Daiwei (David) Zhang, PhD, is an Assistant Professor (tenure-track) in the Department of Biostatistics at the University of North Carolina at Chapel Hill School of Medicine, with a joint appointment in the Department of Genetics. His research focuses on developing AI frameworks for analyzing high-dimensional biomedical data, particularly in spatial omics, computational pathology, and medical imaging. Education: MS (Biostatistics) and PhD (Biostatistics and Scientific Computing) from the University of Michigan. Postdoctoral Training: University of Pennsylvania. Research interests include applying machine learning to address biomedical challenges such as tumor heterogeneity, immune interactions, and tissue architecture. His work spans computational methods for spatial transcriptomics, proteomics, and histology integration. Recent publications emphasize spatial multi-omics analysis of cancer ecosystems, tertiary lymphoid structures, and metabolic coordination. These studies leverage advanced machine learning algorithms and interdisciplinary approaches to advance precision medicine. No scientific awards are explicitly mentioned, but his work reflects significant contributions to biomedical AI research. Grants and advising details are not provided in the text.
Margaret E. Roberts is a Professor in the Department of Political Science at the University of California, San Diego. She co-directs the China Data Lab at the 21st Century China Center and serves as an affiliate at the UC Institute on Global Conflict and Cooperation. Her academic appointments reflect her interdisciplinary approach combining political science, statistics, and computational methods. University of California, San Diego, Department of Political Science (Current) Co-director, China Data Lab at the 21st Century China Center Affiliate, UC Institute on Global Conflict and Cooperation Roberts earned her PhD in Government from Harvard University (2014), MS in Statistics from Stanford University (2009), and BA in International Relations and Economics from Stanford University (2009). Her educational background bridges political science, statistics, and computational methods, forming the foundation for her interdisciplinary research approach. Professor Roberts' research focuses on the intersection of political methodology and the politics of information, with specific expertise in automated content analysis and the politics of censorship and propaganda in China. Her work employs innovative methods including social media analysis, online experiments, and large-scale text analysis to understand how censorship and propaganda influence information access and political beliefs. She has made significant contributions to text-as-data methodologies, developing tools like the Structural Topic Model (stm) R package that have become widely used in social science research. Roberts' research portfolio demonstrates consistent focus on authoritarian information control, particularly in China, while expanding into broader applications of text analysis in political science. Her publications span top journals in political science, computer science, and interdisciplinary fields, reflecting the cross-disciplinary nature of her work. Goldsmith Book Award Best Book Award in the Human Rights Section Best Book Award in Information Technology and Politics Section Best Book Award of the last decade in the Political Communication Section of the American Political Science Association Chancellor's Associates Endowed Chair at UCSD Foreign Affairs Best Books of 2018 Professor Roberts has secured significant research funding supporting her work on Chinese censorship, propaganda, and text analysis methodologies. Her research has practical applications for understanding digital authoritarianism, content moderation, and the development of computational tools for social science research. She has mentored numerous students and collaborators, contributing to the next generation of scholars working at the intersection of political science and computational methods. Roberts also leads the China Data Lab, which serves as a hub for research on Chinese politics and society using digital methods.
Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.
Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.
Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.
Rida Khatoun is a Professor in Cybersecurity at the Computer Sciences and Networks (Infres) Department at Télécom Paris. He holds a M.Sc. in Computer Engineering and a Ph.D. from the University of Technology of Troyes (UTT), France, awarded in 2004 and 2008, respectively. His research focuses on cybersecurity in networks, including cloud computing security, IoT security, vehicular networks security, intrusion detection systems, and blockchain technology. He has taught at Telecom Paris since 2014, Shanghai Jiao Tong University (SJTU) since 2016, and other institutions in China and France since 2005. His work spans theoretical frameworks and practical solutions for network security challenges. Key research areas include DDoS attack detection, vehicular communication security, and cryptographic protocols. He leads the Cybersecurity and Cryptography (C²) research team and is affiliated with the Laboratoire Traitement et Communication de l'Information (LTCI). His contributions include developing secure routing protocols (e.g., ASROP), statistical trust systems in wireless networks, and blockchain-based authentication for IoT. He has authored over 115 publications, including peer-reviewed articles and conference proceedings, with recent work addressing vehicular platooning security, TLS handshake optimization for C-ITS, and machine learning for cyberbullying detection. Rida’s academic activities include teaching courses on network security protocols, wireless network fundamentals, and QoS mechanisms. He collaborates internationally, contributing to smart city cybersecurity architectures and 6G programmable communications. His research emphasizes practical cybersecurity solutions for emerging technologies, ensuring robust protection against evolving threats in vehicular, IoT, and cloud environments.
Carey Rappaport is a Professor of Electrical and Computer Engineering at Northeastern University. He serves as deputy director of the U.S. Department of Homeland Security’s ALERT Center (Awareness and Localization of Explosive-Related Threats) and associate director of the Gordon-CenSSIS Center (Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems). His research focuses on microwave and millimeter-wave sensing technologies for homeland security and biomedical applications, including tunnel detection, airport security systems, and breast cancer diagnostics. His work on explosives detection involves standoff and portal-based millimeter-wave radar, X-ray backscatter, IR and Terahertz spectroscopy for trace detection, and electronic sensing of hidden triggers. He has emphasized balancing technological effectiveness with privacy, health, and cost considerations in security systems. His projects include developing airport scanners with improved imaging and analyzing challenges in securing "soft targets" like public events. Rappaport's research has been highlighted in media outlets such as Boston Herald, The Economist, and Northeastern University news. He has discussed technical limitations of ground-penetrating radar in tunnel detection and policy implications for international airport security measures, including the 2011 Moscow airport bombing and 2023 Gaza Metro tunnel network analysis.
Teruko Mitamura is a prominent researcher at Carnegie Mellon University with over three decades of contributions to natural language processing, computational linguistics, and artificial intelligence. Her work spans from foundational research in event representation to advanced applications in multimodal systems and question answering. Her research interests focus on event detection and understanding, question answering systems, information retrieval, and multimodal processing. She has made significant contributions to event coreference resolution, timeline construction, and cross-document event analysis, developing methodologies that have become standard in the field. Her work often bridges theoretical advances with practical applications, particularly in complex information environments requiring deep semantic understanding. Natural Language Processing : Specializing in event extraction, coreference resolution, and narrative understanding with over 179 publications Question Answering Systems : Developing advanced techniques for complex question answering, particularly through NTCIR QA Lab and PoliInfo tasks Multimodal Processing : Integrating textual, visual, and temporal information for richer understanding in systems like ProMQA Evaluation Methodologies : Creating robust frameworks for assessing NLP systems through TAC KBP Event Tracks Her recent publication trends show a strong focus on leveraging large language models for event understanding, multimodal question answering, and timeline construction. She has expanded her research into specialized domains including patent analysis and novelty examination, demonstrating the breadth of her research impact across academic and practical applications. Active participant in major NLP conferences including ACL, EMNLP, NAACL, and AAAI with consistent publications Long-standing collaborator with researchers at CMU's Language Technologies Institute including Eduard H. Hovy and Eric Nyberg Contributor to shared tasks that have shaped research directions in event processing and question answering Organizer of multiple NTCIR QA Lab tasks focused on political information question answering Dr. Mitamura has mentored numerous researchers who have gone on to make their own contributions to the field, as evidenced by her extensive co-authorship network and the progression of her former students and collaborators into faculty and research positions. Her work continues to evolve with the field while maintaining her focus on deep semantic understanding of events and narratives.
Carina Mallard is Professor of Experimental Perinatal Brain Injury Research and Pro-Vice-Chancellor at the University of Gothenburg since July 2023, where she oversees research infrastructure and chairs the Research Board. She previously served as Deputy Vice-Chancellor (2021–2023) and Head of Core Facilities (2018–2021). Her academic career began with a Physiology degree from Lund University and a PhD in Pediatrics at the University of Auckland (1995), followed by a professorship appointment in 2006. Her research focuses on perinatal brain injury , particularly in premature infants , investigating neuroinflammatory mechanisms maternal dietary interventions neurovascular unit dynamics microglia activation long-term consequences of maternal obesity . Recent publications highlight her work on neonatal rodent models , transcriptomic profiling , and anti-inflammatory therapies . Key themes include RNA degradation , mitochondrial protection , and immune-neurovascular interactions . She has supervised 20 doctoral students since 2004 and collaborates internationally with institutions like King's College London. Contact details include two institutional emails and physical addresses across Gothenburg's biomedical campuses. Her leadership roles span research strategy, doctoral education, and EU-level scientific initiatives.
Jianghai Hu is a Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering within the College of Engineering. He holds a BE from Xi'an Jiaotong University (1994), MS and MA from the University of California, Berkeley (1999-2000), and a PhD in Electrical Engineering from UC Berkeley (2003). His research focuses on control systems, optimization theory, multi-agent systems, hybrid systems, and energy-efficient building management. Key areas include automatic controls, sensor networks, and signal processing. Research Interests: Hybrid systems and multi-agent coordination Optimal control and optimization Applications in energy-efficient buildings and autonomous systems Stochastic control and game theory Recent publications highlight contributions to zeroth-order learning in games, robust control for autonomous vehicles, and distributed optimization algorithms. His work bridges theoretical foundations with practical applications in robotics, energy systems, and networked control. Jianghai Hu advises numerous graduate students and collaborates on projects involving building control systems and distributed algorithms. His research has been supported through interdisciplinary initiatives at Purdue and industry partnerships.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.
Dr. Binbin Xie is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, where she was a recipient of the prestigious Google PhD Fellowship in Mobile Computing. Her research focuses on wireless sensing, mobile health, cyber-physical systems, smart IoT, and wireless networking. Dr. Xie’s academic journey includes a tenure-track position at UTA since September 2024. She has received notable awards such as the Emerging Rockstar (IEEE Pervasive Computing, 2025), Rising STARs Award (UT System, 2024), and multiple scholarships including the Krithi Ramamritham Computer Science Scholarship (2023). Her work spans innovative applications in wireless sensing, leveraging LoRa, mmWave radar, and RFID technologies for real-world challenges like in-vehicle sensing and indoor localization. Her recent publications highlight advancements in LoRa sensing coexistence with communication, mmWave radar human sensing using secondary reflections, and combating interference in multi-target localization. She actively contributes to the academic community through service roles, including Technical Program Committee memberships for MobiSys 2025 and EWSN 2025, and as a reviewer for top-tier conferences like IMWUT/UbiComp and journals such as ACM Transactions on Sensor Networks. Dr. Xie’s teaching includes courses like CSE 4321 (Software Testing & Maintenance). Her research and educational efforts aim to bridge theoretical innovations with practical implementations in wireless and IoT systems.
Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.
Mingchen Gao is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He serves as Program Director for the Engineering Sciences (Artificial Intelligence) MS Program and is affiliated with the Institute for Artificial Intelligence and Data Science. Previously, he was a Postdoctoral Fellow at the NIH Clinical Center's Radiology and Imaging Science Department (2014–2017). His research focuses on medical imaging informatics, computer vision, and machine learning applications in healthcare. Notable projects include NSF-funded work on continual learning and federated domain adaptation. He teaches advanced courses like CSE674 (Advanced Machine Learning) and CSE703 (Deep Learning for Medical Imaging). Dr. Gao earned his Ph.D. in Computer Science from Rutgers University (2014), advised by Dimitris N. Metaxas, and a B.S. from Southeast University, China (2007). His lab develops AI systems for medical diagnosis, with recent work on robust neural networks and federated learning frameworks. His team has produced impactful algorithms for segmentation, classification, and domain adaptation in imaging tasks. Current research includes NSF CAREER Award (2023–2028) for deployable medical diagnosis systems and collaborations on drug discovery and toxicity prediction. He advises four PhD students and has authored over 60 peer-reviewed publications in top venues like NeurIPS, CVPR, and MICCAI.
