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Jamal (Jimmy) Ansary
Jamal (Jimmy) Ansary
About Me: I am a PhD researcher working at the intersection of machine learning and physical sciences, with a focus on vision–language models and reasoning. My work explores how transformer-based models can learn structure, meaning, and spatial relationships from complex scientific data, particularly in thermal-fluid systems and environmental monitoring.
Research focus: My research focuses on vision–language and multimodal machine learning models for challenging scientific and engineering problems. Although recent advances have improved performance on general visual and textual tasks, models tailored to specialized scientific domains remain limited. My work addresses this gap by grounding multimodal models in real physical systems such as thermal‑fluid processes and environmental monitoring, and by systematically evaluating their reliability, reasoning behavior, and failure modes in data‑scarce and noisy settings.
Future goals: I believe AI is the next major driver of progress in science and industry. I want to take advantage of this shift to work on problems that meaningfully improve people’s lives. Even small contributions matter to me, and my goal is to be part of building AI systems that have real, positive impact.
Saketh Merugu
Saketh Merugu
About me: I am a PhD researcher working on interface engineering, with a focus on developing advanced composite membranes and engineered membrane surfaces for water treatment. My work centers on modifying the solid–liquid interface of membranes using functional nanomaterials to overcome long standing challenges such as fouling, poor wetting behavior, and low stability. I am particularly interested in how nanoscale surface design can fundamentally improve separation
Research Focus: My research focuses on mixed matrix composite membranes for desalination, wastewater treatment, and membrane distillation. I design and fabricate membranes by incorporating nanomaterials, such as copper decorated carbon nanofibers, into polymer matrices to improve selectivity, fouling resistance, and mechanical and thermal stability. I also engineer membrane surfaces to control wetting behavior and enhance vapor–liquid transport by tailoring surface chemistry, roughness, and porosity. In addition, I use machine learning models to predict membrane performance and degradation for data driven membrane optimization.
Future Goals: My future work aims to develop next generation membranes with engineered interfaces for industrial wastewater treatment and desalination. Using fabrication methods such as electrospinning, electroblowing, and 3D printing, I plan to achieve precise control over pore architecture and surface functionality. My long term goal is to create efficient, durable, and sustainable membrane systems that enable advanced separations, including selective recovery of valuable metals from industrial effluents.
Keval Bharatbhai Suthar
Keval Bharatbhai Suthar
About me: I am a PhD student with background in thermal-fluid sciences focused on interfacial heat transfer. My prior experience in academia has provided a strong foundation in thermal engineering and research, while also giving me exposure to machine learning techniques for engineering analysis. This interdisciplinary background enables me to investigate complex thermal-fluid phenomena using computational approaches that complement physical modeling and scientific reasoning.
Research focus: My research focuses on improving thermal management for high‑power electronics, nuclear reactors, and advanced energy systems. By combining data driven methods with physical modeling, I study heat transfer mechanisms in pool boiling and fluid‑particle interactions. This work guides the design of nanomaterials and engineered surfaces that enhance heat transfer performance, informed by analysis at both microscopic and macroscopic scales. My goal is to translate fundamental interfacial heat transfer insights into practical thermal management solutions.
Future goals: My future work aims to address heat dissipation challenges in electronic devices and energy systems through targeted control of interfacial heat transfer. I plan to study the effects of fluid additives, substrate surface modification, and operating conditions on thermal performance, supported by machine learning approaches to accelerate research and optimization. Beyond research, I seek to mentor future engineers and promote the integration of interdisciplinary tools to advance thermal management technologies.
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