People
Khaled Sayed
Assistant Professor of Data Science and AI, Director of the Computational BioSystems Lab.
T: +1-203-479-4783
E: ksayed@newhaven.edu
My research focuses on analyzing microbiome and next-generation sequencing data, with additional interests in computer vision applications for biomedical engineering.
I teach courses in deep learning, natural language processing, and Bayesian data analysis, where I work with students to build both theoretical understanding and practical skills.
My research interests include:
- Microbiome data analysis and NGS data processing
- Applications of computer vision in biomedical engineering
- Statistical approaches using Bayesian methods
- Machine learning applications in computational biology
Open to connecting with fellow researchers and practitioners interested in computational biology, machine learning, or interdisciplinary collaborations.
I teach courses in deep learning, natural language processing, and Bayesian data analysis, where I work with students to build both theoretical understanding and practical skills.
My research interests include:
- Microbiome data analysis and NGS data processing
- Applications of computer vision in biomedical engineering
- Statistical approaches using Bayesian methods
- Machine learning applications in computational biology
Open to connecting with fellow researchers and practitioners interested in computational biology, machine learning, or interdisciplinary collaborations.
Mona Abd El-Aty
Ph.D. Student - Cairo University
I hold a bachelor's degree in Systems and Biomedical Engineering from Cairo University and a master's degree in the same field, with my thesis focusing on graphs and motor imagery classification. With an engineering background, I specialize in machine learning and graph-based analysis. My current research project involves applying graph-based methods to microbiome analysis, aiming to uncover insights into complex biological systems.
Binaya Dhakal
M.Sc. Student - UNewHaven
My research focuses on leveraging deep learning techniques to analyze high-dimensional microbiome data from oral, lung, and gut compartments for mortality risk prediction. By utilizing an innovative autoencoder-based framework, I aim to uncover latent microbial patterns and their impact on systemic health. This work seeks to advance the integration of microbiome analytics into predictive healthcare, contributing to the development of precision medicine applications.
Shruti Brahma
M.Sc. Student - UNewHaven
A data science student interested in blending innovation and empathy. With Guide AI, I craft AR/VR solutions to empower visually impaired individuals, contributing to making assistive technology a little smarter and the world a little kinder. If I am not coding, I am discovering songs that’ll loop for weeks.
Hema Sai Kaja
M.Sc. Student - UNewHaven
As a data science student with a strong focus on leveraging technology to improve lives, I am currently working on the development of deep learning models for brain tumor localization and classification. My objective is to refine tumor detection and classification processes, ultimately contributing to more accurate and effective healthcare solutions.
Yara Elshamy
M.Sc. Student - Cairo University
I am a Master's student in Biomedical Engineering and Systems at Cairo University. My academic journey is complemented by a robust foundation in artificial intelligence, machine learning, and computer vision which helps me to tackle complex healthcare challenges. My current research is focused on building a foundation model for improving the analysis and prediction of microbiome data. The goal of my research is to enhance microbiome data analysis to identify and offer deep insights into microbial communities and the much-needed implications on human health on a broader scale for a better understanding of microbial behavior and its effects on overall well-being
Vaishnavi Kukkala
M.Sc. Student - UNewHaven
I am a passionate data science student and full-stack developer with expertise in Python, machine learning, and statistical analysis. I am currently pursuing my Master’s in Data Science at the University of New Haven and have professional experience as an Associate Software Engineer. My work focuses on developing innovative solutions, and my current research project involves brain tumor detection and classification using deep learning methods to enhance healthcare accuracy and outcomes.
Padmaja Phadke
M.Sc. Student - UNewHaven
I am a data science graduate student with a strong background in machine learning, data engineering, and cloud technologies. My current research focuses on predicting Expected Goals (xG) in soccer games by analyzing factors such as pass angles, distances from the goal, and player locations. The aim is to develop a robust model that provides precise xG predictions, offering valuable insights to enhance strategic decision-making in sports analytics.
Gautam Siwach
Ph.D. Student - UNewHaven
As an L3-Distinguished Data Scientist and Technology Specialist, I guide C-level executives in the banking and financial sectors to modernize their IT infrastructure through AI, automation, and next-generation technologies. My focus is on designing hybrid cloud solutions, ensuring business continuity, and driving performance improvements. Currently, I am leading a project that leverages high-performance compute platforms, including Quantum systems, to develop Confidential AI models for the banking sector, enhancing security, privacy, and operational efficiency while enabling cutting-edge AI automation.
Lakshmi Sai Kishore Savarapu
M.Sc. Student - UNewHaven
Passionate, dedicated and disciplined Data Science Graduate with 3 years of Professional Experience and expertise in Data Analytics, AI, ML, DL and NLP. Proficient in Python programming, SQL querying, Cloud platform usage, and Data Visualization tools. Experienced in leveraging advanced Data Science techniques to enhance model accuracy and reliability across diverse datasets and domains. Skilled in collaborating with cross-functional teams to translate complex data insights into actionable strategies, driving measurable improvements in operational efficiency and business outcomes. My research focuses on leveraging Machine learning techniques to analyze high-dimensional microbiome data from oral, lung, and gut compartments for ARF mortality risk prediction. This work aims to advance the integration of microbiome analytics into predictive healthcare, contributing to the development of precise Healthcare applications.
