Umair Bin Waheed

Umair Bin Waheed
Membership	SEG
PhD	Earth Sciences
PhD university	King Abdullah University of Science & Technology

Umair Bin Waheed is an Assistant Professor of Geophysics at King Fahd University of Petroleum and Minerals. He was a postdoc at the Department of Geosciences, Princeton University, and during this time he also worked as a Writing in Science and Engineering Fellow at the Princeton Writing Program. As part of this fellowship program, he attended a course on scientific writing taught by Dr. Judith Swan and participated in several training workshops on teaching scientific writing. Later he taught courses on scientific writing to graduate students at Princeton University from across science and engineering disciplines. Encouraged by the positive feedback, he has continued to teach the material at other institutions. He has delivered courses to graduate students and postdocs at King Abdullah University of Science and Technology and University of Ontario Institute of Technology, in addition to his current institution, King Fahd University of Petroleum and Minerals.

SEG Outstanding Educator Award 2024

Umair bin Waheed is recognized with the Outstanding Educator Award. Geophysicist Stewart Greenhalgh notes that he has “seldom encountered anyone of Waheed’s caliber” and that “he [Waheed] has undoubtedly sparked a passion for geophysics in countless students.” Waheed received his PhD in 2015 from King Abdullah University of Science and Technology (KAUST) and is now an associate professor at King Fahd University of Petroleum and Minerals (KFUPM). He was a Fellow in the Princeton University writing program. For SEG, he has served as Honorary Lecturer in 2023 and as a continuing education instructor. He is the chair of SEG’s Books Editorial Board and a member of the Continuing Education and Research committees. He has been deputy editor of Geophysical Prospecting and associate editor of IEEE Transactions on Geoscience and Remote Sensing. He made nine presentations at IMAGE ’23 and has written some 54 articles and 50 abstracts along with two patents. He received the KFUPM Excellence in Teaching Award in 2023 and, according to the department chair at KFUPM, “has had the highest ranking of the 18 full-time faculty for my three years of evaluating faculty.” He supervises many graduate students and postdoctoral researchers at KFUPM and other international universities, including KAUST, Massachusetts Institute of Technology, and Brown University. He has created and teaches a writing course and has created a geophysics-related YouTube channel. His influence in shaping the next generation of geophysicists is both profound and far-reaching, marking him as a truly deserving candidate.

Biography Citation for the Outstanding Educator Award

by Tariq Alkhalifah and Roel Snieder

Umair bin Waheed’s commitment to education is evident through his roles at King Fahd University for Petroleum and Minerals (KFUPM) and beyond. As an associate professor of geophysics, his innovative approaches and dedication have impacted the educational landscape significantly. His teaching style and the response of students earned him the KFUPM Distinguished Teaching Award in 2023. Umair's forward-thinking approach to education is exemplified by his pioneering initiative in 2018 to develop the first course on machine learning for geoscience at KFUPM. This innovative curriculum reflects his commitment to staying at the forefront of advancements in geophysics education, preparing students to navigate the evolving landscape of technological integration in the field.

His courses and lectures at KFUPM and beyond reflect his expertise in computational geophysics, inverse problems, seismic modeling, and physics-informed machine learning. He served on the committees of many PhD students at King Abdullah University for Science and Technology (KAUST), which included episodes of mentoring and advising. His significant contributions as a visiting scientist at Brown University and cofounder of Intelligent Geoscience Solutions (iGeos) underscore his multifaceted impact within geophysics. His dissemination of knowledge is also reflected in his many open-source packages, like GroningenNet and PINNeik.

Umair has played a pivotal role in empowering future scientists and researchers to communicate their findings effectively through his teaching of technical writing. His position at Princeton University as a “Writing in Science and Engineering Fellow” attests to his expertise in this area. He has shared his insights into technical writing during his teaching tours and has received high acclaim for the depth, clarity, and impact of those insights on students and fellow academicians. His dedication to enhancing the communication skills of geophysicists underscores his commitment to holistic education.

Umair has been an advocate for disseminating knowledge beyond academia — in most cases as a representative of SEG. He has participated as an SEG continuing education course instructor since 2019. He has served on SEG’s Books Editorial Board since 2020 and became chair of that board in late 2023. During this time, he has assumed the role of managing editor for the books Machine Learning for Science and Engineering, Active Seismic Monitoring of a Small CO2 Injection into a Saline Aquifer: Experience from the CO2CRC Otway Project, and An Elementary Approach to Seismic Data Acquisition and Processing.

Umair's dedication to educating and engaging with a wider audience is exemplified by the numerous webinars and lectures he has conducted, which have received widespread acclaim for their depth, relevance, and applicability. These presentations have not only enriched the academic community but have also bridged the gap between theoretical concepts and practical applications in the field of geophysics.

Umair’s extensive list of awards, recognition, and published works, including numerous journal articles and patents, showcase his dedication, expertise, and significant contributions to geophysics. Notably, his SEG Honorary Lecturer distinction and KFUPM Distinguished Teaching Award highlight his excellence in both academia and teaching methods.

Despite his academic success and his prolific contributions in research and education, Umair stays true to his core values, and he is well-aware that he works with people who he seeks to empower in their growth. He does this in a gentle but determined way and is a true teacher with heart. The combination of this mindset with his outstanding academic contributions makes him an outstanding recipient of the Outstanding Educator Award. His impact as an educator, researcher, and leader in the geophysics community resonates deeply, making this award a testament to his exemplary contributions.

2023 SEG Middle East Honorary Lecturer

Scientific machine learning in geophysical exploration and monitoring

Artificial intelligence, and in particular its subdomain machine learning, has revolutionized many science and engineering disciplines during the past decade. Scientific machine learning (SciML) — often referred to as scientific computing with machine learning — is an emerging interdisciplinary field that integrates traditional scientific computing methods with modern machine learning techniques. The aim of SciML is to augment data-driven learning in scientific applications where traditional machine learning approaches might struggle. Conventional machine learning models typically learn patterns from large quantities of data but may struggle with limited or noisy data sets, or where interpretability, reliability, and robustness are essential. They also often lack the ability to incorporate prior scientific knowledge, and sometimes produce results that, while statistically valid, may be physically impossible. SciML, on the other hand, combines physical models (based on scientific laws and principles) with machine learning techniques. This integration allows the models to effectively learn from smaller or noisier data sets and ensure that the outcomes are consistent with established scientific knowledge. It also offers improved interpretability and generalization capabilities.

Applications of SciML are increasingly being found in a variety of fields such as geophysics, climatology, materials science, biology, and fluid dynamics. A number of advancements have been made in recent years in the domain of geophysical exploration and monitoring using emerging SciML paradigms, including physics-informed neural networks (PINNs), Fourier neural operators (FNOs), and Deep Operator Networks (DeepONets). These developments offer a new pathway to address longstanding computational challenges in the field of geophysics. This lecture will delve into these strides forward, highlighting the potential impact of such methods and the associated challenges in making these methods mainstream.

Additional Resource

A recording of the lecture is available.^[1]