Roozbeh Mottaghi is a Senior AI Research Scientist Manager at Meta's Fundamental AI Research (FAIR) division and an Affiliate Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His career spans roles as Research Manager at the Allen Institute for AI and Postdoctoral Researcher at Stanford University. He holds a Ph.D. in Computer Science from UCLA, advised by Alan Yuille, and advanced degrees from Simon Fraser University, Georgia Tech, and a B.Sc. from Sharif University of Technology. Ph.D., Computer Science, UCLA (2016) M.Sc., Computer Science, Simon Fraser University M.Sc., Electrical Engineering, Georgia Institute of Technology B.Sc., Computer Engineering, Sharif University of Technology His research focuses on Embodied AI, Robotics, Computer Vision, and Multimodal Learning, with key contributions to human-robot collaboration benchmarks (PARTNR), 3D scene understanding, and adaptive navigation systems. Recent work explores zero-shot manipulation via video tracking (Track2Act) and lifelong navigation benchmarks (GOAT-Bench). Publications span 3D vision, reinforcement learning, and visual reasoning, including NeurIPS 2022 Outstanding Paper Award for "Ask4Help" and leadership in organizing challenges at CVPR and ICCV workshops. He advises students and interns who have transitioned to leading institutions like AI2, Stanford, and Brown University.
Omobolanle Ogunseiju is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology . She holds a Ph.D. in Environmental Design and Planning from the Department of Building Construction at Virginia Tech. Education: Ph.D. in Environmental Design and Planning, Virginia Tech Current Role: Assistant Professor, Georgia Tech School of Building Construction Her research focuses on integrating wearable robotics and Artificial Intelligence (via digital twin , cyber-physical systems , and data sensing ) to improve construction workforce safety, health, and well-being . She explores ethical implications of automation in construction, particularly in human-technological dynamics. Key research trends include: Advancing smart communities through robotics and AI Exoskeleton evaluation for ergonomic risk reduction Mixed reality environments for construction education Data analytics for cognitive and physical risk assessment Professional identity development in construction engineering students Industry-academia alignment for sensing technology integration Scientific awards: Outstanding Doctoral Candidate, Myers-Lawson School of Construction Outstanding Doctoral Student, College of Architecture and Urban Studies at Virginia Tech Teaching philosophy emphasizes experiential learning , engagement techniques , and hierarchical assessments . She developed the Construction Cost Management course at Georgia Tech and will lead Construction Technology courses. Previously, she taught Smart Construction , Building Systems Technology , and Wireless Sensing in Construction Management at Virginia Tech.
Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.
Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994
Dr. Andrea Bastoni is a Postdoctoral Researcher and Research Fellow at the Chair of Cyber-Physical Systems in Production Engineering at Technical University of Munich (TUM), Faculty of Mechanical Engineering. He is also the CTO and co-founder of Minerva Systems , developing operating system solutions for AI-ready embedded applications. His expertise spans real-time operating systems, cyber-physical systems, and predictable system design for heterogeneous platforms. His research focuses on enhancing predictability of memory hierarchies in complex SoCs through techniques like memory bandwidth regulation and cache partitioning. This work has industrial applications in safety-critical domains such as avionics and railways, where he contributes to certifiable hypervisors and operating systems. As former Software Architect of the PikeOS hypervisor at SYSGO GmbH (2012-2020), he specialized in DO-178C, IEC 61508, and EN 50128 standards. His academic background includes a Ph.D. in Computer Engineering from the University of Rome Tor Vergata (2007-2011), where he developed LITMUS^RT as part of UNC's Real-Time Systems Group during a visiting researcher period (2009-2010). His publications reflect ongoing work on Multicore Real-Time Scheduling , Mixed-Criticality Task Isolation, and Arm DynamIQ shared unit analysis. He actively participates in program committees for conferences like RTSS, DSN, and DATE.
Roberto Martinez-Maldonado is an Associate Professor in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. He holds a PhD in Human-Computer Interaction and Educational Data Mining from the University of Sydney. His research focuses on Learning Analytics, Artificial Intelligence in Education, and Collaborative Learning, with applications in healthcare and classroom settings. Prior to Monash, he worked at the Connected Intelligence Centre (CIC) and as a lecturer at the University of Technology, Sydney. His academic journey includes a Master's in Information Technology from Universidad Tecmilenio and a Bachelor's in Computer Systems Engineering from Instituto Tecnológico de Mérida. Key research projects include developing analytics dashboards for healthcare simulations, teamwork analytics for professional education, and AI-driven tools for reflective practice. His innovations include the HuCETA framework and the Data Storytelling editor, which enhance educational data visualization and teacher-student interaction. He has received multiple awards, including the 2022 Best Student Paper and 2020 Best Paper Award. His work contributes to UN Sustainable Development Goals related to quality education. He supervises PhD students in Multimodal Teamwork and Classroom Analytics. Roberto is actively involved in academic conferences, serving on program committees for LAK and AIED. Media engagements include ABC Radio interviews discussing classroom design and AI in education.
Dr Tanya Tierney serves as Assistant Dean, Clinical Communication at the Lee Kong Chian School of Medicine, Nanyang Technological University. She is a资深 medical educator specializing in patient-centered communication and simulation-based teaching methodologies. Previously, she held roles at Imperial College London, including Course Leader for Clinical Communication and Head of Year for Graduate Entry Medicine students. Her expertise includes simulated patient (SP) training, curriculum development, and student welfare initiatives. Dr Tierney holds a PhD in Neuroendocrinology from the National Institute for Medical Research (1998) and transitioned to medical education research in 2004. Her work focuses on simulation-based learning, ethnographic approaches to non-technical skills development, and stress management in healthcare settings. Key contributions include developing assessment tools like the Imperial Stress Assessment Tool (ISAT) and advancing hybrid simulation methodologies. Her research portfolio spans over two decades, with recent emphasis on empathy development in healthcare professionals, global perspectives on equity in medical education, and optimizing simulated participant programs. She has authored numerous studies on clinical communication, surgical training, and interprofessional collaboration.
Yuning Jiang is a Visiting Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Automatic Control Laboratory (LA3) within the School of Engineering (STI). He teaches the doctoral course Optimal Control for Dynamic Systems and contributes to research in distributed optimization, model predictive control (MPC), and smart grid technologies. His work bridges theoretical advancements in control systems with practical applications in power networks and autonomous systems. Current research emphasizes scalable solutions for AC optimal power flow, real-time MPC for embedded systems, and robust optimization under uncertainty. His research interests span Optimal Control , Power Systems , Smart Grids , and Federated Learning . Notable contributions include distributed algorithms for large-scale power systems and privacy-preserving co-simulation frameworks. Recent publications focus on microservice deployment in satellite-terrestrial networks and real-time pricing mechanisms for vehicle-to-grid (V2G) integration. Yuning holds a position in the EDEE-ENS unit under EPFL’s Academic Affairs division (VPA-AVP-DLE), reflecting his role in academic administration and teaching infrastructure. His lab, the Automatic Control Laboratory, focuses on cutting-edge research in control theory and its interdisciplinary applications.
Professor Washington Yotto Ochieng serves as Head of the Department of Civil and Environmental Engineering and Chair Professor in Positioning and Navigation Systems at Imperial College London. He directs the Centre for Active Resilience and Security (CARS) and maintains key affiliations with the Centre for Systems Engineering and Innovation, Centre for Transport Engineering and Modelling, Institute for Molecular Science and Engineering, and Space Lab. His extensive advisory roles include the Science Museum Group Board of Trustees, Royal Institute of Navigation Presidency, and Royal Academy of Engineering Africa Steering Committee. His educational background includes a BSc (First Class) in Engineering from the University of Nairobi and MSc (Distinction) and PhD in Civil Engineering from the University of Nottingham. He received an honorary DSc from Technical University of Kenya in 2023. Ochieng's research pioneers critical infrastructure resilience, user-centric mobility, and positioning/navigation/timing (PNT) systems. He has designed satellite navigation systems (including Europe's EGNOS and GALILEO) for multi-domain applications and advanced Air Traffic Management and Intelligent Transport Systems. His work integrates geomatics, transportation engineering, and sustainable mobility to solve global urban infrastructure challenges, with recent emphasis on decarbonization and AI-driven solutions. His 2024-2025 publications reveal strong trends in sustainable transportation decarbonization, AI-optimized traffic management, and resilient urban positioning systems. Research focuses on hydrogen fuel cell trains, carbon-efficient aviation, and deep reinforcement learning applications for emission reduction, demonstrating interdisciplinary integration of engineering, environmental science, and artificial intelligence to address climate challenges. Fellow of the Royal Academy of Engineering (2013) Harold Spencer-Jones Gold Medal from Royal Institute of Navigation (2019) Doctor of Science (honoris causa) from Technical University of Kenya (2023) Elder of the Order of the Burning Spear (EBS) from Kenya (2023) Commander of the Order of the British Empire (CBE) (2024) Ochieng provides strategic guidance to UK Government bodies (Government Office for Science, Department for Transport, FCDO), European Parliament, and European Court of Auditors. His advisory work shaped the Blackett Review on Satellite-derived Time/Position, UK Space Strategy, and Future of Mobility report. He chairs the Science Museum London Advisory Board and leads FCDO's Sustainable Urban Economic Development program in Africa, with significant grant influence through UK National Physical Laboratory and Department for International Development. He directs the Centre for Active Resilience and Security (CARS) and leads Space Lab initiatives, focusing on mission-critical PNT systems and infrastructure resilience. His teams collaborate with international consortia including RTCM Special Committee 134 and US Institute of Navigation, developing next-generation navigation solutions for safety-critical applications across transport, aviation, and urban environments.
Yunan Yang is the Goenka Family Assistant Professor in Mathematics at Cornell University, within the Department of Mathematics, College of Arts and Sciences. He holds a Ph.D. from the University of Texas at Austin (2018), supervised by Prof. Björn Engquist. Previously, he was a Courant Instructor at NYU (2018–2021), Simons-Berkeley Research Fellow (2021), and Advanced Fellow at ETH Zürich (2022–2023). His research focuses on computational mathematics, including inverse problems, optimal transport, machine learning, and nonconvex optimization. Notable contributions include applications of optimal transport to seismic inversion and PDE-constrained optimization. He has advised numerous students, including undergraduates and Ph.D. candidates at Cornell and other institutions. Yang teaches courses such as MATH 6220 (Applied Functional Analysis) and has published extensively in journals like SIAM Journal on Scientific Computing and Communications on Pure and Applied Mathematics. His work bridges theoretical foundations with practical applications in geophysics and computational science.
Professor Yanghua Wang is a leading academic in Geophysics at Imperial College London's Faculty of Engineering. He serves as Principal of the Resource Geophysics Academy and Director of the Centre for Reservoir Geophysics. His career spans over four decades, with roles including Research Manager at Robertson Research and a PhD from Imperial College London (1995–1997). He holds prestigious awards such as Fellow of the Royal Academy of Engineering (2021) and membership in the Chinese Academy of Engineering (2023). Education highlights include a BSc (1983) and MSc (1994) in Geophysics, followed by a PhD in Geophysics (1997). His research focuses on seismic inversion, reservoir geophysics, and time-frequency analysis, with notable monographs on seismic inversion and signal processing. He leads interdisciplinary projects combining machine learning with geophysical modeling, addressing challenges in reservoir characterization and seismic data processing. Research interests emphasize geophysical inversion techniques, anisotropic media analysis, and applications in energy exploration. He has pioneered methods like the W transform for seismic signal analysis and contributed to advancements in physics-informed neural networks. His work bridges theoretical geophysics with practical reservoir engineering solutions. Prof. Wang’s lab, the Resource Geophysics Academy, focuses on innovative geophysical methodologies for subsurface characterization. His recent projects include AI-driven data assimilation for large-scale systems and high-resolution seismic imaging techniques. Collaborations span academia and industry, addressing global energy and resource challenges.
Marc GENDRON-BELLEMARE is an Associate Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Université de Montréal. He is also a Chief Scientific Officer at Reliant AI, Adjunct Professor at McGill University, Canada CIFAR AI Chair at Mila, and Associate Fellow at CIFAR LMB Program. His research focuses on reinforcement learning, deep learning, and generative models, with notable contributions to the Atari 2600 benchmark and applications in robotics and stratospheric balloon navigation. He has advised multiple PhD and MSc students, including Pierluca D'Oro and Rishabh Agarwal. Education: PhD from University of Alberta under Michael Bowling and Joel Veness. Notable collaborations include work at Google Brain and DeepMind. Research Interests: Reinforcement Learning, Deep Learning, Probabilistic Models, Online Learning, Generative Models, and Information Theory. Awards: Best Paper Awards at NeurIPS 2021, ICLR 2020, and ICML Exploration Workshop 2019. His work on stratospheric balloon navigation using RL was published in Nature (2020). Grants and Labs: Core member of Mila, involved in projects like the Dopamine research framework and the Arcade Learning Environment (ALE). Active in open-source contributions and industry partnerships through Reliant AI.
Juan Francisco Jiménez-Alcázar is a Professor at the Universidad de Murcia, affiliated with the Department of Prehistory, Archaeology, Ancient History, Medieval History and Historiographic Sciences and Techniques within the Faculty of Arts and Humanities. His research focuses on Medieval History, Digital Humanities, and the intersection of history with video games and digital media. He holds a Doctorate from the Universidad de Murcia (1993) with a thesis on 'Espacio, poder y sociedad en Lorca (1460-1521)'. His work explores historical representation in modern media, particularly analyzing how video games depict medieval soundscapes, warfare, and cultural frontiers. Key themes include the study of linguistic diversity in medieval Spain, governance structures in frontier regions like Murcia, and the impact of historical epidemics such as the 1507-08 plague in Murcia. Recent publications include books on Digital Humanities and video games (2020), medieval warfare simulations, and interdisciplinary studies on migration and historical identity. He collaborates with institutions like CONICET and the CNR in Italy, and his research contributes to both academic and public understanding of medieval history through digital tools and cultural analysis.
Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.
Professor Michael Keidar holds the A. James Clark Professorship at the George Washington University (GW) , School of Engineering and Applied Science, within the Mechanical and Aerospace Engineering department. He leads the Micropropulsion and Nanotechnology Lab , pioneering research in plasma medicine, micropropulsion systems, and plasma nanoscience. His lab collaborates with industry partners like Vector (licensed plasma thruster technology) and US Patent Innovations, LLC (a $5.3M grant for cold plasma cancer therapy). Key research areas include: Cold plasma applications in biomedical treatment Microthrusters for nanosatellites Synthesis of graphene and carbon nanotubes Multi-scale plasma simulations Scientific accolades include the 2017 Ronald C. Davidson Award and AIAA Engineer of the Year (2016-2017), alongside leadership in interdisciplinary projects with GW’s Global Food Institute .
