Welcome to "FAIR"

Fardina Alam Interdisciplinary Research

Broad

Representation Learning

Development of General Representation Learning Methods for Protein Tertiary Structures

Research

Deep Learning

Design Novel Generative Models for generating physically-realistic protein tertiary structures

Areas

Machine Learning

Applications, Downstream Tasks and Analysis

For more publication updates, check here.

Research Track 01: Representation Learning: The key role that the three-dimensional structure of a protein molecule plays in its function and activities in the cell continues to motivate computational research. In particular, we now know that proteins harness their ability to access different structures to regulate their interactions with other molecules.

My research leverages the growing momentum in generative AI and contributes increasingly sophisticated deep latent variable models that learn informative representations of protein structures. Rigorous empirical evaluation demonstrates the capabilities of these models in sampling the protein structure space and additionally addressing important protein modeling tasks, linking protein structure and function.

I have designed different generative neural network models that learn directly from experimentally-available structures of different protein molecules and generate physically-realistic structures of a target protein, enabling us to expand our in-silico characterization of these ubiquitous molecules beyond the static, single-structure view.

My work advances bioinformatics research in molecular biology.

Research Track 02: Data Science Education, Responsible AI and Data Ethics: This research explores the integration of Artificial Intelligence (AI) tools into academic courses, focusing on their potential benefits for students and the overall learning process. Furthermore, the research addresses the ethical dimensions of working with data science in the educational context. It explores how data collection, analysis, and AI deployment should be conducted responsibly, emphasizing privacy, security, and informed consent when dealing with student data. The aim is to provide guidelines for educational institutions on how to ethically incorporate AI tools while fostering an inclusive, transparent, and equitable learning environment.

For questions on any specific research, feel free to email me.

Current Students

Graduate Students

Mariia Vetluzhskikh

I am a first-year Master's student in Machine Learning at the University of Maryland. I received my BS in mathematics at Central Michigan University in 2024. During my undergraduate studies, I received several awards: International President’s Award (2023), Richtmeyer-Foust Award for Outstanding Senior in Mathematics (2024), Summa Cum Laude (2024).

My current research focuses on ethical ML usage, particularly in academia and higher education.

Research Interests: Ethical AI, Machine Learning, Natural Language Processing, LLM

Swattik Maiti

I completed my undergraduate degree in Computer Science following which I worked for 2 years as a data scientist at a major Fintech Organization.

Currently, I am pursuing an MS in Data Science at the University of Maryland. As an avid Kaggler, I am passionate about participating in machine learning competitions to explore innovative solutions. My primary research focuses on improving credit risk scorecards by model stacking and ensemble techniques.

Research Interests: Credit Risk Modelling, Deep Learning Neural Networks, Survival Analysis

Ritik Pratap Singh

I am an MS student specializing in data science, machine learning, and cloud computing at the University of Maryland, College Park.

My recent work includes designing and implementing end-to-end data pipelines, automating loan document analysis using AI-powered computer vision and Optical Character Recognition (OCR), and developing machine learning models for customer behavior prediction and risk analytics. Additionally, I have experience working on computer vision projects, leveraging tools like OpenCV and TensorFlow for object detection, image classification, and OCR-based text extraction.

Research Interests: Data Engineering, Credit Risk Modeling, Machine Learning, Predictive Analytics, and AI-driven Automation.

Undergraduate Students

Shraddha Pattre

I am a junior computer science major at the University of Maryland graduating in 2025. My primary research focus revolves around neural networks and representation learning within the domain of computer science.

Research Interests: Protein Structure Representation Learning, Protein Fold Classification

Ria Kanani

I am a senior computer science and immersive media design double major at the University of Maryland. My current research focuses on improving a machine learning model that can classify protein structure data after learning its latent representation.

Research Interests: Protein Fold Classification, ML, Data Science and AI.

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