Associate Professor Binghao Li leads the MIoT & IPIN Lab at the School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney. He holds a PhD in Spatial Information Systems from UNSW and advanced degrees in Civil and Electrical/Mechanical Engineering from Tsinghua University and Beijing Jiaotong University. Expertise in indoor/outdoor positioning systems Pioneering mine IoT applications Leader in pedestrian navigation research His research spans indoor positioning technologies, satellite navigation, and mining IoT solutions. Key grant projects include: 2021 CRC-P grant ($2m) for underground mine LoRa networks 2020 ARC Research Hub ($5m) for connected sensors 2018 Digital Grid Seed Funding for indoor navigation 2015-2019 ARC Linkage grants for positioning systems Award highlights: 2019 Best Paper & Presentation Awards 2010 VC's Post-Doctoral Fellowship 2004-2005 student research awards He supervises research in indoor positioning and mine IoT, and teaches courses including ENGG1000 Engineering Design and MINE8710 Mine Slope Stability.
David Bani-Harouni is a researcher at the Chair of Computer Aided Medical Procedures at Technische Universität München (TUM). His work focuses on Medical Informatics , Artificial Intelligence , and Deep Learning , with an emphasis on Clinical Decision Support and Medical Image Analysis . Research Interests : Large Language Models (LLMs), Vision Language Models (VLMs), interpretability in deep learning, multimodal clinical decision support, and medical image analysis. Teaching : He contributes to lectures and practical courses such as Computer Aided Medical Procedures I , Medical Augmented Reality , and Deep Learning for Medical Applications . Publications : His research spans reinforcement learning for clinical decision-making, multimodal operating room datasets, toxin prediction systems (e.g., ToxNet), and graph convolutional networks for intoxication prediction. Contact : david.bani-harouni@tum.de
Natalia Sergeevna Belova is an active Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University). She has been with HSE since 2012, accumulating 13 years of scientific and teaching experience. Her academic journey began with engineering education and progressed through postgraduate studies to earning her Candidate of Technical Sciences degree. Her educational background includes: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specializing in Mathematical and Software Support for Computing Machines 2009: Postgraduate studies at the same institution 2005: Engineering degree from Moscow State Academy of Instrument Engineering and Computer Science Belova's research interests span automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work demonstrates a strong focus on practical applications of computer science, particularly in face recognition, pattern recognition, and educational methodologies for software engineering. She has made significant contributions to the fields of embedded database systems and computer vision. Her publication record shows a clear evolution from foundational work on embedded databases (2009) to advanced research in computer vision and deep learning (2015-2025). The most recent publications focus on artificial intelligence applications in transport design and affect recognition in video, demonstrating her ability to adapt to emerging technologies while maintaining expertise in her core areas. Among her notable achievements: Gratitude from HSE University leadership (2023, 2025) Multiple publication bonuses for high-impact research Recognition as Best Teacher (2016-2017) Membership in the High Professional Potential Group (HSE personnel reserve) Belova has supervised numerous bachelor's theses, guiding students through projects ranging from mobile applications to complex software systems. She has also secured significant research funding, including a Presidential Grant for young doctors of science (2017-2018) for developing pattern recognition methods. Her teaching portfolio includes courses on Group Dynamics and Communication in Software Engineering Professional Practice and Software Engineering Economics, reflecting her dual expertise in technical and soft skills development for future software engineers.
Charisse Pickron serves as Assistant Professor at the University of Minnesota's Institute of Child Development, where her research examines socio-cognitive development in infancy with emphasis on perception of social groups across gender and race dimensions. She employs multimodal methodologies including behavioral observation, electrophysiology, and eye-tracking to investigate how early experiences shape infant face processing. Her academic credentials include: BA in Psychology with minor in Race and Racial Identity Development from Mount Holyoke College (2008) MS in Developmental Psychology from University of Massachusetts Amherst (2015) PhD in Developmental Psychology from University of Massachusetts Amherst (2018) Dr. Pickron's research program centers on how infants categorize social groups and the cognitive consequences of such categorization. The Child Brain and Perception Lab investigates perceptual and socio-cognitive development through questions about environmental influences on face processing, with particular focus on neural and behavioral responses to gender and racial stimuli. Her approach integrates community engagement with scientific rigor, valuing input from students and Twin Cities residents in shaping research directions. Analysis of her 2016-2024 publications reveals consistent methodological evolution from basic face perception studies toward complex socio-cognitive questions. Key trends include increasing emphasis on stimulus validation (e.g., Diverse Face Images database), developmental timing effects across infancy, and intersectional analysis of race/gender in attention mechanisms. Her work demonstrates growing sophistication in linking behavioral observations with neural correlates. Dr. Pickron maintains an open advising policy for PhD students starting Fall 2026, envisioning a collaborative research environment that integrates undergraduate and graduate students with community partners. Her laboratory structure emphasizes mutual learning between researchers and the communities they serve, reflecting commitment to translational developmental science. The Child Brain and Perception Lab operates as a community-engaged research hub focused on understanding infant-world interactions. Dr. Pickron's leadership philosophy centers on celebrating infant knowledge acquisition through ecologically valid paradigms, with research directions co-developed through dialogue with Twin Cities stakeholders and student collaborators.
Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.
Hien Nguyen is an Associate Professor in the Department of Computer Science at the University of Wisconsin-Whitewater , where she has been employed since Fall 2005. She holds a Ph.D. in Computer Science from the University of Connecticut (2005), a Master’s degree from the University of Wisconsin-Milwaukee (1999), and a Bachelor of Science in Informatics and Applied Mathematics from Hanoi University of Technology (Vietnam). Her career spans industry experience at 3C Inc. and the United Nations Development Programme before transitioning to academia. Education: B.Sc. Informatics and Applied Mathematics, Hanoi University of Technology M.Sc. Computer Science, University of Wisconsin-Milwaukee Ph.D. Computer Science, University of Connecticut Research Interests: Hien Nguyen’s interdisciplinary research focuses on user modeling , information retrieval , human-computer interaction , collaborative filtering , and human factors . She integrates system-centered and user-centered approaches to improve information retrieval performance through formal knowledge representations in artificial intelligence. Article Trends: While her core research aligns with computer science, recent publications span medical informatics, pharmacology, public health, and environmental science, indicating interdisciplinary collaborations. Topics include genomic studies in Vietnam , transnational caregiving , AI in healthcare , and environmental monitoring systems . Grants: Notable grants include a $200,000 award (2007–2010) from IAPRA/DTO for collaborative problem-solving frameworks and a $50,000 award (2015–2016) from the Office of Naval Research for real-time inverse reinforcement learning . Professional Service: She has served on program committees for conferences like User Modeling and Personalization (2010) and FLAIRS (2008–2016). She has chaired departmental committees at UW-Whitewater and reviewed for journals such as IEEE Transactions on Systems, Man and Cybernetics and Journal of Intelligent Information Systems .
Dr. Lijing Zhu serves as an Assistant Professor of Data Science within the College of Science and Engineering at the University of Houston-Clear Lake, where she teaches foundational data science courses and conducts research in artificial intelligence. Academic Background Ph.D. in Data Science, Bowling Green State University (August 2025) Research Focus Dr. Zhu's research spans machine learning, graph-based deep learning, continual graph learning, and computer vision. Her work addresses critical challenges in knowledge representation through continual knowledge graph learning, human-object interaction detection, and graph representation learning. She develops innovative algorithms that enhance the robustness and efficiency of deep learning models for complex structured data, with particular emphasis on overcoming catastrophic forgetting in dynamic knowledge graphs. Publication Trends Her active 2024-2025 publication record in venues like ECML PKDD, CIKM, and IEEE Big Data demonstrates a cohesive research trajectory across three interconnected domains: (1) advancing graph neural network robustness against adversarial attacks, (2) developing continual learning frameworks for evolving knowledge graphs, and (3) applying multimodal deep learning to drug discovery and computer vision problems. This cross-cutting work positions her at the intersection of theoretical machine learning and practical applications. Teaching and Mentorship Dr. Zhu teaches DASC 5133 (Introduction to Data Science), DASC 5333 (Database Systems for Data Science), and DASC 5431 (Data Analytics and Machine Learning). As an early-career faculty member building her research program, she offers graduate students opportunities to contribute to high-impact publications while developing expertise in graph-based AI systems and multimodal learning.
Dr Shahana Bano is a Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University, specializing in interdisciplinary applications of Artificial Intelligence. Her research spans biomedical imaging, environmental monitoring, infrastructure analytics, and socio-technical systems through the Machine Vision Research Group and Cybersecurity Research Group. Her educational background includes a PhD in Computer Science and Engineering, M.Tech in Computer Science and Engineering, MSc in Information Systems, and BCA - all completed full-time. Her research philosophy centers on convergence, bringing together diverse data types, technologies, and disciplines to create impactful, adaptive systems that bridge academic innovation with societal relevance. Dr Bano's research interests focus on Computer Vision, Image Analysis, Machine Learning, Data Analytics, Internet of Things, Text to Speech Systems, and Social Media Threat Intelligence. Her recent work demonstrates strong application of these interests to medical diagnostics (CAR-T cell classification), environmental monitoring (geothermal reservoir modeling), infrastructure analytics (pipeline defect detection), and social systems (hate detection in football communities). Her publication record shows consistent output with 32 research outputs from 2015-2025, demonstrating increasing focus on medical and environmental applications of AI in recent years. The work spans theoretical computer vision advancements to practical implementations addressing UN Sustainable Development Goals. Associate Fellow (AFHEA) 2023 from Advance HE Dr Bano actively supervises PhD students and mentors interns on diverse projects. Current PhD supervision includes research on morphological classification of CAR-T cell images for leukemia diagnosis and acoustic emission-based pipeline defect detection. She also guides students on projects involving AR-based navigation, NDVI vegetation mapping, hate detection in football communities, multimodal sensor fusion, and airport runway object detection. Her lab resources are supported through university research groups and collaborations focused on machine vision and cybersecurity applications.
Hajar Homayouni is an Assistant Professor in the Department of Computer Science at San Diego State University's College of Sciences. She specializes in Data Science, Database Systems, Applied Machine Learning, and Big Data Quality Assurance. Her work bridges theoretical research with practical applications in healthcare informatics, data privacy, and educational technology. Ph.D. in Computer Science from Colorado State University (2020) MS in Computer Science (Colorado State University, 2017) MS in Artificial Intelligence (Alzahra University of Tehran, 2013) BS in Software Engineering (University of Kashan, 2008) Her research focuses on developing advanced methodologies for anomaly detection, synthetic data generation, and data quality assurance, with applications in public health (particularly COVID-19 analysis), cardiovascular research, and medical imaging. She has pioneered approaches using transformer architectures and federated learning frameworks for improved data analysis and privacy preservation. Recent publications highlight her work in machine learning interpretability , multimodal medical data generation , and time-series anomaly detection . Her research has been recognized with multiple awards including the Harvard PingPong Generative AI Tutor Award and Teach Access Fellowship . Harvard PingPong Generative AI Tutor Award ($1,000) Teach Access Fellowship ($4,000) SDSU Inclusive Excellence Faculty Fellowship STARS Computing Corps Fellowship ($1,000) Grace Hopper Celebration Scholarship She supervises numerous graduate and undergraduate students in research projects covering synthetic data generation, visual phishing detection, and medical data analysis. Her grants portfolio includes over $1 million in federal and private funding, with notable awards from NSF, NIH, and Microsoft Research.
José Rouillard is a Lecturer-Researcher in Computer Science (section 27) at Université de Lille, affiliated with the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille) where he works in the Brain-Computer Interface (BCI) research team. His academic position combines teaching responsibilities with active research in human-computer interaction, particularly focusing on novel interface technologies and assistive applications. Dr. Rouillard's primary research interests center around Brain-Computer Interfaces with particular expertise in Steady-State Somatosensory-Evoked Potentials (SSSEP). His work explores multimodal interaction techniques, virtual reality integration with BCI systems, and applications for individuals with motor disabilities such as Duchenne muscular dystrophy. Recent publications (2023-2024) demonstrate continued innovation in BCI technology, including Wizard of Oz studies on user perception, advanced SSSEP recording techniques with cEEGrid systems, and multimodal cobot interaction frameworks using MQTT protocol. His research bridges theoretical neuroscience with practical applications for assistive technologies. As an educator, Dr. Rouillard has created one of the most comprehensive App Inventor 2 teaching resources available, with 76 detailed projects covering the full spectrum of mobile application development. His course materials progress from basic applications to advanced implementations involving Bluetooth communication with Arduino, Firebase database usage, and AI APIs including OpenAI's ChatGPT and DALL-E. His teaching spans multiple academic years, with documented student projects from 2013 through 2023 across various Master's programs including MMD IAE, MIAGE, and e-Services. Supervised PhD thesis: "Hybrid brain-machine interface to overcome disability caused by Duchenne muscular dystrophy" (Alban Dupres, 2016) Supervised PhD thesis: "Filtrage somesthésique pour des interfaces cerveau-ordinateur utilisant des stimulations vibro-tactiles" (Jimmy Petit, 2022) Dr. Rouillard maintains a strong educational presence through his extensive online resources, including YouTube video tutorials, NextCloud file sharing for course materials, and detailed project guides. His student projects demonstrate practical applications of mobile development across diverse domains including health monitoring, gaming, social networking, and educational tools. The breadth of his educational impact is evident in the hundreds of student applications documented from 2013-2023, showcasing his commitment to practical, hands-on learning in computer science education.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
Prof. Dr. Jens Allmer is a full Professor of Medical Informatics and Bioinformatics at Ruhr West University of Applied Sciences (HRW) in Mülheim an der Ruhr, Germany. He previously held academic positions as Cluster Leader at Wageningen University and Research in the Netherlands (2017-2018) and as Assistant and Associate Professor at Izmir Institute of Technology in Turkey (2008-2016). His academic journey began with a PhD in Biology with distinction from the University of Münster in 2006. Prof. Allmer's research spans multiple omics disciplines with a primary focus on microRNA regulation and pathogen-host interactions. He employs machine learning techniques to explore these complex biological systems. His work has evolved from foundational bioinformatics methods to sophisticated integrative analyses, particularly in proteogenomics and computational miRNomics. He has made significant contributions to understanding microRNA detection algorithms, proteogenomic peptide mapping, and the development of comprehensive frameworks for omics data analysis. His recent publications demonstrate a strong trend toward integrative approaches that combine multiple data types, with increasing emphasis on machine learning applications in bioinformatics. The research spans from fundamental database development for noncoding RNAs to clinical applications in disease mechanisms, particularly in HIV infection and cancer pathways. His work shows consistent progression from method development to application in biological and medical contexts. Outstanding Young Scientist in Bioinformatics by the Turkish Academy of Sciences (2010) EMBO Short Term Fellowship Award (2013) DAAD Research Stays for University Academics and Scientists (2013, declined) Outstanding Young Researcher, Turkish Academy of Science (2010) Prof. Allmer has advised numerous doctoral and master's students throughout his career, primarily during his tenure at Izmir Institute of Technology. He has secured substantial research funding, with over 650,000 euros received for his projects. His teaching portfolio includes courses in medical informatics, bioinformatics, data mining, machine learning, and database systems across multiple institutions in Germany, Turkey, and through ERASMUS programs. He maintains active collaborations with researchers across Europe and continues to contribute significantly to both theoretical and applied aspects of bioinformatics.
