جست‌وجوی استاد و پژوهشگر

Sharona Hoffmanمشاهده پروفایل

استاد

• Health Law
• Employment Discrimination
• Health Information Technology
• +۴ مورد دیگر

Sharona Hoffman is the Edgar A. Hahn Professor of Law and Professor of Bioethics at Case Western Reserve University , where she also serves as Co-Director of the Law-Medicine Center . Since joining the faculty in 1999, she has become a leading voice in health law, employment discrimination, and the legal and ethical implications of health information technology and artificial intelligence. Education: Doctor of Juridical Science (SJD) in Health Law – Case Western Reserve University Master of Laws (LLM) in Health Law – University of Houston Juris Doctor (JD) – Harvard Law School Bachelor of Arts – Wellesley College Research Focus: Hoffman's scholarship spans health law , employment discrimination , health information technology , AI and the law , public health emergencies , and elder law . She has authored over 70 scholarly articles and two books: Electronic Health Records and Medical Big Data: Law and Policy (Cambridge University Press, 2016) and Aging with a Plan: How a Little Thought Today Can Vastly Improve Your Tomorrow (2nd ed., 2022). Awards & Honors: 2021 CWRU Faculty Distinguished Research Award Elected to the American Law Institute (2017) 9th most-cited health law scholar in the U.S. (2016–2020, Leiter’s Law School Reports) First Year Teacher of the Year – 2011, 2012, 2019, 2024 Grants & Fellowships: Hoffman has held prestigious fellowships including the Robert Wood Johnson Foundation Scholar-in-Residence (2013), and visiting scholar positions at the CDC (2007, 2014), Emory University (2014), and NIH Bioethics Department (2022). Professional Roles: Prior to academia, Hoffman served as a Senior Trial Attorney at the EEOC, litigation associate at O'Melveny & Myers, and judicial clerk for U.S. District Judge Douglas W. Hillman.

Ngoc Thanh Nguyenمشاهده پروفایل

استاد

• Computational Collective Intelligence
• Knowledge Integration
• Data Mining
• +۶ مورد دیگر

Ngoc Thanh Nguyen is a Full Professor at Wroclaw University of Science and Technology where he serves as Head of the Department of Applied Informatics. He holds the prestigious title of Professor granted by the President of Poland and has been recognized as a Distinguished Scientist of ACM since 2009. He serves as Editor-in-Chief of both the Journal of Information and Telecommunication (JIT) and the Vietnam Journal of Computer Science (VJCS), and chairs the IEEE SMC Technical Committee on Computational Collective Intelligence. His research spans computational collective intelligence, knowledge integration, data mining, social media analysis, and sentiment analysis. Professor Nguyen has pioneered significant methodologies in spatial data clustering within network space, inter-sequence pattern mining, and graph neural network applications. His work bridges theoretical computer science with practical applications in intelligent information systems, demonstrating particular expertise in handling complex spatial and sequential data structures. His research has evolved from foundational pattern mining techniques to sophisticated neural network approaches for geospatial and social data analysis. The analysis of his recent publications reveals a strong focus on spatial data analysis in network environments, with significant contributions to clustering algorithms, graph neural networks, and pattern mining. His work consistently addresses efficiency challenges in data processing while expanding into emerging areas like Vietnamese language processing and topological data analysis. The research demonstrates a clear trajectory from traditional data mining techniques toward more sophisticated AI-driven approaches that incorporate spatial relationships and network topologies. Distinguished Scientist of ACM (2009) ACM Distinguished Speaker (2009-2013) IEEE Distinguished Visitor (2009-2013) Title of Professor granted by the President of Poland Professor Nguyen has supervised over 20 PhD students to completion and currently mentors several ongoing doctoral candidates. His academic leadership extends to founding two major conference series: the Asian Conference on Intelligent Information and Database Systems (ACIIDS) and the International Conference on Computational Collective Intelligence (ICCCI), which have become significant venues in their respective fields. His collaborative network spans multiple institutions, particularly with Yeungnam University as evidenced by several co-supervised PhD projects. As founder and chair of the IEEE SMC Technical Committee on Computational Collective Intelligence, he leads an international community of researchers advancing this specialized field. His departmental leadership at Wroclaw University of Science and Technology positions him at the center of applied informatics research and education in Poland, with particular emphasis on computational intelligence applications.

Ross Koppelمشاهده پروفایل

استاد مدعو

• Healthcare Information Technology
• Medication Errors
• Technology in Society
• +۲ مورد دیگر

Ross Koppel is an Adjunct Professor of Sociology with expertise in healthcare information technology, medication errors, and ethics in social research. His work explores the intersection of technology and societal impacts, focusing on data governance, clinical workflows, and human factors in health IT systems. Research Interests Context-sensitive understanding of medication errors Societal implications of clinical data sharing Ethical challenges in AI and informatics Healthcare cost analysis Scientific Awards Fellow of the American College of Medical Informatics (FACMI)

• Topology of Complex Systems
• Higher-order Networks
• Topological Neuroscience
• +۷ مورد دیگر

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, where he joined in June 2023. Previously, he held positions at CENTAI as a Principal Researcher and at IMT Lucca as a Guest Scholar, with earlier affiliations at ISI Foundation and Imperial College London. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Petri's research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems. His work in Topological Neuroscience explores brain architecture using algebraic topology, while his research in Cognitive Neuroscience focuses on neural mechanisms underlying human cognition. He is particularly known for his work on higher-order networks, using mathematical frameworks like hypergraphs and simplicial complexes to model systems with multi-way interactions. His recent publications (2023-2025) demonstrate a strong focus on higher-order network theory applied to neuroscience, with particular emphasis on topological approaches to brain connectivity, social contagion models, and the physics of complex systems. These works reveal consistent themes in understanding how multi-body interactions shape system dynamics across biological, social, and technological domains. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems, 2024) As Principal Investigator of the NPLab, Petri advises numerous PhD and postdoctoral researchers including Marilyn Gatica, Andrea Santoro, and Simone Poetto. His RUNES project, funded by the ERC Consolidator Grant, represents a significant research initiative. The lab maintains active collaborations with CENTAI, Project CETI (Cetacean Translation Initiative), and various international institutions. The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society, using statistical mechanics, algebraic topology, and innovative computational approaches. Current projects include Topological Neuroscience, Cognitive Neuroscience, Higher-order Networks, Project CETI, and RUNES.

Cynthia D. Rudinمشاهده پروفایل

استاد

• Interpretable Machine Learning
• Sparse Models
• Causal Inference
• +۷ مورد دیگر

Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.

Stefan Gösslingمشاهده پروفایل

استاد

• Tourism and climate change
• Sustainable tourism
• Mobility studies
• +۶ مورد دیگر

Stefan Gössling is a Professor and Research Coordinator specializing in sustainable tourism and climate change research with dual appointments at Lund University and Kalmar University. He currently serves as Research Coordinator at the Research Centre for Sustainable Tourism, affiliated with Vestforsk (Western Norway Research Institute), where he has been employed since 2007. Gössling earned his BSc in biology and MSc in geography from Westfälische Wilhelms-Universität, Münster, followed by a doctoral degree in Human Ecology from Lund University in 2001. After completing a postdoc at Freiburg University (2002-2003), he became Associate Professor at Lund University in 2005 and was promoted to full Professor in Human Ecology in 2008. He also holds a Professorship in Tourism Research at Kalmar University since 2009. His research focuses on tourism and climate change , sustainable tourism development , mobility studies , and low-carbon tourism systems . His fieldwork spans the Western Indian Ocean region (Mauritius, Seychelles, Zanzibar, Madagascar, Sri Lanka) and Jordan, with significant contributions to understanding tourism's environmental impacts, particularly in aviation and transport systems. Analysis of his recent publications reveals a strong trajectory toward policy-relevant research on carbon management in tourism, with increasing focus on net-zero aviation, economic leakage in digital platforms, and climate risk modeling for tourism destinations. His work bridges academic research with practical policy applications across multiple European contexts. Contributing author of IPCC AR4 Gössling has led numerous significant research projects including TourRisk (modeling climate risk for Norwegian tourism), ECOVET (effects of COVID-19 on cruise tourism in Western Norway), NTRANS (Norwegian Centre for Energy Transition Strategies), and sustainable cruise research examining social, environmental and economic effects. His influential books include Tourism and Global Environmental Change (2006), Sustainable Tourism Futures (2009), and Climate Change and Aviation (2009).

Lisa Saum-Manningمشاهده پروفایل

استاد

• Security Cooperation
• Strategic Planning
• Conflict Prevention
• +۱۶ مورد دیگر

Lisa Saum-Manning is the Associate Director of the International Security and Defense Policy Program, a Senior Political Scientist, and Professor of Policy Analysis at the RAND School of Public Policy, RAND Corporation. She is actively engaged in research that bridges defense policy, international security, and public administration. RAND Corporation – Associate Director, International Security and Defense Policy Program RAND Corporation – Senior Political Scientist RAND School of Public Policy – Professor of Policy Analysis Her educational background includes a Ph.D. in Political Science from the University of California, Los Angeles. Ph.D., Political Science – University of California, Los Angeles Lisa Saum-Manning's research focuses on strategic planning for the U.S. Armed Forces and Department of State, particularly in the areas of security cooperation, conflict prevention in fragile states, WMD nonproliferation, and unconventional warfare. She has also led field research on building sustainable security capacity in Africa, Central and South America, and disaster recovery in Puerto Rico. Her work frequently addresses diversity and inclusion in the military, homeland security policy, and U.S. foreign military sales accountability. She recently served as a senior advisor to the Assistant Secretary of State for Conflict Stabilization Operations during implementation of the Global Fragility Act. Her recent publications span topics such as Foreign Military Sales (FMS) accountability, Army readiness, disaster recovery in Puerto Rico, and diversity in military personnel. The body of her work demonstrates a consistent focus on policy-relevant research that informs national security decision-making, with an interdisciplinary approach combining political science, security studies, and public policy analysis. Scientific Awards and Recognitions: No specific awards listed in the provided text. Lisa Saum-Manning has advised on major defense and homeland security initiatives, including FEMA’s terrorism risk formula, U.S. Army personnel policy, and Coast Guard diversity strategies. She has received funding for multiple RAND studies from U.S. government agencies, including the Department of Defense and Department of Homeland Security. Her advisory roles include direct engagement with senior officials in the State Department and Congress, particularly through her testimony before the U.S. Senate Committee on Armed Services. She leads and contributes to research teams at RAND focused on international security, defense policy, and stability operations. Her projects often involve interdisciplinary collaboration, field data collection, and policy implementation analysis, particularly in complex operational environments such as post-conflict zones and disaster-affected regions.

• Business Focused Predictive Analytics
• Social Media for Business Transformation
• Management of Innovation
• +۵ مورد دیگر

Indranil Bose is a Distinguished Professor and Head of the Area of Excellence in AI, Data Science, and Business at NEOMA Business School, with a Ph.D. in Management from Purdue University. He specializes in business analytics, big data, and digital transformation, leveraging expertise from prior roles at IIM Calcutta, University of Hong Kong, University of Florida, and UT Arlington. Education: Ph.D. in Management (Purdue University, 1997) M.S. (Purdue University) M.S. (University of Iowa) B.Tech (IIT Kharagpur) Research Focus: His work spans predictive analytics, social media impact on business, innovation management, cybersecurity, fintech, and AI ethics. Key themes include consumer behavior in digital ecosystems, AI-driven decision systems, and ethical implications of emerging technologies. Publication Trends: Recent articles emphasize AI ethics, digital misinformation, and predictive modeling in finance/healthcare, reflecting a focus on societal impacts of technology. Methodologies include deep learning, sentiment analysis, and causal inference. Awards & Recognition: Winner, EFMD 2017 Case Competition Ranked 29th globally for Information Systems research productivity (2005–2014) Editorial Leadership: Senior Editor for Decision Support Systems and Pacific Asia Journal of the AIS ; Associate Editor for Information & Management , Journal of the AIS , and Communications of the AIS .

Benjamin Rechtمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Statistics
• +۵ مورد دیگر

Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).

Christine Currieمشاهده پروفایل

استاد

• Operational Research
• Simulation Optimisation
• Healthcare Management
• +۹ مورد دیگر

Professor Christine Currie is a Professor of Operational Research within the School of Mathematical Sciences at the University of Southampton. She is a Fellow of the Alan Turing Institute and previously served as Director of the Centre for Operational Research, Management Science and Information Systems (CORMSIS). Her research is primarily funded by EPSRC and spans healthcare, disaster relief, and pricing optimisation. Research Interests: Simulation Optimisation Healthcare Management Decision Making Under Uncertainty Disaster Relief Logistics Optimal Pricing and Revenue Management Her recent work focuses on real-time simulation for emergency departments, infectious disease modelling, patient flow optimisation, food relief procurement in Indonesia, and robust pricing models in transportation and leisure sectors. The trend in her publications shows a strong emphasis on applied operational research with societal impact, particularly in digital twin integration and stochastic optimisation under uncertainty. Scientific Awards: Companion of Operational Research (2024) Professor Currie actively supervises PhD students and has secured multiple research grants, primarily from EPSRC. She has led projects such as 'Dial-a-Ride', 'CREST-OR', and 'Designing a resilient food supply network for natural disasters in West Java, Indonesia'. She also collaborates with external organisations and has supervised student projects with industry partners. Leadership and Editorial Roles: Editor-in-Chief, Journal of Simulation (2015–2024) Member, Editorial Board, Royal Society Open Science (2021–present) Co-chair, OR63 National Operational Research Conference (2021) Member, Operational Research Society Research Panel She is actively involved in the operational research community and leads interdisciplinary research teams including the Operational Research group, Institute for Life Sciences, CORMSIS, and the Centre for Healthcare Analytics.

Tesary Linمشاهده پروفایل

استادیار

• Consumer Privacy
• Marketing Analytics
• Digital Policy
• +۷ مورد دیگر

Tesary Lin is the Isabel Anderson Career Development Assistant Professor of Marketing at Boston University's Questrom School of Business. She is also a Dean’s Research Scholar, a Junior Faculty Fellow at the Hariri Institute for Computing, and a Faculty Affiliate of the Technology & Policy Research Initiative. Her research centers on the interplay between consumer privacy, data sharing, and marketing strategy. Key interests include how firms adapt analytics in a privacy-first world, how choice architecture influences data consent, and the impact of regulations like COPPA on digital content. Her work integrates behavioral economics, marketing science, and public policy. Her recent publications explore consumer privacy preferences, identity fragmentation bias, and the role of dark patterns in data sharing. These works span venues such as Marketing Science and the ACM Conference on Economics and Computation, reflecting interdisciplinary engagement with computer science, policy, and behavioral research. John D.C. Little Best Paper Award Alessandro di Fiore Best Paper Award Sheth Foundation ISMS Doctoral Dissertation Award MSI Alden G. Clayton Doctoral Dissertation Award Professor Lin advises on data policy and consumer behavior, with ongoing collaborations involving regulatory bodies such as the FTC and academic institutions like MIT and Brookings. She does not currently list formal advisees. She is involved in research teams at the Hariri Institute and the Technology & Policy Research Initiative, focusing on ethical data use and digital governance.

Tim Rogersمشاهده پروفایل

استاد

• Stochastic Processes
• Random Matrix Theory
• Collective Behavior
• +۴ مورد دیگر

Professor Tim Rogers is affiliated with the University of Bath as a faculty member in the Department of Mathematical Sciences . He is actively involved in research spanning complex systems, network theory, and stochastic processes. PhD in Random Matrix Theory from King's College London (2010) His research focuses on emergent behavior in random systems , including: Collective Behavior : Crowd dynamics, lane formation, and noise-enhanced synchronization Epidemics & Networks : Spread prediction, node risk assessment, and misinformation impacts Ecology & Evolution : Trait emergence, species boundaries, and demographic noise effects Random Matrix Theory : Spectral analysis and applications to complex systems Publication trends reflect interdisciplinary work bridging Physics, Biology, and Mathematics , with a focus on network structures , stochastic modeling , and emergence phenomena . Scientific awards include: 2015 : Editor's Choice for Europhys. Lett. 109, 28005 2016 : Highlight of Journal of Physics A 2017 : Editor's Suggestion for Phys. Rev. E 92, 032708 He has supervised numerous PhD students and postdocs on projects related to stochastic dynamics , network modeling , and mathematical biology , with ongoing grants from agencies like EPSRC and The Leverhulme Trust .

Lawrence M. Weinمشاهده پروفایل

استاد

• Operations Management
• Public Health
• Homeland Security
• +۵ مورد دیگر

Lawrence M. Wein is the Jeffrey S. Skoll Professor of Management Science at Stanford University's Graduate School of Business, where he also serves as Senior Associate Dean of Academic Affairs. He holds a PhD in Operations Research from Stanford (1988) and has taught operations management at MIT (1988–2002) and Stanford since 2003. His research spans operations management, public health, and homeland security, with notable contributions to HIV therapy optimization, bioterror response logistics, and forensic science innovations. His academic roles include Senior Fellow at Stanford’s Center for International Security and Cooperation, and editorial leadership as Editor-in-Chief of Operations Research (2000–2005). Notable awards include the Kimball Medal, Lanchester Prize, and membership in the National Academy of Engineering. Research interests emphasize applying operations research to real-world challenges, such as optimizing radiation detection systems, analyzing sexual assault kit backlogs, and improving ballistic imaging techniques. His work bridges theoretical models with practical policy, impacting areas like healthcare, counterterrorism, and criminal justice. Key contributions include developing the workload regulating release policy in semiconductor manufacturing, influencing U.S. smallpox vaccination strategy, and advancing mathematical models for cancer treatment and pandemic influenza control. His recent focus includes forensic genetic genealogy and crime-solving technologies. Grants and collaborations span industries, governments, and academic institutions, reflecting interdisciplinary impact. His teaching includes courses on operations management, stochastic networks, and homeland security applications.

Mehrdad Arashpourمشاهده پروفایل

دانشیار

• Automation and Robotics
• Computer Vision
• Deep Learning
• +۷ مورد دیگر

Summary Associate Professor Mehrdad Arashpour is an internationally recognized researcher and educator in construction and civil infrastructure, focusing on automation and information technologies. He leads the ASCII Lab at Monash University's Department of Civil and Environmental Engineering. His academic roles include Head of Construction Engineering and membership in the CIB's Working Commission on Off-site Construction (W121) and Infrastructure Task Group (TG91). Education: Ph.D., RMIT University, Australia M.Sc., Grenoble University, France B.Sc., IU University, Iran Research Interests: Digital twins, computer vision, robotics, BIM integration, sustainable construction, and automation in construction processes. His work contributes to UN Sustainable Development Goals, particularly in sustainable cities and communities. Grants & Awards: Over $6M in grants from ARC, Austroads, and industry partnerships. Recognitions include Editor's Choice Paper (ASCE, 2019) and Outstanding Reviewer (Elsevier, 2016). Teaching: Courses like Risk Management in Engineering Projects and Infrastructure Research Project. Advises on PhD topics in computer vision, robotics, and BIM. Labs & Collaborations: ASCII Lab focuses on smart, sustainable solutions for construction. Collaborates with global researchers and organizations like SPARC Hub and Building 4.0 CRC.

Angelica Limمشاهده پروفایل

استادیار

• Human Robot Interaction
• Affective Computing
• Multimodal Perception and Learning
• +۶ مورد دیگر

Angelica Lim is an Assistant Professor of Professional Practice and Rajan Family Scholar in the School of Computing Science at Simon Fraser University. Her research focuses on Human Robot Interaction, Affective Computing, and Multimodal Perception with applications in healthcare and developmental robotics. She holds a PhD in Informatics from Kyoto University (2014), an M.Sc. from Kyoto University (2012), and a B.Sc. in Computing Science from SFU (2008). Her work bridges robotics and human-centered AI through projects like the ROSIE Lab, exploring emotion-aware systems, socially assistive robots, and VR programs for aging populations. Key contributions include benchmarking emotional speech recognition (BERSting), developing embodied emotion models for robots, and co-designing healthcare technologies with patient partners. Recent publications emphasize ethical AI, multimodal perception systems, and human-robot collaboration in dynamic environments. Teaching includes courses on software engineering, artificial intelligence, and introductory computer science. Her research has been applied in dementia care through VR programs, robotic companionship for older adults, and emotion-aware human-robot communication systems. Current initiatives focus on inclusive HRI design and sim2real methodologies for underrepresented data in affective computing.