Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.
Dr. Wenjing Zhang is an Assistant Professor in the School of Computer Science at the University of Guelph, Canada. She holds a Ph.D. in Computer Science from the University of Guelph (2024) and was a visiting research scholar at the University of Arizona's Department of Electrical and Computer Engineering (2016–2018). Her research focuses on cybersecurity in AI/ML, including security threats to models, privacy-preserving techniques, and data privacy in generative AI. She leads projects on robust defenses against adversarial attacks, secure federated learning, and privacy-preserving prompt engineering for LLMs. Dr. Zhang’s research areas include: Security in AI/ML (e.g., poisoning, evasion, prompt injection attacks) Model Privacy (protection of internal parameters) Data Privacy (synthetic data generation, privacy-preserving prompt engineering) She has secured a five-year NSERC Discovery Grant (2025–2030) for her research on enhancing security, privacy, and fairness in generative AI. Her work has been published in top-tier venues such as NeurIPS, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. She is a recipient of the 2022 Westin Scholar Award and serves on technical committees for IEEE conferences including CNS 2025 and ICC 2025. Her team collaborates on interdisciplinary projects involving federated learning, information theory, and reinforcement learning. She actively seeks partnerships in security, privacy, and generative AI applications.
Dr. Sheryl Staub-French is a Professor and Head (Pro Tem) in the Department of Civil Engineering at the University of British Columbia (UBC), Faculty of Applied Science. She directs the BIM TOPiCS Lab, focusing on digital delivery methods for sustainable construction using BIM. Her research spans over 100 publications in BIM, VDC, and collaboration frameworks. She previously served as Associate Dean of Equity, Diversity, and Inclusion (EDI), advancing inclusive practices in engineering education. She holds a BS from Santa Clara University and MS/PhD from Stanford University. Her research interests include BIM implementation challenges, design coordination, 4D visualization, and sustainable construction innovation. She teaches courses like CIVL 300 (Construction Engineering), CIVL 426 (Virtual Design and Construction), and advanced BIM topics. Her lab collaborates with industry/government to develop BIM guidelines and tools. Notable projects include UBC’s Brock Commons Tall Wood Building and studies on IPD adoption barriers in Canada. Her work bridges academia and industry, emphasizing digital transformation and EDI. She has pioneered BIM-based methods for facility management and energy modeling, addressing gaps in information quality and handover processes. Her contributions span policy development, tool innovation, and educational reforms to enhance construction sector efficiency and inclusivity.
Pelin Bolat is an Associate Professor in the Department of Fundamental Sciences at Istanbul Technical University (ITU), College of Maritime Studies. She is actively engaged in maritime cybersecurity, risk assessment, and maritime safety research. Her work is supported by multiple BAP and EU-funded projects, with ongoing research extending into 2025. Her research interests include cybersecurity in maritime navigation systems, dynamic positioning, port state control, and GHG emissions in the maritime sector. She applies advanced methodologies such as fuzzy FUCOM, CORAS framework, and association rule mining to analyze cyber and operational risks. Her recent publications (2023–2025) reflect a strong trend in maritime cybersecurity, focusing on ECDIS, RADAR, ransomware, and cyber hygiene. These works span high-impact journals in maritime engineering and technology, demonstrating interdisciplinary engagement with computer science, safety engineering, and policy analysis. She serves as a principal investigator on several key projects, including cyber risk assessment of bridge navigation equipment and system dynamic modeling of maritime GHG emission measures. She also mentors 15 theses in progress, indicating her active role in student supervision. Her collaborative network includes researchers like Gökhan Kayişoğlu and international partners. While no formal awards are listed, her leadership in EU and BAP projects underscores her academic prominence.
Muchao Ye is an Assistant Professor in the Department of Computer Science at the University of Iowa. He earned his Ph.D. from Pennsylvania State University's College of Information Sciences and Technology in 2024 and a Bachelor of Engineering in Information Engineering from South China University of Technology. Ph.D., Information Sciences and Technology, Pennsylvania State University (2024) B.Eng., Information Engineering, South China University of Technology His research focuses on the intersection of Artificial Intelligence, Machine Learning, and AI Safety, particularly adversarial robustness in language models and vision-language models. He designs methods to enhance the security and reliability of deep learning systems for safety-critical applications like video surveillance and healthcare. Recent publications highlight adversarial robustness frameworks (e.g., UniT , PAT ), vision-language models for explainable video anomaly detection ( VERA ), and healthcare risk prediction techniques ( MedPath , MedRetriever ). His work appears in top venues such as NeurIPS, KDD, AAAI, ACL, and CVPR. Professional experience includes Applied Scientist internships at Amazon (2022–2023) and teaching roles at the University of Iowa and Pennsylvania State University. He serves as a reviewer for conferences like NeurIPS, ICML, and journals including IEEE TPAMI.
Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.
Aybars Tuncdogan is a Reader in Digital Innovation and Information Security at King’s Business School, King’s College London. He holds affiliations with the King’s AI Institute, King’s Cybersecurity Centre (Informatics), and King’s Cybersecurity Group (War Studies). A Fellow of the Higher Education Academy, he also serves on the editorial review board of Industrial Marketing Management . Education : PhD in Management, Rotterdam School of Management, Erasmus University MPhil in Business Research (Distinction), Erasmus University Bachelor’s in Business Management & Computer Science (Honors), Earlham College Research Interests : His work focuses on three pillars of digital innovation: generation (crowdsourcing, AI), marketing (digital brand personality, sales ambidexterity), and protection (information security, corporate espionage). He integrates psychology (individual differences, social identity) and computer science (machine learning/AI) frameworks. Publications Trends : Recent work addresses cybersecurity challenges in retail, AI ethics in healthcare, and interdisciplinary innovation. He frequently publishes in top-tier journals like Journal of Management and Scientific American , blending academic rigor with practitioner impact. Awards & Contributions : 2015 Best Paper Award (shared) Edited books including Oxford Handbook of Individual Differences and Strategic Renewal Teaching & Pedagogy : Develops innovative teaching methods like inquiry-based learning to foster student creativity. Taught modules on digital marketing, consumer behavior, and research methods. Labs/Teams : Contributes to cybersecurity initiatives at King’s, focusing on AI-driven defense mechanisms and organizational cyber resilience strategies.
Dr. Nemanja Stanišić is a Full Professor at Singidunum University's Faculty of Business, with a distinguished academic career spanning over 15 years. He holds a Ph.D. in Corporate Finance from Singidunum University (2010), an MBA in Finance from Lincoln University (2007), and a Bachelor's in Accounting from the University of Belgrade (2005). His expertise focuses on Corporate Finance, Banking, Audit, and Applied Statistical Analysis. His research integrates quantitative methods with economic theory, addressing topics such as audit opinion prediction using AI, tourism destination competitiveness, financial distress dynamics, and air pollution health impacts. He co-authored textbooks including Contemporary Exchange and E-business (2010) and Financial Statement Analysis (2024), and served as Editor-in-Chief of The European Journal of Applied Economics . He teaches courses from Financial Accounting to Advanced Financial Engineering at undergraduate, master's, and Ph.D. levels. The 15 most recent publications highlight his interdisciplinary approach: 7 in Finance/Audit, 5 in Tourism/Hospitality, and 3 in Environmental Health. Key trends include applying machine learning to audit quality (2023), multilevel modeling for hospitality satisfaction (2015-2019), and air pollution mortality analysis (2016). His work appears in high-impact journals like Tourism Management (IF 10.125) and Environmental Health (IF 4.986). He held administrative roles including Rector (2020-2021) and Vice President of Singidunum University. He served as Vice Dean for Student Affairs (2010-2011) and participated in TEMPUS projects for educational reform. He mentors graduate students extensively, advising 100+ bachelor's, 57 master's, and 4 doctoral theses, including international candidates. His visiting professorship at Bangkok's ICO NIDA and teaching in Austria-Singidunum joint programs reflect global engagement. Professional development includes advanced training at Utrecht University (Bayesian Modeling, 2019), Stanford (Mentoring, 2010), and NYU (Valuation, 2012). He reviews for top journals like Annals of Tourism Research and Cornell Hospitality Quarterly , with 1017 Google Scholar citations and 349 Scopus citations. Current research involves the Science Fund of Serbia's TOURCOMSERBIA project evaluating tourism competitiveness models.
Yu Sun is an assistant professor in the Department of Electrical and Computer Engineering at Johns Hopkins University with a joint appointment at the Data Science and Artificial Intelligence (DSAI) Institute. His research integrates machine learning, computer vision, optimization, and physics to advance computational imaging frameworks for reliable AI-driven imaging systems. He earned a BEng in electronics and information from Sichuan University (2015) and a PhD in computer science from Washington University in St. Louis (2022), where his dissertation received the Turner Dissertation Award. His academic journey includes a postdoctoral fellowship at Caltech's Department of Computing and Mathematical Sciences. Dr. Sun's research spans biomedical imaging, computational imaging, inverse problems, and machine learning, focusing on interpretable AI integration for next-generation imaging. His work bridges theoretical foundations with practical applications in medical and scientific imaging domains. Recent publications reveal a dominant trend in diffusion models for scientific imaging problems, including plug-and-play priors for reconstruction (NeurIPS 2024) and benchmarks for diffusion-based scientific problem-solving (ICLR 2025 Spotlight), demonstrating cross-disciplinary impact from biomedical engineering to cell biology. Key honors include: Turner Dissertation Award for doctoral contributions Rising Star Award from the Conference on Parsimony and Learning (CPAL, 2025) He serves as a consultant associate editor for the IEEE Open Journal of Signal Processing and actively participates in the IEEE Signal Processing Society’s Computational Imaging Technical Committee. His research is supported by institutional funding through the Hopkins Computational Imaging Group. The Hopkins Computational Imaging Group, which he leads, unites AI, mathematics, and data science to develop principled algorithms for imaging systems, with emphasis on biomedical applications and novel computational frameworks.
Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.
Dr. Alex S Clark is an Associate Professor in Quantum Technologies at the University of Bristol's School of Physics, where he serves as a Senior Lecturer and Royal Society University Research Fellow. He is a key member of the Quantum Engineering Technology Labs (QETLabs) and leads the Interfaces Work Package in the EPSRC Programme Grant 'Quantum Science with Ultracold Molecules (QSUM).' Additionally, he holds a Visiting Academic position at Imperial College London and serves as Honorary Secretary for the QQQ Group at the Institute of Physics. His research focuses on Solid State Quantum Nanophotonics, exploring the use of atoms, molecules, and solid state defects to develop quantum technologies. Dr. Clark's work spans quantum imaging, quantum sensing, and quantum information processing, with particular emphasis on creating on-demand photon sources, quantum memories, photonic quantum gates, and hybrid interfaces to link disparate quantum systems. His research integrates experimental and theoretical approaches across quantum photonics, nanophotonics, and quantum technology. Analysis of his recent publications reveals a strong trend toward practical quantum applications, particularly in quantum sensing and imaging using undetected light. His work demonstrates increasing focus on real-world applications including methane sensing, medical diagnostics, and environmental monitoring, while maintaining fundamental research in quantum optics and nanophotonics. The interdisciplinary nature of his research bridges physics, engineering, and materials science. Among his notable achievements is the prestigious Royal Society University Research Fellowship, recognizing his significant contributions to quantum technology research. His work has resulted in numerous publications and patents in quantum photonics and related fields. Dr. Clark leads multiple major research initiatives, including the Quantum Positioning, Navigation, and Timing Hub (2024-2029) and the Integrated Quantum Networks project. His research has secured substantial funding through EPSRC grants and other sources, supporting a vibrant research group focused on advancing quantum technologies from fundamental principles to practical applications. Within the Quantum Engineering Technology Labs (QETLabs), Dr. Clark's research group works at the intersection of quantum optics, nanophotonics, and quantum information science. His team develops novel photonic platforms for quantum applications, with particular expertise in quantum imaging with undetected photons, quantum sensing, and integrated quantum photonics.
Suradip Das is a Research Assistant Professor in the Department of Neurosurgery at the Perelman School of Medicine, University of Pennsylvania, where he serves as a Senior Research Investigator. His work bridges neural engineering and regenerative medicine to address critical challenges in nerve and muscle repair. His academic training includes: B.Tech in Biotechnology from Heritage Institute of Technology (2010) PhD in Biosciences and Bioengineering from Indian Institute of Technology Guwahati (2016) Dr. Das specializes in biomaterials development , peripheral nerve injury models , neuromuscular interface engineering , and stem cell-based regeneration . His research pioneers innervated tissue-engineered muscle constructs, demonstrating how motor neurons and endothelial cells synergistically enhance skeletal myocyte maturation. He innovates custom mechanobioreactors that apply tensile forces to guide nanofiber alignment for optimal myofiber formation, significantly advancing volumetric muscle loss treatments. Analysis of his 15 most recent publications reveals a dominant focus on neuromuscular regeneration (75% of articles), with emerging exploration of psychedelic compounds in neural repair. His work consistently integrates human iPSC-derived models , multi-cellular co-cultures , and large-animal validation to address translational gaps. Key trends include optogenetic control of motor units (2023), porcine nerve injury models (2020), and the critical role of pre-innervation in creating pro-regenerative microenvironments (2020-2022). As a core member of the Cullen Lab, Dr. Das collaborates on developing biofabricated neural microtissues for delayed nerve fusion and rapid functional recovery. His research directly informs clinical strategies for peripheral nerve repair and muscle regeneration through rigorous mechanistic studies and innovative engineering solutions.
Nuri Yazdani is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, Switzerland. Based at the Institute for Electronics (Institut für Elektronik) in Zurich, Dr. Yazdani contributes to both teaching and research in advanced materials and nanotechnology. His work spans multiple interdisciplinary areas connecting physics, chemistry, and electrical engineering, with particular emphasis on nanocrystal-based materials and their applications in electronics and optoelectronics. Dr. Yazdani's research focuses on the synthesis, characterization, and application of nanomaterials, particularly semiconductor nanocrystals and quantum dots. His work explores the fundamental physical properties of these materials, including exciton-phonon interactions, structural ordering in multicomponent systems, and charge transport mechanisms in nanocrystal assemblies. He investigates how nanoscale phenomena affect macroscopic material properties, with applications ranging from catalysis to optoelectronic devices. His approach combines experimental techniques like small-angle X-ray scattering with theoretical modeling to understand structure-property relationships in nanomaterials. Analysis of Dr. Yazdani's recent publications reveals a strong emphasis on perovskite and chalcogenide nanocrystals, with particular interest in how structural features like cation distribution, octahedral tilting, and surface chemistry affect optical and electronic properties. His work bridges fundamental physics with practical applications, spanning from quantum optics to energy conversion technologies. A recurring theme is the investigation of size-dependent phenomena and the role of phonons in determining material behavior at the nanoscale. Dr. Yazdani collaborates extensively with researchers across multiple institutions and disciplines, as evidenced by his authorship on numerous multi-investigator publications. His work appears in high-impact journals including Nature Communications, Journal of the American Chemical Society, and Nature Physics, reflecting the significance and interdisciplinary nature of his contributions to nanoscience and nanotechnology.
Eric Green is an Adjunct Assistant Professor in the Department of Civil Engineering at the University of Kentucky , affiliated with the Kentucky Transportation Center . He holds a Ph.D., M.S., and B.S. in Civil Engineering from the same institution. Ph.D., Department of Civil Engineering, University of Kentucky M.S., Department of Civil Engineering, University of Kentucky B.S., Department of Civil Engineering, University of Kentucky His research focuses on highway safety , spatial analysis (GIS) , crash modeling , and software development for traffic safety . Recent work includes text mining for secondary crash detection and GPS-based horizontal curve analysis. Publications highlight trends in crash analysis , Highway Safety Manual methodologies , and data integration for asset management . Key subfields include GIS applications, safety modeling, and automated regression techniques.
Irina Oleinikova is a Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Electric Energy, Faculty of Information Technology and Electrical Engineering. She leads the Power System Operation and Analysis research group and serves as the NTNU Smart Grid Team Leader. She is a steering committee member of the European Energy Research Alliance (EERA) Joint Programme on Smart Grids and an expert in the International Smart Grid Action Network (ISGAN) WG6. Research Interests : Power System Operation, Digital Power System Protection and Control, Grid Resilience, Energy Flexibility, Cybersecurity in Power Systems, and Hydrogen Technology Integration. Her work focuses on advancing smart grids, grid flexibility, and cybersecurity through projects like FME CINELDI, HONOR, ASAP, and ZeroKyst. Key Projects : CINELDI : Developing intelligent electricity distribution grids. HONOR : Cross-sectoral energy flexibility markets. ASAP : Next-generation system protection schemes. ZeroKyst : Hydrogen and charging infrastructure along Norway’s coast. COSPAT : Stability of AC/DC transmission grids via co-simulation. Advising & Grants : Supervises PhD students in digital protection and cybersecurity. Active in projects funded by RCN, STATNETT, and EU Horizon 2020. Leads the Power System Operation and Analysis group and collaborates with SINTEF and industry partners. Labs/Teams : NTNU Smart Grid Team and the Power System Operation research group.
