People
We are currently a lab of 8 PhD students, 5 MS students (AI&CS), 11 undergraduates, and an additional 4 capstone student teams each semester, working together on a variety of projects, with student interests described below
The team:
Director
PhD students
MS Students
Undergraduate
Lab affiliated students
Capstone groups for Fall 2020 (4 groups)
Capstone groups for Spring 2020 (4 groups)
Past students
Lab Alumni... Where have they gone?
Director
Director
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 (10)
Ph.D. Student, also with Dr. Ting Xiao
Developing medical outcome measures using PCA/autoencoders, speaker segmentation for speech pathology applications
ThasinaTabashum@my.unt.edu
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. Her first project was creating a unified measure of health outcomes for individuals with transfemoral amputations to indicate a consistent improvement in outcomes when using a microprocessor controlled knee. She is currently working on a mobile phone application to automatically segment speakers in a conversation which was originally used to moderate group conversations, but she will use the tool to quantify speaking and social engagement for people undergoing therapy, including individuals with aphasia, to improve their ability to communicate.
Steve (Shou-Jen) Wang
Ph.D. Student
Spasticity prediction with wearables, Surgical outcomes prediction for gait impairments
Shou-JenWang@my.unt.edu
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.
Sahar Behpour
PhD Student, also with Dr. Ting Xiao
Machine Learning, Natural Language Processing, Computational Neuroscience, Efficient Coding, Models of sensory neural development
Sahar.Behpour@unt.edu
Sahar comes to the lab with experience in natural language processing and using machine learning to extract valuable information from diverse text corpora. Her long-term interests are in using machine learning to build models of sensory neural processing and relating that knowledge to neural models of language learning. In particular, she is studying how the visual system in developing animals uses an "Innate visual learning" strategy in which spontaneous neural activity patterns train the visual system prior to eye opening in the same way the visual system adapts to information after eye opening.
Himanshu Sharma
PhD Student
himanshusharma@my.unt.edu
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@my.unt.edu
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 Student
sabayousefianjazi@my.unt.edu
Saba joined the lab as a PhD Student in Fall 2020, and is pursuing combined Kalman filter and deep learning strategies to improve markerless tumor tracking to improve radiation oncology outcomes.
Riyad Bin Rafiq
PhD Student, also with Dr. Ting Xiao
RiyadBinRafiq@my.unt.edu
Riyad has been working as a remote student at Chittagong University of Engineering and Technology and will be formally joining the lab in Spring 2021. He has a strong interest in developing validated machine learning models, collaborating with the Biomedical AI lab and submitting a paper to ITiCSE noting high experiences and interests in proper machine learning model validation. Continuing his work in automated sign language recognition, he will continue to collaborate on projects related to gesture recognition for use in medical applications, particular for individuals unable to speak.
Md Abdullah Al Forhad
PhD Student
MdAbdullahAlForhad@my.unt.edu
Shabbab Algamdi
PhD Student
Ziruo Yi
PhD Student
MS Students (10+)
Chengping Yuan
MS Student
Reinforcement Learning, Decision Making, Game Theory
ChengpingYuan@my.unt.edu
Chengping came to UNT in 2018 and is engaged in Masters thesis work in reinforcement learning. He is the project leader in this research effort with three Texas Academy of Math and Science students, creating and studying the behavior of a system that learns both how to play arbitrary games (tactics) and how to optimally engage opponents for maximum rewards while learning (strategy).
He received his MBA from Missouri State University, and received his BS in Information Systems from Fuzhou University (福州大ĺ¦).
Theo Medeiros
MS in AI, also with Dr. Ting Xiao
TheophilusMedeiros@my.unt.edu
Theo is an Artificial Intelligence graduate student who joined the lab in 2020. He is currently working on Stock2vec Vector Embeddings using deep learning neural networks and dimensionality reduction techniques. He leads a team of two Texas Academy Math and Science students researching vector representations of stocks to predict various business outcomes. He has done some research work in recommender systems and also the application of NLP methods in intellectual properties.
Ryan Hunter Moye
MS in AI
ryanmoye@my.unt.edu
Ryan is pursuing his masters in artificial intelligence with a concentration in biomedical engineering. He is interested in computational neuroscience and image processing. His current work in the lab is in applying efficient coding techniques and ICA to an Android app. The app will demonstrate the receptive field filters, similar to what is observed in our own brains, that are associated with user given or predefined natural images and sounds.
Akansha Goel
MS in AI
akanshagoel@my.unt.edu
Akansha joined the lab in Spring 2020 and led the research effort creating a visual dashboard system to reduce excessive sound exposure during music instruction. Currently, she is a member of the ECG vest team using predictive modeling to identify features of cardiac arrythmias in an ECG vest created by EE faculty and students at UNT.
Phillip Merritt
MS in AI
phillipmerritt@my.unt.edu
Syed Araib Karim
MS in AI
SyedAraibKarim@my.unt.edu
Lakshmi Vandana Nunna
MS in AI
LakshmiVandanaNunna@my.unt.edu
Jerline Jeyaraj
MS in AI
JerlineJeyaraj@my.unt.edu
Irina Maystorovich
MS in AI
irinamaystorovich@my.unt.edu
Cooper Snyder
MS in AI
robertsnyder@my.unt.edu
Aditya Pujari
MS in AI
adityapujari2@gmail.com
Kishen Prakashlal Patel
MS in AI
KishenPatel@my.unt.edu
Divya Geethanjali Birudharaju
MS in AI
divyageethanjalibirudharaju@my.unt.edu
Annie Liu
MS in AI
annieliu@my.unt.edu
Tanuja Polineni
Masters in Computer Science
tanujapolineni@my.unt.edu
Rickey Dixon Jr.
MS in AI
Undergraduate (10+)
including TAMS, excluding capstone and summer research groups
Rhea Pookulangara
William Flinchbaugh
William Zamudio
Lab Affiliated (5)
Namratha Urs
Ph.D. Student, member of HiLT lab
Efficient neural coding of sensory signals
NamrathaUrs@my.unt.edu
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.
Ishan Ranasinghe
Ph.D. Student
IshanRanasingheArachchilage@my.unt.edu
Trevor Exley
Ph.D. Student
trevorexley@my.unt.edu
Md Mosharaf Hossain
Ph.D. Student, member of HiLT lab
mdmosharafhossain@my.unt.edu
Abdullah Albanyan
Ph.D. Student, member of HiLT lab
abdullahalbanyan@my.unt.edu
Capstone groups for Fall 2020 (4 groups)
Efficient coding approaches to neural processing in the early visual and auditory systems
An app demonstrating the role of efficient coding in understanding sensory neuroscience has been created, but is not released on the android market. The current version can not only use some polish prior to release but also has features related to processing phone-acquired images and sounds that were disabled due to lack of time to develop. It has applications to neuroscience, but neuroscience knowledge is not required. If interested, here is the thesis about the app [PDF] including defense slides [gSlides] and an APK of the current version [apk]
Temporally-biased clustering in contiguous time to identify scientific trends in academic papers
A paper is currently being finalized analyzing the scientific trends in abstracts from finance journals over the past 50 years. The same methodology can be readily applied to journals or scientific areas to automatically identify trending topics. One approach is to create a pipeline to scrape data from additional sources and document the results in different fields or journals. Alternately, analyses can be performed offline and a web tool can be created to help users sift through the trends that are found. NLP knowledge is not needed, though it is beneficial.
Using a two-level reinforcement learning model to excel at strategic and tactical decision making in competitive games.
A two-tier reinforcement learning model has been created to play tic-tac-toe, dots and boxes, and connect 4 by not only learning to play each game, but also how to engage opponents optimally in tournament settings to maximize winnings. A poster will be presented in the Tapia 2020 conference [abstract available here] along with an upcoming paper submission. However, the code is currently in Jupyter notebook form and is no fun to play. Help us create a playable version of this system for a future educational workshop to engage students about reinforcement learning and its many applications.
Research and Projects Portal
Students in Spring and Summer 2020 created a web portal to organize project ideas and results in order to bring students, instructors, researchers, and project proposers together to move projects forward and provide continuity. The current web version is not ready for prime time use, but the goal is to have the system up and running to help organize AI projects across the university with summer 2021 for a test run. Here is the poster summarizing the most recent version [PDF]
Capstone groups from Spring 2020 (4 groups)
Uzair Akram, Cooper Vick, Paris Estes, Thien-An Vu, and Mark-Anthony Andrade are undergraduates working with Dr. Ting Xiao and Dr. Albert on building a tool to be used to identify an early biomarker for Parkinson's disease. The tools integrate automated pupil size tracking in a robust user interface for an experimental paradigm by Ophthamologist Bruce Gaynes.
Adam Spinhirne, Lance Wahlert, Jovanny Frias, Dain, and Jorge Martinez are undergraduates working with Thasina Tabashum to build a dashboard system to indicate cumulate levels of exposure to sound energy. This prototype will be used to encourage safer sound exposure levels during music instruction during ensemble session in order to avoid noise-induced hearing loss, which has been observed in this context. This work is in collaboration across UNT Gopal Kamakshi, Kris Chesky, and Sara Champlin.
Dominic Whiting, Ashley Torres, Colton Estes, Parker Hansen, and Maira Rivera are undergraduates working with Havish Nallapareddy to build a tool to better manage the coordination of research projects across courses, capstones, directed studies, and thesis efforts - Research and Projects Portal (RAPP) with an outward focus to connect them to interested stakeholders outside CS.
Phillip Nelson, Ranak Bansal, and Kaushik Akula are Texas Math and Science Academy (TAMS) students engaged in research led by Chengping Yuan, developing and analyzing models of tactical and strategic decision making in adversarial game playing. Ultimately the goal is to further demonstrate the benefits of hierarchical AI decision systems
Past students
Havish Nallapareddy
MS in AI
Automated fall detection and mitigation with wearables
Gloria Kim
Undergraduate
Efficient neural coding of sensory signals
Kiana Poole
MS Student
MS Student, Biomedical Engineering
KianaPoole@my.unt.edu
Sri Sravya Comerica
MS Student
Interests: General applications of predictive models
Munazza Ali
MS Student
Interests: Hidden Markov Models, toddler activity recognition
Most recent publication: Physiological Measurement 2020: Hidden Markov Model-based Activity Recognition for Toddlers
Shiva Ebrahimi
PhD Student
Interests: Machine Learning applied to graph theory. Active Learning.
Bassam Metwally
Undergraduate
Interests: Speaker diarization for speech pathology
Hannah Helgesen
MS in AI
Austin Meek
BS in CS
Ted Kwee-Bintoro
Arvind Ganesh
Brianna Chan
Adheesh Kadiresan
Kaushik Akula
Lisa Li
Kanav Bengani
Brian (Joonghyun) Kim
Phillip Nelson
Abhijay Achukola
Cindy Liang
Ranak Bansal
Nora Xiao
Kane Dong
Sarvesh Sathish
Zoe El-Zayaty
Venkat Ayalavarapu
Lab Alumni... Where have they gone?
Select Alumni
Former Thesis Graduate Students
Pinky Sindhu (Fall 2017 - Summer 2018) --> Allstate
Ilona Shparii (Aug 2015 - Aug 2017) --> Google
Anne Zhao (Aug 2015 - Aug 2017) --> Panasonic
Pichleap (Jessie) Sok (Sep 2015 - Aug 2016) --> Amazon
Former Graduate Students, non-thesis research
Albert Sugianto (Summer 2018 - Spring 2019)
Rejoice Jabamalaidass (Fall 2018 - Spring 2019)
Liz Sink (Summer 2017 - Fall 2017) --> Avant
Irina Rabkina, Computer Science graduate student (Spring - Summer 2015) --> Northwestern University PhD Program in Computer Science
Lailson Nogueira (Fall 2014 - Spring 2015) --> Oracle
Asma Mehjabeen (Spring 2013 - Fall 2014) --> Procured Health
Daneih Ismail (Fall 2013 - Spring 2014) --> DePaul Ph.D. Program in Computer Science
Former Undergraduate Students
Zhihao Zhou (Spring 2018 - Fall 2018) --> MS program at Carnegie Mellon University, Silicon Valley campus
Sam Sendelbach (Fall 2017 - Spring 2018) --> Founder, TensorTask
David Saffo (Summer 2016 - Spring 2018) --> PhD program at Northeastern University
Jack Blandin (advised post graduation in 2017/18) --> GoHealth, then Ph.D. Program at University of Illinois Chicago
Anirrudh Krishnan (Spring 2018) --> Quansight
Mary Makarious (Spring 2015 - Fall 2016) --> NIH
Gordon Kratz (Spring - Summer 2014) --> Group One Trading
Neil Rao (Spring - Fall 2014) Biology undergrad --> Co-founder REPRIMX