Eric Poe Xing is a Professor at Carnegie Mellon University's School of Computer Science, holding joint affiliations with the Machine Learning Department, Language Technology Institute, and Computer Science Department. He also serves as President of the Mohamed bin Zayed University of Artificial Intelligence. His research focuses on machine learning methodology, statistical systems, and large-scale computational architectures, with recent work on foundation models for biology (AIDO), world/agent models (PAN), and open-source LLM initiatives (LLM360). He advises numerous students and postdocs in areas like AI, NLP, and computational biology. He teaches graduate courses in Machine Learning and Probabilistic Graphical Models, and actively contributes to academic leadership roles, including ICML program chairs and editorial boards. Research interests span automated reasoning, AI ethics, and scalable computing. His lab, SAILING, develops cutting-edge models for vision-language tasks, bioinformatics, and multi-modal learning. Notable projects include LLM360's open-source AGI efforts and innovations in distributed ML systems. His work emphasizes interdisciplinary applications, from healthcare decision-support (PetuumMed) to climate modeling (ClimSatDiff).
Dong Jin Song is a full Professor at the National University of Singapore's School of Computing, Department of Computer Science. He joined NUS in 1998 and was promoted to Professor in 2016 after serving as Associate Professor (2005) and Assistant Professor. He has held various leadership roles including Deputy Head of CS Department (2023-2024), NUS Senate Member (2020-current), and Assistant Dean (Graduate Office, SoC). PhD, University of Queensland, Australia (1993-1995) BInfTech with First Class Honours, University of Queensland, Australia (1989-1992) - Major in Software Engineering Professor Dong's research spans formal methods, safety and security systems, probabilistic reasoning, sports analytics, and trusted machine learning. He is best known for co-founding the PAT verification system which has attracted thousands of registered users from over 150 countries and won the 20-year ICFEM Most Influential System Award in 2018. He also co-founded 'Silas: Trusted Machine Learning' and the Dependable Intelligence company. His work bridges formal verification with practical applications in security, AI, and even sports analytics where he developed Markov Decision Process models for tennis strategy analysis. His recent publications show a strong trend toward integrating formal methods with modern AI systems, particularly focusing on trustworthy AI, LLM verification, and security applications. The research spans multiple high-impact venues including ICML, NeurIPS, IEEE Transactions, and top security conferences like USENIX Security, reflecting his interdisciplinary approach that combines formal verification with machine learning, security, and practical applications. Professor Dong has received numerous honors including the ACM SIGSOFT Distinguished Paper Award for ICSE 2020, the 20-Year ICFEM Most Influential System Award (2018), and being named a Fellow of the Institute of Engineers Australia (2018). His awards reflect both theoretical contributions to formal methods and practical impact on software engineering. ACM SIGSOFT Distinguished Paper Award for ICSE 2020 NUS Research Recognition Award (2020) Fellow of Institute of Engineers Australia (2018) 20-Year ICFEM Most Influential System Award (2018) Best Paper Award at ICECCS (2015 and 2012) Professor Dong has successfully supervised 33 PhD students, many of whom have become tenured faculty members at leading universities worldwide including The University of Auckland, Aston University, Singapore Management University, and Monash University. His students have gone on to successful careers in both academia and industry at organizations like Google, Apple, HP Research Lab, and IBM. He has served on the editorial boards of prestigious journals including ACM Transactions on Software Engineering and Methodology and has been active in numerous conference organizing committees. Through his research group and commercial ventures (Dependable Intelligence), Professor Dong has built a strong team focused on formal verification, trusted AI systems, and practical applications of model checking. His work has evolved from foundational formal methods research to cutting-edge applications in AI safety and security, maintaining a consistent thread of rigorous verification throughout his career.
Christopher Metzler is an Assistant Professor in the Department of Computer Science at the University of Maryland (UMD), with appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and a courtesy appointment in the Electrical and Computer Engineering Department. He leads the UMD Intelligent Sensing Laboratory, focusing on computational imaging, machine learning, and wireless communications. His research develops novel systems and algorithms for imaging through scattering media, multimodal sensor fusion, and self-supervised AI techniques. Education: PhD in Electrical and Computer Engineering (Rice University, 2019), MSEE (2014), BSEE (2013). Postdoctoral Fellowship: Stanford Computational Imaging Lab (2020). Awards: AFOSR Young Investigator Program (2022), NSF CAREER (2023), ARO Early Career Award (2024), and multiple fellowships (NSF GRFP, NDSEG). Research interests include computational imaging, machine learning, statistical signal processing, and AI-driven sensor fusion. His work addresses challenges in non-line-of-sight imaging, turbulence mitigation, and hardware-aware algorithms. He advises 10 PhD students and collaborates on projects funded by the Air Force, NSF, and other agencies. Key contributions include neural wavefront shaping, adversarial sensing frameworks, and high-resolution non-line-of-sight imaging. His lab develops open-source software and datasets, such as the Transmission Matrix Dataset and learned compressive sensing tools.
Amitabh Varshney is the Dean of the College of Computer, Mathematical, and Natural Sciences and Professor of Computer Science at the University of Maryland, College Park. He previously directed the UMD Institute for Advanced Computer Studies (2010–2018) and served as interim Vice President for Research (2016–2017 and 2021). His research focuses on virtual/augmented reality (VR/AR), scientific visualization, molecular graphics, and high-performance computing. Collaborations include NVIDIA, Honda, IBM, and the University of Maryland, Baltimore (UMB). Education: B.Tech. (IIT Delhi, 1989), M.S. and Ph.D. (UNC Chapel Hill, 1991 and 1994). Research highlights include molecular surface algorithms, GPU computing, and immersive technologies for healthcare and education. Awards include the NSF CAREER Award (1995), IEEE Visualization Technical Achievement Award (2004), and IEEE Fellow (2010). He leads the NVIDIA CUDA Center of Excellence and co-founded the Maryland Blended Reality Center. Recent work explores nanophotonics for AR/VR displays, VR medical training, and bias detection in AI systems. His interdisciplinary projects address challenges in personalized medicine, pain management, and implicit bias training through extended reality (XR). Key Projects: Augmentarium (immersive infrastructure), CHIB (healthcare bioinformatics), Immersive Media Design Program. Grants: NSF, NIH, industry partnerships. Awards: NSF CAREER, IEEE Technical Achievement Award, IEEE Fellow.
Haizhao Yang is an Associate Professor of Mathematics and Computer Science at the University of Maryland College Park (UMCP). He holds affiliate appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Applied Mathematics and Scientific Computation (AMSC) program. Previously, he served as an Assistant Professor at Purdue University and the National University of Singapore, and as a Visiting Assistant Professor at Duke University (2015–2017). His education includes a B.Sc. from Shanghai Jiao Tong University (2010), M.Sc. from The University of Texas at Austin (2012), and Ph.D. from Stanford University (2015). Yang's research focuses on machine learning theory, scientific computing, and applied mathematics. Key areas include AI for scientific discovery, high-performance computing, and algorithm development for differential equations. His work bridges mathematical rigor with practical applications, such as developing neural network-based solvers for high-dimensional PDEs and quantum computing integration for uncertainty quantification. He leads a dynamic research group with over 30 students and postdocs across PhD, master's, and undergraduate levels. Notable achievements include the NSF CAREER Award (2020), ONR Young Investigator Award (2022), and DARPA Young Faculty Award (2024). His laboratory collaborates with institutions like Lawrence Berkeley National Lab and Duke University, emphasizing interdisciplinary projects in data science and computational physics. Recent publications highlight innovations in operator learning for PDEs, quantum algorithm integration, and adversarial reinforcement learning. His group actively explores AI-driven approaches to overcome computational bottlenecks in scientific simulations, with techniques like the Finite Expression Method and OptimAI framework for automated problem-solving.
Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on developing trustworthy machine learning models for medical AI and robotics applications, emphasizing uncertainty estimation, computer vision, and explainable AI. He holds a PhD from Heriot-Watt University (2019) and a Master of Science in Autonomous Systems from Bonn-Rhein-Sieg University (2014). His work contributes to UN Sustainable Development Goals related to quality education and industry innovation. Notable achievements include Best Reviewer awards at leading machine learning conferences and highlighted contributions to uncertainty quantification research. He teaches courses such as 'Introduction to Machine Learning' and 'Deep Learning' at the university's AI programs. Recent research explores adaptive prompt tuning for few-shot learning, Bayesian neural networks for uncertainty modeling, and neuromorphic approaches to robotics. His collaborations include the German Research Center for Artificial Intelligence and European summer schools on AI. Key datasets include the Japanese Uncertain Scenes Dataset.
Chun-Hung Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University (MSU). He previously held positions at the University of Michigan, National Chiao Tung University, and Qualcomm Inc. His research focuses on machine learning theory, stochastic control, data science, wireless communication, and cyber-physical systems security. Prof. Liu has received prestigious awards including the Air Force Research Lab Faculty Fellowship (2023), Taiwan’s Young Scholar Award (2015), and an IEEE Globecom Best Paper Award (2014). Education: Ph.D., Electrical and Computer Engineering, University of Texas at Austin M.S., Mechanical Engineering, MIT M.S., Electrical Engineering, National Taiwan University B.S., Mechanical Engineering, National Taiwan University Research Interests: Dr. Liu’s work spans theoretical and applied domains, including deep learning topology analysis, edge computing optimization, federated learning architectures, and underwater acoustic communication systems. He explores practical solutions for energy-efficient computing, cyber-physical system security, and next-generation wireless networks (e.g., 5G/6G, RIS-enabled systems). Publications: His recent work emphasizes interdisciplinary approaches, with articles addressing topics like RIS-assisted MIMO systems, federated learning over UAV networks, and solar-powered edge computing. These studies highlight innovations in both algorithm design and system-level performance evaluation. Awards: Air Force Research Lab Faculty Fellowship (2023) Taiwan’s Young Scholar Award (2015) IEEE Globecom Best Paper Award (2014) Advising & Grants: While specific grant details are not provided, his publications indicate sustained research activity supported by military and academic collaborations. His lab focuses on experimental platforms for federated learning and wireless mesh networks, as evidenced by a mini-PC-based testbed described in 2022 work. Labs/Teams: Leads a research group at MSU investigating edge computing, federated learning, and reconfigurable intelligent surface technologies. Collaborates with industry partners (e.g., Qualcomm) and international institutions on communication systems and AI integration.
Gabriel Ghinita is an Associate Professor in the Department of Computer Science at the University of Massachusetts at Boston. He holds a Ph.D. in Computer Science from the National University of Singapore (2008) and a B.S. from the 'Politehnica' University of Bucharest (2003). His research focuses on databases, information security, privacy, and spatio-temporal data management. During the 2018/19 academic year, he was a Visiting Associate Professor at the University of Southern California. Key research interests include privacy-preserving sharing of location data, secure data provenance, and privacy-aware network measurement. He teaches courses such as Database Management Systems and IT Network Security. Professional activities include serving on program committees for top venues like SIGMOD, ICDE, and ACM GIS, and as a PC Chair for CODASPY 2016. His work emphasizes balancing utility and privacy in data-driven systems, with contributions to differential privacy, secure spatial queries, and IoT security.
Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).
Raju Rangaswami is an Eminent Scholar Chaired Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), where he directs the Systems Research Laboratory. His academic roles include serving as Associate Professor (2009-2019), Assistant Professor (2004-2009), and a visiting role at IIT Bombay (2015). He holds a Ph.D. in Computer Science from the University of California, Santa Barbara, an M.S. from the same institution, and a B.S. in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur. His research focuses on operating systems , storage systems , virtualization , real-time systems , and computer security , with applications in cloud computing, distributed systems, and mobile computing. Key contributions include work on caching strategies, persistent memory, and energy-efficient storage architectures. Rangaswami has received prestigious awards such as the NSF CAREER Award (2008-2013), DOE ECPI Award (2006-2010), and multiple NetApp Faculty Fellowships. He actively contributes to the field through roles like Program Chair for the USENIX File and Storage Technologies Conference, editorial work for the Springer Journal of Distributed and Parallel Databases , and NSF panel service. His research spans over 50 publications in top venues like FAST, USENIX ATC, and IEEE conferences, focusing on topics like cache replacement policies, storage tiering, and secure data management. Current projects explore AI-driven caching systems and energy-efficient hybrid memory architectures.
Tianyi Chen is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where he has been since August 2019. He specializes in theoretical and algorithmic aspects of bilevel optimization, multi-objective learning, and their applications in AI systems, including large language models (LLMs) and next-generation wireless communications. Education: Ph.D., Electrical and Computer Engineering, University of Minnesota, Twin Cities (2019) M.S., Electrical and Computer Engineering, University of Minnesota, Twin Cities (2016) B.S., Communication Science and Engineering, Fudan University, China (2014) His research focuses on advancing optimization frameworks for AI, particularly in meta-learning, LLM fine-tuning, and safety-aware AI systems. He has pioneered work on bilevel optimization for diffusion models and federated learning, addressing challenges in safety-capability trade-offs and algorithmic efficiency. Awards: IEEE Signal Processing Society Best PhD Dissertation Award (2020) NSF CAREER Award (2021) Amazon Research Award (2022) Best Student Paper Awards at NeurIPS Federated Learning Workshop (2020), ICASSP (2021), and IEEE Young Author Best Paper Award (2024) Chen’s work bridges theoretical foundations and practical applications, with contributions to AI safety, wireless systems, and scalable machine learning algorithms. His research has been recognized for its impact on both academic and industrial domains.
John Mitchell is a Professor and Graduate Program Director in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. His research focuses on optimization theory and applications, including integer programming, conic optimization, and complementarity constraints. He holds an additional affiliation with the Department of Industrial and Systems Engineering. Key research interests include sparse/low-rank optimization with applications in compressed sensing, genome-wide association studies, and financial optimization. His work extends to practical domains like disaster management (resource allocation, humanitarian logistics) and resilient supply chain design. Mitchell's methods address both theoretical challenges (e.g., exact formulations) and real-world problems such as network interdiction and infrastructure restoration. Publications from 2015-2025 highlight interdisciplinary applications: from ridesharing algorithms and trafficking network disruption to community detection in graphs and neuromorphic computing optimization. His work bridges mathematical rigor with societal impact in areas like emergency evacuation modeling and fair division approaches for humanitarian logistics. His research emphasizes algorithm development (e.g., branch-and-cut methods) and convex reformulations for nonconvex problems. Current projects explore quantitative resilience metrics for multi-echelon supply chains and ground-truth community detection via modularity optimization.
Ali Ebnenasir is an Associate Professor of Computer Science at Michigan Technological University, specializing in software engineering, formal methods, and fault-tolerant distributed systems. He holds a B.E. (1994) from the University of Isfahan, M.E. (1998) from Iran University of Science and Technology, and Ph.D. (2005) from Michigan State University. His doctoral work was nominated for the ACM Doctoral Dissertation Award. He worked as a postdoctoral researcher at Michigan State University’s Software Engineering and Network Systems Lab (2005–2006). Research focuses on formal verification of distributed systems, quantum computing algorithms, and fault-tolerant embedded systems. Notable areas include automated analysis of fault tolerance, self-stabilizing protocols, and specification languages for safety-critical systems. He has published extensively in venues like IEEE TDSC, ACM TOSEM, and ICDCS. Educations: B.E. in Computer Engineering, University of Isfahan, Iran (1994) M.E. in Software Engineering, Iran University of Science and Technology (1998) Ph.D. in Computer Science, Michigan State University (2005) His work bridges theoretical foundations with practical implementations, addressing challenges in distributed computing and quantum algorithm optimization. Awards include an IEEE Travel Grant and Michigan State University fellowships. Current research explores quantum circuit distribution and formal methods for legal system modeling. Grants/Awards: AF: Small: Framework for Algorithmic Design of Self-Stabilizing Protocols (2011) Teaching emphasizes software engineering principles and parallel computing. Active in advising graduate students on distributed systems and formal verification methodologies. His lab focuses on GPU-based algorithm acceleration and fault-tolerant system design.
Enrique Barbieri is a Professor in the Department of Engineering Technology at the University of Houston's College of Technology. He holds a Ph.D. in Electrical Engineering from The Ohio State University. His academic career includes roles as department chair and associate dean, with leadership in research and education across Tulane University, the University of Houston, and the University of North Texas. Barbieri specializes in Systems and Control, focusing on applications in robotics, manufacturing, and biomedical systems. His work spans over 90 publications and $4M in grant funding, emphasizing interdisciplinary research and translational education. Education: Ph.D. in Electrical Engineering (Control Systems), The Ohio State University, 1988 M.S. in Electrical Engineering (Digital Systems), The Ohio State University, 1984 B.S. in Electrical Engineering (Computer Option), The Ohio State University, 1981 Research Interests: Systems Control Technology, robotics, industrial processes, infectious disease modeling, and educational innovation. His projects include defibrillation waveform optimization, rocket propulsion control, and automation of manufacturing processes like heat shrink tubing systems. Grants & Awards: Over $4M in research funding, including NSF, NASA, and industry partnerships. Notable awards include the 1995-96 Tulane Teaching Excellence Award and a U.S. patent for an ultrasonic ranging system. He has led major initiatives like the Invenciones de Nuestra Inventiva program to enhance Hispanic STEM awareness. Service & Leadership: Served on ASEE committees, chaired the Texas Manufacturing Assistance Center Executive Council, and contributed to academic program development at multiple universities. His service includes external reviews for engineering programs and editorial roles in control systems conferences. Labs & Teams: Directed the Center for Technology Literacy (2006–2010) and collaborated with industry partners like SBC/ATT for technology labs. His current research integrates SIR models for disease control and applied data science for industry needs.
Fan Ye is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, serving as Graduate Program Director. His research focuses on mobile/embedded sensing systems, AI/ML for Computational Screening and Surveillance (CSS), data-centric wireless communication, edge computing, and IoT. He leads projects in health monitoring infrastructure (e.g., Proteus) and vehicular peer model training (e.g., Roadtrain), emphasizing scalability and manageability. His work bridges hardware-software integration for real-world applications like non-contact vital signs monitoring and aging-in-place sensor tech. Research trends reflect a strong emphasis on practical systems: 2024 publications highlight RF-based health monitoring innovations, edge computing optimizations, and user-centric sensor development. Earlier work includes contributions to decentralized machine learning, self-calibrating indoor tracking, and UWB-depth sensor fusion. Collaborations span academia and industry, addressing challenges in privacy, energy efficiency, and real-time data processing. Ye’s lab develops scalable solutions for pervasive edge environments, addressing issues like multicast rate control, optimal producer selection (OPSEL), and blockchain resource allocation. His teaching contributions include collaborative approaches to aging technology education. Current projects aim to engage older adults in shaping low-cost sensor systems and improving post-COVID health monitoring through unobtrusive home sensors. Notable infrastructure includes the Proteus health monitoring platform and SCALING for plug-and-play indoor tracking. His systems emphasize robustness and adaptability, leveraging acoustic, radar, and vision modalities for multi-factor authentication and indoor mapping.
