Mark V. Albert


Machine learning to inform clinical care, focusing on rehabilitation and wearables.

"If you have build castles in the air, your work need not be lost; that is where they should be, now put the foundations under them." - Thoreau

Dr. Albert's professional goal in life is to leverage machine learning to automate the collection and inference of clinically useful health information to improve clinical research. His projects in wearable sensor analytics have improved the measurement of health outcomes for individuals with Parkinson's disease, stroke, and transfemoral amputations with a variety of additional populations and contexts including children with cerebral palsy as well as healthy toddler activity tracking.  Current projects include video-based activity tracking and mobile robotic platforms, all in an effort to improve measures of clinical outcomes to justify therapeutic interventions.

PhD Students (9)

Thasina Tabashum

Ph.D. Candidate, also with Dr. Ting Xiao

Predicted modeling of clinically relevant score outcomes using natural language processing. 


ThasinaTabashum at 

Thasina is our lab manager coming all the way from Bangladesh in 2019. Generally, she is applying machine learning strategies to more precisely measure health outcomes due to therapeutic interventions of cerebral palsy subjects. Her current research focus is applying in-context learning methods utilizing large language models to predict clinical outcomes such as pain scores and fall risk scores. 

Steve (Shou-Jen) Wang

Ph.D. Candidate

Masters of Science in Technology from Eastern Illinois University


Shou-JenWang at 

Steve has developed a machine learning model for sensors placed on the arm to automatically measure spasticity (a condition in certain muscles are continuously contracted causing stiffness or tightness of the muscles and can interfere with normal movement) to enable more precision in discerning spasticity treatments. 

He is currently working with the Shriners Hosptials for Children on a predictive model of surgical outcomes for children with cerebral palsy, aiding surgeons in the selection of therapeutic options to improve gait. 

Himanshu Sharma 

PhD Candidate


Himanshu joined UNT in Fall 2018 and completed his Master’s in Computer Science (Spring 2020). Now he has joined Computer Science PhD program in Fall 2020. He is currently working on EKG signal compression using deep neural network autoencoders. His interest includes computational neuroscience and natural language processing. He has interest in cognitive computing, natural language processing and computational robotics.  

Chandrashekhar Ramamurthi  

PhD Student

ChandrashekharRamamurthi at

Chandrashekhar joined UNT in 2020 currently pursuing his PhD with specialization in Machine learning and Deep learning. His is currently working on scoring symptoms of Parkinson's Disease through measurements during quiet standing. He is currently involved in applying the predictive models to clinical decision support. He is interested in application of Artificial Intelligence in clinical care. 

Saba Yousefian Jazi

PhD Candidate

Linkedin, GitHub, GoogleScholar


I am a Computer Science and Engineering PhD student at the University of North Texas, involved in different research project such as non-marked tumor tracking in lung imaging, emotion based recommender system, multispectral image analysis, point cloud clustering and many more. My most recent project, involves developing and assessing physiologically appropriate efficient coding models for LGN/V1 spontaneous neural activity and describing their role in binocular vision evolution, and other modalities of data such as audio, images, and video. 

Riyad Bin Rafiq

PhD Candidate, also with Dr. Weishi Shi


RiyadBinRafiq at 

Riyad joined the Biomedical AI Lab in the Spring of 2021. His research interest lies in the field of biomedical application and deep learning. He is specifically interested in exploring the use of transfer learning, continual learning, and meta-learning for custom gesture learning strategy in the small-data paradigm. Currently, he is working on wearable gesture recognition for use in medical applications, particularly for individuals unable to speak. Previously, Riyad completed his undergrad in CSE at Chittagong University of Engineering and Technology (CUET) in Bangladesh.

Ziruo Yi

PhD Candidate

LinkedIn, Google Scholar 

ziruoyi at

Ziruo is a Computer Science and Engineering PhD student in our lab. Her research focuses on multimodal machine learning, natural language processing, computer vision, large language models, image captioning, visual question answering and artificial intelligence in healthcare. 

Guna Sindhuja Siripurapu

PhD Student

LinkedIn, GunaSindhujaSiripurapu at

Sindhuja joined the lab as a PhD student in Fall 2023. She is currently working on a project that focuses on Bayesian Analysis to decode Human Preferences, combining the elements of both computer science and neuroscience. Previously, Sindhuja worked as a Research Assistant in the domain of VR/AR/XR and Game Development, which provided her with a strong foundation for exploring new research areas and expanding her knowledge horizons. Now, she is passionate and curious about delving deeper into the human mind through computational neuroscience and cognitive computing. Additionally, she has a keen interest in the domains of Artificial Intelligence, Machine Learning, and Human-Computer Interaction and desires to make significant contributions in these fields.

Sekhar Lanka

PhD Student

MS Students

Ernest William Cubit

BS in Materials Science 

MS in Artificial Intelligence 


William joins us with a BS in Materials Science. Pursuing a MS in Artificial Intelligence, his research is focused on ML-based alloy design, particularly studying deformation-induced transformations in High Entropy Alloys using Few-Shot Learning, Active Learning, and NLP. Additionally, William is exploring predictive modeling of crystallographic shifts through nanoindentation using various ML techniques such as Logistic Regression, SVM, and Decision Trees. His work aims to bridge the gap between traditional materials science and modern machine learning approaches to unlock new potentials in alloy design and predictive modeling. 

Hari Kiran Keerthipati 

Bhavani Rachakatla 

Master of Science in Computer Science

Linkedin,  BhavaniRachakatla at 

Bhavani Rachakatla combines a solid 4.5 year background in software engineering at Vistex Inc with a burgeoning interest in machine learning and predictive analytics. Her existing publications in CRC Press and Springer highlight her commitment to applying data insights in solving real-world problems, a focus that will continue in her forthcoming MS thesis on Fall Detection and Real-time response.

Usha Chandrashekar 



Arlene Makia 

Aditya Nallaparaju

General Science and Mathematics Track at the Texas Academy of Mathematics and Science 

aditya.nallaparaju at 

As a junior at the Texas Academy of Mathematics and Science pursuing the General Science and Mathematics Track, I am fascinated by the intersection of biomedicine, computer science, and health administration. The opportunity to conduct research in this lab aligns perfectly with my academic interests, allowing me to examine the connections between biomedical engineering and computer science firsthand. With my background in the sciences and mathematics, I am well-prepared to contribute to the innovative work being done in the lab while gaining invaluable hands-on research experience at the interface of these exciting fields. 

Emily Godinez-Martinez 


Kaylyn King 


Lab Affiliated

Namratha Urs

Ph.D. Student, member of HiLT lab

Efficient neural coding of sensory signals

NamrathaUrs at 

Namratha regularly participates in projects through the adjoining Human Intelligence and Language Technologies (HiLT) Lab. She is pursuing a computational neuroscience project demonstrating how the early visual and auditory systems can be understood through efficient coding - in essence you can "derive" a visual system from a (neurally) appropriate efficient coding of natural scenes. She has created a Jupyter notebook demonstrating this in a number of modalities (black and white, color, and "natural" audio input) and presented at SfN 2019 (the Society for Neuroscience conference), with a goal of making the notebook readily accessible to anyone, computational or not, that is interested in understanding a link between computer science and neuroscience.

Past students



Former Thesis Graduate Students

Former Graduate Students, non-thesis research

Undergrad and TAMS