“I’ve received just so much positive reinforcement from my mentors and advisors – now I hope I can pass that on to my students,” says Tingting Zhao, Ph.D., Assistant Professor of Information Systems and Analytics and Faculty Fellow for the Center of Health and Behavioral Sciences, explaining why she chose to teach.  

Zhao has published impactful research and made significant contributions to the field of data science with these advisors (see recent scholarship below), with an interest in machine learning and life sciences, specifically. Machine learning is a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way that humans learn and is an in-demand field.

Now she is bringing these rewarding experiences—and her expertise—to Bryant. She arrived at Bryant in January 2022, and after a full semester of teaching core data science courses for the Data Science degree program and the Information Systems and Analytics Department, she’s familiar with the talents of Bryant students, and her plans for helping them to succeed in their endeavors.

“The most effective way of teaching is to create a learning environment in which students are likely to feel involved and excited with the material.”

“I sometimes can’t believe they are just second-year or third-year undergraduate students because I also have experience teaching at a master’s of data science program. They’re highly motivated. And each class is very interactive. I really enjoy every class,” says Zhao.

“Our students are so good at communication, even with a more technical course, like my machine learning course. They are very smart. I feel they have unlimited potential.”

As an educator, she hopes to provide her students with exposure to the most recent advances in the field, as well as practical experience. Given her expertise and experience, she’s well-positioned to do both.

After earning her Ph.D. in Statistics at the University of British Columbia, and with an interest in the “engineering side” of machine learning, she explored cutting edge applications in data science within the health field. She joined a post doctorate research program at Northeastern University, the Signal Processing, Imaging, Reasoning, and Learning Group, with Jennifer Dy, Ph.D., a leading figure in the machine learning community, where in a collaboration with Harvard University she used machine learning applications methods in healthcare, specifically on Chronic Obstructive Pulmonary Disease (COPD). In a second post-doc research program, with Patrick Flaherty, Ph.D., at the UMass Amherst TRIPODS Institute for Theoretical Foundations of Data Science, she pursued computational biology in the new drug discovery field. 

“I encourage my students to aim high, to not set limits on themselves.”

In her data visualization and machine learning courses, students can find themselves working with real data sets to solve challenges, as she’s done throughout her career. And she has plans to introduce them to experts from her network—including a colleague working at a leading tech giant to discuss deep learning from that industry’s perspective in her classes. In these ways, she hopes to inspire students while helping them to gain knowledge as well as expertise in real-world problems that prepare them for their careers. 

“If you want to build a ship, do not drum up your men to collect wood, give orders, and distribute the work. Instead, teach them to yearn for the vast and endless sea,” said Zhao, quoting Antoine de Saint-Exupéry, the author of “The Little Prince.” “Similarly, in my opinion, the most effective way of teaching is to create a learning environment in which students are likely to feel involved and excited with the material. I encourage my students to aim high, to not set limits on themselves.”

In addition to the University’s reputation, she was attracted to Bryant’s supportive environment. “It feels like a supportive family with a fantastic interdisciplinary research environment. My personal teaching goals and research interest resonate with the Vision 2030 strategy to build business-STEM,” says Zhao. Bryant's Vision 2030 plan details the University's strategic efforts currently underway to take Bryant’s unique interdisciplinary curriculum and vibrant student life to the next level by building on the strong foundation of its transformative learning experience and commitment to student learning, engagement and success. The plan adds innovative academic programs, supports experiential learning and invests in world-class facilities and technology.

“I love to have those interactive, close connections with the students.”

To her Bryant is a place where her goal to be a supportive advisor, as others have been for her, can be achieved. Where even a casual conversation outside of class can empower students and lead to big things. For example, she says, “In my data visualization course, several students combined several different NBA data sets (National Basketball Association), which takes a lot of data manipulation skills, and one of them is even applying for a sports analytics internship. He's very interested in the field, and we’ve had several conversations after class. I love to have those interactive, close connections with the students.”

Recent Research Highlights:

• Deep Bayesian Unsupervised Lifelong Learning, published in Neural Networks (Impact Factor 8.05) May 2022 and authored by Professors Tingting Zhao, Zifeng Wang, Aria Masoomi and Jennifer Dy, focuses on lifelong learning, the world's the most recent field in machine learning.

• Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler, authored by Professor Zhao and her Ph.D. advisor, Professor Alexandre Bouchard-Côté, and published May 2021 in Journal of Machine Learning Research, considered one of the best machine learning journals. A theoretical paper with applications to phylogenetics, the field for learning relationships between species or strains, the work offers a current, state-of-the-art computational algorithm for continuous time Markov chains.

Ongoing research projects highlights:

• A collaboration with Bryant faculty in a new interest area in natural language processing with applications in finance, with Professor Chen Zhang, Professor Hakan Saraoglu, and Professor David Louton.

• Continuing her interest in developing novel machine learning algorithms that could be useful for the field of healthcare and life science, as lead author she is working on a project with Professor James Ross (Harvard Medical School) and Professor Jennifer Dy at Northeastern with implications in precision medicine. The project involves applying machine learning algorithms to reveal patient characteristics subtypes and relationship to COPD severity. 

• In a project related to a large research program called NIH Links, as lead author she is using machine learning techniques helpful to the new drug discovery field to identify common characteristics of small molecule, a type of medicine, and identify new small molecule medicines. The NIH Links project spans across 15 institutions, including Harvard University, MIT and others.

• In a project with Professor Patrick Flaherty, she is using the power of multilevel analysis to discover genetic modulators of protein homeostasis systems via transposon sequencing.