Guodong Shi is Associate Professor at the University of Sydney's Australian Centre for Robotics, heading the Centre for Robotics and Intelligent Systems. His research develops theoretical frameworks for multi-agent coordination, distributed optimization, and networked control systems. Current projects investigate collective decision-making under information constraints, privacy-preserving optimization, and game-theoretic formulations for social and robotic networks. His group develops algorithms for distributed solution of linear equations, Boolean networks, and equilibrium seeking. Doctoral supervision includes projects on acrobatic legged robots, reinforcement learning for robotic stability, and safe control under dynamic environments. Laboratory capabilities support theoretical and experimental validation. Research has applications in autonomous swarm robotics, smart grid optimization, and social network analysis. Teaching includes graduate courses on networked systems and optimization.
Professor Eduardo Velloso is an academic staff member at the School of Computer Science , University of Sydney . He is a member of the Centre for AI Trust and Governance and holds a PhD in Computer Science from Lancaster University and a Bachelor of Computer Engineering from Pontifical Catholic University of Rio de Janeiro. Teaches COMP4447/5047 - Pervasive Computing and INFO1111 - Computing Professionalism Research Interests focus on distributed collaboration in mixed reality , human-AI interaction , and HCI theory and methodology . His work integrates Engineering, Design, and Psychology to explore gaze interaction, adaptive agents, and multimodal interfaces. Key projects include Blended Whiteboard for remote MR collaboration and GazeGrip for mobile accessibility. Publication Trends show expertise in Virtual Reality , Mixed Reality , and Human-AI Interaction , with recent work on Algorithmic Recourse and Immersive Educational Tools . Awards include ACM Best Paper Awards at CHI, UIST, TOCHI, and DIS venues. Scientific Awards 2024 ACM CHI & DIS Honorable Mentions 2022 UoM-FEIT Teaching & Learning Award 2019 UoM-CIS Excellence in Research Award 2015 ACM UIST Best Paper Award Advising includes supervision of research students Marvin, Tinghui LI, and Wendi YU in projects on asynchronous MR collaboration , situationally-induced impairments , and physical environment integration . His lab explores AI-assisted interaction and context-aware computing through projects like SpinalLog and LiftSmart .
Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.
Henrique O'Neill is an Associate Professor (with Habilitation) in the Department of Marketing, Operations and General Management at ISCTE - University Institute of Lisbon, Portugal. He is an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture. His research focuses on information systems adoption, organizational strategy, and process optimization in healthcare, finance, and public administration. Education: PhD in Business Organization and Management - University of Cranfield (1995) Master's in Electrical and Computer Engineering - Higher Technical Institute (1987) Bachelor's in Electrical Engineering - Higher Technical Institute (1983) Research Interests: His work spans business/IT strategy, systems modeling, process analysis, and technology implementation. Key domains include healthcare informatics, banking systems, and Industry 4.0 applications. He emphasizes practical solutions for organizational performance through technology integration. Publication Trends: Recent articles explore intelligent business systems, telemedicine, design science methodologies, and supply chain innovation. His work consistently bridges theoretical frameworks with sector-specific applications in healthcare, education, and logistics. Professional Engagement: Member: Portuguese Telemedicine Association, Order of Engineers Commissioner: INEM (National Institute of Medical Emergency) reform study Director: Center for IT Development (2010-2014) Advising & Projects: Supervises 7 graduate students (1 PhD, 6 Master's). Leads EU-funded projects including: Atlantic Crossing (2024-2025): US-Portugal academic collaboration in AI/cybersecurity AAL4ALL (2011-2015): Ambient Assisted Living ecosystem UNITE (2000-2002): Ubiquitous teamwork platforms
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
Laurent Lessard is an Associate Professor of Mechanical and Industrial Engineering at Northeastern University, with courtesy appointments in Electrical and Computer Engineering and the Khoury College of Computer Sciences. He is a core faculty member of the Institute for Experiential AI. Previously, he was at the University of Wisconsin-Madison. His research focuses on control theory, optimization algorithms, machine learning, and decentralized systems. He holds a PhD from Stanford University (2011) and completed postdocs at Lund University and Berkeley. Education: PhD in Aeronautics and Astronautics, Stanford University, 2011 MS in Aeronautics and Astronautics, Stanford University, 2005 BASc in Engineering Science (Aerospace), University of Toronto, 2003 Research Interests: Control theory, optimization algorithms, machine learning, distributed systems, and robust control. His work bridges control theory and optimization, emphasizing algorithm design and analysis with applications to AI and autonomous systems. Key Projects: Includes NSF-funded research on distributed optimization, Army-funded work on cognitive distributed sensing, and studies on algorithm equivalence and robustness. Awards: NSF CAREER Award (2018), Hugo Schuck Best Paper Award (2013), Gerald Holdridge Teaching Excellence Award (2019). Grants & Labs: Principal investigator on multiple NSF grants, including work on decentralized control and optimization. Active in the Kostas Research Institute for Homeland Security. Leads a research group with a focus on theoretical and applied control systems. Publications: Over 50 peer-reviewed articles, including foundational work on optimization algorithms and control theory. Recent work emphasizes robustness, distributed systems, and machine learning integration.
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.
Habeeb Olufowobi is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), where he leads the Cyber-Physical System Security Lab. He holds a PhD in Computer Science from Howard University (2019) and previously served as a Lecturer at Howard before joining UTA in 2020. His research is centered on the security and trustworthiness of embedded and distributed systems, particularly in the domains of autonomous vehicles, IoT, and healthcare AI. He investigates cybersecurity challenges at the hardware-software interface in real-time systems and develops AI/ML models that are transparent, explainable, and equitable. His interdisciplinary work integrates principles from cybersecurity, real-time systems, and machine learning. The recent publications reflect a strong trend in securing cyber-physical systems using advanced AI techniques, with emphasis on intrusion detection, secure communication (e.g., named data networking), and robustness of autonomous systems. His work frequently appears in top-tier venues such as IEEE Transactions, VehicleSec, and ICMLA. Project Management Professional (PMP), PMI (2012–Present) Member, Institute of Electrical and Electronics Engineers (IEEE) (2020–Present) Habeeb has secured significant research funding, including an NIH grant on ethical AI for Chagas disease prediction and an AIM-AHEAD grant focused on health equity. He mentors several graduate students, including Paul Agbaje and Afia Anjum, who have received awards and internships at prestigious institutions like Los Alamos National Laboratory. He teaches courses in cloud computing, embedded systems, and information security, and serves as a faculty advisor for the National Society of Black Engineers (NSBE) at UTA. His lab, the Cyber-Physical System Security Lab, focuses on developing scalable and reliable security solutions for critical infrastructure, with growing emphasis on healthcare applications and fairness in algorithmic decision-making.
Likoebe Maruping is a Professor of Computer Information Systems and Director of the Ph.D. program at Georgia State University's J. Mack Robinson College of Business. He is a member of the Center for Digital Innovation and serves as a senior editor for MIS Quarterly , having previously held editorial positions at Information Systems Research and the Journal of the Association for Information Systems . Previously, he taught at the University of Arkansas and the University of Louisville. Education: Ph.D. in Information Systems, University of Maryland, Robert H. Smith School of Business B.S. in Information Systems, University of Maryland, Robert H. Smith School of Business B.S. in International Business, University of Maryland, Robert H. Smith School of Business Maruping's research spans digital innovation , open source software development , agile methodologies , and team collaboration in dispersed settings . His work explores how leadership styles, particularly empowering leadership , can mitigate stress and improve performance in software development teams. He examines governance in digital platform ecosystems and investigates the relationship between digital technologies and social justice, as evidenced by his 2024 MIS Quarterly special issue contribution. His research often employs multilevel analysis approaches to connect micro and macro phenomena in information systems. Maruping's recent publications reveal a strategic shift toward examining AI innovation frameworks, platform ecosystem governance, and the social implications of technology. His 2025 work on AI innovation contrasts with traditional IT innovation through patent analysis, while his research on digital skill visibility in open source communities demonstrates practical applications of theoretical frameworks. These publications consistently bridge academic rigor with practical business implications. Editorial Recognition: Senior Editor, MIS Quarterly Former Associate Editor, Information Systems Research Former Associate Editor, MIS Quarterly Former Senior Editor, Journal of the Association for Information Systems As Ph.D. program director and former CIS department Ph.D. coordinator, Maruping has advised numerous doctoral students. His leadership extends to strategic program development, curriculum innovation, and enhancing recruitment of diverse doctoral candidates. He frequently leads workshops on multilevel theorizing, as evidenced by his 2023 workshop at Hong Kong Baptist University. His research has been supported through collaborations with major institutions and has influenced both academic discourse and industry practices in information systems management. Maruping is actively engaged with the Center for Digital Innovation at Robinson College of Business, which serves as his primary research hub. His work often involves cross-institutional collaborations with researchers from leading universities worldwide, reflecting the global nature of contemporary information systems research.
Trevor J Jones is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he leads the Mechanically Intelligent Engineered Structures (MInEnS) Lab. His research integrates soft matter mechanics, nonlinear dynamics, and indigenous knowledge to develop novel technologies in soft robotics, meta-materials, and manufacturing. Education: Ph.D., Chemical Engineering, Princeton University (2023) B.S., Chemical Engineering, Vanderbilt University (2017) His research focuses on harnessing mechanical instabilities, fluid-solid interactions, and granular matter to create intelligent, adaptive materials. Inspired by natural phenomena and Ojibwe beadwork traditions (reflected in the MInEnS Lab's name from the Ojibwemowin word manidoominens ), his work spans soft robotics, deployable structures, and beadwoven metamaterials. He employs an interdisciplinary approach combining crafting, experimentation, and theoretical modeling. His recent publications (2022–2024) demonstrate a strong trend in leveraging buckling, plasticity, and fluid dynamics to achieve emergent intelligence and multifunctionality in soft engineered systems, particularly through innovative fabrication techniques like bubble casting and beadwork-inspired design. Scientific Awards: AISES Lighting the Pathway Fellow Trailblazer in Engineering Rising Star in Soft and Biological Matter Jones actively mentors graduate and undergraduate researchers, including PhD students Eddie Beck and Angela Lee, and undergraduates Eleni Georgountzos and Adela Qiu. He is currently recruiting PhD students and postdocs for projects in bead-woven materials and soft matter mechanics. The MInEnS Lab fosters a highly interdisciplinary environment that values curiosity, craftsmanship, and the integration of diverse cultural perspectives in scientific inquiry.
Xiaowen Zhang is a Professor of Computer Science at the College of Staten Island (CSI), City University of New York (CUNY), and a Doctoral Faculty Member at the CUNY Graduate Center. His academic work bridges theoretical and applied research in cybersecurity, information systems, and network technologies. Dr. Zhang holds a Ph.D. in Computer Science from the CUNY Graduate Center (2007) and a Ph.D. in Electrical Engineering from Northern Jiaotong University (1999), along with an M.A. from CUNY Queens College, an M.S. from Northern Jiaotong University, and a B.S. from Shanxi University. His research focuses on Cryptography, Information Security, Cybersecurity, Secure Biometrics, RFID Security & Privacy, Information Retrieval, and Wireless Sensor Networks . He explores both foundational cryptographic methods—such as secret sharing schemes and hash functions—and their practical implementations in secure systems, including RFID authentication protocols and data visualization platforms for sensor networks. The analysis of his recent publications reveals a consistent focus on security mechanisms in distributed and wireless environments . His work frequently combines cryptographic theory with system-level implementations, particularly in RFID and sensor networks. There is a strong trend toward privacy-preserving protocols, efficient data retrieval, and secure information sharing , often leveraging mathematical structures like Latin squares and Bloom filters. Dr. Zhang has been actively involved in mentoring students, as evidenced by numerous co-authored publications with graduate and undergraduate researchers. His contributions span journals such as Security and Communication Networks , Journal of Applied Security Research , and International Journal of Security and Networks , as well as major conferences including IEEE LISAT, ACM CODASPY, and IEEE Sarnoff Symposium.
Sanjeev Baskiyar is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. He has been actively involved in research, teaching, and academic leadership, with a strong focus on computer systems, real-time and embedded computing, scheduling, cloud and fog computing, and energy-aware architectures. Education: Ph.D., Electrical and Computer Engineering, University of Minnesota M.S., Electrical and Computer Engineering, University of Minnesota B.S., Electronics and Communications, Indian Institute of Science, Bangalore B.S., Physics (with honors), and distinction in Mathematics Dr. Baskiyar’s research interests span scheduling, real-time and embedded systems, computer architecture, fog/cloud computing, thermal/energy-aware computing, and STEM education. His recent work explores machine learning applications in scheduling and quantum computing for fake news detection. He has supervised over 25 graduate students, many of whom now hold academic and industry positions. His recent publications emphasize fog computing simulation, service placement, quantum-inspired fake news detection, and adaptive scheduling using machine learning. These works reflect a trend towards intelligent, scalable, and energy-efficient computing systems, particularly in distributed and edge environments. Scientific Awards and Honors: Walker Teaching Excellence Award, Auburn University, 2020 Summer Faculty Fellow, Air Force Research Labs, 2020 Nominated Best Teaching Assistant, University of Minnesota, 1992 Multiples Merit and State-merit Scholarships Honors in Physics and Distinction in Mathematics Dr. Baskiyar has successfully advised numerous MS and PhD students and secured over $2 million in research funding as Principal Investigator from the National Science Foundation, DARPA, NASA, and industry partners like Wind River Systems and Mentor Graphics. His grants focus on parallel computing education, real-time micro-architectures, and embedded systems. He has also served on editorial boards, program committees, and as a reviewer for NSF and IEEE journals. He has held leadership roles including Senator in the University Faculty Senate and Chair of the E-day Committee. Labs and Research Groups: While not explicitly named, Dr. Baskiyar leads a research group focused on computer systems, scheduling, and embedded computing, as evidenced by his long list of graduate student supervision and funded projects in fog, cloud, and real-time systems.
Abderrahim Benslimane is a Full Professor of Computer Science at the University of Avignon, France, where he serves as Vice Dean of International Relations at the UFR STS (Unité de Formation et de Recherche en Sciences et Technologies). He is also Head of the master degree SICOM (Systèmes Informatiques Communicants: réseaux, services et sécurité) program at the university. His extensive academic career spans several decades with significant contributions to computer science, particularly in networking and security domains. Professor Benslimane holds a HDR (Title to supervise researches) from the University of Cergy-Pontoise, a Ph.D. from the University of Franche-Comté, along with M.S. and B.S. degrees in Computer Science from the same institution and the University of Nancy respectively. His research interests primarily focus on distributed computing, networking and communication protocols, with particular emphasis on modeling, describing and implementing secure communication protocols and multimedia applications in heterogeneous network architectures. He combines engineering and theoretical approaches using graphs, distributed algorithms, transition systems, and performance evaluation models. Benslimane's scholarly work demonstrates a strong trend toward addressing security and privacy challenges in emerging technologies. His recent publications focus on cybersecurity applications for wireless sensor networks, Internet of Things, blockchain implementations, UAV communications, and vehicular networks. He has pioneered research in energy attack mitigation, trust management systems, and secure group communications, often employing game theory and novel cryptographic approaches. His work bridges theoretical foundations with practical implementations in next-generation networking technologies. IEEE VTS Distinguished Lecturer (2020-2022) Best Paper award at IEEE ICC 2019 Multiple Prime d'Encadrement et de Recherche Doctorale awards (1998-2021) Prime d'Excellence Scientifique (2011-2015) IEEE Senior Member As an academic leader, Benslimane has served as Editor in Chief of Multimedia Intelligence and Security Inderscience Journal, Area Editor of IEEE Internet of Things Journal, and Associate Editor for multiple prestigious publications including IEEE Transactions on Multimedia and IEEE Wireless Communication Magazine. He has founded and led research centers including the Informatics Research center (CRI) at the French University in Egypt and the Multimedia and networking team (RAM) at the Laboratoire d'Informatique d'Avignon (LIA). His laboratory research focuses on security, communication protocols, graphs and distributed algorithms, with applications in ad hoc networks, sensor networks, vehicular networks, and IoT.
Emma Mercier is an Associate Professor and Associate Head & Director of Graduate Programs in the Department of Curriculum & Instruction at the University of Illinois, Urbana-Champaign's College of Education. She also holds a secondary appointment in the Department of Educational Psychology, demonstrating her interdisciplinary approach to educational research. Dr. Mercier's research focuses on the relationship between social interaction and learning, with particular emphasis on collaboration and computer-supported collaborative learning (CSCL) in classroom settings. Her work examines how technology influences group interactions and learning, especially through the use of multi-touch tables in classrooms. She investigates between-group and whole-class interactions, device ecologies, teacher tools, and classroom contexts that shape learning opportunities in technology-enhanced environments. Her research spans K-12 and higher education settings, with significant contributions to engineering education and the design of collaborative learning spaces. Analysis of Dr. Mercier's recent publications reveals a strong focus on orchestration tools that support instructors in facilitating collaborative learning, the role of technology (particularly augmented and virtual reality) in collaborative problem solving, and the design of effective collaborative tasks in engineering education. Her work bridges educational theory with practical classroom applications, often employing design-based implementation research methodologies. A notable trend is her increasing focus on machine learning applications to analyze and support collaborative interactions in real-time classroom settings. Dr. Mercier has been actively involved in mentoring graduate students and teaching courses related to educational research methods, child development and technology, and advanced study of education. Her work has involved significant collaboration with researchers across institutions and disciplines, particularly in the fields of educational technology, learning sciences, and engineering education. Her research has been supported through various projects, including the CSTEPS (Collaborative Support Tools for Engineering Problem Solving) initiative, which has developed and evaluated tools to support collaborative learning in engineering classrooms. This work has involved partnerships with teaching assistants, course assistants, and faculty to implement and refine collaborative learning approaches in undergraduate engineering courses.
Albert S. Berahas is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan's College of Engineering. He joined the university in 2020 after completing postdoctoral positions at Lehigh University (2018-2020) and Northwestern University (2018). He holds a PhD in Engineering Sciences and Applied Mathematics from Northwestern University (2018), an MS in Applied Mathematics from Northwestern (2012), and a BSE in Operations Research and Industrial Engineering from Cornell University (2009). His research focuses on designing, developing, analyzing, and implementing algorithms for solving large-scale nonlinear optimization problems. His work spans multiple sub-fields including constrained optimization, optimization for machine learning, stochastic optimization, derivative-free optimization, and decentralized optimization. He is affiliated with the Michigan Institute for Data Science (MIDAS), the Michigan Institute for Computational Discovery and Engineering (MICDE), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). Berahas has received numerous honors including the Charles Broyden Prize (2025), the Air Force Office of Scientific Research Young Investigator Program award (2025), the IISE Operations Research Division Teaching Award (2024), and the North Campus Dean's MLK Spirit Award for Community Building & Impact (2024). His recent publications demonstrate strong activity in developing novel optimization frameworks with theoretical guarantees for challenging problem settings. His research has been supported by significant grants including from the Office of Naval Research (ONR) and the Air Force Office of Scientific Research. He actively mentors PhD students and has successfully advised Jiahao Shi, who defended his dissertation in March 2025 and joined Amazon. Berahas is also engaged in community outreach, particularly through initiatives like Engage Detroit that aim to empower Detroit's next generation of engineers.
