Find Academic Supervisors

• Sleep
• Circadian rhythms
• Bioinformatics
• +4 more

Ron C Anafi serves as Associate Professor of Medicine (Sleep Medicine) at the Hospital of the University of Pennsylvania and holds key affiliations as Faculty Scholar at the Penn Institute for Bioinformatics, Co-Investigator at the Center for Subcellular Genomics, and member of both the Chronobiology and Sleep Institute and ITMAT. His work bridges computational biology with clinical sleep medicine within the Perelman School of Medicine. His educational trajectory includes a B.S.E. in Biomedical Engineering and Philosophy from Tulane University (1995), followed by a Ph.D. in Applied Mechanics (2002) and M.D. (2004) from the University of Minnesota. This dual training underpins his unique approach to circadian research. Dr. Anafi's laboratory pioneers computational methods to decode sleep and circadian influences on human physiology, focusing on neurodegeneration and cancer. By applying machine learning and systems biology to large-scale omics data, his team reconstructs molecular rhythms from cross-sectional human tissue databases—overcoming the critical barrier of limited longitudinal sampling in patients. His research demonstrates how circadian disruptions in ribosomal function link to Alzheimer's pathology and how tumor clock strength predicts breast cancer outcomes. Analysis of his 15 most recent publications (2022-2025) reveals intensifying focus on cell-type-specific circadian disruptions in neurodegenerative diseases, with significant expansion into cancer chronobiology and contactless sleep monitoring technologies. His methodological innovations like CYCLOPS enable rhythmic signal extraction across diverse tissues—from brain to skin—while increasingly integrating multi-omics layers to identify therapeutic targets. As mentor in the Genomics and Computational Biology and Neuroscience graduate groups, he guides students through rotation projects at his Translational Research Laboratories facility (Suite 2100, 125 South 31st Street). His research is supported by NIH grants investigating circadian mechanisms in Alzheimer's disease and cancer progression, with recent work exploring sex-specific metabolic impacts of circadian genes and non-invasive sleep monitoring.

Kobus BarnardView profile

Professor

• Computer Vision
• Machine Learning
• Scientific Applications
• +3 more

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

Simon DeakinView profile

Professor

• Labour Law
• Economics of Law
• Law and Development
• +3 more

Simon Deakin is a Professor of Law at the University of Cambridge and Director of the Centre for Business Research (CBR) . He is a Fellow of Peterhouse and contributes to law and economics , empirical legal studies , and labour law . Education : M.A., Ph.D. (Cantab.), Ph.D. (h.c.) (Louvain-la-Neuve) His research focuses on labour law , law and technology , and corporate governance , with datasets like the CBR Labour Regulation Index and Platform Work Index covering 117+ countries. He has conducted fieldwork in Europe, Asia, and Africa. Key publications include Is Law Computable? (2022) and The Law of the Labour Market (2005). Recent work explores AI's legal implications and labour law's economic impact , funded by DIGIT and ESRC . He edits the Industrial Law Journal and Cambridge Journal of Economics . Scientific Awards : Fellow of the British Academy (2005) Honorary Doctorate, Catholic University of Louvain (2012) ECGI and Allen & Overy Prizes for Corporate Governance Research He leads projects like the Global Comparison of Legal Protections for Platform Workers under Digital Futures at Work (Digit) , collaborating with institutions like the International Labour Organization (ILO) and British Department of Business and Trade .

• Financial Engineering
• High Frequency Trading
• Algorithmic Trading
• +3 more

David Lariviere is a Clinical Professor in the Industrial & Enterprise Systems Engineering (ISE) Department at the Grainger College of Engineering , University of Illinois at Urbana-Champaign. He is also the Founder and Director of the FinTech Lab at the university and affiliated with the Master of Science in Financial Engineering (MSFE) Program through both the Grainger College of Engineering and the Gies College of Business Finance Department. Research Interests include high-frequency trading technologies, algorithmic market microstructure, FPGA applications in financial trading, low-latency networking, and quantitative finance. His work bridges computer science, electrical engineering, and financial markets, focusing on real-world trading system design and optimization. Teaching Highlights : Directed 21 student groups in Spring 2025 with projects spanning agentic AI trading systems, FPGA development, crypto arbitrage, and GPS timing servers Received 8 teaching excellence awards (2019-2024) including Outstanding recognition for FIN556 Designed courses integrating cutting-edge technologies like OneTick databases, Strategy Studio backtesters, and PyTorch-based ML models Awards & Patents : 2007 Andrew P. Kosoresow Memorial Award 7 issued/filings including latency determinism enforcement and FPGA-based trading architectures

• Fuzzy Logic
• Soft-Computing
• Health Monitoring
• +9 more

Nishchal K. Verma is a Professor at the Department of Electrical Engineering, Indian Institute of Technology Kanpur. He holds a PhD from IIT Delhi (2007), an M.Tech from IIT Roorkee (2003), and a B.Tech from DEI Agra (1996). His postdoctoral research includes work at the University of Tennessee (2009) and Louisiana Tech University (2008). Specialization: Fuzzy Logic, Health Monitoring, Intelligent Informatics Current Research Interests: Intelligent Data Mining, Computer Vision, Smart Grids, Biomedical Applications His research focuses on Fuzzy Systems , Machine Learning , and Health Monitoring with applications to power systems, biomedical data, and wireless sensor networks. He has developed technologies like the Transducers and Instrumentation Virtual Laboratory and Brain Computer Interface Laboratory , emphasizing predictive modeling and fault diagnosis. Key sponsored projects include DST-funded Fuzzy Rule-Based Image Prediction and DRDO-supported Visual Surveillance Systems . His work spans 15+ years of interdisciplinary publications in journals and conferences. Scientific Awards : Devendra Shukla Young Faculty Research Fellowship (2013-16) He has served as Associate Editor for journals and Chairman of IEEE chapters, with leadership roles in academic administration at IIT Kanpur.

• Information and Knowledge Management
• Product Configuration Systems
• Supply Chain Management
• +5 more

Anders Haug serves as Associate Professor at the Department of Business and Sustainability (DBS) within the University of Southern Denmark's Kolding campus. Having joined the university in 2008 as Assistant Professor in the Department of Entrepreneurship and Relationship Management before transitioning to his current role in 2010, his academic career spans over 15 years of research and teaching in operations, supply chain, and digital transformation contexts. His work bridges theoretical rigor with practical industry applications, particularly in engineer-to-order manufacturing and logistics sectors. Education: PhD in communication, representation and automation of design knowledge (2005-2007) Haug's research centers on information and knowledge management systems, with deep expertise in data quality frameworks, knowledge-based configuration, and digitalization of business processes. His fingerprint reveals distinctive contributions to product configuration systems, digital twin applications, and supply chain resilience—particularly examining how configurators transform warehouse services, manufacturing processes, and product-service ecosystems. Recent work increasingly addresses sustainability through green dynamic capabilities frameworks and life cycle assessment tools, maintaining strong empirical grounding via case studies in Danish manufacturing. Analysis of his 2024-2025 publications shows converging trends: digital technologies (configurators, digital twins) are examined through operational performance lenses while addressing sustainability imperatives. These works span operations management, information systems, and strategic management disciplines but consistently prioritize practical implementation frameworks for manufacturing SMEs. The research demonstrates methodological diversity—from conceptual modeling to empirical case studies—with strong industry relevance in logistics, engineering-to-order contexts, and manufacturing digitization. Scientific Awards: Top read paper in Business 2017/18 (Wiley) (2019) Haug has supervised 34 teaching courses between 2018-2024 covering business information systems, digitalization projects, and supply chain management. His academic service includes extensive peer reviewing for conferences like NOFOMA and DRS, plus organizational roles in Nordic business research networks. While specific grant details aren't provided, his 175+ research outputs and industry collaborations (evidenced by consultant work since 2006) indicate substantial research funding engagement. Media contributions on 3D printing and business process efficiency demonstrate effective knowledge transfer to practitioners. Though no dedicated research lab is specified, Haug's extensive co-authorship network—including collaborations on projects like digital twin implementation and configurator development—reveals embeddedness in multiple research collectives. His industry-facing approach manifests through case studies with logistics providers, manufacturer partnerships, and practical frameworks for warehouse service design and supply chain resilience.

Gerhard WolberView profile

Professor

• Computational Drug Design
• G-Protein Coupled Receptors (GPCRs)
• Cytochrome P450 Enzymes
• +3 more

Professor Gerhard Wolber leads the Molecular Drug Design research group at the Institute of Pharmacy , Freie Universitaet Berlin. His work focuses on computational approaches to drug discovery, with expertise in G-protein coupled receptors (GPCRs) , cytochrome P450 enzymes , Toll-like receptors , and viral protease inhibitors . He supervises a team of 13 PhD candidates 3 researchers 2 Master's students engaged in projects ranging from calcium channel blockers to CYP enzyme modulators for cancer therapy. Recent publications highlight his lab's contributions to pan-coronavirus drug discovery, TLR8 antagonism, and calcium channel inhibition. The team employs advanced methodologies including Molecular dynamics simulations Fragment-based de novo design Bayesian neural networks DFT calculations 3D pharmacophore modeling to bridge computational predictions with experimental validation. Notable projects include Virtual screening for TREM2-targeted glioblastoma therapeutics Allosteric communication path analysis via MDPath Immune checkpoint inhibitors for cancer immunotherapy Biased GPCR ligand development demonstrating a multidisciplinary approach to contemporary drug design challenges.

• Organic Functional Materials
• Materials Informatics
• Structural Organic Chemistry
• +4 more

Takuya Taniguchi is an Associate Professor at the Center for Data Science, Waseda University, where he has been employed since 2019, first as an Assistant Professor (2019-2021) before promotion to his current non-tenure track position. His interdisciplinary research bridges organic chemistry, materials science, and data science, with a focus on developing and analyzing functional organic materials, particularly photo-responsive molecular crystals. He maintains active collaborations with research groups including Hideko Koshima and Toru Asahi, and serves on professional committees such as the Manufacturing Industry AI Promotion Association. Education Doctor of Engineering, Waseda University (2019) Graduate studies at Waseda University's Graduate School of Advanced Science and Engineering Undergraduate studies at Waseda University's School of Advanced Science and Engineering, Department of Life Science and Medical Bioscience Research Focus Professor Taniguchi's work centers on structural organic chemistry and physical organic chemistry , with particular emphasis on organic functional materials and the application of materials informatics to crystal engineering. His research investigates how molecular structure influences macroscopic mechanical properties, especially photo-mechanical behaviors in organic crystals. He has pioneered the use of machine learning techniques to predict crystal properties, optimize materials performance, and design novel functional materials with applications in soft robotics and energy conversion. His recent work demonstrates a clear progression from fundamental studies of photo-mechanical crystals toward increasingly sophisticated applications of machine learning in materials science. The 15 most recent publications reveal a strong focus on neural network potentials for crystal property prediction, optimization of photo-actuated materials, and exploration of phase transitions in molecular crystals. This research trajectory shows consistent innovation in combining experimental chemistry with advanced computational methods. Awards and Recognition Inoue Research Encouragement Prize (2019) Ono Azusa Memorial Award from Waseda University (2019) Student Presentation Award at Japan Chemical Society 99th Spring Annual Meeting (2019) Poster Presentation Award at Chirality 2017 Multiple poster and presentation awards at international crystallography conferences Research Leadership Dr. Taniguchi leads multiple research projects funded by JSPS, JST, and industry partners including ENEOS Corporation and Sumitomo Foundation. His current projects include 'Efficient crystal structure prediction for organic porous materials,' 'Machine learning simulation of HOF material stability,' and 'Machine learning application for organic solid-phase transition.' He has successfully secured competitive funding including JST ACT-X and JSPS Early-Career Scientist grants. His work has received significant media attention, with coverage in Nikkan Kogyo Shimbun and Kagaku Kogyo Nippo highlighting breakthroughs in light-driven organic crystals with 3.7x improved output force. Academic Contributions Beyond research, Professor Taniguchi teaches extensively in Waseda's Global Education Center, offering courses in data science, statistical analysis with R, and statistics literacy across multiple academic quarters. He has contributed chapters to technical publications on process informatics, Bayesian optimization, and crystallization process design. His scholarly impact is evidenced by an h-index of 11 (Google Scholar) with 581 total citations, demonstrating significant influence in the emerging field of materials informatics for organic crystals.

• Molecular Imaging
• Positron Emission Tomography (PET)
• Cancer
• +4 more

Professor Andreas Kjær (MD, PhD, DMSc) serves as Head of the Cluster for Molecular Imaging (CMI) at the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen. He is also Chief Physician at Rigshospitalet and co-founder of Minerva Imaging, a CRO established in 2011. University of Copenhagen Department of Biomedical Sciences Cluster for Molecular Imaging Rigshospitalet His research focuses on translational molecular imaging with PET/PET-MRI and targeted radionuclide therapies (theranostics) for precision medicine in cancer, cardiovascular, and inflammatory diseases. CMI bridges basic research to clinical implementation, leveraging interdisciplinary expertise in physics, chemistry, biology, and medicine to develop imaging agents and therapies. Recent publications highlight advancements in theranostics , PET/CT , hyperpolarized MRI , and radiopharmaceutical development . Collaborations span oncology, cardiology, and nuclear medicine, with clinical trials in neuroendocrine tumors and metabolic imaging. Current affiliations include University of Copenhagen (Professor, Department of Biomedical Sciences) Rigshospitalet (Chief Physician)

• Database Systems
• Distributed Systems
• Machine Learning Systems
• +3 more

Tim Kraska is an Associate Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) within the School of Engineering. His research spans foundational areas in modern data systems: Database Systems Distributed Systems Machine Learning Systems Learned Indexes Cloud Databases Dr. Kraska pioneers the integration of machine learning into core database components, developing "learned systems" that replace traditional algorithms with ML models for significant performance gains. His work on learned indexes redefined indexing paradigms, while recent research extends this approach to garbage collection, query optimization, and concurrency control. His publication trends reveal a strategic shift toward applying ML to low-level systems problems, particularly in memory management and runtime optimization as demonstrated by his PLDI 2020 paper on Learned Garbage Collection. Award highlights include: ACM SIGMOD Jim Gray Doctoral Dissertation Award VLDB Early Career Research Contribution Award Marie Curie Fellowship At MIT, he leads a research group focused on next-generation data systems, securing grants from NSF and industry partners to explore the theoretical and practical boundaries of learned components in database engines. His team collaborates extensively with industry research labs on real-world deployment challenges. As a core member of CSAIL's distributed systems group, he contributes to MIT's leadership in reimagining data infrastructure for the AI era through cross-lab initiatives on scalable machine learning systems.

Heather MillerView profile

Assistant Professor

• Distributed Systems
• Concurrent Programming
• Programming Languages
• +4 more

Heather Miller is a tenure-track Assistant Professor in the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. Her academic journey includes prior roles as an Assistant Clinical Professor at Northeastern University's College of Computer and Information Science and as Executive Director of the Scala Center at EPFL. Miller's research centers on distributed and concurrent computation through the lens of programming languages, with particular emphasis on data-centric systems, big data processing, and edge computing. A defining theme throughout her work is composability - enabling construction of complex distributed systems through composition of components that are correct by construction. Her projects span distributable closures, flexible serialization techniques, futures and promises for asynchronous programming, and deterministic concurrent dataflow models. Her recent publications demonstrate strong trends in applying programming language theory to practical distributed systems challenges, with increasing focus on WebAssembly instrumentation, microservice resilience, and language model pipelines. This evolution reflects her commitment to bridging theoretical foundations with real-world system requirements. Dahl-Nygaard Junior Prize (2023) Mentorship forms a significant component of Miller's academic work. She actively supervises multiple PhD, MS, and undergraduate researchers at CMU, including Christopher Meiklejohn, Matthew Weidner, Huairui Qui, Ria Pradeep, and Luke Dramko. Her service contributions span numerous top-tier conferences including PLDI, SPLASH, ECOOP, and ICSE where she has served as committee member, chair, and keynote speaker. Miller co-founded the Curry On conference to foster industry-academia dialogue, hosting successful editions in Prague, Rome, Barcelona, Amsterdam, and London. She leads research groups focused on distributed programming models and maintains strong industry connections through Two Sigma, where she holds an affiliation. Her work consistently emphasizes practical open-source implementations, primarily within the Scala ecosystem where she's been a core contributor since 2011.

Mariaelena PierobonView profile

Associate Professor

• Biomedical Research
• Medical Proteomics
• Precision Medicine
• +3 more

Mariaelena Pierobon is an Associate Professor at George Mason University's School of Systems Biology, affiliated with the Institute for Advanced Biomedical Research and the Center for Applied Proteomics and Molecular Medicine (CAPMM). Her research focuses on precision medicine for cancer patients, integrating translational studies and clinical trials with emphasis on metastatic disease and tumor microenvironment analysis. MD, University of Padova (Italy), School of Medicine MPH, George Mason University Her work leverages in vitro models and patient-derived specimens with state-of-the-art technologies to explore cancer dynamics and develop tailored therapies. Recent projects include proteogenomic profiling of breast and ovarian tumors, KRAS mutation targeting, and spatial heterogeneity mapping in gynecologic cancers. Scientific trends in her publications include precision oncology, signaling pathway inhibition in KRAS-mutant cancers, tumor microenvironment analysis, and multi-omic diagnostics for therapeutic response prediction. Fulbright Scholar (2019) Pierobon collaborates with clinical teams using reverse phase protein arrays (RPPA) and laser capture microdissection to dissect molecular mechanisms in pancreatic, breast, and ovarian cancers. Her lab contributes to databases like the Side-Out Foundation Metastatic Breast Cancer portal for open-access multi-omic research.

• Blockchain Technology
• Internet of Things (IoT)
• Cybersecurity
• +4 more

Ali Dorri is an Associate Professor in the School of Computer Science at Queensland University of Technology's Faculty of Engineering. His research focuses on the intersection of blockchain technology, Internet of Things (IoT), and cybersecurity, with significant contributions to privacy-preserving systems and energy trading applications. He maintains an active research profile with consistent publications in top-tier venues including IEEE Transactions, ACM Computing Surveys, and various IEEE conferences. Dr. Dorri's research interests center on blockchain technology and its applications to real-world problems. His work addresses critical challenges in IoT security, privacy-preserving systems, energy trading mechanisms, and supply chain management. He has developed innovative solutions including Tree-Chain (a lightweight consensus algorithm for IoT-based blockchains), LSB (a lightweight scalable blockchain for IoT security), and various blockchain storage optimization techniques. His research bridges theoretical concepts with practical implementations, often targeting specific industry challenges in manufacturing, energy, and supply chain sectors. Analysis of his recent publications reveals a strong focus on optimizing blockchain for resource-constrained environments like IoT networks, developing privacy-preserving mechanisms for sensitive applications, and creating practical implementations for energy trading systems. His work shows increasing sophistication in addressing scalability challenges while maintaining security and privacy guarantees. The interdisciplinary nature of his research connects computer science fundamentals with applications in energy systems, manufacturing, and supply chain management. Dr. Dorri has established productive research collaborations with colleagues at QUT including Raja Jurdak, Salil Kanhere, and Gowri Ramachandran, as well as international collaborators. His publications demonstrate consistent productivity with multiple high-impact papers each year, including several that have received significant citations within the blockchain and IoT research communities.

• AI Agents
• Multi-Agent AI
• Artificial Intelligence
• +14 more

Saptarashmi Bandyopadhyay is a Tenure-Track Assistant Professor of Computer Science at the City College of New York and the Graduate Center at the City University of New York (CUNY). Her research focuses on Artificial Intelligence Agents and Autonomous Decision Making, with special emphasis on Multi-Agent Reinforcement Learning, Multi-Agent Imitation Learning, and related paradigms. She has established significant collaborations with leading institutions including Google DeepMind, Carnegie Mellon University, Oxford University, and MIT. Dr. Bandyopadhyay received her PhD from the University of Maryland, College Park, where she was advised by Professor John Dickerson and Professor Tom Goldstein. Prior to that, she graduated from Penn State in 2020 with a thesis on Multimodal Computer Vision in Medical Domain advised by Prof. William Evan Higgins. Her research expertise spans multiple domains of AI including Multi-Agent Systems, Reinforcement Learning, Imitation Learning, and Multimodal Perception. She specializes in developing AI agents for applications in climate conservation, economic systems, and AI safety. Her work integrates techniques from computer vision, natural language processing, and robotics to create more robust and explainable AI systems that can operate effectively in complex, real-world scenarios. Current work includes improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Analysis of Dr. Bandyopadhyay's publication record reveals a clear progression from foundational work in medical imaging and natural language processing toward increasingly sophisticated multi-agent AI systems. Her recent publications demonstrate a strong focus on Multi-Agent Reinforcement Learning frameworks like JAXMARL, with applications spanning from supply chain orchestration to climate conservation. The interdisciplinary nature of her work is evident in publications spanning computer vision, NLP, and multi-agent systems conferences including AAAI, NeurIPS, AAMAS, EMNLP, and ACL. DoGood Fellow (2022) UMD Dean's Summer Fellow (2021) Dr. Bandyopadhyay has been actively involved in securing research funding from major agencies including NSF, NIH, DoD, and ARL. She served as the lead PhD student RA in a DoD project for Multi-Agent Explainable AI to improve AI trustworthiness. Her service to the academic community includes membership on program committees for major conferences including IJCAI 2024, KDD 2024, ACL 2024, and AAMAS 2023-2024. She has also created the AI Agents Seminar Series at UMD in 2022 with over 1,000 participants from six continents. Currently, Dr. Bandyopadhyay leads research on improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Her lab collaborates prominently with researchers from Google DeepMind, Carnegie Mellon University, Oxford University, University of Sheffield, Waymo, Meta AI, and MIT, with special focus on Dr. Jakob Foerster's and Dr. Robert Loftin's groups.

• Climate variability
• Palaeoclimatology
• Hydrology
• +5 more

Dr. Danielle Verdon-Kidd is an Associate Professor in Earth Sciences at the University of Newcastle, specializing in climate variability and extreme event analysis. Her research integrates palaeoclimatology, hydrology, and climatology to understand and mitigate climate-driven risks. She has held roles in the School of Environmental and Life Sciences since 2003, after completing her PhD in Civil Surveying & Environmental Engineering and Bachelor of Engineering (Environmental Eng) at the University of Newcastle. Education: PhD (Civil Surveying & Environmental Engineering), University of Newcastle Bachelor of Engineering (Environmental Eng), University of Newcastle Research Interests: Her work focuses on climate extremes (droughts, bushfires, tropical cyclones), palaeoclimate reconstructions, and interdisciplinary approaches to risk assessment. She emphasizes leveraging long-term data (e.g., tree rings, coral records) to inform water management, infrastructure planning, and community resilience. Key projects include collaborations with governments and industries to develop tools like CoastAdapt for coastal risk management. Publications & Awards: Over 100+ publications, including peer-reviewed journal articles and conference contributions. Notable awards include the Women in Research Fellowship (2018) and Envisage Program mentorship (2019). Advising & Partnerships: Supervises undergraduate and graduate students, fostering the next generation of climate researchers. Collaborates with federal/state agencies (e.g., NSW Department of Planning), water authorities, and international partners (e.g., University of Melbourne, Hydro Tasmania) to bridge scientific research and practical applications. Labs & Teams: Leads projects in palaeoclimate reconstruction, stochastic modeling, and climate adaptation. Active in interdisciplinary teams addressing climate change impacts on agriculture, biodiversity, and urban planning.