The MIT-Pillar AI Collective has announced six fellows for the spring 2024 semester. With support from this system, the graduate students, who’re of their final 12 months of a master’s or PhD program, will conduct research within the areas of AI, machine learning, and data science with the aim 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 students conducting research on AI, machine learning, and data science. Supported by a present from Pillar VC and administered by the MIT Deshpande Center for Technological Innovation, the mission of this system is to advance research toward commercialization.

The spring 2024 MIT-Pillar AI Collective Fellows are:

Yasmeen AlFaraj

Yasmeen AlFaraj is a PhD candidate in chemistry whose interest is in the applying of information science and machine learning to soft materials design to enable next-generation, sustainable plastics, rubber, and composite materials. More specifically, she is applying machine learning to the design of novel molecular additives to enable the low-cost manufacturing of chemically deconstructable thermosets and composites. AlFaraj’s work has led to the invention of scalable, translatable latest materials that would address thermoset plastic waste. As a Pillar Fellow, she is going to pursue bringing this technology to market, initially specializing in wind turbine blade manufacturing and conformal coatings. Through the Deshpande Center for Technological Innovation, AlFaraj serves as a lead for a team developing a spinout focused on recyclable versions of existing high-performance thermosets by incorporating small quantities of a degradable co-monomer. As well as, she participated within the National Science Foundation Innovation Corps program and recently graduated from the Clean Tech Open, where she focused on enhancing her marketing strategy, analyzing potential markets, ensuring a whole IP portfolio, and connecting with potential funders. AlFaraj earned a BS in chemistry from University of California at Berkeley.

Ruben Castro Ornelas

Ruben Castro Ornelas is a PhD student in mechanical engineering who’s keen about the long run of multipurpose robots and designing the hardware to make use of them with AI control solutions. Combining his expertise in programming, embedded systems, machine design, reinforcement learning, and AI, he designed a dexterous robotic hand able to carrying out useful on a regular basis tasks without sacrificing size, durability, complexity, or simulatability. Ornelas’s progressive design holds significant business potential in domestic, industrial, and health-care applications since it may very well be adapted to carry every little thing from kitchenware to delicate objects. As a Pillar Fellow, he’ll give attention to identifying potential business markets, determining the optimal approach for business-to-business sales, and identifying critical advisors. Ornelas served as co-director of StartLabs, an undergraduate entrepreneurship club at MIT, where he earned an BS in mechanical engineering.

Keeley Erhardt

Keeley Erhardt is a PhD candidate in media arts and sciences whose research interests lie within the transformative potential of AI in network evaluation, particularly for entity correlation and hidden link detection inside and across domains. She has designed machine learning algorithms to discover and track temporal correlations and hidden signals in large-scale networks, uncovering online influence campaigns originating from multiple countries. She has similarly demonstrated using graph neural networks to discover coordinated cryptocurrency accounts by analyzing financial time series data and transaction dynamics. As a Pillar Fellow, Erhardt will pursue the potential business applications of her work, resembling detecting fraud, propaganda, money laundering, and other covert activity within the finance, energy, and national security sectors. She has had internships at Google, Facebook, and Apple and held software engineering roles at multiple tech unicorns. Erhardt earned an MEng in electrical engineering and computer science and a BS in computer science, each from MIT.

Vineet Jagadeesan Nair

Vineet Jagadeesan Nair is a PhD candidate in mechanical engineering whose research focuses on modeling power grids and designing electricity markets to integrate renewables, batteries, and electric vehicles. He’s broadly concerned about developing computational tools to tackle climate change. As a Pillar Fellow, Nair will explore the applying of machine learning and data science to power systems. Specifically, he’ll experiment with approaches to enhance the accuracy of forecasting electricity demand and provide with high spatial-temporal resolution. In collaboration with Project Tapestry @ Google X, he can be working on fusing physics-informed machine learning with conventional numerical methods to extend the speed and accuracy of high-fidelity simulations. Nair’s work could help realize future grids with high penetrations of renewables and other clean, distributed energy resources. Outside academics, Nair is lively in entrepreneurship, most recently helping to arrange the 2023 MIT Global Startup Workshop in Greece. He earned an MS in computational science and engineering from MIT, an MPhil in energy technologies from Cambridge University as a Gates Scholar, and a BS in mechanical engineering and a BA in economics from University of California at Berkeley.

Mahdi Ramadan

Mahdi Ramadan is a PhD candidate in brain and cognitive sciences whose research interests lie on the intersection of cognitive science, computational modeling, and neural technologies. His work uses novel unsupervised methods for learning and generating interpretable representations of neural dynamics, capitalizing on recent advances in AI, specifically contrastive and geometric deep learning techniques able to uncovering the latent dynamics underlying neural processes with high fidelity. As a Pillar Fellow, he’ll leverage these methods to realize a greater understanding of dynamical models of muscle signals for generative motor control. By supplementing current spinal prosthetics with generative AI motor models that may streamline, speed up, and proper limb muscle activations in real time, in addition to potentially using multimodal vision-language models to infer the patients’ high-level intentions, Ramadan aspires to construct truly scalable, accessible, and capable business neuroprosthetics. Ramadan’s entrepreneurial experience includes being the co-founder of UltraNeuro, a neurotechnology startup, and co-founder of Presizely, a pc vision startup. He earned a BS in neurobiology from University of Washington.

Rui (Raymond) Zhou

Rui (Raymond) Zhou is a PhD candidate in mechanical engineering whose research focuses on multimodal AI for engineering design. As a Pillar Fellow, he’ll advance models that would enable designers to translate information in any modality or combination of modalities into comprehensive 2D and 3D designs, including parametric data, component visuals, assembly graphs, and sketches. These models could also optimize existing human designs to perform goals resembling improving ergonomics or reducing drag coefficient. Ultimately, Zhou goals to translate his work right into a software-as-a-service platform that redefines product design across various sectors, from automotive to consumer electronics. His efforts have the potential to not only speed up the design process but additionally reduce costs, opening the door to unprecedented levels of customization, idea generation, and rapid prototyping. Beyond his academic pursuits, Zhou founded UrsaTech, a startup that integrates AI into education and engineering design. He earned a BS in electrical engineering and computer sciences from University of California at Berkeley.