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

Mark V. Albert

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)

Thasina Tabashum

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.

Himan Namdari

Ph.D. Student

Machine learning, Tumor tracking, Kalman filters

HimanNamdari@my.unt.edu

"Yesterday I was young, and I wanted to change the world, today I am wise, and I am changing myself" - Rumi

Himan joined UNT in 2017 as a research scholar from La Sapienza University. His current research approach uses a variety of computational strategies, including kalman filters and deep learning, to track tumors during breathing so that radiation oncologists are able to minimize irradiating healthy tissue during radiation therapy on cancer cells in the abdomen. He enjoys music, kalman filters, movies, convolutional neural networks, and being social.

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



MS Students (5)

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 (福州大学).

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

irinamaystorovich@my.unt.edu

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

Hannah Helgesen



MS in AI


Austin Meek


Bs in CS


Undergraduate (18)

including TAMS, excluding capstone and summer research groups

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

Rhea Pookulangara

Kane Dong

Sarvesh Sathish

Zoe El-Zayaty

Venkat Ayalavarapu

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

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