جست‌وجوی استاد و پژوهشگر

Sidonie Christopheمشاهده پروفایل

عضو هیئت علمی

• Geographic Information Sciences
• Geovisualization
• Map Design
• +۱۲ مورد دیگر

Sidonie Christophe is a Senior Researcher (Directrice de Recherche, DR1) at UMR LASTIG, a joint research unit of Université Gustave Eiffel, IGN-ENSG, and EIVP. She serves as co-director of the LASTIG laboratory and leads research in geovisualization, map design, and interactive spatial data exploration. She also holds a part-time advisory role (60%) at the French Ministry of Higher Education and Research in the domain of digital technology, environment, climate, and sustainable urban development. PhD in Geographic Information Sciences Senior Researcher, DR1, MTECT Co-Director, LASTIG Laboratory (since 2021) Former Team Leader, GEOVIS (Geovisualization, Interaction, and Immersion) Advisor, Environment and Urban Climate, French Ministry of Higher Education & Research Her research centers on innovative methods for 2D/3D and nD geospatial data visualization, with a focus on enabling spatio-temporal understanding through visual and non-visual spatial thinking. Her work integrates principles from geographic information science, human-computer interaction, and computer graphics. Key areas include urban climate visualization, tactile and augmented reality for accessibility, expressive cartographic rendering, and cognitive aspects of map design. She investigates how aesthetic and semiotic choices impact map comprehension and utility. The 15 most recent publications reflect a strong trend in interactive and accessible geovisualization, particularly for urban and environmental applications. Topics include neural map style transfer, 3D urban climate analysis, tactile maps for the visually impaired, augmented reality in geography, and visual analytics for crisis and climate data. There is a consistent emphasis on user-centered design, interdisciplinary integration, and the development of tools for decision-making under uncertainty. Scientific Recognition and Service: Invited speaker at major conferences (IEEEVIS, AGILE, ICC, CPGIS) Co-organizer of international workshops (e.g., GeoVIS, ISPRS AR/VR sessions) Leader of national research projects (ANR ORACLES, ANR ACTIVmap, ANR ECOCIM) Recipient of international mobility grants (AMICI I-SITE FUTURE) Contributor to national glossaries and research strategy (e.g., French photogrammetry glossary) Advising and Grants: Sidonie Christophe actively supervises PhD students and postdoctoral researchers, including Markie Jiang, Maria-Jesus Lobo, and Alexandre Mielniczek. She leads or participates in multiple funded research projects such as ANR ORACLES (marine flooding visualization), ANR ACTIVmap (tactile 3D maps), and ANR ECOCIM (eco-design of city information models). Her advisory role at the MESR involves shaping national research strategy in digital and environmental sciences. Labs and Research Teams: She is a core member of the GEOVIS team (Geovisualization, Interaction, and Immersion) at LASTIG and previously served as its leader. As co-director of LASTIG, she plays a central role in the leadership and strategic direction of the entire laboratory, which comprises over 100 researchers across four teams: ACTE, GEOVIS, MEIG, and STRUDEL.

Jesse Crossonمشاهده پروفایل

دانشیار

• American Political Institutions
• Legislative Politics
• Political Economy
• +۳ مورد دیگر

Jesse Crosson is an Associate Professor at Purdue University's Department of Political Science and Co-Director of the Program on American Institutional Renewal (PAIR) . His work investigates hyperpartisan institutional transformation , focusing on how insecure majorities , interest groups , and legislative centralization affect policy change and decentralized expertise . He also contributes to Cornerstone Integrated Liberal Arts and Cornerstone for Business teaching sequences. Ph.D. , Political Science, University of Michigan (2019) B.A. (summa cum laude), Political Science, Hofstra University (2013) His research examines: Institutional gridlock mechanisms Legislative polarization dynamics Interest group depolarization roles Electoral reform effectiveness Legislative capacity erosion Artificial intelligence policy development PAIR activities highlight constitutional legacy through Sen. Birch Bayh's work and executive power analysis . Recent publications analyze legislative strategy , AI regulation , and polarized pluralism . The program has expanded with postdoctoral and graduate fellows , including Ayoung Chun (money in politics) and Rylee Crum (economics).

Jarno Vanhataloمشاهده پروفایل

استاد

• Statistics
• Probability
• Ecology
• +۶ مورد دیگر

Professor Jarno Vanhatalo is a Professor of Statistics at the University of Helsinki, serving as vice director of the Research Center for Ecological Change. He is affiliated with both the Faculty of Biological and Environmental Sciences and the Faculty of Science, where he leads the Environmental and Ecological Statistics Group. His work bridges advanced statistical methodology with pressing ecological and environmental challenges. His research focuses on the development and application of statistical models for ecological and environmental data. Key areas include: Bayesian statistics and hierarchical modeling Gaussian processes and spatial statistics Species distribution modeling Ecological risk assessment Climate change impact analysis Biodiversity monitoring and conservation Professor Vanhatalo's publication record shows a consistent focus on integrating sophisticated statistical methods with ecological applications. His recent work demonstrates increasing emphasis on climate change impacts on biodiversity, spatially explicit modeling of ecological processes, and the development of Bayesian methods for uncertainty quantification in environmental predictions. His research spans terrestrial and marine ecosystems, with particular attention to Nordic and Baltic regions. Among his notable contributions are advancements in: Joint species distribution modeling Bayesian calibration of environmental models Spatiotemporal survey design Model-based variance partitioning in ecological studies Statistical approaches to Arctic risk management Professor Vanhatalo has received significant research funding, including an ERC grant for "Predective Understanding of the effects" (2024-2029) and leadership roles in multiple Academy of Finland and EU-funded projects focused on biodiversity, ecological change, and risk management in polar waters. He actively contributes to the academic community through: Supervising doctoral students in Wildlife Biology and Mathematics and Statistics programs Serving on editorial boards for journals including Conservation Biology and Ecology Letters Peer reviewing for numerous statistical and ecological journals Organizing workshops on statistical methods in ecology

• Mechanism Design
• Auction Theory
• Blockchain Economics
• +۴ مورد دیگر

Mallesh M Pai is the Lay Family Associate Professor in the Department of Economics at Rice University and a CEPR Research Fellow. His research bridges mechanism design, auction theory, and blockchain economics, with applications to decentralized finance (DeFi) and privacy economics. Prior to Rice, he was a Janice and Julian Bers Assistant Professor at the University of Pennsylvania. PhD in Managerial Economics and Strategy, Kellogg School of Management, Northwestern University Bachelor's in Computer Science and Engineering, Indian Institute of Technology, Delhi His work focuses on designing robust mechanisms in environments with information asymmetry, privacy constraints, and decentralized systems. Recent publications address dynamic transaction fees, blockchain centralization, and algorithmic collusion. He serves on program committees for major conferences in computer science and economics. Indirect Persuasion (Journal of Political Economy) Dynamic Transaction Fee Mechanism Design (with Max Resnick) Collusive Outcomes via Pricing Algorithms (Marketing Science) Mallesh has received significant recognition for his work, including the Best Paper award at the American Economic Journal: Microeconomics (2021) and Best Pricing Paper from the American Marketing Association RAPSIG (2023). His research is supported by NSF grants on fair data analysis and digital privacy foundations. He teaches graduate and undergraduate courses in microeconomic theory and market design at Rice, emphasizing practical applications of theoretical models. His Erdős number is 3, reflecting interdisciplinary collaborations.

Qiang Zhuمشاهده پروفایل

استاد

• Data Science
• Data Management
• Machine Learning
• +۳ مورد دیگر

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Charles E. Leisersonمشاهده پروفایل

استاد

• Parallel Computing
• Performance Engineering
• Algorithms
• +۶ مورد دیگر

Charles E. Leiserson is a Professor of Computer Science and Engineering at MIT, holding the Edwin Sibley Webster Professorship in Electrical Engineering and Computer Science. He leads the Supertech Research Group and is Faculty Director of the MIT-Air Force AI Accelerator. His work focuses on parallel computing, performance engineering, and algorithms. Leiserson is renowned for co-authoring the foundational textbook Introduction to Algorithms , widely used in computer science education globally. He has pioneered technologies like the Cilk multithreaded programming language and contributed to supercomputing architectures such as the Connection Machine CM-5. His research bridges theoretical computer science with practical applications, emphasizing cache-oblivious algorithms and compiler optimizations. Leiserson has received multiple awards for his academic contributions and educational impact, including the ACM-IEEE Ken Kennedy Award and Margaret MacVicar Fellow distinction at MIT. Education: B.S., Yale University, 1975 Ph.D., Carnegie Mellon University, 1981 Research Interests: Leiserson’s work addresses performance engineering challenges in post-Moore’s Law computing. His group develops algorithms, software systems, and hardware strategies for scalable parallelism. Key areas include parallel programming frameworks (e.g., OpenCilk), cache-aware algorithms, and compiler optimizations. He emphasizes making parallel computing accessible to mainstream programmers through tools like Cilk and educational initiatives such as MIT’s Software Performance Engineering course. Projects & Leadership: Leiserson leads the Supertech Research Group and contributed to the Cilk Arts Inc. venture, acquired by Intel. He chairs the MIT Undergraduate Practice Opportunities Program (UPOP) and teaches courses on algorithms and discrete mathematics. His leadership workshops for faculty have educated hundreds worldwide on team management in academia. Awards & Recognition: 2014 ACM-IEEE Ken Kennedy Award IEEE Taylor L. Booth Education Award ACM Paris Kanellakis Theory and Practice Award Member of the National Academy of Engineering Labs & Teams: Active in MIT’s CSAIL, Leiserson collaborates through the Supertech Group and Theory of Computation communities. His current projects include Tapir compiler infrastructure, graph neural network applications for anti-money laundering, and deterministic parallel scheduling algorithms.

Mostafa Milaniمشاهده پروفایل

استادیار

• Data Management
• Databases
• Data Cleaning
• +۷ مورد دیگر

Mostafa Milani is an Assistant Professor in the Department of Computer Science at Western University. His research focuses on data management, databases, and their applications in data cleaning, privacy, provenance, and fairness. Before joining Western, he held postdoctoral positions at the University of British Columbia and McMaster University, and earned his Ph.D. from Carleton University under Dr. Leopoldo Bertossi. Education: Ph.D. in Computer Science from Carleton University (supervised by Leopoldo Bertossi), Postdoctoral Fellowships at University of British Columbia and McMaster University. Research Interests: Data Quality, Privacy, Provenance, Fairness, Entity Matching, Query Optimization, and Database Systems. His work emphasizes ethical data practices and integrates machine learning for improved database interactions. He has contributed to projects like Building Trust in Data (privacy/fairness integration) and Unified Data Exploration (provenance and query recommendations). Courses taught include Databases I/II, Applied Logic, and Web Systems. Current advisees include 7 MSc and 1 PhD student. Former students have graduated across MSc and undergraduate programs. His research is supported by grants and collaborations, and he actively participates in program committees for top conferences like SIGMOD and VLDB.

Dora Erdosمشاهده پروفایل

مدرس ارشد

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +۳ مورد دیگر

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

• Privacy-Preserving Algorithms
• Differential Privacy
• Federated Learning
• +۶ مورد دیگر

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.

• Serious Games
• Healthcare Technology
• Gamification
• +۴ مورد دیگر

Simon McCallum is an Associate Professor at NTNU’s Faculty of Information Technology and Electrical Engineering. Born in New Zealand, he transitioned from commercial game development in Norway to academia in 2009. His research focuses on Serious Games, particularly in healthcare contexts, gamification, and virtual reality applications. He holds a PhD from the University of Otago (2007), where he studied Computer Science, Mathematics, Psychology, and Philosophy. McCallum has contributed to over 30 publications, including works on cognitive health gaming, software engineering education, and AR applications. He teaches courses such as IMT4307 (Serious Games Research) and IMT3603 (Game Programming). His key projects include the Smartkuber AR game for cognitive screening and the Traction Trebuchet historical engineering study. McCallum has collaborated with institutions like the University of Otago and has engaged in outreach through conferences and media appearances, promoting game technology in healthcare and education.

Stephen C. Smithمشاهده پروفایل

استاد

• Development Economics
• Poverty Alleviation
• Economics of Organization
• +۷ مورد دیگر

Stephen C. Smith is Professor of Economics and International Affairs and Chair of the Department of Economics at George Washington University. He is also Research Fellow at the Institute of Labor Economics (IZA), Nonresident Senior Fellow at the Brookings Institution, and member of the BRAC-USA Advisory Council. He previously served as Director of the Institute for International Economic Policy (IIEP) and as founding director of GW’s International Development Studies Program. Ph.D. in Economics, Cornell University His research focuses on development economics, particularly poverty reduction, climate change adaptation, and the economics of cooperatives and employee participation. His work combines rigorous empirical analysis with policy relevance, drawing on field research in Bangladesh, India, Uganda, Senegal, Germany, and other countries. He has authored or co-authored over 50 journal articles and several influential books, including the widely adopted textbook Economic Development (13th ed., 2020) with Michael Todaro and Ending Global Poverty: A Guide to What Works . His recent publications span topics such as agricultural extension in Uganda, works councils in Germany, multidimensional poverty targeting in Bangladesh, and climate adaptation in India. These works reflect a consistent focus on policy evaluation, institutional design, and the economic mechanisms underlying poverty traps and development outcomes. Fulbright Research Scholar Jean Monnet Research Fellow IZA Research Fellow Visiting Fellow, Brookings Institution Nonresident Senior Fellow, Brookings Institution Fulbright Senior Specialist UNICEF Senior Research Fellow Smith has supervised numerous doctoral students and taught core courses in development economics at undergraduate, master’s, and PhD levels. He has been Principal Investigator on major research projects funded by USAID/BASIS, including work on training, technology, and credit in smallholder agriculture. He has also served as Associate Editor of the Journal of Economic Behavior and Organization and consulted for the World Bank, UNDP, ILO, and USAID. His research program integrates fieldwork, policy engagement, and academic scholarship to advance understanding of effective strategies for global poverty reduction.

Marianna Cavadaمشاهده پروفایل

مدرس ارشد

• Smart Cities
• Urbanism
• AI Urbanism
• +۴ مورد دیگر

Marianna Cavada is a Senior Lecturer at the Manchester School of Architecture (MSA), where she leads the MA Architecture & Urbanism program as the Prototype Urbanism Lab Lead and serves as the Department International Lead. Her research focuses on AI-driven urbanism, smart and liveable cities, and policy design for sustainable urban transformation. Education: PhD in Civil Engineering (2019, University of Birmingham) PG Dip Architecture (International) RIBA II (2008, Huddersfield University) BA Hons Architecture RIBA I (2005, Huddersfield University) BA Hons Interior Design (2001, De Montfort University) Marianna bridges theoretical and practical urbanism through collaborations with local policymakers (Lancaster City Council, Karystos Municipality) and international bodies like the WHO. Her work addresses inequality, climate emergency, and global health within the UN Sustainable Development Goals framework. She contributed to the British Academy-funded 'Caring-with Cities' project and EPSRC initiatives including Liveable Cities and the Green Infrastructure Corridors program. Her research outputs include the book The Little Book of Smart Cities and publications on topics ranging from CO2 emissions in smart cities to gamification in post-pandemic urban evaluation. Marianna has presented at institutions such as the University of Birmingham, Donghua University, and Xi’an Jiaotong University, and has led workshops like the Smart Cities Alliance in Birmingham. Key Scientific Awards: First Prize – Peter Stead Award (2008) Higher Commendation in PG Dip Architecture (2008) Marianna supervises PhD projects in AI Urbanism, Smart Cities, Liveable Cities, and Urban Engineering. Her professional background includes architectural practice in the UK (ATKINS), UAE (Al Falah project), and China (GDG Ltd, Twin Cities), alongside academic roles at the University of Birmingham and Lancaster School of Architecture. She operates from the Manchester Technology Centre and maintains memberships in the International Society for Urban Health and the Royal Institution of Chartered Surveyors (RICS).

Ahmad Alsharifمشاهده پروفایل

استادیار

• Applied Cryptography
• IoT Security
• Blockchain Applications
• +۲ مورد دیگر

Dr. Ahmad Alsharif is an Assistant Professor in the Department of Computer Science at the University of Alabama's College of Engineering. His research expertise spans applied cryptography, IoT security, cyber-physical systems security, and blockchain applications. He received his B.S. and M.S. in Electrical Engineering from Benha University, Egypt, and Ph.D. in Electrical and Computer Engineering from Tennessee Tech University. Research focuses on security challenges in critical infrastructure systems including smart grids, IoT networks, and UAV systems. Current projects investigate privacy-preserving machine learning techniques, adversarial attack resilience, secure data marketplaces, and attack detection mechanisms for distributed energy systems. His work combines cryptographic protocols with machine learning for trustworthy systems. Awards include the NSF Research Initiation Initiative Grant (NSF CRII) and Young Innovator Award from Egyptian Industrial Modernization Center.

• Signal Processing
• Computational Imaging
• Data Science
• +۵ مورد دیگر

Professor Minh N. Do is the Thomas and Margaret Huang Endowed Professor in Signal Processing & Data Science at the University of Illinois at Urbana-Champaign (UIUC), with primary appointment in the Department of Electrical and Computer Engineering. He holds multiple affiliate appointments across campus including with the Coordinated Science Laboratory, Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Computer Science, Institute for Genomic Biology, College of Medicine, and School of Computing and Data Science. Additionally, he serves as Director of the joint VinUni-Illinois Smart Health Center and holds an Honorary Vice-Provost position at VinUniversity. Professor Do received his B.Eng. in Computer Engineering (First Class Honors) from the University of Canberra, Australia in 1997, followed by his Dr.Sci. in Communication Systems from the Swiss Federal Institute of Technology Lausanne (EPFL) in 2001. His educational journey was marked by exceptional achievement, earning the University Medal from the University of Canberra and a Silver Medal from the 32nd International Mathematical Olympiad. Professor Do's research focuses on developing new multidimensional signal processing tools with applications across several domains. His primary research interests include smart health, data science, computational imaging, and signal processing. His work spans biomedical imaging, machine learning, computer vision, and robotics, with particular emphasis on geometric image representations, integrating image formation and processing, and image processing from multiple sensors. His research bridges theoretical investigations with practical applications, creating impactful solutions in healthcare, diagnostics, and AI systems. His recent publications demonstrate a consistent trajectory toward multimodal AI systems, robust learning frameworks, and healthcare applications. Professor Do's work increasingly integrates signal processing with deep learning approaches to address challenges in medical imaging, cross-modal transfer, and real-world deployment of AI systems. His research shows strong emphasis on practical applications with societal impact, particularly in healthcare diagnostics and smart health technologies. Professor Do's scientific achievements have been recognized with numerous prestigious awards: Member of the National Academy of Artificial Intelligence (2025) Fellow of Asia-Pacific Artificial Intelligence Association (2023) Thomas and Margaret Huang Endowed Professor, UIUC (2020-present) Fellow of IEEE (2014) Young Author Best Paper Award, IEEE Signal Processing Society (2008) CAREER award from the National Science Foundation (2003) Best Doctoral Thesis Award from EPFL (2001) As an educator, Professor Do has taught numerous courses spanning digital signal processing, probability, data science, and image processing. His teaching excellence has been recognized with multiple "Teachers Ranked as Excellent" awards at UIUC. He also maintains active industry connections through tech-transfer efforts, having co-founded Personify and served as Chief Scientist of Misfit. His leadership extends to administrative roles, having served as Vice-Provost for VinUniversity during 2020-2021. Professor Do leads research initiatives at the intersection of signal processing and healthcare applications, with particular focus on the Smart Health Center collaboration between UIUC and VinUniversity. His lab develops innovative solutions for medical diagnostics, point-of-care testing, and neurological assessment using advanced signal processing and AI techniques.

Mahsa Salehiمشاهده پروفایل

مدرس ارشد

• Data Mining
• Machine Learning
• Time Series Analysis
• +۵ مورد دیگر

Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids. Education: PhD in Computer Science, University of Melbourne (2016) MSc in Software Engineering, Amirkabir University of Technology (2009) BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006) Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks. Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016). Grants & Projects: Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027) AI for Clean Energy & Sustainability (Monash, 2023–2027) Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025) Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.