Ammar Alsheghri | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Ammar Alsheghri | Artificial Intelligence | Best Researcher Award

Assistant Professor at king fahd university of petroleum and minerals, Saudi Arabia

Assist. Prof. Dr. Ammar Alsheghri is a distinguished academic and researcher specializing in materials and mechanical engineering. He currently serves as an Assistant Professor in the Department of Mechanical Engineering at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia. Dr. Alsheghri earned his Ph.D. in Materials Engineering from McGill University, Canada, in 2020, following his Master’s in Mechanical Engineering from Khalifa University in collaboration with MIT, and a Bachelor’s degree from Khalifa University, UAE. His research spans bioinspired materials design, geometric deep learning, and the optimization of materials for prosthetics and medical applications. He has extensive experience in academia and industry, contributing to innovative projects and holding patents in dental restoration technologies. Dr. Alsheghri’s work is highly recognized through numerous fellowships, scholarships, and awards for research excellence.

Profile:

Education:

Assist. Prof. Dr. Ammar Alsheghri holds a Ph.D. in Materials Engineering from McGill University, Canada, which he completed in May 2020 with a perfect GPA of 4.0/4.0. During his Ph.D., he was awarded the prestigious Graduate Excellence Fellowship. He earned his Master of Science in Mechanical Engineering from the Masdar Institute of Science and Technology, Khalifa University, UAE, in collaboration with the Massachusetts Institute of Technology (MIT), USA, in May 2015, also achieving a GPA of 4.0/4.0. Dr. Alsheghri obtained his Bachelor of Science in Mechanical Engineering from Khalifa University, UAE, in June 2013, graduating with the highest honors and an impressive GPA of 3.98/4.0.

Professional Experience:

Assist. Prof. Dr. Ammar Alsheghri has an extensive professional background in both academia and industry. Currently, he is an Assistant Professor in the Department of Mechanical Engineering at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia, a position he has held since August 2023. Prior to this, he served as a Postdoctoral Fellow at Polytechnique Montreal from 2020 to 2022, where he worked on geometric deep learning for dental applications, collaborating with industrial partners to develop customized dental crowns. Dr. Alsheghri also contributed as a Research Assistant at McGill University from 2015 to 2020, where his thesis focused on bioinspired design and optimization of materials for dentures and cellular structures. He has previous experience as a Research Assistant at Masdar Institute of Khalifa University, working on self-healing materials using finite element analysis. In addition to his academic roles, Dr. Alsheghri has substantial industrial experience. He was a Technology Developer at Dragonfly, part of the Comet Group, from 2022 to 2023, focusing on AI-driven scientific imaging and software development. He also worked as a Research & Development Consultant and Intern at Dragonfly, leading projects involving artificial intelligence for material image processing and mechanical properties analysis. His industry roles also include an internship at Diehl Aircabin GmbH in Germany, where he performed stress analysis for the Airbus A380 ceiling and assisted with composite material testing.

Research Interests:

Assist. Prof. Dr. Ammar Alsheghri’s research interests are deeply rooted in the intersection of materials engineering, mechanical engineering, and cutting-edge technologies such as artificial intelligence. His work focuses on bioinspired design and optimization of materials, particularly for dental applications, including the development of dentures and customized prosthetic structures. Dr. Alsheghri is also highly engaged in the field of geometric deep learning, specifically 3D deep learning for applications in dental segmentation and the generation of dental crowns from scanned dental arches. His expertise extends to self-healing materials, mechanics modeling, and finite element analysis (FEA), which he has applied to enhance the structural properties of materials. Additionally, he is interested in AI-driven image processing and computational mechanics, applying these techniques to scientific imaging and material analysis, with the aim of optimizing mechanical properties and structures for various engineering applications.

Skills:

Assist. Prof. Dr. Ammar Alsheghri possesses a diverse and advanced skill set that spans both academia and industry. He is highly proficient in materials engineering, with expertise in bioinspired design and optimization of materials, specifically for dental and prosthetic applications. Dr. Alsheghri excels in finite element analysis (FEA) and computational mechanics, which he has applied to projects involving self-healing materials and lightweight structures. He is also skilled in computer-aided design (CAD) and 3D modeling, having worked on the design and development of dental prostheses and optimized cellular structures. In addition, Dr. Alsheghri has strong capabilities in artificial intelligence, particularly in geometric deep learning and 3D deep learning, which he has applied to the segmentation of dental structures and the design of customized dental crowns. He is experienced in data analysis, image processing, and the use of AI for scientific imaging. His skills in laboratory testing and materials characterization complement his technical expertise, allowing him to contribute to multidisciplinary research and development efforts.

Conclusion:

With an outstanding academic record, a strong research portfolio, multiple patents, and a track record of successful collaborations in both academia and industry, Dr. Ammar Alsheghri stands out as a highly qualified candidate for the Best Researcher Award. His interdisciplinary expertise, particularly in materials engineering, AI, and bioinspired design, positions him as a leader in advancing research and technology in both mechanical and biomedical engineering fields.

Publication Top Noted:

  • Removable partial denture alloys processed by laser‐sintering technique
    • Authors: O Alageel, MN Abdallah, A Alsheghri, J Song, E Caron, F Tamimi
    • Journal: Journal of Biomedical Materials Research Part B: Applied Biomaterials
    • Volume/Issue: 106(3)
    • Pages: 1174-1185
    • Year: 2018
    • DOI: 10.1002/jbm.b.33910
  • Design and cost analysis of a solar photovoltaic powered reverse osmosis plant for Masdar Institute
    • Authors: A Alsheghri, SA Sharief, S Rabbani, NZ Aitzhan
    • Journal: Energy Procedia
    • Volume: 75
    • Pages: 319-324
    • Year: 2015
    • DOI: 10.1016/j.egypro.2015.07.439
  • Finite element implementation and application of a cohesive zone damage-healing model for self-healing materials
    • Authors: AA Alsheghri, RKA Al-Rub
    • Journal: Engineering Fracture Mechanics
    • Volume: 163
    • Pages: 1-22
    • Year: 2016
    • DOI: 10.1016/j.engfracmech.2016.05.020
  • High strength brushite bioceramics obtained by selective regulation of crystal growth with chiral biomolecules
    • Authors: H Moussa, W Jiang, A Alsheghri, A Mansour, A El Hadad, H Pan, R Tang, …
    • Journal: Acta Biomaterialia
    • Volume: 106
    • Pages: 351-359
    • Year: 2020
    • DOI: 10.1016/j.actbio.2020.01.004
  • Composition and characteristics of trabecular bone in osteoporosis and osteoarthritis
    • Authors: I Tamimi, ARG Cortes, JM Sánchez-Siles, JL Ackerman, …
    • Journal: Bone
    • Volume: 140
    • Pages: 115558
    • Year: 2020
    • DOI: 10.1016/j.bone.2020.115558
  • Thermodynamic-based cohesive zone healing model for self-healing materials
    • Authors: AA Alsheghri, RKA Al-Rub
    • Journal: Mechanics Research Communications
    • Volume: 70
    • Pages: 102-113
    • Year: 2015
    • DOI: 10.1016/j.mechrescom.2015.09.007
  • Determining the retention of removable partial dentures
    • Authors: O Alageel, AA Alsheghri, S Algezani, E Caron, F Tamimi
    • Journal: Journal of Prosthetic Dentistry

Moran Cerf | Artificail Intelligence | Best Researcher Award

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Prof Dr. Moran Cerf, Artificail Intelligence, Best Researcher Award

Professor at Columbia University, United States

Dr. Moran Cerf is a renowned neuroscientist known for his expertise in the field of brain research. He earned his PhD in Neuroscience from the California Institute of Technology (Caltech) in 2009, following which he pursued a Master of Arts in Philosophy at Tel-Aviv University in 2001. Dr. Cerf also holds a Bachelor of Science degree in Physics from Tel-Aviv University, completed in the years 1998 to 2000. His work spans across various domains including consciousness, decision-making, and brain functions. Dr. Cerf has received numerous awards and grants for his contributions to neuroscience and has been actively involved in teaching, research, and consulting projects in academia and industry.

Profile:

Education:
  • PhD in Neuroscience from the California Institute of Technology (Caltech), completed from 2005 to 2009.
  • MA in Philosophy from Tel-Aviv University, obtained in 2001.
  • BSc in Physics from Tel-Aviv University, completed from 1998 to 2000.

Professional Experience:

Dr. Moran Cerf has a distinguished professional experience in the field of neuroscience, academia, and industry. He has served as a President of the Human Intracranial Research Foundation since 2010, overseeing research initiatives and advancements in brain studies. Dr. Cerf has been actively engaged in teaching at prestigious institutions, including Columbia Business School at Columbia University, Northwestern University, Kellogg School of Management, and Stern School of Business, NYU, where he has taught courses in marketing, neuroscience, and cognitive neuroscience. His expertise extends to executive education programs, where he has delivered lectures on topics like blockchain applications in business and using neuroscience in business. Apart from teaching, Dr. Cerf has contributed significantly to professional services and advisory roles. He has been a member of various professional organizations such as the Association for Consumer Research, Society for Neuroscience, and American Marketing Association, among others. Dr. Cerf has also been involved in reviewing scientific publications in neuroscience, psychology, and engineering fields, showcasing his expertise and commitment to advancing knowledge in these areas. In addition to his academic and professional engagements, Dr. Cerf has been an entrepreneur and consultant, contributing his expertise to technology companies, entertainment industries, financial institutions, and policy organizations. He has been a financial stakeholder in various ventures related to blockchain, climate, and neuroscience, demonstrating his multidisciplinary approach to solving complex challenges. Dr. Cerf’s extensive professional experience and contributions have made him a highly respected figure in the field of neuroscience, academia, and business.

Research Interest:

Dr. Moran Cerf’s research interests encompass a wide array of topics at the intersection of neuroscience, decision-making, and human behavior. His work delves into understanding the neural processes that underlie complex cognitive functions, including decision-making, perception, consciousness, and social interactions. Dr. Cerf employs neuroimaging techniques such as fMRI (functional magnetic resonance imaging) and EEG (electroencephalography) to investigate brain activity patterns and their correlation with various cognitive and behavioral phenomena. One of his key research areas involves studying the neural correlates of decision-making, exploring how the brain processes information and makes choices in uncertain or high-stakes situations. He also investigates the neural mechanisms of perception, seeking to unravel how the brain interprets sensory inputs and constructs our subjective experiences of the world. Furthermore, Dr. Cerf is interested in consciousness research, aiming to uncover the neural signatures of consciousness and explore fundamental questions about self-awareness and subjective experience. His research in this area contributes to the broader understanding of consciousness from a neuroscientific perspective. In addition, Dr. Cerf explores the dynamics of social behavior and interaction from a neural standpoint, examining how neural activity patterns relate to social cognition, empathy, trust, and decision-making in social contexts. This research sheds light on the neural basis of human social interactions and provides insights into interpersonal dynamics and group behavior. Overall, Dr. Moran Cerf’s research interests span a wide spectrum of cognitive neuroscience, with a focus on decision-making, perception, consciousness, and social behavior, aiming to unravel the mysteries of the human brain and its intricate workings.

Publication Top Noted:

Title: Predicting human gaze using low-level saliency combined with face detection

  • Authors: M Cerf, J Harel, W Einhäuser, C Koch
  • Conference: Neural Information Processing Systems (NIPS) 20
  • Year: 2008
  • Cited By: 661

Title: Faces and text attract gaze independent of the task: Experimental data and computer model

  • Authors: M Cerf, EP Frady, C Koch
  • Journal: Journal of Vision
  • Volume: 9
  • Issue: 12
  • Year: 2009
  • Cited By: 539

Title: Breathing above the brainstem: Volitional control and attentional modulation in humans.

  • Authors: JL Herrero, S Khuvis, E Yeagle, M Cerf, AD Mehta
  • Journal: Journal of Neurophysiology
  • Year: 2017
  • Cited By: 299

Title: Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe

  • Authors: F Mormann, S Kornblith, RQ Quiroga, A Kraskov, M Cerf, I Fried, C Koch
  • Journal: The Journal of Neuroscience
  • Volume: 28
  • Issue: 36
  • Pages: 8865-8872
  • Year: 2008
  • Cited By: 293

Title: On-line, voluntary control of human temporal lobe neurons

  • Authors: M Cerf, N Thiruvengadam, F Mormann, A Kraskov, RQ Quiroga, C Koch, …
  • Journal: Nature
  • Volume: 467
  • Issue: 7319
  • Pages: 1104-1108
  • Year: 2010
  • Cited By: 218