Nicole C. Riddle is a Professor and Associate Chair for Research and Facilities in the Department of Biology at the University of Alabama at Birmingham (UAB). She holds a B.S. in Biology from the University of Missouri Columbia and a Ph.D. in Evolutionary and Population Biology from Washington University in St. Louis. Her research focuses on epigenetics and chromatin dynamics, particularly in the context of aging and sex differences using Drosophila melanogaster as a model system. Dr. Riddle's work explores how epigenetic mechanisms influence lifespan, genome stability, and phenotypic variation. She has pioneered the use of Drosophila to study exercise-induced physiological changes and their genetic underpinnings. Her lab investigates the roles of HP1 proteins in transcriptional regulation and chromatin organization, with recent studies emphasizing cross-species comparisons of aging mechanisms. Her research has been supported by grants including the BII: IISAGE project on sex-specific aging mechanisms. Notable contributions include developing novel tools like the Rotating Exercise Quantification System (REQS) to measure Drosophila activity levels. Dr. Riddle actively mentors students and postdoctoral researchers, inviting inquiries via riddlenc@uab.edu to join her lab.
Dr. Georgie Agar is a Lecturer at the School of Life & Health Sciences, Aston University , affiliated with the College of Health and Life Sciences and the Applied Health Research Group . Her research focuses on sleep disorders, self-injurious behavior, and caregiver impact in individuals with intellectual disabilities and rare genetic syndromes like Smith-Magenis and Angelman syndromes. Research Interests : Dr. Agar specializes in developing inclusive clinical assessment tools for sleep, pain, and behavior in neurodevelopmental populations. She employs mixed-methods, meta-analytic approaches, and longitudinal studies to explore sleep trajectories, behavioral conditioning models, and caregiver experiences. Publications : Her work spans sleep management in Smith-Magenis syndrome, overactivity metrics in genetic conditions, and systematic reviews on self-restraint in autism. Key journals include Molecular Autism , Orphanet Journal of Rare Diseases , and Research in Developmental Disabilities . Labs & Collaborations : She collaborates with the Aston Institute of Health & Neurodevelopment , focusing on neurodevelopmental research and cross-institutional partnerships in sleep and behavioral science.
James T. Hamilton is the Vice Provost for Undergraduate Education and Hearst Professor of Communication at Stanford University, where he also directs the Stanford Journalism Program. He previously taught at Duke University’s Sanford School of Public Policy and led the De Witt Wallace Center for Media and Democracy. His academic career spans over three decades, with a focus on media economics, investigative journalism, and environmental policy. Hamilton holds a B.A. (summa cum laude) and Ph.D. in Economics from Harvard University. His research explores how markets shape news content, the economics of investigative reporting, and the societal impact of information access. He co-founded the Stanford Computational Journalism Lab and is a Senior Fellow at the Stanford Institute for Economic Policy Research. His work emphasizes computational tools to enhance journalism’s accountability role, including automated fact-checking and data-driven story discovery. Key contributions include groundbreaking books like Democracy's Detectives (2016) and All the News That’s Fit to Sell (2004), which analyze media markets and transparency policies. Scientific Awards: David N Kershaw Award, Goldsmith Book Prize (twice), Frank Luther Mott Research Award (twice), Tankard Book Award Teaching Honors: Allyn Young Prize, Trinity College Distinguished Teaching Award, Susan Tifft Mentoring Award His current research addresses digital inequality, the psychological effects of online financial ads, and algorithmic transparency in journalism. He advises on media innovation through affiliations with the Brown Institute for Media Innovation and the JSK Fellowships Board.
Michael C. Frank is the Benjamin Scott Crocker Professor of Human Biology at Stanford University and Director of the Symbolic Systems Program. He leads the Stanford Language and Cognition Lab and has pioneered large-scale collaborative projects including Wordbank (open vocabulary data), MetaLab (developmental meta-analyses), ManyBabies (replication network), childes-db (language transcripts), and Peekbank (eye-tracking repository). His research examines children's language learning and its interaction with social cognition, utilizing computational modeling, large datasets, and open science frameworks. Key interests include: Mechanisms of early language acquisition Pragmatic inference in social contexts Cross-cultural variability in cognitive development Data-driven approaches to developmental science Reproducibility and meta-scientific innovation Recent publications (2022-2025) demonstrate strong emphases on: 1) Novel methods for measuring language environments and cognitive abilities, 2) Computational models of learning and perception, 3) Cross-cultural investigations of social cognition, and 4) Infrastructure for open developmental science. The majority employ multimodal data, meta-analytic techniques, and large-scale collaborations. He teaches courses including Experimental Methods, Developmental Psychology, and interdisciplinary seminars on language, cognition, and computation. His lab maintains active research teams across multiple continents through initiatives like ManyBabies and LEVANTE.
Asst. Prof. OU Pengfei is an Assistant Professor and NUS Presidential Young Professor in the Department of Chemistry at the National University of Singapore, Faculty of Science. He leads the AI for Chemistry (AI4Chem) research group, focusing on computational catalysis, machine learning, and materials science. Previously, he was a Research Associate at Northwestern University and a Postdoctoral Fellow at the University of Toronto under Prof. Edward H. Sargent, and earned his Ph.D. from McGill University. Education: Ph.D., McGill University, 2020 M.Eng., Central South University, 2015 B.Eng., Central South University, 2012 Research interests include catalyst design for electrochemical reactions using ab initio DFT, molecular dynamics simulations, and AI-driven methods. He develops dynamic simulations of chemical processes under reaction conditions and machine learning tools for accelerated catalyst discovery. His work addresses challenges in energy and environmental applications such as CO2 reduction and hydrogen evolution. Notable awards include the NUS Presidential Young Professorship (2024), Climate Positive Energy Postdoctoral Fellowship (2021), and Chinese Government Award for Outstanding Self-Financed Students Abroad (2020). Labs/Teams: The AI4Chem group integrates theory-guided and data-driven approaches to advance computational catalysis, with three core research directions: (1) reaction mechanism exploration and catalyst optimization, (2) dynamic structure-performance relationships under reaction conditions, and (3) machine learning algorithms for high-throughput screening.
Mark W. Newman is an Associate Professor in the School of Information at the University of Michigan, with a joint appointment in the EECS Department. His research focuses on human-computer interaction, ubiquitous computing, and health informatics, emphasizing the design of technologies that integrate seamlessly into everyday life. He teaches courses on user experience research, programming, and application development, contributing to curriculum design efforts such as the User Experience Design track and programming curricula for undergraduate and graduate programs. His work spans smart home technologies, mobile health interventions, and collaborative health management systems. Notably, he collaborates across disciplines to address challenges in healthcare technology, including patient-generated data integration and sustainable energy solutions. Newman actively mentors students and leads design projects that prioritize user-centered approaches, such as the UX Research & Design Specialization on Coursera. His recent scholarship explores adaptive mHealth interventions, context-aware systems, and participatory design methods to enhance health equity.
Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).
Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.
Dr. Jean Goodwin is the SAS Institute Distinguished Professor of Rhetoric & Technical Communication at NC State University's Department of Communication, part of the College of Humanities and Social Sciences. She specializes in science communication ethics, civil argumentation, and the rhetoric of controversial scientific topics such as climate change and GMOs. Her work bridges theory and practice, including NSF-funded initiatives like Teaching Responsible Communication of Science, which develops case studies for STEM graduate students. She also leads the Leadership in Public Science cluster through NC State's Chancellor’s Faculty Excellence Program. Education: B.A. in Mathematics (University of Chicago, 1979), J.D. (University of Chicago, 1984), and Ph.D. in Communication/Rhetoric (University of Wisconsin-Madison, 1996). Her career includes over 25 years of teaching rhetoric and mentoring students across communication subfields. Research focuses on how scientists communicate effectively with non-experts, leveraging discourse analysis and conceptual frameworks like speech act theory. Key themes include trust-building in contentious contexts, ethical advocacy roles for scientists, and historical rhetorical strategies from Cicero to modern policy debates. Her work often intersects with civic engagement and interdisciplinary collaboration, such as organizing conferences for science communication scholars and advising organizations like the AAAS. Major publications explore ethical dimensions of science communication, including climate change advocacy, disinformation detection, and pandemic messaging. Awards include the EB Knight Journal Award (2007) and her distinguished professorship. Current initiatives emphasize translating scholarly insights into practical tools for scientists and fostering dialogue between scientific and public communities through funded research partnerships.
Dr Raquel Campos is an Assistant Professor (Education) of Management at the Department of Management, London School of Economics and Political Science (LSE). Her work focuses on the impact of information technologies on institutional performance across firms and universities. She holds dual expertise in economics (PhD, MSc from Universidad Rey Juan Carlos) and engineering (BSc in Electrical Engineering and Computer Science). Prior to academia, she worked in IT project management and consulting in Spain and the US. Key qualifications include a PhD in Economics (2013) and MSc in Economics (2009), both from Universidad Rey Juan Carlos. She also completed the University of London’s BSc in Economics and Management via its external program. Her multidisciplinary background spans electrical engineering, computer science, and law. Research interests center on organizational economics, personnel economics, and ICT applications in labor markets. Notable works include studies on hurricane impacts on academic collaborations, internet recruitment strategies in Spain, and gender disparities in engineering careers. She has presented at major labor economics workshops and serves as a referee for Economics of Innovation and New Technology . Teaching responsibilities include leading the Strategy module (MG301) at LSE, where she received top teaching awards including the 2019 Excellence in Education Award. Her professional experience includes roles as Visiting Researcher at University of Kent (2015-2017), and prior IT project management roles in sectors like banking and real estate. Awards highlight her educational contributions: Top 10% performer in LSE Management Department teaching evaluations (2018-19) and Excellence in Education recognition. Current research explores missing skills for EU entrepreneurs/managers using PIAAC data in collaboration with M. Arrazola and J. de Hevia.
Richard Garner is a lecturer at Macquarie University's School of Mathematical and Physical Sciences, Faculty of Science and Engineering. He specializes in teaching mathematics to engineering and computing students in units like MATH2055 and MATH1007, focusing on problem-solving and real-world applications. His teaching philosophy emphasizes authentic mathematical experiences, blending abstract concepts with practical examples, such as connecting multivariable calculus to AI technologies. School: School of Mathematical and Physical Sciences University: Macquarie University Teaching Areas: Mathematics for engineering and computing, convolution, multivariable calculus Richard won a Student Nominated Award in the 2023 Vice Chancellor’s Learning and Teaching Awards, reflecting his commitment to student-centered education. He prioritizes clarity in course design, using visual tools and accessible materials to enhance learning, and fosters a supportive environment where students feel comfortable asking questions. Key Teaching Strategies Organized iLearn layouts following Macquarie University's standards Multiple formats for lecture materials (diagrams, color-coded slides) Weekly task clarity and real-world problem framing Live worked examples and transparent success criteria His research spans category theory, computational effects, and homotopy theory, with publications on topics like comodels, monoidal bicategories, and enriched categories. Richard's work bridges abstract mathematics with applications in computer science and logic. Scientific Awards 2023 Vice Chancellor’s Learning and Teaching Award (Student Nominated) Students praise his ability to make complex concepts intuitive, his enthusiasm for mathematics, and his dedication to explaining the 'why' behind the subject. His teaching design, including time-sensitive banners and structured weekly content, has been highlighted as exemplary.
Roxana Geambasu is an Associate Professor at Columbia University's Department of Computer Science, with affiliations to the Software Systems Lab and the Cybersecurity Committee . She specializes in systems security, differential privacy, and resource management for modern computing environments. PhD: University of Washington (2011) Undergraduate: Polytechnic University of Bucharest, Romania Her research focuses on integrating differential privacy as a first-class computing resource in infrastructure systems, with key contributions in: Privacy Budget Management (PrivateKube, DPack, Turbo) Web & Mobile Privacy (Cookie Monster, Big Bird) Security Abstractions (POSIX, Vanish, XRay) Article trends show a strong emphasis on differential privacy (8/15), resource scheduling (5/15), and privacy-preserving advertising (3/15). Key subfields include budget optimization, browser APIs, and ML pipeline privacy. Scientific awards include: Alfred P. Sloan Fellowship NSF CAREER Award Google Ph.D. Fellowship in Cloud Computing Best Paper Awards at SOSP, EuroSys She advises M.S. and undergraduate students, teaching courses in Distributed Systems and Privacy Curriculum . Her lab develops tools like PixelDP and Sunlight for operationalizing privacy in real-world systems.
Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.
Danyang Zhuo is an Assistant Professor of Computer Science at Duke University, Trinity College of Arts & Sciences, with expertise in datacenter/cloud computing and machine learning systems. He joined Duke in 2020 after postdoctoral research at UC Berkeley under Ion Stoica and a PhD at the University of Washington advised by Tom Anderson and Arvind Krishnamurthy. Education: PhD in Computer Science (University of Washington, 2019) His research focuses on improving cloud infrastructure through systems like Phoenix (application-level abstractions) and Phantora (GPU cluster simulation). Recent work explores LLM verification, tensor compression via video codecs, and fairness in LLM serving. His 15 most recent publications span operating systems, machine learning, and networked systems conferences like HOTOS, NSDI, SIGCOMM, and OSDI. Scientific honors include NSF CAREER Award (2023), USENIX Security Distinguished Paper (2023), and multiple industry research awards. He has secured major NSF grants for projects including "OS-Managed Remote Procedure Call" and "Campus-level RDMA Networking." At Duke, he advises PhD students and teaches courses such as Introduction to Operating Systems (CompSci 310) and Systems for Machine Learning (CompSci 590.05). His work appears in leading conferences and journals, with collaborations across institutions including UC Berkeley, University of Washington, and industry partners.
Stephen W. Keckler is an Adjunct Professor at the Department of Computer Science , The University of Texas at Austin , and serves as Vice President of Architecture Research at NVIDIA . He is an ACM Fellow , IEEE Fellow , and Sloan Foundation Research Fellow . Education: BS in Electrical Engineering, Stanford University (1990) SM in Computer Science, Massachusetts Institute of Technology (1992) PhD in Computer Science, MIT (1998) Research Interests focus on computer architecture for deep learning , GPU computing , and energy-efficient systems . His work explores memory compression , network-on-chip designs , and heterogeneous computing . Publication Trends highlight advancements in deep learning accelerators , GPU memory systems , and energy-efficient architectures . Notable themes include sparsity exploitation , multi-chip modules , and fault-tolerant GPU pipelines . Scientific Recognition : ACM Fellow IEEE Fellow Sloan Foundation Research Fellow Best Paper Awards at ASPLOS 2009 and ISPASS 2011 Laboratory Affiliations : Computer Architecture and Technology Laboratory (CART) TRIPS Project (Tera-Op Reliable Intelligently adaptive Processing System) NVIDIA Research
