Matthew Stephenson is a Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence applications for games. He leads the Data for Decisions initiative within the Factory of the Future Transdisciplinary Hub, focusing on AI-powered scenario generation for smart digital twins. Additionally, he is a member of IRL CROSSING, an international lab studying human-autonomous agent teaming dynamics. PhD in Computer Science (Australian National University, 2019) B.Sc.(Hons) in Computer Science (University of Canterbury, 2015) His research applies AI, Machine Learning, and Data Science to game domains, including intelligent agent development for physics-based environments, procedural content generation, and game analytics. He also investigates deceptive behaviors in multi-agent systems and leverages games as testbeds for real-world AI solutions. Recent publications focus on large language models for game benchmarking, physical reasoning challenges, and evolutionary game generation. Scientific awards include an honourable mention at Foundations of Digital Games (FDG'18). He supervises students in procedural generation, game AI, and physics-based task creation, with teaching roles in computational intelligence and neural networks courses.
Leid Zejnilovic is an Assistant Professor at Nova School of Business and Economics (Nova SBE), where he co-founded the Data Science Knowledge Center and serves as Academic Director, and co-founded the Open and User Innovation Knowledge Center as Scientific Deputy Director. He also co-founded the Patient Innovation platform, enabling patients and caregivers to share self-made healthcare solutions. With a double PhD from Carnegie Mellon University and Católica-Lisbon School of Business and Economics, his career spans over 20 years of international consulting, academic entrepreneurship, and teaching at institutions like Imperial College Business School and Ludwig Boltzmann Institute. PhD in Strategy, Entrepreneurship and Technological Change (Carnegie Mellon University / Catholic University of Portugal, 2014) Master in Engineering and Public Policy (Carnegie Mellon University, 2012) Master in Information Technology (Dzemal Bijedic University, 2007) Bachelor in Telecommunications (University of Sarajevo, 2002) His research focuses on Technology and Innovation Management, Human-Computer Interaction, and data-driven solutions across healthcare, tourism, and education. He has published extensively in journals like California Management Review , PLoS ONE , and Marine Policy , with recent work analyzing big data in tourism, machine learning for oral health, and pandemic impacts on fisheries. As an Associate Editor for Data & Policy Journal , his contributions bridge academic research and real-world applications. Co-founding the Data Science for Social Good Foundation and leading over 100 talks in industry and academia, Zejnilovic's career emphasizes translating innovation into social and economic impact through platforms, policy, and education.
Jindal Shah is a Professor and holds the Anadarko Petroleum Chair in Chemical Engineering at Oklahoma State University, where he also serves as the Graduate Program Director. He is affiliated with the Department of Chemical Engineering within the College of Engineering at Oklahoma State University. Dr. Shah received his educational training from prestigious institutions worldwide. He earned his Ph.D. in Chemical Engineering from the University of Notre Dame in 2005, followed by an M.S. in Environmental Engineering from the University of Cincinnati in 1999, and completed his undergraduate education with a B.Tech. in Chemical Engineering from the Indian Institute of Technology (IIT) Bombay in 1996. Dr. Shah's research focuses on the application of molecular simulation methodologies to understand molecular-level interactions that give rise to macroscopic phenomena. His primary research interests include Monte Carlo and Molecular Dynamics Simulations, Phase Equilibria, Ionic liquids, and Dye-sensitized solar cells. A significant portion of his work centers on designing novel biodegradable ionic liquids with properties suitable for chemical processes, with applications in next-generation batteries and carbon capture. He also investigates molecular-level interactions responsible for device efficiency in dye-sensitized solar cells to rationally design novel dye molecules. Additionally, Dr. Shah employs data science and machine learning techniques to correlate properties of ionic liquids and generate new molecules with desired properties. An analysis of Dr. Shah's recent publications reveals a strong focus on ionic liquids and their applications in energy storage and carbon capture technologies. His work consistently bridges fundamental molecular-level understanding with practical applications, particularly in developing electrolytes for batteries and CO2 capture systems. A notable trend is the integration of machine learning techniques with traditional molecular simulation methods to accelerate materials discovery and optimization. His research demonstrates a progression from fundamental molecular simulations toward applied technologies with significant environmental impact, particularly in climate action (SDG 13) and affordable clean energy (SDG 7). Dr. Shah has secured substantial research funding from multiple prestigious sources including the National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, and industry partners. His funded projects include 'Collaborative Research: Cyber Training-Implementation, Medium, Establishing Sustainable Ecosystem for Computational Molecular Science Training & Education' (NSF), 'Ionic Liquids for Direct Air Capture of CO2 using Electric-Field-Mediated Moisture Gradient Process' (DOE), and 'CAREER: Computation-Enabled Rational Design of Cytochrome P450 for Ionic Liquid Biodegradation' (NSF). These grants support his research in computational molecular science, CO2 capture technologies, and the development of biodegradable ionic liquids. As an educator, Dr. Shah has been actively involved in teaching graduate courses including Principles of Chemical Engineering Thermodynamics, Doctoral Thesis supervision, and specialized courses such as Machine Learning for Chemical Processes and Introduction to Chemical Process Analytics. His teaching philosophy integrates cutting-edge research with educational practice, preparing students for the computational challenges of modern chemical engineering. He has also mentored numerous doctoral students through their dissertation research, contributing to the development of the next generation of chemical engineers and computational scientists.
Dr. Jake O'Brien is a Senior Research Fellow at the Queensland Alliance for Environmental Health Sciences (QAEHS) within the University of Queensland. He holds an NHMRC Emerging Leadership Fellowship and serves as Chair of the EMCR@UQ Committee. His work focuses on wastewater-based epidemiology, antimicrobial resistance, and environmental chemical exposure assessment. O'Brien co-leads the National Wastewater Drug Monitoring Program and has contributed to understanding pharmaceutical impacts on wastewater systems. His research spans analytical chemistry, environmental toxicology, and public health, with particular emphasis on antimicrobial resistance gene mobility and plastic pollution in air/water matrices. Key research areas : Wastewater-based epidemiology, antimicrobial resistance surveillance, plastic pollution, pharmaceutical fate in environment Techniques : High-resolution mass spectrometry, non-target screening, in-sewer stability analysis O'Brien's recent publications examine antidepressant correction factors, tobacco product monitoring, and antimicrobial resistance gene dynamics. He has advised over 15 PhD students on topics including microplastics in biosolids, SARS-CoV-2 wastewater tracking, and novel psychoactive substance detection. Scientific Awards NHMRC Emerging Leadership Fellowship His team's work impacts national drug policy evaluation, environmental health monitoring, and wastewater treatment regulations.
Christopher G. Healey is the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics and a Professor in the Department of Computer Science at North Carolina State University. His research spans visualization, data analytics, text analytics, sentiment analysis, machine learning, cognitive psychology, computer graphics, and social media analytics. He has graduated 15 Ph.D. and 26 master's students and secured over $6 million in research funding from agencies including the National Science Foundation, Department of Defense, National Security Agency, Army Research Office, and various industry partners. He has published over 100 peer-reviewed articles and is a senior member of both IEEE and ACM, as well as a member of the NC State Academy of Outstanding Teachers. His research focuses on developing visualization techniques that leverage visual perception to support rapid, accurate, and effective analysis of large, complex datasets. More recently, he has been investigating machine learning for natural language processing and text analytics. His work includes projects on visualizing election results, sentiment estimation for social media, and wildfire narratives using large-scale social media data. His publications demonstrate a strong trend toward integrating machine learning with visualization, particularly for text analytics and social media analysis. He has made significant contributions to visualizing deep neural networks, cyber situation awareness, and pandemic response analytics, showing how visualization can enhance understanding of complex systems and large datasets across multiple domains. IBM Faculty Award (2007, 2008, 2010, 2011, 2012) Senior member, Association of Computing Machinery (ACM) (2007) Senior member, Institute of Electrical and Electronics Engineers (IEEE) (2007) NC State Academy of Outstanding Teachers inductee (2003) National Science Foundation Faculty Early CAREER Award (2001) He has successfully mentored numerous graduate students and secured significant research funding across multiple projects. His work with the Laboratory for Analytic Sciences, National Science Foundation, and Department of Defense demonstrates strong industry and government partnerships. His recent projects focus on visualizing social media narratives, deep neural networks for text understanding, and predictive analytics for large document collections. He leads research groups focused on visualization and analytics, working with teams to develop innovative approaches for data exploration and analysis. His current work continues to push the boundaries of how visualization can be used to enhance understanding of complex data across domains including public health, cybersecurity, and social media analysis.
Inseok Hwang is the Paul Stanley Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics. He earned his Ph.D. from Stanford University, specializing in multiple-vehicle control systems. His research focuses on hybrid systems, air traffic control, unmanned systems, and cybersecurity of cyber-physical systems. He leads the Flight Dynamics and Control/Hybrid Systems Laboratory and has received numerous awards, including the NSF CAREER Award and AIAA Associate Fellow designation. His work spans theoretical advancements in control theory and practical applications in aerospace systems. He has over 150 peer-reviewed publications and actively collaborates with industry and government agencies like NASA and the FAA. Education: B.S. (Seoul National University, 1992), M.S. (KAIST, 1994), Ph.D. (Stanford, 2004). Professional memberships include AIAA and IEEE. Research Interests: Hybrid systems analysis, air traffic surveillance and control, fault detection and isolation, spacecraft control, and cybersecurity for autonomous systems. His lab develops algorithms for safe and efficient operation of networked systems, including UAS traffic management and resilient control protocols against cyberattacks. Awards: NSF CAREER (2008), AIAA Associate Fellow (2012), University Faculty Scholar (2017), C.T. Sun Award (2019), multiple Seed for Success Awards (2020–2024), and Paul Stanley Professorship (2024). Grants and Collaborations: Active projects funded by NSF, NASA, FAA, and industry partners. Focus areas include resilient navigation, anomaly detection in air traffic systems, and cyberattack mitigation for autonomous vehicles.
Hua Cai is the Thomas and Jane Schmidt Rising Star Associate Professor at Purdue University's Edwardson School of Industrial Engineering with a joint appointment in Environmental & Ecological Engineering. She holds a PhD in Environmental Engineering & Natural Resources from the University of Michigan, an MS in Environmental Engineering from Penn State, and a BS from Tsinghua University. Her research integrates operations research and production systems to address sustainability challenges, with focus areas including: Environmental implications of emerging technologies Urban sustainability modeling and infrastructure resilience Industrial ecology and complex adaptive systems Sustainable transportation and mobility systems Recent publications demonstrate strong focus on sustainable transportation systems, with extensive analysis of shared mobility patterns (bike/e-scooter systems), autonomous vehicle impacts, and microgrid resilience. Her work consistently applies advanced computational methods including reinforcement learning, agent-based modeling, and spatiotemporal analysis to urban sustainability challenges. Dr. Cai has received recognition including the Thomas and Jane Schmidt Rising Star Professorship for her contributions to sustainable systems engineering. She leads research on renewable energy integration in transportation infrastructure and advises projects on climate-resilient urban systems.
Kevin Chenchuan Chang is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the FORWARD Data Lab and the Data and Information Systems Laboratories. His research focuses on bridging structured and unstructured data through natural language processing, data mining, machine learning, and information retrieval, with applications in web search, social media analytics, and knowledge acquisition. He co-founded Cazoodle and developed GrantForward.com, a funding discovery platform used by leading institutions globally. Education: Ph.D. in Electrical Engineering from Stanford University (2001), B.S. from National Taiwan University. Professional roles include service on program committees for SIGMOD, VLDB, KDD, and NeurIPS, as well as editorial roles for PVLDB, TKDE, and the Encyclopedia of Database Systems. His awards include the ICDE 10-Year Test of Time Award (2022), NSF CAREER Award (2002), and multiple UIUC teaching excellence recognitions. He teaches courses such as CS 411 (Database Systems), CS 598 KCC (Understanding LLMs), and CS 511 (Advanced Data Management). Research contributions span graph algorithms (e.g., Geom-GCN, SimRank), social network analysis (ROSE), and NLP (DEER, Open Relation Modeling). The FORWARD Lab emphasizes real-world impact through systems like GrantForward and tools for analyzing large-scale data.
Paulo Blikstein is an Associate Professor of Communication, Media, and Learning Technologies Design at Teachers College, Columbia University. He holds affiliations with the Mathematics, Science & Technology department and the Communication, Media, and Learning Technologies Design program. His expertise spans curriculum design, digital innovation, science education, and educational technology. Dr. Blikstein earned a Ph.D. in Learning Sciences from Northwestern University (2009), M.Sc. in Media Arts & Sciences from MIT Media Lab (2002), and degrees in Engineering from the University of São Paulo (Brazil). His research focuses on leveraging technology to enhance learning through computational modeling, maker education, and tangible interfaces. He leads the Transformative Learning Technologies Lab and the FabLearn Program, which develop innovative tools like MoDa and PlayData, integrating computational thinking with real-world science experiments. His work emphasizes equitable access to technology-driven education, particularly in the Global South. Recent projects include deploying cloud labs for biology education, analyzing disinformation dynamics via agent-based models, and exploring how social media influences political radicalization. He critiques commercial education technology discourse through a critical pedagogy lens, advocating for culturally responsive, hands-on learning. Blikstein’s research bridges theory and practice, addressing systemic challenges in science education through participatory design with teachers and communities. His labs create sustainable educational technologies, such as DIY liquid handling robots and haptic feedback systems, to democratize STEM access. He also investigates computational identity formation in K-12 students and the role of making in fostering gender equity in STEM.
Prof. Zena Moore is a renowned academic and clinician serving as Professor and Head of the School of Nursing & Midwifery at RCSI, University of Medicine and Health Sciences. She holds adjunct professorships at Curtin University (Australia), Griffith University (Australia), Cardiff University (UK), Ghent University (Belgium), and Fakeeh College for Medical Sciences (Saudi Arabia). Her research focuses on wound healing, pressure ulcer prevention, and nursing education, with over 300 publications. She leads the Skin Wounds and Trauma (SWaT) Research Centre and chairs multiple international bodies including the European Pressure Ulcer Advisory Panel. Education: PhD in Wound Healing (RCSI), MSc in Leadership in Health Education (RCSI), MSc in Wound Healing (University of Wales), FFNMRCSI, and Diplomas in Management and Nursing. Research Interests: Wound pathophysiology, pressure ulcer risk assessment, technologies for early detection, and healthcare equity. Awards: 2022 Lifetime Achievement Award from World Union of Wound Healing Societies. Her work spans over 50 funded projects, including grants from Science Foundation Ireland and the National Health and Medical Research Council. She has supervised numerous research projects on topics like pressure ulcer prevention algorithms and eHealth interventions.
Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.
Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.
Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.
Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.
Christopher Piech is an Assistant Professor (Teaching) in the Department of Computer Science at Stanford University, with a courtesy appointment in the Graduate School of Education. He serves as a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and is affiliated with the Symbolic Systems Program. Current courses: AI for Social Good (CS 21SI), Introduction to Probability for Computer Scientists (CS 109), Researching Presenting and Publishing Work in AI & Education (CS 220/EDUC 481) Advises 11 Master's students and co-advises 3 Doctoral students His research focuses on computational education, leveraging artificial intelligence to enhance learning analytics, student collaboration detection, and knowledge tracing in programming education. Publications span ACM Technical Symposium on Computer Science Education (SIGCSE) and NeurIPS conferences. Key article trends include: (1) AI-driven educational tools for code analysis, (2) collaboration monitoring in large classes, and (3) probabilistic models for student learning trajectories.
