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.
Rachit Agarwal is an Associate Professor in the Department of Computer Science at Cornell University, with research focusing on systems, networking, and theoretical problems arising in practical systems. He leads a research group working on resource disaggregation, host architecture, secure cloud storage, and datacenter design. PhD in Computer Science, Cornell University Undergraduate, IIT Kanpur His research spans three major directions: Resource Disaggregation (with $3M NSF and Google awards), Host Architecture (exploring terabit interconnects), and PANCAKE (secure oblivious cloud storage with $1M NSF award). He also contributed to foundational work in Near-optimal Datacenter Design and Data Plane Monitoring . Awards include the Sloan Fellowship, NSF CAREER, IRTF Applied Networking Prize, and multiple best paper awards. His recent publications focus on host network architecture, congestion control, oblivious data access mechanisms, and secure cloud storage systems. He has advised multiple Ph.D. and postdoctoral researchers who now hold faculty positions at leading institutions. Sloan Research Fellowship NSF CAREER award Kavli Fellowship IRTF Applied Networking Research Prize SIGCOMM Best Student Paper Award Usenix Security Distinguished Paper Award Tau Beta Pi Professor of the Year 2025 Rachit has advised numerous students including current Cornell advisees like Midhul Vuppalapati, Shreyas Kharbanda, and Omar Eqbal. Former advisees include Saksham Agarwal (UIUC), Qizhe Cai (UVA), and Mina Tahmasbi Arashloo (University of Waterloo). His research is supported by large NSF grants and industry awards, with deployments in real-world systems and open-source contributions.
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.
Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.
Majid Ghayoomi is a Professor and Civil Engineering Undergraduate Coordinator in the Department of Civil and Environmental Engineering at the University of New Hampshire's College of Engineering and Physical Sciences. His research focuses on geotechnical engineering and geomechanics, particularly unsaturated soil mechanics and geotechnical earthquake engineering. He teaches courses such as Soil Mechanics, Engineering Behavior of Soils, and Geotechnical Modeling. Dr. Ghayoomi holds a Ph.D. from the University of Colorado at Boulder, an M.S. from Sharif University, and a B.S. from the University of Tehran. His research interests include bioremediation, hazards mitigation, soil-structure interaction, and materials testing. He leads the Geotechnical Modeling and Innovation lab, advancing bio-inspired solutions and remote sensing applications in geotechnical systems. His recent work emphasizes climate change impacts on seismic resilience, microbial stabilization of soils, and satellite-based soil moisture monitoring. Key contributions include studies on liquefaction mitigation, seismic site response, and infrastructure vulnerability in dynamic environments. His research spans theoretical, experimental (centrifuge modeling), and computational approaches to address complex geotechnical challenges.
Frank Corsetti is a Professor of Earth Sciences at the University of Southern California (USC), leading the Corsetti Lab. His research focuses on the co-evolution of Earth and its biosphere, particularly during extreme events like the 'Snowball Earth' glaciation and mass extinctions. He holds a Ph.D. in Geological Sciences from UC Santa Barbara (1998) and a B.S. in Geology from UC Davis (1989). Research Interests: Corsetti studies geobiological processes such as stromatolite formation, microbial carbonate systems, and the environmental impacts of mass extinctions. His lab investigates biosignatures, diagenetic processes, and astrobiological applications. Current projects include the end-Triassic mass extinction and microbial interactions in extreme environments. Advising & Grants: He mentors a team of PhD students (including Reena Joubert, Alison Cribb, and others) and has secured funding from NASA for projects like stromatolite carbonate formation studies. His work bridges paleontology, geochemistry, and microbiology, contributing to understanding Earth’s long-term climate and life history. Labs/Teams: The Corsetti Lab at USC explores stromatolites, microbial mats, and ancient Earth systems. Collaborations involve institutions like JPL and international universities.
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.
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.
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.
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.
Leena Järvi is a Professor at the Institute for Atmospheric and Earth System Research (INAR) and Helsinki Institute of Sustainability Science (HELSUS) , University of Helsinki. Her work bridges urban climate science , air pollution , and greenhouse gas dynamics through experimental and theoretical approaches. Research Interests : Urban micrometeorology, carbon sequestration in green spaces, climate mitigation strategies, and air quality modeling. Key Projects : CO-CARBON (Strategic Research Council), GHUGS (Research Council of Finland), and PAUL (EU Horizon 2020). Her recent publications focus on urban CO2 fluxes , carbonyl sulfide as a carbon proxy , and climate impacts of urban vegetation . She has supervised 12 PhD students, 7 postdocs, and 17 undergraduates, while serving on editorial boards and organizing international workshops. Scientific Awards : Timothy Oke Award 2021 (IAUC). Teaching : Courses on Urban Climate and Atmospheric Sciences .
