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Research Grants Awarded for Pioneering Work in STEM, Health Sciences

Recipients of faculty research grants: top row, l-r, Irina Catrina, Anderson Oliveira, Rajalakshmi Viswanathan, David Sweet; second row, Rana Khan, Margarita Vigodner, Amiya Waldman-Levi, Honggang Wang; third row, Youshan Zhang, Radhashree Maitra, Ramesh Natarajan, Mehdi Hasan.

The Katz School of Science and Health has awarded 10 research grants to faculty for pioneering projects that address critical issues in STEM and the health sciences. These interdisciplinary initiatives range from developing artificial intelligence tools to assessing children’s playfulness to designing new treatments for women’s infertility and creating digital healthcare solutions for underserved communities.

In collaboration with academic and industry partners, including Montefiore Medical Center, 鶹ýӳ's Stern College for Women and Yeshiva College, the projects aim to tackle key challenges in medicine, technology and social equity.

“This initiative is designed to stimulate new research and expand existing studies that increase the Katz School’s visibility while also creating research opportunities for full-time graduate students,” said Dr. Paul Russo, dean of the Katz School and vice provost of 鶹ýӳ. “The diverse research initiatives reflect the school’s commitment to advancing science, technology and healthcare to solve pressing real-world problems.”

High-Dimensional Bayesian Optimization Expanding and Enhancing Faculty Research in STEM and Health (David Sweet, M.S. in Artificial Intelligence) aims to improve a method called Bayesian Optimization, which helps design and enhance systems like AI models, social media platforms and pharmaceuticals by running experiments. These experiments are essential for seeing how well a system works, but they can be expensive and time-consuming. His project will focus on dealing with complex systems that have many variables, or dimensions. More dimensions usually mean more experiments, but his goal is to reduce the number of experiments needed while still finding the best solutions. He and his eam are developing a new algorithm to make this process faster and more efficient, especially for systems with many variables.

From Ocean Depths to Biotechnological Innovations: Characterizing Novel Luciferases (Anderson Oliveira, Stern College for Women) will focus on the study of bioluminescence—how certain living organisms produce light. The project aims to discover and study new enzymes, called luciferases, from deep-sea animals called cnidarians. These luciferases are responsible for the light emission when they interact with a chemical called coelenterazine. The team will use advanced techniques, like RNA sequencing and molecular cloning, to understand how these luciferases work, while modifying these luciferases to make them more efficient. This could lead to improvements in various biological studies and practical applications, like drug screening and molecular imaging.

Identification of Molecular Glues for Targeted Protein Degradation (Rajalakshmi Viswanathan, Yeshiva College) This project will investigate a new approach to treating diseases, such as cancer, inflammatory and immune disorders, by targeting proteins that are traditionally considered “undruggable.” These proteins are difficult to attack with regular drugs because they lack suitable sites for binding or can’t interact with the enzyme Ubiquitin ligase, which helps degrade and remove unwanted proteins in cells. To solve this problem, Viswanathan’s team is using a new technique involving small molecules called molecular glue degraders. These molecules help bring the problematic protein, called the protein of interest, and the enzyme Ubiquitin ligase, called the effector protein, close together. The project aims to find the best molecular glue from a library of small molecules that can form this ternary complex and effectively degrade the target protein.

Developing Tools for Fast and Sensitive Detection and Functional Analysis of Any RNA Target (Irina Catrina, Yeshiva College, and Rana Khan, M.S. in Biotechnology Management and Entrepreneurship) This project will focus on understanding how RNA, a molecule that plays a key role in cellular functions, moves within cells. The project will use advanced techniques to design special probes that can detect and visualize RNA in cells, helping scientists better understand how it contributes to processes like gene expression—how information from DNA is used—and mRNA translation, the process of making proteins. The researchers will also focus on improving the probes to make them more efficient and cost-effective, which could lead to breakthroughs in diagnosing and treating diseases, such as cancer and genetic disorders.

Regulation of Transcription Factors by SUMO Proteins in Testicular Sertoli Cells (Margarita Vigodner, 鶹ýӳ Stern College for Women, and Rana Khan, M.S. in Biotechnology Management and Entrepreneurship) The research will use transgenic mice to explore how these proteins influence fertility at the cellular level, potentially leading to new fertility treatments. This project focuses on the misregulation of the cell cycle in testicular cells and studies how it can lead to infertility or testicular cancer. The goal is to obtain new information about cell cycle regulation in normal and cancerous cells, particularly through studying a recently identified protein that may play an important role.

AI-Powered Play Assessment (Amiya Waldman-Levi, OT Doctorate, and Honggang Wang, Graduate Computer Science and Engineering program) involves the development of an innovative AI tool to streamline behavioral assessment processes in clinical practice and research. The AI-powered tool will provide accurate and reliable scoring for video footage, saving clinicians’ time, ensuring accuracy and reliability of assessments, and reducing costs while improving clinical outcomes.

Clinical Innovation in Rare Diseases Interactive Information Hub (Rana Khan, M.S. in Biotechnology Management and Entrepreneurship, and Youshan Zhang, M.S. in Artificial Intelligence) is an interdisciplinary project that seeks to compile comprehensive information on rare diseases that is currently difficult to find and access into an interactive, centralized digital platform. The hub will streamline access to information for patients, caregivers and healthcare providers, improving the likelihood of diagnosis and treatment for rare diseases affecting over 300 million people worldwide.

Viral-based Treatment Approach Using Oncolytic Viruses and Targeted Delivery to Treat the Polycystic Ovary Syndrome (Radhashree Maitra, Montefiore Medical Center). Maitra is exploring viral-based treatments for polycystic ovary syndrome (PCOS) in her project. PCOS is a leading cause of infertility and other metabolic disorders in women. The innovative approach involves using oncolytic viruses to correct hormonal imbalances and improve ovarian function, offering a more targeted and effective treatment than conventional therapies.

Designing Need-Based Digital Healthcare Service for Underserved Communities (Mehdi Hasan, Yeshiva College) This project aims to change healthcare delivery by creating a digital platform that increases access to care in underserved areas. In communities with limited access to healthcare, environmental health records make it easier to provide consistent care and track patients’ conditions over time. By incorporating telemedicine, mobile health apps and electronic health records, the system seeks to improve patient outcomes and address healthcare inequities. This project is expected to serve 200 to 300 patients each month.

Deep Learning Model Interventions with Applications (Ramesh Natarajan, Yeshiva College) This project concerns the use of large “foundation” AI models for new tasks by modifying them instead of creating new models from scratch. These foundation models take a lot of computing power to train, but they show good, “out-of-the-box” baseline performance in many tasks. The project aims to take these pre-trained models and improve and adjust them for new tasks, a process called “transfer learning." The goal is to create tools that help apply this method to real-world problems, such as analyzing medical and remote-sensing images, by starting with large models trained on different types of data and improving them for specific tasks.

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