Dr. Zhibao Mian is a Lecturer in the School of Computer Science at the University of Hull, UK, and previously held an Associate Professor position at Northwest Normal University. He specializes in trustworthy AI, machine learning, and intelligent maintenance systems. His research integrates AI with IoT, blockchain, and digital twins in Industry 4.0/5.0 contexts. He leads projects on predictive maintenance for offshore wind turbines and AI-driven sustainable energy solutions. Dr. Mian holds a PhD from the University of Hull and an MSc from the University of Nottingham. Research interests include AI ethics, model-based safety analysis, and RCM. He has secured grants such as the CPHC-funded study on AI in software education and oversees multiple PhD scholarships. Notable roles include Editorial Board member of the American Journal of Artificial Intelligence and Reviewer for high-impact journals/conferences like JSS and IEEE. He is a Senior Fellow of the Higher Education Academy and received the Royal Academy of Engineering's 2024 Exceptional Talent designation. Recent publications (2023-2025) focus on ordinal networks, outlier detection, Belt and Road trade analysis, and carbon emissions modeling. He actively advises PhD students on topics like UAV-based anomaly detection and predictive maintenance frameworks.
Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.
David Wentzlaff is a Professor of Electrical and Computer Engineering at Princeton University, with associated faculty roles in Computer Science and the High Meadows Environmental Institute (HMEI). He leads research in computing architecture, green computing, and sustainable system design. As Director of Undergraduate Studies, he shapes educational programs in his field. Education: Ph.D., Electrical Engineering, MIT (2012) M.S., Electrical Engineering and Computer Science, MIT (2002) B.S., Electrical Engineering, University of Illinois at Urbana-Champaign (2000) Research Focus: Future Computing Systems: Designing manycore architectures, cloud computing infrastructure, and chiplet-based systems for exascale computing. Sustainability: Developing energy-efficient hardware, recyclable computing systems, and eco-friendly decommissioning strategies. Hardware-Software Co-Design: Exploring FPGA integration, in-memory computing, and parallel processing frameworks. Advising & Grants: Advises 8 current graduate students, focusing on topics like chiplet design, neural acceleration, and sustainable computing. Recipient of NSF grants for projects like OpenPiton (open-source manycore research platform) and CAREER awards for energy-efficient architectures. Labs & Collaborations: Leads the Wentzlaff Research Group at Princeton. Develops open-source frameworks like PRGA (FPGA prototyping) and OpenPiton (manycore processor).
Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .
Steve Sprecher is a Lecturer in the Department of Computer Science at Northeastern University. His research focuses on network and web security, systems security, practical applications of machine learning in security, censorship measurement, and privacy. He holds a PhD from Northeastern University, advised by Engin Kirda, and earned his MS and BS in Computer Science from the University of Michigan, where he worked with J. Alex Halderman and Roya Ensafi on censorship, ransomware, and network measurement. His teaching experience includes serving as the Instructor of Record for Foundations of Computer Security & Privacy at Northeastern (Winter 2023), and as Head Graduate Student Instructor for the introductory computer security course at the University of Michigan (2017-2019). He has also held roles as a Teaching Assistant and Guest Lecturer in multiple security-related courses. Sprecher's publications address cutting-edge security challenges such as HTTP protocol vulnerabilities, third-party script management, and decentralized internet control mechanisms. His work bridges theoretical research with practical applications, emphasizing both technical depth and real-world impact.
Dr. Ava Hedayatipour is an Assistant Professor of Electrical Engineering at California State University at Long Beach (CSULB), where she joined in Fall 2020. She holds a Ph.D. from the University of Tennessee, Knoxville (2020), and degrees from Iran University of Science and Technology (B.S., 2012) and Shahid Rajaee Teacher Training University (M.S., 2015). Her research focuses on analog/mixed-signal circuit design, bio-implantable devices, low-power systems, and hardware security. Notable contributions include a first-of-its-kind integrated secure multimodal sensor and a flexible paper electrode for remote electrochemical experiments. Education: Ph.D., Electrical Engineering, University of Tennessee, Knoxville, 2020 M.S., Electrical Engineering, Shahid Rajaee Teacher Training University, Iran, 2015 B.S., Electrical Engineering, Iran University of Science and Technology, 2012 Research Interests: Analog and mixed-signal circuit design Biomedical devices and lab-on-chip applications Low-power, low-noise microelectronics Hardware security for IoT and biomedical sensors Flexible electrodes for wearable systems Awards: University of Tennessee Fellowship Award (2019) Outstanding Teaching Assistant Award (2018) BEST PAPER AWARD at IEEE DCAS 2025 2nd Place Winner at IEEE BIOCAS 2023 Innovation Challenge Advising & Grants: Lead CSULB LEAP program project on medical imaging braces Funded NSF project on chaotic analog security (2018–present) Collaborated with industry partners like Applied Medical and Synaptics Labs & Teams: Next Generation Wearable Lab at CSULB Focus on sensor design, hardware security, and biomedical applications
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
Dr. Pascale Biron is a Professor in the Department of Geography, Planning and Environment at Concordia University, Montreal. Her research focuses on river dynamics, stream restoration, flood modeling, and climate change impacts. She has been at Concordia since 1998 and holds professional affiliations with key organizations like the Canadian Geomorphology Research Group and the American Geophysical Union. Education: PhD from Université de Montréal and Leeds University (fluvial geomorphology) Research Interests Her work integrates fluvial geomorphology, computational fluid dynamics, and societal aspects of river management. Key themes include: River restoration for fish habitat Floodplain modeling using LiDAR Climate adaptation strategies Socio-hydrological dimensions of restoration Hydrodynamic processes at river confluences Recent Research Trends Recent publications emphasize global salmonid habitat analysis, machine learning for fluvial hazard detection, and the socio-environmental impacts of urban flooding. Her work bridges technical hydrology with policy-relevant solutions for sustainable water management. Grants & Advising Supervises over 15 graduate students (PhD/MSc) in topics ranging from flood modeling to bioengineering. Active in collaborative projects like the 'Freedom Space for Rivers' initiative and large-scale floodplain mapping. Labs & Teams Member of GRIL (Limnology Research Group) and RIISQ (Quebec Flood Network), contributing to interdisciplinary water security research.
Marco Di Renzo is a CNRS Professor (Directeur de Recherche Titulaire) at University of Paris-Saclay, affiliated with CentraleSupelec and the Signals and Systems Laboratory (L2S). He serves as Coordinator of the Communications Networks Area at the DigiCosme Laboratory of Excellence and Editor-in-Chief of IEEE Communications Letters. His academic leadership includes membership in the Ph.D. School on ICT Admission Committee at Paris-Saclay University. His educational background includes a Laurea (cum laude) and Ph.D. in Electrical Engineering from University of L'Aquila, Italy (2003, 2007), and a Habilitation à Diriger des Recherches from University Paris-Sud (2013). Laurea (cum laude), Electrical Engineering, University of L'Aquila (2003) Ph.D., Electrical Engineering, University of L'Aquila (2007) Habilitation à Diriger des Recherches, University Paris-Sud (2013) Di Renzo's research focuses on next-generation wireless communications, particularly reconfigurable intelligent surfaces (RIS), 6G technologies, and stochastic geometry modeling. His work bridges theoretical communication theory with practical implementations in cellular networks, millimeter-wave communications, and ultra-wide band systems. Recent publications demonstrate leadership in holographic metasurfaces, integrated sensing and communication (ISAC), and AI-empowered network design, establishing him as a pioneer in electromagnetic wave manipulation for future networks. His award-winning publications span RIS-aided communications, channel modeling, and security frameworks. Analysis of his recent work reveals consistent focus on three pillars: (1) fundamental electromagnetic theory for wave manipulation, (2) practical RIS implementations across frequency bands, and (3) integration with AI for network optimization. His articles frequently address industrial applications including factory automation and space-air-ground networks. Di Renzo's scientific recognition includes: IEEE Fellow (2020) and IET Fellow (2020) Highly Cited Researcher (Web of Science, 2019) SEE-IEEE Alain Glavieux Award (2017) Multiple Best Paper Awards (IEEE ICC, EURASIP) Nokia Foundation Visiting Professorship (2020) As Principal Investigator for CNRS, he coordinates multiple Horizon 2020 projects including SURFER, PathFinder, and MetaWireless. His leadership extends to serving as Project Coordinator for H2020 5Gwireless, 5Gaura, MAPNET, and REDESIGN. With over 350 publications, 17,000+ citations, and h-index of 66+, his research group maintains strong industry partnerships with Nokia and other telecommunications leaders. Di Renzo directs the Signals and Systems Laboratory (L2S) at Paris-Saclay and coordinates the DigiCosme Excellence Lab's Communications Networks Area. His team specializes in electromagnetic modeling for wireless networks and has pioneered the European Telecommunications Standards Institute (ETSI) Industry Specification Group on RIS. The group maintains active collaborations with Aalto University (Finland), University of Technology Sydney (Australia), and University of L'Aquila (Italy).
Ravi Aron is a Professor of Healthcare Strategy & Technology at the C. T. Bauer College of Business, University of Houston, and Research Director of the Healthcare Business Institute. He holds a joint appointment in the Department of Health Systems & Population Health Sciences at the Tilman J. Fertitta Family College of Medicine. He earned his Ph.D. in Management Information Systems from New York University's Stern School of Business. His research focuses on healthcare IT, emergent technologies in healthcare operations, valuation of healthcare startups, and AI applications in healthcare. He has published widely in top journals like Management Science and Information Systems Research, and his work bridges information systems, operations management, and technology strategy. Dr. Aron has extensive teaching experience at The Wharton School, Johns Hopkins Carey Business School, and NYU Stern, winning multiple teaching awards. He advises Fortune 500 firms, startups, and policymakers on technology strategy, digital transformation, and risk assessment. His executive education programs address AI, machine learning, and digital business models for global executives. Key awards include the Dean's Faculty Excellence Award (2016), multiple teaching accolades from Wharton and Johns Hopkins, and the Herman E. Kross Best Dissertation Award (1999). His current projects explore healthcare supply chains, predictive models using machine learning, and valuing technology-enabled startups. He regularly participates in global forums like the World Economic Forum, advising on healthcare innovation and technology policy.
Dr. Nilanjan Banerjee is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He leads the Mobile, Pervasive, and Sensor System Lab, focusing on embedded and distributed systems for mobile, pervasive, and sustainability-based computing. His research spans renewable energy-driven systems, health diagnostics, mobile usability, and experimental testbed design. He holds a Ph.D. in Computer Science from the University of Massachusetts (2009), an M.S. from the same institution (2007), and a B.Tech. (Hons) from the Indian Institute of Technology (2004). Dr. Banerjee's work emphasizes interdisciplinary innovation, including low-power wearable devices for health monitoring (e.g., RestEaZe), cybersecurity frameworks for embedded systems (e.g., CARE), and sensor-based solutions for environmental sustainability. His contributions address challenges in mobility, energy efficiency, and accessibility, such as the Presight sidewalk localization system for visually impaired riders and the Inviz gesture-recognition textile sensors. His recent publications (2018–2021) reflect a focus on health technology, cybersecurity, and sustainable systems. Notable trends include: Integration of machine learning with sensor data for medical applications (e.g., sleep analysis, infection detection) Development of lightweight security protocols for embedded devices Exploration of renewable energy solutions for mobile and sensor networks No scientific awards are explicitly listed in the provided text. His academic advising and grant activities are not detailed here, but his lab's active research suggests significant collaborative projects. The lab also pioneers educational strategies in mobile app development and inclusive faculty recruitment through peer education programs like STRIDE.
Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.
Dongming Xu is an Associate Professor in Business Information Systems at the University of Queensland Business School. She holds a PhD from the City University of Hong Kong in Information Systems and has established herself as a prominent researcher in the field of information systems with over 100 publications in top-tier journals and conference proceedings. Her educational background includes a PhD from City University of Hong Kong in Information Systems, though specific details about earlier degrees are not provided in the available text. Dr. Xu's research focuses on the confluence of information technology use and innovation, with particular emphasis on IT entrepreneurship, social media applications in business contexts, and business intelligence systems. Her work explores how information systems influence society and business performance, with applications spanning disaster management, eFinance, eHealth, and knowledge management. She combines theoretical model building with laboratory and field experiments, often developing prototype systems to validate her research. Her publication record demonstrates consistent high-quality output across multiple domains of information systems research, with recent work emphasizing digital disruption, platform ecosystems, social media in disasters, healthcare technology, and micro-learning applications. Her research shows a clear trajectory from foundational work on intelligent agents and decision support systems toward contemporary topics in digital transformation and platform-based innovation. Associate Editor, Information & Management Associate Editor, Journal of Electronic Commerce Research Associate Editor, Australasian Journal of Information Systems Dr. Xu has supervised numerous PhD students to completion, with research topics spanning digital disruption, IT startup development, social media in disasters, conceptual modeling, and environmental management. She has received multiple research grants, including current funding for 'Empowering Australia's Visual Arts via Creative Blockchain Opportunities' (2023-2026) and past projects on 'Smart micro learning with open education resources' (2018-2022). Her research has been supported by various agencies including the Hong Kong Government Research Grant Council, The National Natural Science Foundation of China, The University of Queensland, and City University of Hong Kong. She leads research in several key areas including IT entrepreneurship, business intelligence systems, and social media applications across multiple domains. Her work often involves developing innovative systems such as web-service-agent-based family wealth management systems, decision support systems for securities exception management, and knowledge management systems for disaster management.
Dr. Michael Gubanov is an Assistant Professor in Computer Science at Florida State University and founder of BigLab!, specializing in scalable data systems for scientific knowledge discovery. Research: Develops hybrid polystore/LLM systems for cancer research (CancerKG.ORG), COVID-19 knowledge graphs (COVIDKG.ORG), and aging studies (AgingGraph.ORG). Focuses on metadata classification, tabular embeddings, and web-scale knowledge extraction. Funding: Secured $1.8M+ from NSF, Florida Department of Health, and AWS for projects bridging data management and AI. Awards: IEEE ICDE Best Paper (2017), ACM SIGMOD Research Highlight (2018), CACM Research Highlight (2020). Elected to Sigma Xi. Education: PhD in Computer Science (University of Washington); Postdoc at MIT CSAIL.
Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.
