The MIT-Pillar ai Collective has announced six fellows for the spring 2024 semester. With support from the program, graduate students, who are in the final year of a master's or doctoral program, will conduct research in the areas of artificial intelligence, machine learning and data science with the aim of commercializing their innovations.
Launched by the MIT School of Engineering and Pillar VC in 2022, the MIT-Pillar ai Collective supports faculty, postdocs, and students conducting research in ai, machine learning, and data science. Supported by a gift of Pilar VC and managed by MIT Deshpande Center for Technological InnovationThe program's mission is to advance research toward commercialization.
The Spring 2024 MIT-Pillar ai Collective Fellows are:
Yasmeen Al Faraj
Yasmeen AlFaraj is a PhD candidate in chemistry whose interest is the application of data science and machine learning to the design of soft materials to enable next-generation sustainable plastics, rubber and composites. More specifically, she is applying machine learning to the design of new molecular additives to enable low-cost manufacturing of chemically deconstructable composites and thermostables. AlFaraj's work has led to the discovery of new scalable and translatable materials that could address thermoset plastic waste. As a Pillar Fellow, she will seek to bring this technology to market, initially focusing on the manufacturing of wind turbine blades and conformal coatings. Through the Deshpande Center for technology Innovation, AlFaraj acts as leader of a team developing a spin-off company focused on recyclable versions of existing high-performance thermosets by incorporating small amounts of a degradable comonomer. Additionally, he participated in the National Science Foundation's Innovation Corps program and recently graduated from the Clean tech Open, where he focused on improving his business plan, analyzing potential markets, securing a complete intellectual property portfolio, and connecting with potential funders. AlFaraj earned a bachelor's degree in chemistry from the University of California, Berkeley.
Ruben Castro Ornelas
Rubén Castro Ornelas is a mechanical engineering PhD student passionate about the future of multipurpose robots and designing the hardware to use them with ai control solutions. Combining his expertise in programming, embedded systems, machine design, reinforcement learning, and artificial intelligence, he designed a dexterous robotic hand capable of performing useful everyday tasks without sacrificing size, durability, complexity, or simulability. Ornelas' innovative design has significant commercial potential in domestic, industrial and healthcare applications because it could be adapted to hold everything from kitchen utensils to delicate objects. As a Pillar Fellow, he will focus on identifying potential business markets, determining the optimal approach to business-to-business sales, and identifying critical advisors. Ornelas served as co-director of StartLabs, a university entrepreneurship club at MIT, where he earned a bachelor's degree in mechanical engineering.
Keeley Erhardt
Keeley Erhardt is a PhD candidate in media arts and sciences whose research interests lie in the transformative potential of ai in network analysis, particularly for entity correlation and the detection of hidden links within and between domains. She has designed machine learning algorithms to identify and track temporal correlations and hidden signals in large-scale networks, uncovering online influence campaigns originating in multiple countries. Similarly, she has demonstrated the use of graph neural networks to identify coordinated cryptocurrency accounts by analyzing financial time series data and transaction dynamics. As a Pillar Fellow, Erhardt will pursue potential commercial applications of her work, such as detecting fraud, propaganda, money laundering and other covert activities in the finance, energy and national security sectors. She has interned at Google, Facebook, and Apple and held software engineering positions at multiple tech unicorns. Erhardt earned a master's degree in electrical and computer engineering and a bachelor's degree in computer science, both 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 renewable energy, batteries and electric vehicles. He is broadly interested in developing computational tools to address climate change. As a Pillar Fellow, Nair will explore the application of machine learning and data science to energy systems. Specifically, he will experiment with approaches to improve the accuracy of electricity demand and supply forecasting with high spatio-temporal resolution. In collaboration with Project Tapestry @ Google Nair's work could help create future networks with high penetration of renewables and other clean, distributed energy resources. Outside of academia, Nair is active in entrepreneurship and most recently helped organize the MIT Global Startup Workshop 2023 in Greece. He earned a master's degree in engineering and computer science from MIT, a master's degree in energy technologies from the University of Cambridge as a Gates Scholar, and a bachelor's degree in mechanical engineering and a bachelor's degree in economics from the University of California at 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 unsupervised methods to learn and generate interpretable representations of neural dynamics, taking advantage of recent advances in ai, specifically contrastive and geometric deep learning techniques capable of uncovering the latent dynamics underlying neural processes with high fidelity. As a Pillar Fellow, he will leverage these methods to gain a better understanding of dynamic patterns of muscle signaling for generative motor control. By complementing current spinal prostheses with ai generative motor models that can streamline, accelerate and correct limb muscle activations in real time, as well as potentially using multimodal vision and language models to infer high-level intentions of the patients, Ramadan aims to build truly scalable, affordable and capable commercial neuroprostheses. Ramadan's entrepreneurial experience includes co-founder of UltraNeuro, a neurotechnology startup, and co-founder of Presizely, a computer vision startup. He earned 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 for engineering design. As a Pillar Fellow, he will develop models that could enable designers to translate information in any modality or combination of modalities into comprehensive 2D and 3D designs, including parametric data, component visuals, assembly graphics, and sketches. These models could also optimize existing human designs to achieve goals such as improving ergonomics or reducing the drag coefficient. Ultimately, Zhou aims to translate his work into a software-as-a-service platform that redefines 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 customization, idea generation, and rapid prototyping. Beyond his academic pursuits, Zhou founded UrsaTech, a startup that integrates ai into engineering education and design. He earned a bachelor's degree in electrical engineering and computer science from the University of California at Berkeley.
