Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.
Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.
Giuseppe Ateniese is a Professor and Eminent Scholar in Cybersecurity at George Mason University, affiliated with the Department of Computer Science and Cyber Security Engineering. Formerly, he held the Farber Endowed Chair and chaired the Department of Computer Science at Stevens Institute of Technology. His research focuses on cloud security, applied cryptography, blockchain technology, and AI-driven cybersecurity solutions. He has received prestigious awards including the NSF CAREER Award and Google/IBM Faculty Awards. AIFurther, he teaches courses on blockchains, cryptography fundamentals, and cryptofinance. His work includes pioneering contributions to rewritable blockchains, password cracking via AI (e.g., PassGAN), and secure decentralized systems. Education: PhD in Computer Science, University of Genoa (2000) Laurea (M.Sc.), University of Salerno (1995) Master of Engineering (Honoris Causa), Stevens Institute of Technology (2020) Research Interests: Ateniese’s work bridges theoretical and applied cryptography, with emphasis on blockchain scalability, privacy-preserving machine learning, and secure data storage. He has developed foundational techniques like proxy re-cryptography and accountable storage systems. His recent efforts address AI’s dual role as both a cybersecurity tool and threat vector, including AI-based password cracking and defense strategies against LLM-driven attacks. Awards: NSF CAREER Award (for privacy/security research) Google Faculty Research Award (cloud security) IBM Faculty Award IEEE CISTC Technical Recognition Award Teaching & Labs: He teaches advanced courses on blockchains (CS695), cryptography fundamentals (CYSE/ECE476), and introductory cryptography (CS487/587). His research team focuses on cryptographic protocols, decentralized systems, and AI-security intersections. Collaborations include projects with Accenture on editable blockchain prototypes and NSF-funded work on password security.
Maryam Alemzadeh is Associate Professor in History and Politics of Iran at the Oxford School of Global and Area Studies (OSGA), University of Oxford, and a Middle East Centre Fellow. She holds a Ph.D. in Sociology from the University of Chicago (2018) and previously held postdoctoral positions at Brandeis University and Princeton University. Education Ph.D. in Sociology, University of Chicago (2018) Research Interests Dr. Alemzadeh specializes in revolutions, state-building, militias, and modern Iran, examining how individual actions shape political structures through micro-level analysis. Her work integrates sociological theory with Middle Eastern studies to explore the interplay of religion, military institutions, and everyday practice in revolutionary contexts, with a current focus on the Islamic Revolutionary Guards Corps (IRGC). Publication Trends Her recent publications analyze Iran's regional dynamics following Soleimani's assassination and review scholarly works on Iranian history. These demonstrate consistent expertise in military institutions, gender dynamics, and post-revolutionary state consolidation, bridging sociology and Middle Eastern studies while emphasizing micro-foundations of political phenomena.
James K. Russell is a Full Professor in Earth, Ocean and Atmospheric Sciences at The University of British Columbia, where he has been faculty since 1999. He directs the Volcanology and Petrology Laboratory and maintains an active research program in volcanology and petrology with significant international collaborations including appointments at LMU Munich, University of Torino, and Monash University. His educational background includes a Ph.D. (1984) and M.Sc. (1980) from the University of Calgary, and a B.Sc.(H) (1976) from the University of Manitoba. He previously worked at Cominco Exploration Ltd in Vernon, British Columbia before joining UBC. Russell's research focuses on understanding magmatic processes through multiple approaches. His primary interests include glaciovolcanism as a tool for paleoclimate reconstruction, predictive modeling of magma transport properties, and high-temperature rock mechanics experiments. His work bridges theoretical modeling with field observations, particularly in the Canadian Cordillera. He has developed influential models for magma viscosity that are widely cited in the volcanology community. His publication record shows consistent high-impact research over several decades, with recent work focusing on ultramafic melt viscosity, kimberlite transport mechanisms, and glaciovolcanic interactions. The research demonstrates a strong emphasis on quantitative approaches combining thermodynamics, rheology, and field-based observations to solve fundamental problems in volcanology. Scientific Awards: 2024 Research Award of the Alexander von Humboldt Foundation, LMU, Munich, Germany 2022 DAAD Research Stay for Visiting Academics Scholarship (3 months) 2019 Visiting Professorship, University Roma Tre 2019 Visiting Fellow at Centre for Advanced Studies, University of Munich 2016 Fellow of Mineralogical Society of America 2013 NSERC Discovery Accelerator Supplement Award 2010 Career Achievement Medal, Volcanology Division, Geological Association of Canada 2008 Peacock Medal, Mineralogical Association of Canada Russell has served in numerous editorial and professional capacities, including as Co-Editor-in-Chief for Journal of Volcanology & Geothermal Research (2016-2022), Associate Editor for multiple prominent journals, and as evaluator for research grants including ERC Consolidator grants and NSERC competitions. He has provided media consultation for National Geographic and Nova/Pioneer Productions. He maintains active laboratory facilities for volcanology and petrology research at UBC, with equipment for high-temperature experiments and magma rheology studies. His research group collaborates internationally with institutions in Germany, Italy, and Australia, reflecting his extensive network built through sabbatical visits and visiting professorships.
Dr. Athos Agapiou serves as an Assistant Professor at the Department of Civil Engineering and Geoinformatics, Cyprus University of Technology, where he holds the 'Geoinformatics and Earth Observation' chair. He also serves as Deputy Director of the UNESCO Chair on Digital Cultural Heritage and coordinates the EOCult Laboratory, a specialized research group focused on applying Earth Observation and Geoinformatics for archaeological research and cultural heritage landscapes. His research interests center on developing and applying innovative remote sensing technologies for cultural heritage preservation, addressing challenges posed by environmental and anthropogenic factors. Dr. Agapiou has led numerous high-profile research initiatives at European and national levels, including Horizon Europe and Horizon 2020 projects, as well as programs funded by the Cyprus Research and Innovation Foundation. His publication record demonstrates consistent output in remote sensing applications for archaeology, with particular focus on satellite image fusion, UAV technologies, spatial resolution optimization, and advanced data analysis techniques for archaeological prospection. His work bridges technical remote sensing methodologies with practical archaeological applications. Young Scientist Award from International Society for Digital Earth (2023) Young Researcher Award by Cyprus Research Awards (2018) Top 2% researcher worldwide in field (Stanford studies 2020-2024) Top three global researchers in Geoinformatics for cultural heritage (2024) Dr. Agapiou actively contributes to major research projects and collaborations, with his work influencing both academic research and practical applications in cultural heritage management. His laboratory (EOCult) serves as a hub for interdisciplinary research connecting geospatial technologies with archaeological practice.
Martin Jakobsson is a Professor of Marine Geology and Geophysics at Stockholm University's Department of Geological Sciences. His career spans international research expeditions and leadership roles, including Dean of Earth Science (2021) and Head of Department (2012-2018). A key member of the Marine Geology group, he studies Arctic and Antarctic glacial history through sediment analysis and seafloor mapping. PhD from Stockholm University (2000) Academy Fellow, Royal Swedish Academy of Sciences (2004-2009) Professor II position at University Centre of Svalbard (2011-) His research focuses on marine geological archives to reconstruct glacial cycles, ice-ocean interactions, and bathymetric mapping. As GEBCO Vice-Chair (2013-2020), he led initiatives for global seabed mapping through the Seabed 2030 project. Recent publications reveal expertise in: Arctic Ocean paleocirculation Methane release from thawing permafrost Ice sheet sensitivity to oceanic warming High-resolution bathymetric analysis Scientific recognition includes: Election to Royal Swedish Academy of Sciences (2012) 1st Vice President, Royal Swedish Academy of Sciences (2016-2019) Membership in Norwegian Scientific Academy for Polar Research (2016) He leads international collaborations on polar research vessels, with over a year of cumulative ship time and multiple Co-Chief Scientist roles on Arctic expeditions.
Steve Mussmann serves as an Assistant Professor in the School of Computer Science at the Georgia Institute of Technology, where he joined in Fall 2024. His research centers on data-centric machine learning, with emphasis on active labeling, data selection, and adaptive experimental design methodologies. He maintains active collaborations through Georgia Tech's Foundations of AI (FoAI) and ML@GT research groups. Mussmann earned his PhD in Computer Science from Stanford University in 2021 under Percy Liang's supervision, following a BS in Math, Statistics, and Computer Science from Purdue University in 2015. His professional trajectory includes a machine learning researcher role at Coactive AI and an IFDS postdoctoral fellowship at the University of Washington's Paul Allen School of Computer Science and Engineering. His research program investigates theoretical and practical aspects of data efficiency in machine learning systems, particularly focusing on active learning frameworks, statistical properties of data algorithms under concept drift, and task specification via prompts or demonstrations. Current projects address challenges in label-efficient training of large language models and multimodal dataset development. Analysis of his 15 most recent publications reveals a consistent focus on advancing data-centric methodologies, with increasing emphasis on large-scale applications like multimodal datasets and language model fine-tuning. His work bridges theoretical guarantees in experimental design with practical frameworks like LabelBench for benchmarking label efficiency. Mussmann has received recognition through the IFDS postdoctoral fellowship. His contributions to the field include foundational work on active learning theory and data selection algorithms. IFDS postdoctoral fellow He currently advises five graduate students including PhD candidates Kangping Hu (CS) and Hangyu Zhou (ML), alongside MS students Kabir Kang and Kalp Vyas, and undergraduate Saloni Bedi. Former advisee Wei-Liang (Edison) Liao completed BS research under his supervision. His teaching portfolio includes graduate courses CS 7545 (Machine Learning Theory) and CS 8803-DML (Data-centric Machine Learning). Mussmann operates within Georgia Tech's Foundations of AI initiative and ML@GT collective, which provide infrastructure for large-scale data-centric research. His lab develops open-source tools like LabelBench for reproducible evaluation of data selection techniques, with ongoing projects exploring video data exploration systems and adaptive finetuning frameworks for foundation models.
Carol Smith is an Associate Professor in the Department of Soil & Physical Sciences at Lincoln University, New Zealand, where she serves as Head of Department since 2017. She has been an elected academic staff member of the Lincoln University Council from 2018-2022. Dr. Smith holds a PhD from the University of Aberdeen, an MSc from the University of Reading, and a BSc(Hons) from the University of Portsmouth, forming the foundation of her expertise in soil science and physical geography. Her research spans both fundamental and applied aspects of pedology. On the fundamental side, she investigates Quaternary pedology and paleoenvironmental reconstruction using loess stratigraphy, geomorphology, and micromorphology, which provides critical data for verifying future climate change predictive models. Applied research focuses on sustainable use of recycled organic matter and rehabilitation of degraded soils. She collaborates internationally on multidisciplinary projects involving paleoclimate reconstruction, paleoliquefaction, Antarctic soils, and viticulture. Dr. Smith is passionate about teaching and science communication, employing experiential learning methods to develop practical field skills in soil science through 'soil judging competitions' in New Zealand and Australia. Her research addresses UN Sustainable Development Goals including Life on Land (15), Climate Action (13), and Quality Education (4). Among her notable recognitions are the Norman H Taylor Memorial award 2020 from the New Zealand Society of Soil Science for outstanding contributions to soil science in New Zealand and Fellowship in the Royal Geographical Society, London. She serves as Associate Editor of Natural Sciences Education and was previously editor of Quaternary Australasia. Dr. Smith has supervised numerous graduate students through research-based supervision, with completed projects covering diverse topics from soil patterns in Southland to Antarctic soil ecology. She teaches advanced courses in field research, soil science, and physical landscapes, and has developed innovative approaches to teaching during the pandemic, including virtual field trips.
Matthew Osman is an Assistant Professor of Climate Science in the Department of Geography at the University of Cambridge. He leads the Cambridge Computational Climate and PaleOceanography (C3PO) group and serves as a supervisor for the Cambridge NERC Doctoral Landscape Awards (DLA). Dr. Osman's research focuses on understanding climate dynamics across various timescales by integrating geochemical proxy records, modern observations, and climate model simulations. His work centers on: Developing quantitative tools to reconstruct past climates and constrain future projections Using data assimilation and modeling methods for key climate intervals (mid-Pliocene, Last Ice Age, interglacials) Creating probabilistic frameworks combining proxies with climate simulations Developing statistical proxy system models for ice cores and marine records Investigating cryosphere-climate feedbacks, particularly ice sheets and sea ice His research spans sub-seasonal to millennial time intervals, specializing in bridging climate proxies with global climate models. He works closely with the international PMIP/CMIP community on projects spanning Arctic sea ice sensitivity, AMOC weakening, and carbon cycle feedbacks. Dr. Osman actively supervises PhD students through the Department of Geography PhD Program and encourages students interested in quantitative climate science to develop projects in: Using paleo data to constrain future climate projections Developing fingerprinting techniques for proxy records Building probabilistic proxy system models Applying paleoclimate data assimilation during ice sheet collapse Climate risk modeling using physics-informed statistics The C3PO group maintains a strong commitment to diversity and inclusion, welcoming researchers from all backgrounds to address the climate crisis through quantitative, multidisciplinary approaches.
Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.
Elliott Ash is an Associate Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics. He holds a Ph.D. in Economics and J.D. from Columbia University, a B.A. in Economics, Government, and Philosophy from the University of Texas at Austin, and an LL.M. in International Criminal Law from the University of Amsterdam. His research focuses on empirical legal studies using econometrics, NLP, and ML, examining topics like judicial behavior, legislative impact, and AI-driven governance. He has been funded by the ERC, Swiss NSF, and others. Research Interests: Elliott explores automation of legal decisions, text-as-data analysis in law, and the intersection of AI with legal systems. He develops tools like BallotBot and LePaRD to enhance legal transparency and public understanding. His work bridges law, economics, and computer science, with publications in top journals like the American Economic Journal and Review of Economics and Statistics . Teaching: Courses include Building a Robot Judge , Natural Language Processing for Law , and Big Data for Public Policy . He co-organizes the Zurich Workshop in AI+Economics and Monash-Warwick-Zurich Text-as-Data Workshops. Awards: European Research Council Starting Grant, Swiss National Science Foundation Grant, and multiple grants from U.S. and Swiss institutions. His work has been featured in NPR , VoxEU , and Georgetown Law Journal . Labs/Teams: Leads the Swiss AI Initiative's Human-AI Alignment team, collaborates with the CEPR on Political Economy research, and serves as an Economic Journal Associate Editor.
Roya Ensafi is the Morris Wellman Associate Professor in the Department of Computer Science & Engineering at the University of Michigan. She is the Founder and Director of the Censored Planet Lab, which focuses on Internet censorship measurement and digital equity. Her research lies at the intersection of networking, security, and privacy, with a strong emphasis on detecting censorship, surveillance, and digital inequity through scalable systems. Positions: Associate Professor (University of Michigan), Lab Director (Censored Planet) Recent Awards: Sloan Research Fellowship, NSF CAREER (2023), IRTF Applied Networking Research Prize (2016, 2022, 2023), USENIX Security Internet Defense Prize (2022) Her work develops systems like Censored Planet for global censorship monitoring, VPNalyzer for evaluating commercial VPN security, and Splintering Net for studying regionalized Internet access. By combining remote measurement techniques with user studies, her research addresses both technical and policy dimensions of digital freedom. Key methodologies include TLS handshake analysis, cross-layer latency metrics, and large-scale network probing. The Censored Planet project operates a global censorship detection network covering 221 countries, while VPNalyzer received the Consumer Reports Digital Lab fellowship. Collaborations include Google Jigsaw for data visualization systems used by over 100 organizations. Her 2024 work on digital discrimination in sanctioned states extends earlier groundbreaking research on Kazakhstan's HTTPS interception (2019) and Russia's Twitter throttling (2021). Scientific Awards: 2024: Distinguished Paper Awards at USENIX Security Symposium 2023: NSF CAREER Award 2022: IRTF Applied Networking Research Prize, USENIX Security Internet Defense Prize, First Prize in Internet Defense Prize, CSAW '22 Applied Research Competition First Place 2021: Recognized as Highest Scoring Short Paper at ACM IMC 2015: IRTF Applied Networking Research Prize 2022: Finalist for ACUM Outstanding Advisor Award Her lab trains both current and alumni PhD/Master's students including Ram Sundara Raman, Diwen Xue, Reethika Ramesh (now at Palo Alto Networks), and Victor Ongkowijaya (PhD at Princeton). She teaches EECS 388 Introduction to Security at the University of Michigan, covering software and network security principles. Her work has been featured in The Economist, New York Times, and BBC for analyzing global censorship trends.
Tracey Galloway is an Associate Professor in the Department of Anthropology at the University of Toronto Mississauga (UTM), where she conducts critical research on Indigenous health disparities and policy interventions in northern Canada. Her work bridges medical anthropology, public health, and community-based participatory research to address systemic inequities affecting circumpolar populations. Education: PhD, McMaster University, 2008 MA (institution unspecified) BA (institution unspecified) BScN (institution unspecified) Dr. Galloway's research program centers on chronic disease risk assessment and health system improvement in Indigenous communities, with specific expertise in nutrition transition, food security, child growth patterns, and public health policy evaluation. She examines the impact of federal programs like Nutrition North Canada while developing community-led solutions for health equity. Her methodological approach combines quantitative analysis of health outcomes with qualitative community engagement, emphasizing Indigenous research sovereignty and decolonizing methodologies. Her publication record reveals consistent thematic focus across 15 recent articles, demonstrating interdisciplinary collaboration between anthropology, epidemiology, and health economics. Key trends include rigorous evaluation of colonial impacts on Indigenous food systems, innovative analysis of subsidy program effectiveness, and centering Indigenous patient experiences in healthcare design. Her work consistently prioritizes community-defined research questions and actionable policy recommendations. Dr. Galloway actively mentors graduate students including Darci Belmore, Carly Checholik, Neda Maki, and Hiliary Monteith, guiding research on Indigenous health determinants and policy interventions. While specific grant details aren't publicly enumerated, her collaborative projects involve partnerships with Indigenous communities across Northern Canada and interdisciplinary teams addressing complex health system challenges. She maintains strong community partnerships for her applied research, particularly in Nunavut and Northwestern Ontario, working directly with Anishinabeck and Inuit communities to translate findings into culturally safe health initiatives and policy reforms that address the root causes of health inequities.
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.
