Hakima Mokrane PhD

I am reviewer in various journals of physics and machine learning applied to science and technology such as : Machine Learning : Science and Technology, International Journal of Physics: Study and Research and International Journal of Fundamental Physical Science (IJFPS). - Machine Learning: Science and Technology is a multidisciplinary journal that bridges the application of machine learning across a broad range of subject disciplines (extending to physics, materials science, chemistry, biology, biomedicine, earth science and space science). A particular focus is on new conceptual advances in machine learning methods motivated by the physical sciences. Particular areas of interest include (but are not limited to): Atomistic and coarse-grained simulation Design and discovery of novel materials and molecules Quantum machine learning Simulation of molecules, chemical processes and biological systems Applications of machine learning in physics and space science Applications of machine learning in biomedicine and imaging Applications of machine learning in geoscience (including natural disaster prediction) and climatology Neuro-inspired computing (including neuromorphic computing) New conceptual advances in machine learning methodology motivated by physical insights Applied algorithms and high-performance computing

Advisor to a hand-picked portfolio of deep-tech startups at UC Berkeley’s elite SkyDeck accelerator (sub-1% acceptance). I help founders convert world-class research into scalable products, investor-ready businesses, and durable competitive advantage. Shape scientific and technical strategy: define milestone-driven R&D roadmaps, validation experiments, and reproducibility standards. Translate research into product: prioritize MVPs, design technical architectures, and set data/ML validation metrics. Build go-to-market and fundraising readiness: refine value proposition, craft investor pitches, size markets (TAM/SAM/SOM) and prepare for VC diligence. Accelerate commercialization: secure pilot partnerships, de-risk regulatory/operational pathways, and optimize technical-business handoffs. Mentor founders and teams: hiring strategy, leadership coaching, scaling engineering/ML talent, and instilling ethical AI practices. Empowering ambitious teams to turn scientific breakthroughs into market-leading companies.

Scientific reviewer and panelist for NASA and the Natural Sciences and Engineering Research Council of Canada (NSERC). I help shape national research strategy in space science, physics, AI and STEM by reviewing high-impact proposals, guiding federal investments, and advising on long-term priorities and mission readiness. Review proposals in astrophysics, space engineering, AI/ML and deep-tech: assess scientific excellence, technical approach, feasibility and innovation potential. Review methodological rigor: reproducibility, data quality, model validation, uncertainty quantification, and safety/ethical safeguards for missions and AI systems. Chair and participate in panels to harmonize assessments, calibrate scoring, and translate peer review into robust funding recommendations. Advise on strategic roadmaps, mission-readiness, translational pathways and workforce development to maximize scientific return and societal benefit. Mentor junior reviewers and contribute templates, checklists and best practices to raise review standards across agencies. Driving rigorous, transparent funding choices that accelerate discovery in space science and deploy trustworthy AI and deep-tech for public benefit.

Scientific advisor focused on astrophysics, AI and deep-tech, reviewing and shaping high-impact research funding across Europe. I ensure proposals demonstrate rigorous science, methodological robustness and clear paths to discovery and real-world deployment — accelerating breakthroughs from telescope data and theory to production-ready AI systems and deep-tech solutions. • Review competitive proposals in astrophysics, space science, AI/ML and deep-tech: judge scientific excellence, technical approach, feasibility and innovation potential. • Review methodological rigor: reproducibility, data quality, model validation, uncertainty quantification and ethical safeguards. • Advise on research priorities, translational roadmaps and commercialization pathways to maximize scientific and societal impact. • Serve as quality-checker: validate scores, audit panel consistency, detect/mitigate bias, verify compliance with the European Research Executive Agency (REA) & SMEs Executive Agency (EISMEA) guidelines and resolve conflicts of interest. • Harmonize assessment outcomes, maintain evaluation templates and best-practice checklists, and mentor junior reviewers to raise overall evaluation standards. Driving fair, transparent funding decisions that translate scientific excellence in astrophysics and AI into measurable advances for Europe.

As a Board Advisor, my role is to provide domain-relevant technical insights to differents AI and scientific projects. I am working alongside with the CEO and the greater team in the commercialization of these technologies. I lead a team of machine learning to build an end-to-end neural systems application for facial recognition, quantum computing, AI human-centric, deep tech explorations, and solving some of the world's biggest problems… I'm in charge of the management of the R&D projects and teams, budgets, and fundraising to develop our activities. Our main activities are artificial intelligence, deep neural networks, quantum computing, banking, marketplace, social network, fintech, financial inclusion, business inclusion, defi, blockchain, quantum encryption, DAO, erp, cryptography, e-learning, business building, decentralized, and entrepreneurship.

The Newchip Accelerator is an immersive accelerator program for Early Stage to Serie A startups serious about scaling their businesses to the next level. We provide founders with all of the tools and skills necessary to build, scale, and fund their startups. I help founders to solve real-world problems in AI, Robotics, healthcare, and many others …

Fountech group of companies is the home of thinking artificial intelligence, a home for a deep tech AI startups. Having spun-out from Fountech.ai, where theoretical research and the commercialization of artificial intelligence ideas has been our thing for longer than most - we embrace startups in artificial intelligence, robotics, quantum computing and blockchain at its core. As a Board Advisor, my role is to provide domain-relevant technical insights to the portfolio companies, and I am working alongside with the CEO and the greater team in the commercialization of these technologies.

Development of Eye tracking & Gaze prediction software using Deep learning technologies - Interaction human computer and computer vision - Design algorithm and development in python The goal of this project is to put the power of eye tracking by building a software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices. The prediction of gaze is an important tool in many domains from scientific research to commercial applications for example: human–computer interaction techniques to medical diagnoses to psychological studies to computer vision, eye tracking has applications in many areas such as AR, VR, Robotic... In these fields, the trend to capture subconscious and unbiased data through implicit methods is growing. Eye tracking is among the most effective of these techniques. Increase in use of eye tracking technology in the military and aerospace sectors drive the growth of the global eye tracking market. Moreover, the rise in investment on smart & wearable technology across the industry verticals and increase in demand for assistive communication devices, also fuel the growth of the eye tracking market. The growth in automation and rapid acceptance of robotics technology across the industry verticals restrict the market growth. Conversely, the rapid growth of eye tracking in new applications such as lie-detecting systems, video gaming industry, and cognitive testing, also in aviation industries and R&D in the field of augmented reality, virtual reality, and others are projected to drive the market in near future. Eye tracking lets you see how consumers react to different marketing messages and understand their cognitive engagement, in real time. Below the link for project : https://platform.insightdata.com/projects/deepgaze-ml-powered-eye-tracking-gaze-prediction Technical skills : Python, opencv, TensorFlow, ..

Astronomy, Astrophysics, Astrochemistry. - Gas grain chemical modeling of the interstellar medium, in low mass and high mass star forming region. - Improvement of grain surface reaction modeling. My research has contributed to our understanding of how complex molecules form in space, which is crucial for understanding the origins of planets and life. My research involves conducting experiments with laboratory ice and making astronomical observations to investigate the formation of crucial astrochemical molecules and their potential transformation into larger molecules relevant to the origins of life. An integral part of this research is to refine our understanding of the fundamental physical and chemical processes that drive ice chemistry. In addition to studying the chemical evolution in space, our work provides a foundation for creating molecular probes for a variety of astrophysical phenomena. Technical skills : Fortran, IDL, LATEX, mathematical modeling ...

Facial emotion analysis, facial micro-expression detection and analysis, object detection and predictive analysis. Facial micro-expressions and voice tonality analysis for Lie Detection. Tools : Python, Scikit learn, Numpy, Deep Neural Network, OpenCV, Computer vision, Computer Humain Interaction ...

Molecular Astrophysics, Astrochemistry. - Atomic and molecular physics applied to the study of molecules of astrophysical interest (planetary atmospheres, cometary, interstellar medium ...) - I use laboratory experiments to expand our understanding of physical processes and then apply these results to the processes throughout the Universe. In some cases laboratory experiments can reproduce similar physics for understanding of some of the key astrophysical processes in the interstellar medium and circumstellar environments. The knowledge of these processes helps us to understand the spectra, composition, and structure of astronomical objects. Dust and molecules can be observed in nearly all astrophysical environments including evolved stars, star-forming regions, protoplanetary disks, comets, and the surface of planets.