The MIT-Pillar AI Collective has announced six fellows for the spring 2024 semester. With the program’s support, postgraduate students who are in their final year of master’s or doctoral programs will conduct research in the areas of AI, machine learning, and data science with the goal of commercializing their innovations.
Launched by MIT’s School of Engineering and Pillar VC in 2022, the MIT-Pillar AI Collective supports faculty, postdocs, and graduate students conducting research in artificial intelligence, machine learning, and data science. Supported by a donation from VC Pillar and administered by MIT Deshpande Center for Technology InnovationThe mission of the program is to prepare scientific research for commercialization.
The MIT-Pillar AI Collective fellows for spring 2024 are:
Yasmeen AlFaraj
Yasmeen AlFaraj is a PhD candidate in chemistry whose interests focus on applying data science and machine learning to the design of pliable materials to enable the production of next-generation sustainable plastics, rubbers, and composites. More specifically, she applies machine learning to the design of recent molecular additives to enable the low-cost production of chemically deconstructible thermosets and composites. AlFaraj’s work has led to the discovery of scalable, translatable recent materials that could solve the problem of thermoset waste. As a Pillar Fellow, she will work to bring this technology to market, initially focusing on wind turbine blades and conformal coatings. Through the Deshpande Center for Technological Innovation, AlFaraj is leading a team developing a spin-out focused on recyclable versions of existing high-performance thermosets by incorporating diminutive amounts of a degradable co-monomer. Additionally, she participated in the National Science Foundation Innovation Corps program and recently completed the Spotless Tech Open, where she focused on refining her business plan, analyzing potential markets, securing a complete IP portfolio, and networking with potential funders. AlFaraj earned a bachelor’s degree in chemistry from the University of California, Berkeley.
Ruben Castro Ornelas ’22
Ruben Castro Ornelas is a mechanical engineering doctoral candidate who is passionate about the future of multi-functional robots and designing hardware to leverage AI control solutions. Combining his background in programming, embedded systems, machine design, machine learning, and AI, he has designed a dexterous robotic hand capable of performing useful everyday tasks without sacrificing size, durability, complexity, or simulation capabilities. Ornelas’ novel design has significant commercial potential in home, industrial, and healthcare applications, as it can be customized to hold everything from kitchenware to fine items. As a Pillar Fellow, he will focus on identifying potential commercial markets, determining the optimal approach to business-to-business sales, and identifying key advisors. Ornelas served as co-director of StartLabs, an entrepreneurship club for students at MIT, where he earned his bachelor’s degree in mechanical engineering.
Keeley Erhardt ’17, MNG ’17
Vineet Jagadeesan Nair SM ’21
Vineet Jagadeesan Nair is a PhD candidate in Mechanical Engineering whose research focuses on power grid modeling and electricity market design to integrate renewables, batteries, and electric vehicles. He has a broad interest in developing computational tools to combat climate change. As a Pillar Fellow, Nair will explore the application of machine learning and data science to energy systems. In particular, he will experiment with approaches to improve the accuracy of electricity demand and supply forecasting with high spatiotemporal resolution. In collaboration with Project Tapestry @ Google X, he is also working to combine physics-based machine learning with conventional numerical methods to boost the speed and accuracy of high-fidelity simulations. Nair’s work could facilitate realize future grids with high penetration of renewables and other pristine, distributed energy sources. In addition to his academic work, Nair is lively in the entrepreneurial field, most recently helping to organize the MIT Global Startup Workshop 2023 in Greece. He holds a Master of Science in Computational Science and Engineering from MIT, a Master of Philosophy in Energy Technologies from the University of Cambridge as a Gates Scholar, and a Bachelor of Science in Mechanical Engineering and a Bachelor of Science in Economics from the University of California, Berkeley.
Mahdi Ramadan
Mahdi Ramadan is a PhD candidate in brain and cognitive sciences whose research interests lie at the intersection of cognitive science, computational modeling, and neural technologies. His work uses novel methods for unsupervised learning and generating interpretable representations of neural dynamics, leveraging recent advances in artificial intelligence, particularly contrastive and geometric deep learning techniques that can uncover the hidden dynamics underlying neural processes with high fidelity. As a Pillar Fellow, he will apply these methods to better understand lively models of muscle signals for generative motor control. By augmenting current spinal prostheses with AI generative motor models that can streamline, accelerate, and correct limb muscle activations in real time, and potentially using multimodal vision-linguistic models to infer patient intent at high levels, Ramadan aims to build truly scalable, accessible, and commercially viable neuroprostheses. Ramadan’s entrepreneurial experience includes co-founding UltraNeuro, a neurotechnology startup, and co-founding Presizely, a computer vision startup. He holds a bachelor’s degree in neurobiology from the University of Washington.
Rui (Raymond) Zhou
Rui (Raymond) Zhou is a PhD candidate in mechanical engineering whose research focuses on multimodal AI in engineering design. As a Pillar Fellow, he will develop models that enable designers to translate information in any modality or combination of modalities into comprehensive 2D and 3D designs, including parametric data, component visualizations, assembly plots, and sketches. These models can also optimize existing human designs to achieve goals such as improving ergonomics or reducing drag coefficient. Ultimately, Zhou aims to translate his work into a software-as-a-service platform that will redefine product design across sectors from automotive to consumer electronics. His efforts have the potential to not only speed up the design process but also reduce costs, opening the door to unprecedented levels of personalization, ideation, and rapid prototyping. In addition to his academic pursuits, Zhou founded UrsaTech, a startup that integrates AI with education and engineering design. He earned a bachelor’s degree in electrical engineering and computer science from the University of California, Berkeley.