Dr. Ben Swift is a Senior Lecturer at the School of Cybernetics, ANU, specializing in AI, computational art, and cybernetics. He leads the Cybernetic Studio, an interdisciplinary collective exploring cybernetic systems through hardware/software/people collaborations. As a livecoding artist, he performs globally and co-founded the ANU Laptop Ensemble. His research spans generative AI, open-source tools like Extempore, and UX design. Education: PhD in Computer Science (ANU) Projects: Australia's Digital Economy (2022), The Augmented Web (2019) Research focuses on AI creativity, biofeedback interfaces, and computational music. His work bridges technical innovation with artistic expression, evident in projects like TSPNet and adversarial camera systems. Key contributions include Extempore’s development and studies in live coding disruption. Awards unspecified but recognized internationally for interdisciplinary impact.
Sara Wade is a Lecturer in Statistics and Machine Learning at the University of Edinburgh , within the School of Mathematics . Her research focuses on Bayesian statistics, machine learning, and their applications in health sciences, particularly in dementia diagnosis and predictive modeling. She holds a PhD from the University of Milan and has held academic positions at the University of Cambridge and University of Warwick before joining Edinburgh. She teaches a popular Machine Learning and Python course for Master’s and final-year undergraduate students, attracting nearly 200 enrollments annually. Her work integrates Bayesian methods with modern machine learning, emphasizing interdisciplinary applications such as scalar-on-image regression and biomarker analysis. Notable contributions include developing hierarchical Dirichlet processes for clustering and uncertainty quantification in RNA velocity studies. She secured a Royal Society of Edinburgh grant for her dementia research project, which aims to improve early diagnosis through statistical modeling. Education: PhD in Statistics, University of Milan Bachelor’s in Mathematics, University of Maryland Wade advocates for diversity in STEM, actively participating in the Women in Machine Learning community. Her research bridges statistical rigor and computational tools, fostering collaborations across academia and healthcare sectors.
Dr. Pradip Sharma is an Associate Professor of Cybersecurity & AI at the University of Aberdeen, UK, within the School of Natural and Computing Sciences, Department of Computing Science. He is a globally recognized academic and researcher with expertise in Cybersecurity, Artificial Intelligence, Blockchain, and Edge Computing. His research interests span multiple domains including Cybersecurity, Blockchain, Edge Computing, Software-defined Networking, and IoT Security. Dr. Sharma's work focuses on developing innovative solutions for security challenges in emerging technologies, with particular emphasis on privacy-aware AI systems, secure data sharing frameworks, and intelligent network security mechanisms. His interdisciplinary approach bridges theoretical foundations with practical implementations across healthcare, smart mobility, and consumer electronics domains. Senior Fellowship Advance HE (SFHEA) IEEE Senior Member (SMIEEE) Dr. Sharma actively supervises doctoral researchers and is accepting new PhD students in Computing Science. His funded research portfolio exceeds £1M from sources including EPSRC, Innovate UK, and international agencies. Current projects include 'Secure, Privacy-aware, and Trusted Data Share in Smart Mobility' (EPSRC, £200K), 'ZECURE Data Exchange Platform' (Innovate UK, £236K), and 'Quantum-resistant Cybersecurity' (Royal Embassy of Saudi Arabia, £73K). He also serves as an editor for leading journals and is a regular keynote speaker at international conferences.
Chris Bryan is an Assistant Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He leads the Sonoran Visualization Laboratory (SVL @ ASU), focusing on data visualization, human-computer interaction, and advanced interfaces for data science. His research includes explainable AI, augmented/virtual reality, and privacy-preserving visualization techniques. Educations: Ph.D. Computer Science, University of California, Davis (2018) B.S. Computer Science, University of Arkansas (2008) Research Interests: Bryan’s work spans data visualization, human-computer interaction, explainable AI, and immersive visualization. He develops tools for collaborative analysis, privacy-aware systems, and visual analytics for complex data. Current projects involve VR/AR interfaces, bias reduction in NLP tasks, and educational visualization tools. Recent Achievements: Recipient of the 2024 and 2023 Top Five Percent Faculty Award at ASU’s Ira A. Fulton Schools of Engineering. NSF grants for privacy-preserving visualization (SaTC #2224066) and visualization education (IUSE #2216452). Multiple publications in top venues like IEEE VIS, CHI, and EuroVis, including work on differential privacy, mind wandering in visualization, and LLM prompt exploration. Advising & Grants: Advises Ph.D., MS, and undergraduate students on visualization and HCI research. Collaborates with institutions like Los Alamos National Laboratory, Phoenix Children’s Hospital, and Nankai University. Lab focuses on mentoring and preparing students for academic and industry roles in visualization and AI. Labs & Teams: Leads the SVL @ ASU, which uses advanced hardware (HTC Vive, HoloLens 2) and tools like D3.js, React, and LaTeX. The lab emphasizes interdisciplinary projects with domain experts in medicine, engineering, and security.
Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.
Raju Vatsavai is an Associate Professor in the Department of Computer Science at North Carolina State University, affiliated with the Center for Geospatial Analytics. He joined NC State in 2014 as part of the Chancellor’s Faculty Excellence Program cluster hire in Geospatial Analytics. Education: PhD and MS in Computer Science from University of Minnesota Prior Roles: Lead Data Scientist at Oak Ridge National Lab, roles at University of Minnesota, IBM Research, AT&T Labs, and C-DAC (India) His research in geospatial analytics spans big data management , spatiotemporal data mining , deep learning for remote sensing , and high-performance computing , with applications in national security, climate change, and crop monitoring. Recent work includes deep learning frameworks for cloud imputation , multi-sensor satellite data harmonization , and transfer learning applications in crop classification . He has been a leading investigator on grants from the National Geospatial-Intelligence Agency, Department of Energy, and Department of Homeland Security. Labs: Associate Director of the Center for Geospatial Analytics Expertise: Spatial computing, Earth observation, nuclear proliferation detection via remote sensing
Dr. Ali Kashani is a Senior Lecturer at the University of New South Wales (UNSW) within the School of Civil and Environmental Engineering. His research focuses on sustainable and low-carbon concrete materials, robot-aided construction (particularly 3D printing), and Circular Economy-aligned applications. Leadership in cementitious materials innovation Expertise in 3D printing for construction Advocate for waste valorisation and carbon capture Dr. Kashani has secured approximately $7 million in research funding and holds a patent in lightweight concrete foam. His work spans 70+ publications with 9,000+ citations, including media coverage in the Sydney Morning Herald and The Fifth Estate. He actively contributes to professional organizations such as MECLA, RILEM, and ASTM. Recent research trends include AI and optimization algorithms for sustainable concrete mix design, chloride diffusion modeling, and 3D printing performance analysis. His publications often address waste material integration, durability assessment, and eco-friendly construction practices. Scientific Awards: National and NSW Awards for 'Excellence in Concrete' (Technology and Innovation) from the Concrete Institute of Australia Churchill Fellowship for Digital Construction and 3D Printing sponsored by AVJennings Dr. Kashani serves as Co-Chair of the cement and concrete working group at MECLA and contributes to RILEM and ASTM committees. His email is ali.kashani@unsw.edu.au , and his office is located in the Civil Engineering Building (H20), Level 2, Room CE204, UNSW.
Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.
Louis Collins is a Professor in the Department of Biomedical Engineering and Department of Neurology and Neurosurgery at McGill University, with associate membership at the Center for Intelligent Machines. His work focuses on advanced medical imaging techniques for neurological applications. Key Expertise: Non-linear image registration, model-based segmentation, neuroimaging, MRI analysis Applications: Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, schizophrenia Methodology: Development of computer vision algorithms for image-guided neurosurgery (IGNS), automated atlasing, and biomarker quantification Collins' research combines computational neuroanatomy with clinical translation, particularly in: Quantifying brain atrophy and anatomical variability across populations Optimizing MRI templates for improved diagnostic accuracy Developing tools like SEEGAtlas for surgical electrode classification Exploring neurophysiological fingerprints of neurodegenerative diseases His lab (NIST-Lab) actively pursues CIHR-funded projects on ultrasound-based image-guided neurosurgery and machine learning applications in clinical trials.
Fernando Corinto is a Research Fellow at the Department of Electronics and Telecommunications (DET) , Polytechnic University of Turin , and a member of the SmartData@PoliTO Big Data and Data Science Laboratory. He holds a European Doctorate in Electronics and Communications Engineering (2005) and was a Marie Curie Fellow (2004) at University College Dublin, focusing on cardiac fibrillation modeling and chaotic systems. Education : Laurea (2001) and Ph.D. (2005) in Electronics and Communications Engineering from Politecnico di Torino His research spans nonlinear dynamical systems , memristor devices , and complex network modeling , with over 50 publications. Key projects include RECOMMEND (2024–2027) and COSMO (2020–2024), where he served as Scientific Director . His recent work involves memristor-based neuromorphic systems and nonlinear circuit applications in biomedical and industrial contexts. He supervises PhD students Rosanna Cavazzana and Davide Rossetti and teaches Nonlinear Systems for Engineering (Mathematical Engineering) and Memristor-based Neuromorphic Systems (Electrical Engineering). His scientific contributions include the Flux-Charge Analysis Method and Bifurcations without Parameters in memristor circuits. He holds a national/international patent for skin ulcer classification algorithms and has led commercial research projects in biomedical and packaging systems.
Craig Jones is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering. He is affiliated with the Malone Center for Engineering in Healthcare and contributes to the Precision Medicine Analytics Platform's Imaging and Data Science Subcommittees. BSc in Computer Science and Mathematics from Simon Fraser University MSc in Medical Biophysics from the University of Western Ontario PhD in Physics from the University of British Columbia His research focuses on applying artificial intelligence and neural networks to medical image processing, particularly for MRI, CT, optical coherence tomography (OCT), and ultrasound datasets. Key areas include 2D/3D image processing, anomaly detection, segmentation, and uncertainty quantification, with clinical applications in neurosurgery, ophthalmology, and oncology. Projects span robotic imaging, neuroendoscopic guidance, and cancer boundary detection. Recent publications highlight advancements in vision-language models for 3D medical imaging, automated segmentation of venous malformations, and AI-guided neurosurgical tools. Articles emphasize multimodal data fusion, self-supervised learning, and federated learning for rare cancer analytics. He received a $310,000 Department of Defense grant in 2022 to develop AI-guided treatments for venous malformations. His work bridges clinical imaging domains and computer vision as a member of the Radiology AI Lab (RAIL), a collaborative effort across Johns Hopkins Hospital, the Whiting School of Engineering, and the Applied Physics Laboratory.
Richard Futrell is an Associate Professor at the University of California, Irvine (UCI), affiliated with the Department of Language Science. He leads the Language Processing Group, focusing on computational models of human and machine language processing. His work bridges information theory, Bayesian cognitive modeling, and natural language processing (NLP) interpretability. University of California, Irvine Department of Language Science Language Processing Group leader His research examines how linguistic structures emerge from cognitive and communicative pressures. Key areas include dependency locality, surprisal theory in sentence processing, and efficiency-driven language evolution. He investigates how memory constraints, predictability, and information density shape syntactic and morphological patterns across languages. Recent publications analyze code-switching efficiency, syntactic priming, ERP component modeling, and agent-based language contact simulations. His work frequently employs Bayesian modeling, neural network analysis, and cross-linguistic corpora to uncover universal principles in language processing. ACL Best Paper Award (2024) Best Paper Award for Computational Modeling of Language (2023) Marr Prize for Best Student Paper (2017) He has developed datasets like SPACER for error repair analysis and contributed to phonotactic learning frameworks. His collaborations span cognitive scientists, computational linguists, and neuroscientists, advancing understanding of language production, comprehension, and structural optimization.
Wenzhong Li is a Professor at the School of Computer Science, Nanjing University, where he leads research at the State Key Laboratory for Novel Software and Technology. His academic career spans over 15 years with significant contributions to AI-empowered distributed systems, big data mining, and networking applications. He teaches Computer Networks and guides graduate students in Distributed Computing Research. Professor Li's research focuses on cutting-edge areas including AI-Empowered Distributed Systems and Applications (MultiModal Large Models, Embodied Intelligence, Edge Computing), Big Data Mining (Time Series Analysis, Graph Computing, Social Networks Analysis), and AI-Based Distributed Resource Scheduling. His work bridges theoretical foundations with practical implementations in real-world systems. His recent publications demonstrate a strong trend toward integrating deep learning with graph theory and time series analysis, with applications in human activity recognition, network optimization, and multimodal systems. The research spans multiple disciplines including artificial intelligence, computer vision, networking, and data mining, with a particular emphasis on practical implementations for real-world problems. Best Paper Runner Up at KSEM 2023 for 'Learning-based Dichotomy Graph Sketch for Summarizing Graph Streams with High Accuracy' Best Paper Award at APNet 2018 for 'Toward Effective and Fair RDMA Resource Sharing' Professor Li has advised numerous PhD and Master's students who have gone on to prominent positions at institutions like Nanjing University, Huawei, Alibaba, Microsoft, and various international universities. His research is supported by substantial grants from the National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, National Power Grid, and other major funding bodies, totaling multiple multi-year projects with significant budgets. He leads the AINet Group and is affiliated with the Sino-German Institute of Social Computing and MobileCloud research initiatives. His DISLAB provides the organizational framework for his research team, which includes dozens of graduate students and collaborators working on cutting-edge problems in AI, networking, and distributed systems.
Fabio Zanini is an Associate Professor at the University of New South Wales (UNSW) , leading a research group focused on computational biology , single-cell approaches , and transcriptomic analysis across diseases like severe dengue , neonatal lung disease , cancer , and marine biology . He previously conducted postdoctoral research at Stanford University (2016-2019) and earned a PhD in Bioinformatics from the Max Planck Institute for Developmental Biology and the University of Tuebingen (2015). Current Affiliation: Group leader, UNSW Previous Training: Postdoc (Stanford), PhD (Max Planck/University of Tuebingen) His research spans single-cell RNA sequencing , computational virology , developmental cell biology , and bioinformatics tool development , with recent work on: Severe dengue progression (viral-host interactions, immune signatures) Lung development (endothelial cell diversity, hyperoxia-induced injury) Cancer genomics (mutant HSC clones, AZA therapy response) Marine biology (plankton transcriptomics, evolutionary analysis) Bioinformatics (HTSeq 2.0, northstar algorithm) Recent scientific awards include grants from the Chan Zuckerberg Initiative ($270,000), NIH R01 (multiple), ARC Discovery Grant , and NHMRC Ideas Grant . Notable contributions include: Northstar - Cell classification algorithm SpectralSeq - Hyperspectral-transcriptomic integration Tabula Muris - Mouse aging atlas He has supervised research into hematopoietic stem cell regulation , lung vascular development , and autophagy in viral infections , with collaborations across Stanford , University of Sydney , and Harvard .
Prof. Kwang W. Oh is a tenured Professor at the Department of Electrical Engineering and Department of Biomedical Engineering within the School of Engineering and Applied Sciences at University at Buffalo (SUNY at Buffalo) . He serves as the Director of Graduate Studies in Electrical Engineering and Director of SMALL (Sensors and MicroActuators Learning Lab) . His academic journey includes PhD and MS in Electrical and Computer Engineering from University of Cincinnati (2001, 1997) and BS in Physics from Chonbuk National University (1995). Prof. Oh's research expertise lies at the intersection of microfluidics , BioMEMS , and lab-on-a-chip technologies. His lab has pioneered vacuum-driven microfluidic devices , PDMS-based systems , droplet manipulation , and chemical-free fabrication techniques . His work enables point-of-care diagnostics , single cell analysis , and wearable medical sensors , with significant contributions to sample-to-answer nanosystems and world-to-chip interfacing . The scientific awards section highlights his excellence in teaching and research: SUNY Chancellor's Award for Excellence in Teaching (2020) Meyerson Award for Undergraduate Teaching (2019) Qualcomm Faculty Award (2019) Senior Teacher of the Year (2017) Royal Society of Chemistry's Emerging Investigators (2013) Samsung Electronics' CEO Honor (2003) His lab has produced numerous PhD and MS students including Dr. Anyang Wang (2020), Dr. Nikhila Nyayapathi (2020), Mr. Liam Christie (2021), and Dr. Domin Koh (2019). As a conference chair , he has organized symposia at NanoTech (2012-2026) and served as editorial board member for Sensors , Micromachines , and Biomedical Engineering Letters .
