Hi, I'm Vahe Gharakhanyan - Research Engineer @ FAIR at Meta
I am driven by the thrill of turning abstract mathematical models into reality. I thrive in collaborative environments where the pressure is high and the challenge is to solve problems no one has touched before.
At a glance
Curated signals on strengths, focus areas, and how they can help.
Contributed to cutting-edge machine learning research at the Facebook Artificial Intelligence Research lab.
Currently accelerating progress in artificial intelligence as a Research Engineer at Meta.
Offers deep technical expertise and insights into scaling complex neural architectures for production systems.
🚀 Career trajectory
Foundational Research
Completed BS in Materials Science at UC Berkeley, establishing a core understanding of chemical and electrical engineering principles.
✦ Academic Research
✦ Chemical Engineering
Advanced Computational Theory
Earned MS and PhD at Columbia University, specializing in solid state physics and computational material science.
✦ Doctoral Research
✦ Computational Modeling
Industry Implementation
Applied advanced AI at X and Meta, transitioning from theoretical discovery to industry-scale research engineering.
✦ AI Research
✦ Generative Modeling
💪🏻 Superpowers
Algorithmic Material Architect
Pioneering new methods for inverse material design using advanced ML.
✦ Develop generative models to discover novel material properties.
✦ Integrate quantum simulations into scalable data pipelines.
✦ Transform theoretical physics concepts into production-ready software.
Cross-Disciplinary Strategist
Synthesizing chemistry, physics, and computer science to solve complex challenges.
✦ Unite disparate technical fields to innovate beyond traditional silos.
✦ Optimize computational workflows for high-dimensional data sets.
✦ Translate abstract mathematical models into functional physical outcomes.
Moonshot Innovator
Accelerating scientific breakthroughs through experimental AI residency.
✦ Navigate high-uncertainty environments to iterate on disruptive technologies.
✦ Apply neural network architectures to complex physical systems.
✦ Drive research from conceptual modeling to empirical validation.
I'm excited about
✦ Exploring interdisciplinary collaborations with leaders in the AI4Science space.
✦ Finding new avenues to apply quantum-inspired algorithms to sustainability challenges.
✦ Connecting with technical founders who are building the next generation of scientific infrastructure.
I can help with
✦ Provide technical mentorship on applying generative models to physical science problems.
✦ Advise on navigating the transition from academic research to industry engineering roles.
✦ Share insights on optimizing ML workflows for high-performance scientific computing.
I would love your help on
✦ Connect with experts who are successfully deploying AI in hardware and materials manufacturing.
✦ Exchange knowledge on best practices for scaling research projects into industrial products.
✦ Discover new opportunities to engage with emerging communities focused on computational physics.