received significant external funding, including: National Science Foundation ADVANCE Adaptation Track grant for gender equity on campus AccessLex Institute/Association for Institutional Research grant on law school access and enrollment among women of color Collectively, these grants total $1.1 million, supporting her leadership in advancing equity through research and institutional change. While no formal list of advisees or awards is provided, her role as a leading author and grant recipient highlights her active mentorship and scholarly leadership. Dr. Ro has not indicated any lab or research team affiliations in the provided text, but her research profile suggests collaboration with institutional research units and equity-focused initiatives.

• Information Science
• Library Science
• Information Behavior
• +5 more

David Bawden is a Professor in the Department of Library and Information Science (CityLIS) at City, University of London. With a publication record spanning over two decades, he has established himself as a leading scholar in information science, particularly known for his work on the philosophical foundations of information, knowledge organization, and information behavior. His research frequently intersects with Luciano Floridi's Philosophy of Information, which he has applied to library and information science theory and practice. Bawden's research interests focus on the theoretical underpinnings of information science, including information behavior, information literacy, knowledge organization, and the philosophical dimensions of information. His work often explores the conceptual frameworks that shape how we understand information in both physical and digital environments. He has made significant contributions to understanding information privacy through a philosophical lens and has examined temporal aspects of information in contemporary society. His recent publications demonstrate a continued focus on theoretical developments in information science, with particular attention to the implications of Floridi's work for library and information practice. Bawden's scholarship shows a consistent trajectory from examining historical aspects of information to contemporary challenges in the digital age, maintaining a strong theoretical orientation throughout. As a frequent collaborator with Lyn Robinson (also at City, University of London), Bawden has co-authored numerous papers exploring transitions between concepts of information across different domains, information overload, and the philosophical foundations of information science. His work has appeared in leading journals including the Journal of Documentation, Journal of the Association for Information Science and Technology, and Information Research. Bawden has been actively involved in the CityLIS program, contributing to innovative approaches to information science education including the development of curricula addressing data librarianship and the 'onlife' nature of contemporary information experience. His scholarship reflects a deep commitment to advancing the theoretical foundations of the field while maintaining relevance to practical information challenges.

Ruchi ChoudharyView profile

Professor

• Urban Energy Systems
• Building Energy Modeling
• Sustainable Cities
• +7 more

Professor Ruchi Choudhary is a Professor in Architectural Engineering at the University of Cambridge, Department of Engineering, within the School of Technology. She leads the Energy Efficient Cities initiative (EECi), a cross-disciplinary research project focused on strengthening the UK's capacity to address energy demand reduction and environmental impact in cities through research in building and transport technologies, district power systems, and urban planning. Her research interests span urban energy systems, building energy modeling, sustainable cities, geothermal energy systems, and data-driven energy modeling . She has pioneered work in digital twins for energy systems, urban subsurface thermal modeling, and building-integrated agriculture. Her research group develops numerical tools to improve energy efficiency of cities, with particular focus on modeling energy consumption of large building sets at multiple time and spatial resolutions. Analysis of her recent publications reveals a strong trend toward integrating machine learning with physics-based modeling for energy systems, with increasing emphasis on uncertainty quantification, digital twins, and value of information analysis for decision-making. Her work bridges the gap between theoretical modeling and practical urban implementation, with significant focus on city-scale geothermal potential, underground climate change impacts, and energy equity considerations. Professor Choudhary has supervised numerous PhD students who have gone on to prominent positions at institutions including UCL, University of Cambridge, BEIS, Arup, and various international universities. Her research group includes faculty members, research associates, graduate students, and international collaborators from institutions worldwide. Her current research focuses on two parallel investigations: one on using multi-scale multidisciplinary models to address energy use questions in the built environment, and second, on quantifying uncertainties in model outcomes. Current projects include integration of food production in urban environments, analysis of underground transport systems as energy sources, large-scale integration of ground source heat pumps, and distributed energy networks.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +4 more

Danqi Chen is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. Their research focuses on advancing large language models (LLMs), with emphasis on model alignment, safety, and long-context reasoning capabilities. Key research areas: LLMs, AI safety, retrieval systems, and model optimization Recent work explores theorem proving, context encoding, and ethical content generation Their 2025 publications highlight innovations in formal verification scaffolding, attention mechanism efficiency, and copyright-aware generation. 2024 studies investigate continual memorization, rule-based chatbot representations, and scientific literature retrieval benchmarks. Current projects demonstrate commitment to improving model robustness, interpretability, and security compliance in multimodal systems.

Amir HakamiView profile

Professor

• Air Quality Modeling
• Sensitivity Analysis
• Inverse Modeling
• +4 more

Dr. Amir Hakami is a Professor in the Department of Civil & Environmental Engineering at Carleton University , where he leads the Carleton Atmospheric Modelling Group . His research focuses on advanced air quality modeling techniques to inform environmental policy. Degrees: B.Sc. (Polytechnic of Tehran), M.Sc., Ph.D. (Georgia Tech), Postdoc (Caltech) Contact: Office 3454 Mackenzie Building, Phone: 613-520-2600 ext. 8609, Email: amir.hakami@carleton.ca Research Interests: Air quality modeling at multiple spatial scales Adjoint sensitivity analysis for atmospheric response Inverse modeling and data assimilation techniques Uncertainty quantification in environmental systems Interdisciplinary applications in policy, public health, and economics Teaching: Courses include Environmental Engineering Systems Modeling , Contaminant Transport , and Air Pollution & Emissions Control at undergraduate and graduate levels. Research Group: The group includes Ph.D. candidates, postdoctoral fellows, and alumni working on topics ranging from atmospheric chemistry to sustainable energy systems. Members come from diverse backgrounds in engineering, science, and policy disciplines.

• Quantitative PET/CT and SPECT/CT Imaging
• Deep Learning in Medical Imaging
• Motion Correction Algorithms
• +2 more

Chi Liu is a Professor of Radiology & Biomedical Imaging at Yale School of Medicine . He serves as Associate Director of Biomedical Imaging Technology at the Yale Biomedical Imaging Institute and Director for Research Faculty Affairs in the Radiology & Biomedical Imaging department. Education : PhD from Johns Hopkins University (2008) Postdoctoral Training : University of Washington (2010) Certification : American Board of Science in Nuclear Medicine (Nuclear Medicine Physics and Instrumentation) His research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging , emphasizing deep learning algorithms , reconstruction algorithms , data correction , and dynamic imaging . Key clinical applications include early detection of chemotherapy-induced cardiotoxicity , multimodality imaging of heart failure , and motion variability elimination in therapy response assessment . The 15 most recent publications reveal a strong emphasis on deep learning techniques for low-dose imaging , motion correction , and cross-tracer generalizability in PET/SPECT systems. These works span applications in cardiac imaging , neuroscience , oncology , and theranostics . Scientific Award : Bruce Hasegawa Young Investigator Medical Imaging Science Award (2012) Contact: chi.liu@yale.edu | ORCID 0000-0002-7007-1037

Martin Willis MonroeView profile

Assistant Professor

• Ancient Middle Eastern History
• Cuneiform Studies
• Astronomy and Astrology in Mesopotamia
• +3 more

Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.

Nicole M. GaspariniView profile

Associate Professor

• Fluvial Geomorphology
• Tectonic Geomorphology
• Landscape Evolution
• +2 more

Nicole M. Gasparini is an Associate Professor in Tulane University's Department of Earth and Environmental Sciences, School of Science & Engineering. She holds a Ph.D. from MIT (2003) and researches fluvial/tectonic geomorphology, landscape evolution modeling, and climate-erosion interactions using tools like Landlab. Her work investigates sediment transport, river network evolution, and human impacts on landscapes. Research integrates field data with numerical models to quantify erosion processes across diverse environments. Publications demonstrate expertise in geomorphic model development, particularly through contributions to the Landlab toolkit. Recent articles focus on climatic controls on erosion, fault geomorphology, and uncertainty quantification in earth-surface models. Awards: Marguerite T. Williams Award for contributions to geosciences and advocacy against harassment in STEM Leads collaborative projects on landscape response to climate change and mentors students/postdocs in geomorphology and computational modeling.

Daniel BalasubramanianView profile

Adjunct Associate Professor

• Cyber-physical Systems
• Cybersecurity
• Software Verification
• +3 more

Daniel Balasubramanian is an Adjunct Associate Professor of Computer Science and Research Scientist at Vanderbilt University's School of Engineering. His research focuses on cybersecurity, software verification, and cyber-physical systems, with expertise in symbolic execution, code analysis, and formal methods. He contributes to advancing secure systems through frameworks like RAMPART for adversarial defense and Syntheto for formal verification. His work intersects edge computing, hardware security, and autonomous systems resilience. Research Interests: Cybersecurity (including ethical hacking, network defense), formal methods (verification, theorem proving), edge computing (tinyML, cloud integration), and cyber-physical systems (emulation, testbeds). His recent work emphasizes assurance provenance in software documentation and adversarially robust autonomous systems. Publications since 2019 highlight contributions to cybersecurity testbeds, reinforcement learning for penetration resistance, and hardware security against rowhammer attacks. He has explored domain-specific languages (Syntheto), incremental modeling techniques (differential-formula), and cloud-edge service resilience against adversarial perturbations. Labs/Teams: Affiliated with the Institute for Software-Integrated Systems (ISIS), focusing on integrating software with physical systems through model-driven approaches and cybersecurity innovations.

Amir HoumansadrView profile

Associate Professor

• Security and Privacy
• Network Security
• AI/ML Security
• +4 more

Amir Houmansadr is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. His research focuses on network and AI security, privacy-enhancing technologies, and censorship circumvention. He holds a PhD from the University of Illinois at Urbana-Champaign (2012) and a postdoctoral fellowship at the University of Texas at Austin (2012-2014). Key research areas include secure communication systems, adversarial ML attacks, federated learning defenses, and analyzing censorship mechanisms like the Great Firewall of China. He leads the SPIN research group, which develops tools like CensorLab and MassBrowser. Notable contributions include exposing GFW vulnerabilities and advancing privacy-preserving AI models. Awards include the DARPA Director’s Award (2024), ACM CCS Distinguished Paper (2023), and NSF CAREER Award (2016). His work has been featured in media outlets like The Guardian, MIT Technology Review, and MassLive. He advises over 20 students and has served on program committees for top security conferences (IEEE S&P, ACM CCS, USENIX Security). Current courses include CMPSCI 660: Advanced Information Assurance.

Qimin LiuView profile

Assistant Professor

• Psychological Sciences
• Quantitative Methods
• Clinical Science
• +4 more

Qimin Liu is an Assistant Professor at Boston University, serving as Lab Director of the Quantitative Psychopathology Laboratory. He holds a PhD in Psychological Sciences from Vanderbilt University with specializations in Clinical Science and Quantitative Methods. His research focuses on emotional disturbances across development, statistical methodology development, and health equity with an emphasis on intersectional marginalization. He has expertise in analyzing intensive longitudinal data and has published extensively on topics like irritability, suicidality, and mental health disparities among sexual and gender minority populations. Dr. Liu’s work frequently integrates advanced statistical techniques such as latent variable modeling, network analysis, and machine learning. His recent studies explore the temporal dynamics of affect, the impact of stigma on mental health, and the role of childhood adversity in psychiatric outcomes. Notable contributions include developing methods for analyzing zero-inflated longitudinal data and creating algorithms for digital phenotyping of mood disorders through mobile device usage patterns. His scholarship emphasizes bridging clinical phenomena with rigorous quantitative approaches, addressing gaps in understanding how social determinants and individual differences shape mental health trajectories. He has collaborated on large-scale datasets like the Collaborative Psychiatric Epidemiological Surveys and contributed to interdisciplinary research on public health outcomes among aging sexual minority men. Dr. Liu’s methodological innovations include the DACF framework for ceiling/floor effect data and the lamme package for log-analytic multiplicative effects modeling. He actively publishes in high-impact journals such as Psychological Methods and Journal of Abnormal Psychology , focusing on both empirical findings and statistical best practices.