Hewlett Packard Enterprise Data Science Institute

On April 28, the Hewlett Packard Enterprise Data Science Institute and Department of Computer Science hosted the bi-annual AI and Data Science Showcase. The event spotlights the innovative research of University of Houston undergraduate and graduate students.

Ten teams presented projects in artificial intelligence, machine learning, deep learning, and neural networks, followed by a Q&A with the panel of judges. Each project was evaluated based on innovation, feasibility, impact, and presentation quality. Three teams were recognized with certificates of achievement for their outstanding work.

The showcase is part of the institute’s mission to foster research and cross-disciplinary collaboration in emerging data science fields. It also reflects the University's continued investment in student-led research and its dedication to preparing the next generation of AI and data science leaders.

First Place

Team: Adam Nelson-Archer
Project: Synthesis of Lunar Horizon Imagery Using Generative Models

Second Place

Team: Dylan Berens, Kathiana Rodriguez, Shruti Yenamagandla, Carl Aguinaldo, Dominic McDonald
Project: Multi-Class Prediction of Species' Extinction Risk Using​ Deep Learning, Support Vector Machine & Neural Networks

Third Place

Team: Emmanuel Billy Gillis-Harry
Project: AI-Enhanced Resume Analysis for Data Science Career Optimization

Judges: Andrew Kapral, Raunak Sarbajna, Rohan Chaudhary, Nguyen Phan, Alihamza Navroz, Ishita Sharma, Nouhad Rizk, Guoning Chen

On December 2, the Department of Computer Science and the Hewlett Packard Enterprise Data Science Institute hosted the AI and Data Science Showcase. The event highlights student research in artificial intelligence, machine learning, deep learning, and neural networks. 

Claudia Neuhauser, director of HPE DSI, opened the showcase with remarks on the institute's mission and encouraged students to embrace such opportunities to advance their research skills. 26 undergraduate teams presented their research, and a panel of judges evaluated each project on innovation, feasibility, impact, and presentation. Five winning teams were selected and received certificates of achievement. 

The showcase celebrated the creativity of UH undergraduate students and the University’s commitment to fostering cross-disciplinary collaboration and innovation in the rapidly evolving fields of AI and data science. 

First Place 

Team: Victoria Bayang, Phuong Ly, Zoubida H. Rezki, Matthew Yoon 

Project Title: Predicting Breast Cancer Type with mRNA data and FNN 

Second Place 

Team: David Colin Cooper, Victor R. Elkins, Victor Adrian Lopez, Trang N. Nguyen 

Project Title: Breast Cancer Image Modelling Using Convolutional Neural Networks G-0030 

Third Place

Team: Austin L. Lam, Guillermo A. Martinez Somoza, Riley S. Myers, Gabriel Zermeno

Project Title: CBIS-DDSM Breast Cancer Detection 

Fourth Place 

Team: Thinh Quoc Bui, Kevin H. Nguyen, Nathan Daryush Taheri, Loc Trinh 

Project Title: Hyperparameter Optimization in Neural Networks for Bitcoin Price Prediction: A Comparative Study  

Fifth Place 

Team: Sebastian Duran, Khang Duc Duy Huynh, Andrew Q. Pham, Stephanie M. Snow 

Project Title: Brain Hemorrhage Detection 

Judges: Nouhad Rizk, Jerry Ebalunode, Guoning Chen, Jusvin Charles, Rohan Chaudhary, Jianyi Yang 

Organizing Committee: Claudia Neuhauser, Nouhad Rizk, Andrew Kapral, Ishita Sharma

The Department of Computer Science and the Hewlett Packard Enterprise Data Science Institute hosted the Summer AI Student Showcase on August 2. The event brought together graduate research teams from across the University of Houston to share innovative projects in artificial intelligence (AI), machine learning and data science. 

Six teams were invited to present, and three winners were chosen by the judging panel. The projects spanned a wide range of applications, showcasing the versatility and potential of AI in addressing complex challenges.  

Following the success of the Data Science Showcase for undergraduate students, the AI Showcase offered a platform for graduate students to present their research and engage with peers and faculty. Nouhad Rizk, director of undergraduate studies for the UH Department of Computer Science, and Claudia Neuhauser, director of the HPE DSI, led the organizing efforts. 

“We are pleased to see the level of dedication that these students have brought to this showcase,” said Rizk. “This event underscores the important contributions of our graduate students and gives them a place to promote their research and its applications.” 

First Place 

Team Lead: Saikiran Anugam, Department of Engineering Data Science 

Team Members: Hariharan Annadurai, Department of Chemical and Biomolecular Engineering 

Project: Anugam and Annadurai investigated the relationship between chemical compositions and mechanical properties— such as tensile and yield strength—  of industrial steels using machine learning and neural network models. Their research offers insights for optimizing manufacturing processes and improving material performance, with advanced models and robust error analysis enhancing the reliability of their findings. 

Second Place  

Team Lead: Divija Kalluri, Computer Science Department(NSM) 

Team Members: Charan Gajjala Chenchu, Computer Science Department  

Project: Kalluri and Chenchu proposed a method to improve X-ray interpretation accuracy using the DenseNet deep learning architecture. By combining segmented image features with diagnostic prompts and processed report data, they trained a large language model to generate concise medical reports. This system enhances radiologists' efficiency by automating image interpretation and report generation, providing a reliable tool for clinical diagnostics. 

Third Place 

Team: Rabimba Karanjai, Computer Science Department 

Project: Karanjai’s AI-Powered Education Diary leverages advanced large language model technology to generate personalized code examples and study materials grounded in authoritative sources like textbooks and lecture notes. Continuous user feedback will refine the content, resulting in an intuitive, multi-device application that enhances personalized learning. 

The Hewlett Packard Enterprise Data Science Institute at the University of Houston recently hosted the "AI Faculty Symposium: Research and Teaming" on Friday, June 7. This event marked a significant step in UH's commitment to supporting high-quality, interdisciplinary research in artificial intelligence. 

The symposium featured presentations by faculty members on open challenges and funding opportunities in key sectors of AI research: healthcare, security, energy, manufacturing and robotics. These presentations were followed by breakout team activities, which fostered innovative brainstorming from attendees representing the social sciences, natural sciences and engineering disciplines. 

In a move to encourage practical outcomes and partnerships from the symposium, seed funding was made available to support the most promising team proposals.  

This symposium built on the success of the AI Industry Symposium and Career Mixer held earlier in the spring. While that event focused on connecting students with industry opportunities, the faculty symposium aimed to strengthen the university's internal research capabilities and collaborations. 

“This was our first event gauging the interest of faculty in establishing AI research as a focus at UH” said HPE DSI director Claudia Neuhauser. “I’m encouraged to see so many faculty representing almost every college on campus taking part in this event.” 

As artificial intelligence continues to shape various aspects of research and industry, events like the AI Faculty Symposium play a crucial role in positioning UH at the forefront of innovation and interdisciplinary collaboration in this rapidly evolving field. 

On Thursday, April 18, 2024, the University of Houston Hewlett Packard Enterprise Data Science Institute hosted an online lecture titled "Computational Genomics Reveal Insights into the Biology of Non-model Organisms." Richard Meisel from UH's Department of Biology and Biochemistry presented his research on applying genomic methods to non-traditional study organisms. Meisel highlighted how recent technological advancements have made genomic analysis more accessible across various biological fields.  

The lecture focused on projects from Meisel's lab, a key example being their work on the house fly, Musca domestica, which possesses a complex sex determination system. Meisel’s team used advanced sequencing technologies and computational methods to assemble a high-quality genome for the house fly. This improved genome assembly allowed them to investigate the genetic basis of sex determination in this species, revealing variations in the male-determining chromosomes across different populations. The research uncovered how environmental factors, particularly temperature, play a role in the distribution of different male-determining chromosomes.  

Another significant aspect of the talk was the demonstration of how computational genomics can bridge the gap between model and non-model organisms. Meisel’s team used data from the well-studied fruit fly, Drosophila melanogaster, to inform experiments on the house fly, leading to the identification of genes involved in mating behavior.  

Throughout the presentation, Meisel emphasized the importance of integrating various approaches, including computational genomics, organismal experiments and comparative studies between model and non-model organisms. This multifaceted strategy allows researchers to address fundamental questions in biology from both mechanistic and evolutionary perspectives. Meisel highlighted the technical challenges of genomic assembly, such as dealing with repetitive DNA sequences. Long-read sequencing technologies and novel computational approaches have helped overcome these obstacles.

Richard Meisel, Ph.D. completed his postdoctoral training in the Department of Molecular Biology and Genetics at Cornell University. The Meisel lab uses lab experiments, genomic data and molecular biology techniques to study a broad range of questions in evolutionary biology. The research addresses how environmental variation and sex differences influence genetic and phenotypic diversity within populations and between species. Meisel’s lecture is part of an ongoing series hosted by the UH Hewlett Packard Enterprise Data Science Institute, aimed at showcasing cutting-edge research that leverages high-performance computing resources for scientific discovery. 

Containers, which are software packages that house necessary elements for running programs, are often used in high performance computing (HPC) applications. For the University of Houston’s HPC clusters, users can optimize their workloads through Apptainer (formerly Singularity), a flexible and user-friendly container system designed specifically for HPC workloads. The Apptainer platform provides an increased level of security, portability, customization and performance.

In the first half of the seminar, Pasunuri introduced basic commands in Apptainer through practical examples: launching a shell, running Apptainer as an executable and executing container commands. Pasunuri also explained the process of acquiring container images, which can be pulled from the Docker registry through the Singularity hub. Container images can also be built through a definition file on the hub. However, users will need sufficient disk space and memory to create an image. According to Pasunuri, this can be mitigated by requesting more resources and memory space in a user’s project directory.

In the second half of the seminar, Pasunuri discussed the advantage of using Apptainer in UH HPC clusters, highlighting the platform’s ability to create and run commands inside a job script. Pasunuri then demonstrated how to use and run Apptainer image containers inside a job script. This feature enables users to handle dependencies and maintain reproducibility within their workload. Additionally, Apptainer incorporates a bind option in its software, facilitating the integration of directories and external applications, such as UH HPC clusters.

Aravind Kumar Reddy Pasunuri is a teaching assistant and front desk support for the Hewlett Packard Enterprise Data Science Institute. In this role, he has had the opportunity to work with Apptainer, gaining practical experience in containerization technology and its role in modern software development practices.

To access the UH’s HPC clusters, faculty and researchers can request cluster access online. For more information about RCDC resources and information, please visit the RCDC website.

In a strategic collaboration connecting academia and industry expertise, the Hewlett Packard Enterprise Data Science Institute partnered with University Career Services to host a day-long AI Industry Symposium and Career Mixer at the University of Houston. The event, held on March 1, brought together scholars and professionals from diverse industry sectors.

Claudia Neuhauser, director of the HPE DSI, opened the event with a brief introduction of the institute and set the tone for a day of rich discussions. After the welcome, sector experts gave presentations on AI in research, technological advancements, and industry practices, covering oil and gas, energy, supply chain management and other areas.  

The symposium concluded with a panel on career success in AI-dominated industries, featuring speakers from Hewlett Packard Enterprise, Shell, ConocoPhillips, Patterson-UTI, Chevron, Humana and Metegrity.  

The career mixer that followed gave young professionals the opportunity to interact with representatives from ten leading organizations in the field. Recruiters shared insights on career paths and industry demands, outlining potential future collaborations.

Nearly 100 students, alumni, and members of the UH community attended the event. “It shows that we are meeting the interests of our young researchers and providing them with real-world connections to fruitful career paths,” Neuhauser said.  

This event is expected to shape the trajectory of programming, as Neuhauser hopes it will serve as a model for further integration of research, teaching, and career development at the University.

On February 29, Ernesto Lima gave a hybrid Lunch and Learn presentation with the Society of HPC Professionals at the Texas Advanced Computing Center (TACC). Lima is a research associate at UT Austin’s Center for Computational Oncology.

Breast cancer affects millions worldwide. Around 15-20 percent of cases are HER2-positive breast cancers, which are tumors that promote aggressive, rapid cancer cell growth. Current HER2-positive treatments— the most notable being trastuzumab— have proven effective, often given with chemotherapies, such as doxorubicin, to increase response rates. In his study, Lima investigates one main question: how can we optimally combine these two treatments to maximize control and reduce tumor size?

To tackle this problem, Lima and his research team utilized data from a murine model of HER+ breast cancer to analyze changes in tumor volume under various trastuzumab and doxorubicin treatment protocols. Adopting a Bayesian framework, they developed and calibrated ten mathematical models that capture the experiment tumor dynamics and the direct effects of the drug-to-drug interactions. The model calibration was done through Python using a Markov chain Monte Carlo (MCMC) ensemble sampler.

After selecting the model with the highest Bayesian information criterion, they applied the optimal control theory to find two ideal treatment protocols that both deliver trastuzumab prior to doxorubicin. The first one used the same experimental doses for both drugs, predicting an additional 45% tumor burden reduction. However, to limit cardiotoxicity, Lima and his team reduced the total dose of doxorubicin to approximately 43% for the second protocol, which achieved equivalent tumor control as the experimental protocol.

Currently, Lima and his research team are performing the necessary experiments to confirm, or improve, the optimized treatment protocol.

Lima is a research associate at the Center for Computational Oncology at the Oden Institute for Computational Engineering and Sciences and a member of the Life Sciences group at the Texas Advanced Computing Center (TACC). His research focuses on the development of numerical methods, innovative tumor growth models, treatment optimization, and model selection and calibration.

On March 21, 2024, the University of Houston Hewlett Packard Enterprise Data Science Institute hosted an online research lecture titled "Accelerated Materials Design Driven by Machine Learning" featuring Mingjian Wen, an assistant professor of Chemical and Biomolecular Engineering at UH's Cullen College of Engineering.  

Wen’s lecture highlighted two main challenges in creating reliable artificial intelligence models for materials science: uncertainty quantification for neural network models and the incorporation of prior physical knowledge and constraints into such models.  

Wen's research employs graph neural networks to model the elastic properties of materials. From the predicted elastic tensor, one can obtain various mechanical properties, such as bulk modulus (resistance to volume change) and shear modulus (resistance to sliding deformation) across different crystal systems. This capability is particularly valuable because many materials exhibit anisotropy, behaving differently depending on the direction of applied force.

Wen stressed the importance of incorporating physical constraints and uncertainty quantification into these models. He explained how a dropout technique can be used to assess the reliability of predictions, especially when extrapolating beyond the training data. This approach allows researchers to identify when a model might be unreliable for certain materials or conditions.

The lecture highlighted practical applications of these AI-driven methods. Wen demonstrated how the models could accelerate materials innovation by rapidly screening thousands of potential candidates and identifying those with exceptional properties such as strength. Wen emphasized that the true power of these methods lies in combining traditional scientific knowledge with machine learning techniques. This synergy between human expertise and AI tools has the potential to revolutionize materials design and discovery across various industries, creating more effective and physically-justified models for materials science.

Mingjian Wen, Ph.D is a Presidential Frontier Faculty Fellow at the University of Houston. His current work focuses on developing data-driven approaches for computational understanding and design of energy materials and contributing to open cyberinfrastructure for materials modeling. Prior to UH, Wen worked as a postdoctoral scholar on the Materials Project at Lawrence Berkeley National Lab. Wen's lecture is part of an ongoing series hosted by the HPE Data Science Institute, aimed at showcasing cutting-edge research that leverages high-performance computing resources for scientific discovery. 

Machine learning techniques are revolutionizing nuclear data evaluation and reactor design. Vladimir Sobes, assistant professor in the Department of Nuclear Engineering at the University of Tennessee, Knoxville, has dedicated his career to exploring the application of modern artificial intelligence and machine learning algorithms to current problems in nuclear engineering. 

In the realm of nuclear data evaluation, Sobes underscored the significance of ML algorithms in automating tasks such as hyper-parameter tuning and learning complex functions, particularly in the realm of uncertainty quantification. Sobes illustrated the structured nature of nuclear data, stressing the importance of automated feature identification—a task traditionally performed manually by experts. ML/AI, he argued, offers the promise of enhancing efficiency, reproducibility, and uncertainty quantification in nuclear data evaluation, thereby enabling experts to devote their time to more critical tasks. 

Transitioning to the topic of reactor design, AI has the potential surpass human capabilities in autonomous optimization, although Sobes cautioned against the indiscriminate use of surrogate models.  

Sobes demonstrated the effectiveness of the Gaussian Process Learning Algorithm for design optimization using a scenario related to the development of a spacecraft antenna. NASA engineers asked an AI algorithm to imagine an antenna. Despite an initial skepticism of its unintuitive design, rigorous testing demonstrated superior performance compared to human-designed alternatives. In 2006, the NASA ST5 spacecraft antenna, the same model proposed by an evolutionary computer design, was sent to space. 

AI/ML technologies are reshaping the landscape of nuclear engineering, from streamlining nuclear data evaluation processes to pushing the boundaries of reactor design beyond what was previously thought possible. Sobes' insights highlighted the symbiotic relationship between human expertise and AI, emphasizing the potential for collaboration to drive innovation in nuclear science. 

The presentation concluded with a quote by Arthur C. Clarke, adapted to signify the transformative potential of advanced optimization methodologies: "Any sufficiently advanced optimization methodology is going to be indistinguishable from intelligence."  

Sobes envisions a future where AI supplements human expertise, enabling breakthroughs in nuclear science and technology.