AIE 2025

The First International Symposium on Artificial Intelligence for Engineering Innovations

New York University Abu Dhabi, UAE.

Time - Sunday, February 12-13th, 8:30-12:00 + 14:30-18:00
Location - TBD

Overview Schedule Speakers Topics Organizers
ICIP 2024

Overview đź’ˇ

Artificial Intelligence (AI) has proven to be a powerful tool in addressing complex challenges across a broad spectrum of field. The AIE 2025 Symposium is set to convene researchers from diverse backgrounds, spanning fundamental AI research and various engineering disciplines including but not limited to Cybersecurity, Urban Science, Robotics, Water and Environment, Materials, Bioinnovation, Resilient Systems, Quantum Computing, Wireless Communications, and Renewable Energy. This symposium is dedicated to discussing, exploring, and advancing the use of Artificial Intelligence to tackle challenges within these pivotal engineering research areas. Scheduled for February 12-13, 2025, the event seeks to foster interdisciplinary collaboration, drive innovation, and facilitate the sharing of insights on AI's role in propelling engineering forward.

In light of the identified gaps, the aim of the AIE 2025 Symposium is recalibrated to serve as a pivotal bridge connecting the realms of artificial intelligence research with practical engineering applications. Scheduled for early February 2025, this symposium is dedicated to unveiling the vast potential of AI in addressing complex engineering challenges across various domains, including cybersecurity, urban science, robotics, and renewable energy, among others. By fostering an environment of interdisciplinary dialogue, AIE 2025 seeks to enhance the awareness among AI researchers of the impactful applications of their work in engineering, equip engineers with a deeper understanding of AI's foundational advancements, and encourage a collaborative exchange of ideas and solutions across multiple engineering disciplines. The event aims to catalyze innovation, drive technological advancement, and facilitate a comprehensive sharing of insights, ultimately propelling engineering forward through the strategic application of artificial intelligence.

The International Symposium on Artificial Intelligence for Engineering Innovations (AIE 2025) is strategically crafted to connect the forefront of AI research with its practical implementation in various engineering fields. Set for early February 2025, AIE 2025 strives to merge AI developments with engineering challenges, encouraging innovation, cross-disciplinary cooperation, and the sharing of knowledge. This event is dedicated to showcasing AI's powerful applications in engineering, raising AI researchers' awareness of their impact on engineering progress, and providing engineers with an understanding of AI's core technologies. AIE 2025 will bring together experts from academia, the industry, and government to discuss important issues, research directions, and AI's wide-ranging applications in engineering. Attendees will present new research, exchange ideas, and discuss recent advancements across numerous topics,

Schedule ⏰ (Tentative)

Day 1

Time Topic Speaker
8:30 - 9:00 Opening Remarks Yi Fang
9:00 - 9:30 AI for Engineering Innovation TBD
9:30 - 10:00 TBD TBD
10:00 - 10:30 TBD TBD
10:30 - 11:00 TBD TBD
11:00 - 12:00 Coffee Break and Networking
12:00 - 13:00 Lunch Break
13:00 - 13:30 AI for Robotics and Automation TBD
13:30 - 14:00 TBD TBD
14:00 - 14:30 TBD TBD
14:30 - 15:00 TBD TBD
15:00 - 15:30 Coffee Break
15:30 - 16:00 TBD TBD

Day 2

Time Topic Speaker
8:30 - 9:00 AI for Engineering Innovation TBD
9:00 - 9:30 TBD TBD
9:30 - 10:00 TBD TBD
10:00 - 10:30 TBD TBD
10:30 - 11:00 TBD TBD
11:00 - 12:00 Coffee Break and Networking
12:00 - 13:00 Lunch Break
13:00 - 13:30 AI for Environmental Science TBD
13:30 - 14:00 TBD TBD
14:00 - 14:30 TBD TBD
14:30 - 15:00 TBD TBD
15:00 - 15:30 Coffee Break
15:30 - 16:00 Closing Remarks TBD

Invited Speakers 🧑‍🏫 ( List TBD)

Yi Fang

Associate Professor, New York University

Dr. Yi Fang, is an Associate Professor of Electrical and Computer Engineering and an Affiliated Associate Professor of Computer Science at NYU and NYU Abu Dhabi, as well as a member of the Center for Artificial Intelligence and Robotics (CAIR) at NYU. After earning his doctorate from Purdue University with a focus on computer graphics and vision, he gained industry experience at Siemens and Riverain Technologies, and academic experience at Vanderbilt University. His research focuses on embodied AI, general-purpose robots, and humanoids, with applications spanning engineering, social science, medicine, and biology. Dr. Fang founded the NYU AIR Lab (Embodied AI and Robotics Lab), a leading center for research in robotics and AI.

Mengyu Wang

Assistant Professor and Co-Director of Harvard Ophthalmology AI Lab, Harvard University

Dr. Mengyu Wang is a human and computer vision researcher with a faculty appointment in the Department of Ophthalmology of Schepens Eye Research Institute of Massachusetts Eye and Ear at Harvard Medical School. Dr. Wang has a diverse research background, including ophthalmology, radiology, computational mechanics, image processing, and artificial intelligence (AI). For human vision, Dr. Wang’s current research focuses on developing mathematical, mechanical, statistical, and AI models to enhance our knowledge and understanding of eye diseases and ultimately improve clinical treatment of eye diseases. For computer vision, Dr. Wang’s current research is pivoted on innovating various fairness-promoting AI techniques to improve the performance equity of various AI models, including classification models, segmentation models, cross-domain adaptation and generalization models, vision language models, generative models, and self-supervised foundation models.


Yu-Shen Liu

Associate Professor, Tsinghua University

Dr. Yu-Shen Liu is an Associate Professor in School of Software at Tsinghua University. He spent three years as a post doctoral researcher in Purdue University from 2006 to 2009. He earned his PhD in the Department of Computer Science and Technology at Tsinghua University, China, in 2006. He received his BS in mathematics from Jilin University, China, in 2000. His current research interests include algorithms in pattern recognition, machine learning, shape matching and retrieval; Information retrieval, semantic search; Smart building, Building Information Modeling (BIM); Digital Geometry Processing (DGP), shape denoising and smoothing.

Panlong Yang

Professor, University of Science and Technology of China

Dr. Panlong Yang (Senior Member, IEEE) received the B.S., M.S., and Ph.D. degrees in communication and information system from Nanjing Institute of Communication Engineering, Nanjing, China, in 1999, 2002, and 2005, respectively.,From September 2010 to September 2011, he was a Visiting Scholar with Hong Kong University of Science and Technology, Hong Kong. He is currently a Professor with the School of Computer Science and Technology, University of Science and Technology of China, Hefei, China. He has published more than 50 papers in peer-reviewed journals and refereed conference proceedings in the areas of mobile ad hoc networks, wireless mesh networks, and wireless sensor networks. His research interests include wireless mesh networks, wireless sensor networks, and cognitive radio networks.,Prof. Yang has also served as a member of program committees for several international conferences. He is a member of the IEEE Computer Society and ACM SIGMOBILE Society.

Nikolaos Freris

Professor at University of Science and Technology of China and Vice Dean of the International College

Dr. Nick Freris is Professor in the School of Computer Science at USTC, and Vice Dean of the International College. He received the Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece, in 2005, and the M.S. degree in Electrical and Computer Engineering, the M.S. degree in Mathematics, and the Ph.D. degree in Electrical and Computer Engineering all from the University of Illinois at Urbana-Champaign (UIUC) in 2007, 2008, and 2010, respectively.


Muhammad Shafique

Professor, NYU Abu Dhabi

Dr. Muhammad Shafique (M’11 - SM’16) received the PhD degree in computer science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. Afterwards, he established and led a highly recognized research group at KIT for several years as well as conducted impactful R&D activities in Pakistan. Besides co-founding a technology startup in Pakistan, he was also an initiator and team lead of an ICT R&D project. In Oct.2016, he joined the Institute of Computer Engineering at the Faculty of Informatics, Technische Universität Wien (TU Wien), Vienna, Austria as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since Sep.2020, he is with the Division of Engineering, New York University Abu Dhabi (NYUAD), United Arab Emirates.


Zouhair Lachkar

Senior Research Scientist, NYU Abu Dhabi

Dr. Zouhair Lachkar has been a senior scientist at New York University Abu Dhabi, United Arab Emirates, since 2014. He received his PhD from the University of Pierre and Marie Curie, France. He worked as a research scientist at the Swiss Federal Institute of Technology of Zurich, ETH Zurich, Switzerland, between 2007 and 2014. His research interests are in the areas of oceanography, climate, and marine biogeochemistry, with an emphasis on the use of numerical models to study the coupling between ocean circulation and global biogeochemical cycles. The focus of his current research is on the dynamics of oxygen-minimum zones in the north Indian Ocean and in Eastern Boundary Upwelling Systems and their vulnerability to climate change and rising atmospheric carbon concentrations.


Azhar Zam

Associate Professor of Bioengineering, NYU Abu Dhabi

Dr. Azhar Zam is an Associate Professor of Bioengineering. He holds a B.S. from The University of Indonesia, an M.Sc. from the University of Luebeck, Germany, and a Ph.D. from Friedrich-Alexander-University Erlangen-Nuremberg, Germany. Zam’s research interests focused on the development of smart devices for medical imaging, diagnostics, and monitoring using novel optical technologies, which include smart laser surgery, optical coherence tomography (OCT), photoacoustics, biomedical spectroscopy, AI-aided optical diagnostics and imaging, optical-based smart biosensors, and miniaturized systems. He is an associate editor for the Biophotonics section of Frontiers in Photonics and reviews editor for the Retina section of Frontiers in Ophthalmology. He has written over 85 peer-reviewed articles and book chapters, and books and holds more than several patents.


Mostafa Mobasher

Assistant Professor of Civil and Urban Engineering, NYU Abu Dhabi

Dr. Mostafa Mobasher is an Assistant Professor of Civil and Urban Engineering at NYUAD. He holds Associated Affiliations with the Civil and Urban Engineering and the Mechanical and Aerospace Engineering departments at New York University, Tandon School of Engineering. Mobasher leads the Computational Solid Mechanics (CSM) Lab at NYUAD. The lab’s research is focused on modeling fracture and multi-physical response of manmade and natural materials across spatial and temporal scales. Mobasher’s research contributes to the development of a wide range of numerical models to address the needs for non-linear modeling of materials subjected to mechanical and multi-physical loading scenarios. More recently, the CSM lab’s research scope was expanded to explore the integration between the Machine Learning (ML) and Artificial Intelligence (AI) approaches along with well-established computational methods such as Finite Element Method (FEM). The outcomes of this research support various engineering applications including Infrastructure Integrity and Resilience, Energy, Automotive and Aerospace applications. Mobasher is also a Co-PI and the leader of the Energy Research Theme for Sand Hazards and Opportunities for Resilience, Energy, and Sustainability (SHORES), one of the NYUAD Research Institute Centers.


Gregory S. Chirikjian

Professor & Department Chair, University of Delaware

Dr. Gregory S. Chirikjian is the Willis F. Harrington Professor and Chair of the Mechanical Engineering Department at the University of Delaware. A distinguished roboticist and applied mathematician, he is known for his groundbreaking contributions to robotics, particularly in kinematics, motion planning, and the application of group theory to engineering. His research has advanced the understanding of hyper-redundant robots and stochastic methods on Lie groups, and he is actively involved in embodied AI, focusing on affordance-based reasoning to enhance robotic intelligence. Chirikjian's career is marked by numerous honors, including being named an NSF Young Investigator, a Presidential Faculty Fellow, and a Fellow of both IEEE and ASME. Before joining the University of Delaware in 2024, he held leadership roles at the National University of Singapore


Lilas Alrahis

Assistant Professor, Khalifa University

Dr. Lilas Alrahis is an Assistant Professor in the Department of Computer and Information Engineering at Khalifa University, Abu Dhabi, UAE. Previously, she was a Postdoctoral Associate at New York University Abu Dhabi (NYUAD), UAE, from March 2021 to August 2024, and a member of the NYUAD Center for Cyber Security. Dr. Alrahis' research focuses on hardware security, design-for-trust, and applied machine learning. She has received the MWSCAS Myril B. Reed Best Paper Award in 2016, the Best Paper Award at the Applied Research Competition during Cyber Security Awareness Week in 2019 and 2021, and the Karlsruhe Institute of Technology (KIT) International Excellence Fellowship in Germany for 2023. Additionally, in July 2023, she was awarded the NYUAD Collaboration Grant.


Djellel Difallah

Assistant Professor of Computer Science and Global Network Assistant Professor of Computer Science, Courant Institute of Mathematical Sciences, NYU Abu Dhabi

Dr. Djellel Difallah is an assistant professor at NYU Abu Dhabi. He studied computer science and was a recipient of a Fulbright Scholarship. He obtained a PhD at the University of Fribourg in 2015, working at the eXascale Infolab. He was then a Faculty Fellow at the Center of Data Science, NYU. During his professional career, Djellel spent time at the Wikimedia Foundation (Research Team), Schlumberger, interned at Microsoft Research, and participated in the Google Summer of Code. Djellel's research interests include the study of crowdsourcing, human-computation, machine learning, and knowledge discovery and management. His work focuses on the creation of crowdsourcing systems that improve AI models and make a positive impact on the crowd participants and society.


Junzhou Huang

Jenkins Garrett Professo, University of Texas at Arlington

Dr. Junzhou Huang is the Jenkins Garrett Professor in the Computer Science and Engineering department at the University of Texas at Arlington. He has been the director of the machine learning center at Tencent AI Lab. His major research interests include machine learning, computer vision, medical image analysis and bioinformatics. His research has been recognized by several awards including the NSF CAREER Award, Google TensorFlow Model Garden Award, Microsoft Accelerate Foundation Models Research Award, four Best Paper Awards (MICCAI'10, FIMH'11, STMI'12 and MICCAI'15) as well as two Best Paper Nominations (MICCAI'11 and MICCAI'14). His research projects are supported by both federal/state agencies (NSF, NIH, CPRIT) and industry (Google, Amazon, Microsoft, IBM, Samsung, XtaiPi and Nokia). He enjoys to develop efficient algorithms with nice theoretical guarantees to solve practical problems involved large scale data.


Le Song

Professor of Machine Learning, Mohamed Bin Zayed University of Artificial Intelligence

Dr. Le Song's research interests are in machine learning methods and algorithms for complex and dynamic data including structured prediction, neuro-symbolic integration, and AI for healthcare and drug design. Prior to joining MBZUAI, Song was an associate professor of computational science and engineering and the associate director of Center for Machine Learning at the Georgia Institute of Technology in the USA. He spent several years at various institutes such as Georgia Institute of Technology, Google Research, Carnegie Mellon University and National ICT Australia. Song’s remarkable works won several best paper awards at the ACM Conference on Recommendation System (Recsys) in 2016, Artificial Intelligence and Statistics (AISTATS) in 2016, IEEE International Parallel and Distributed Processing Symposium (IPDPS) in 2015, Neural Information Processing Systems (NeurIPS) in 2013, and International Conference on Machine Learning (ICML) in 2010. Song is a chair of the 39th International Conference on Machine Learning (ICML 2022).

Topics đź“ť

  • Embodied AI and Foundation Models for Next-Generation Autonomous Systems
  • Ethics and Bias Mitigation in AI: Developing Fair and Transparent Algorithms
  • Reinforcement Learning: Techniques for Adaptive AI Systems
  • AI-driven cybersecurity solutions and their implications for protecting complex systems
  • Urban science and the role of AI in developing smart city infrastructures
  • Robotics innovations, focusing on AI applications for improved efficiency and adaptability
  • Clinical Computing or Wearable Computing
  • Water and environmental management using AI for sustainable resource allocation
  • Materials science advancements through AI for discovering new materials
  • Bioinnovation, leveraging AI for breakthroughs in medicine and genetics
  • Resilient systems design, enhanced by AI for better disaster preparedness
  • Quantum computing applications in AI, pushing computational boundaries
  • Wireless communications optimization through AI for enhanced connectivity
  • Renewable energy management, using AI for efficient energy production and distribution
  • Machine learning and deep learning techniques for engineering problem-solving
  • AI in the design and operation of autonomous vehicles and drones
  • The intersection of AI and quantum computing for engineering applications
  • Big data analytics and AI for predictive maintenance in engineering infrastructures
  • Human-AI interaction and its impact on engineering design and innovation
  • Ethics, privacy, and security in AI applications within engineering projects
  • Collaborative AI and multi-agent systems for complex engineering tasks
  • AI applications in civil and structural engineering for smarter construction

    • Industry Exhibition 🏭

    This section will showcase the latest innovations and products from leading industry players in the field of AI and engineering. It provides a platform for professionals to network, share ideas, and explore potential collaborations.

    Sponsors đź’Ľ

    We are grateful for the support of our sponsors who make this event possible. Their contributions help us to foster innovation and promote excellence in the field of Artificial Intelligence.

    Organizers

    Meet the organizers who have made this event possible through their dedication and expertise.

    Yi Fang

    Yi Fang

    Program Chair
    Konstantinos Kyriakopoulos

    Konstantinos Kyriakopoulos

    Program Co-Chair
    Rose Faghih

    Rose Faghih

    Program Co-Chair
    Chen Feng

    Chen Feng

    Program Co-Chair
    Hao Huang

    Hao Huang

    Program Co-Chair
    Zhifei Li

    Zhifei Li

    Industrial Partner Chair
    Yu Hao

    Yu Hao

    Website Chair