Pedagogical Innovations and Mentoring/Coaching Activities
Introduction
Teaching is at the heart of my academic career, and I am deeply committed to fostering an inclusive, engaging, and transformative learning environment. I believe in tailoring my instruction to meet the diverse needs of students, empowering them to explore their interests, and equipping them with the skills to tackle real-world challenges in civil and environmental engineering. My goal is to inspire lifelong learning, critical thinking, and innovation in my students, preparing them to become leaders in their fields.
I prioritize innovation in my teaching by integrating active learning strategies, such as project-based learning, collaborative discussions, and real-world case studies, to engage students deeply with the material. I continuously update my curriculum with cutting-edge research insights, industry examples, and emerging technologies to ensure relevance and practical application. By designing new courses and redesigning existing ones, I create opportunities for students to apply theoretical knowledge to real-world problems, fostering critical thinking and problem-solving skills. My approach emphasizes flexibility, collaboration, and hands-on learning, enabling students to connect theory with practice and achieve their full potential.
New Course Developed - CIV_ENV 395 Data Science for Urban Systems
This course introduces students to the most state-of-the-art data science concepts and techniques, equipping them with the ability to select and apply the right algorithms to solve complex problems. It provides a comprehensive foundation in data science, focusing on the practical application of these concepts in real-world scenarios. Additionally, the course covers essential aspects of data management, including the data engineering ecosystem, lifecycle, and tools. This combination ensures that students not only understand how to manage and utilize data effectively but also how to implement advanced data analytics and build machine learning models. These skills are critical for addressing urban systems challenges such as optimizing transportation, enhancing urban design, and managing energy portfolios.
This course is designed for students who want to learn to program in python for data science.
Learning Objectives：
o To provide students a solid starting point for using Data Science in their work and research.
o Students will be able to understand and use standard sequential, conditional, and iterative control structure of automated data analysis through computers.
o To familiarize students with leading tools used in modern data science practice.
o To help students understand how to manipulate data (store, query, and summarize) using a database designed to analyze structured data.
o To help students understand and use computer programming to collect, analyze and visualize data related to various urban systems challenges.
The need for this course arises from the rapidly evolving landscape of data science and the increasing demand for professionals equipped with both theoretical knowledge and practical skills. While the current curriculum tends to focus either more on foundational concepts or high-level applications, it often falls short in introducing the state-of-the-art techniques and tools required to effectively apply these concepts to real-world problems or innovation applications.
This course is designed to bridge this gap by integrating emerging trends in data science, such as machine learning and data engineering. It provides students with hands-on experience in selecting and applying the right algorithms and tools to tackle complex challenges. By emphasizing practical applications, this course ensures that students are prepared to meet the demands of the modern workforce and contribute effectively to their fields. To measure students’ program towards the learning objectives, a variety of methods are employed, including regular assignments and projects that simulate real-world challenges, exams to evaluate their fundamental understanding of key concepts and techniques, instructor feedback on collaborative work which provides insights into their ability to work effectively in teams and communicate their ideas, and regular check-in hours to gauge individual progress and provide personalized support. Additionally, self-assessments and reflective exercises (e.g. through platforms like Datacamp) are incorporated to help students track their own growth and identify areas for improvement. These multifaceted approaches ensure that students not only meet the course objectives but also develop the skills needed to excel in their future professional environments.
Moreover, this course addresses the growing importance of interdisciplinary approaches, particularly in urban systems. As cities become increasingly data-driven, there is a pressing need for professionals who can leverage data science to optimize transportation, improve urban planning, and manage energy resources efficiently. By focusing on these areas, this course aligns with current industry trends and equips students with the skills necessary to drive innovation and solve pressing societal challenges.
Innovative Teaching Methods
Description of Methods
I have implemented a variety of innovative teaching strategies, including problem-based learning (PBL), flipped classrooms, and the integration of technology to enhance student engagement and learning outcomes. In my course Data Analytics for Transportation and Infrastructure Applications, I introduced project-based learning, where students worked on real-world datasets and applied advanced analytics techniques to solve industry-relevant problems. Additionally, I incorporated active learning tools such as Piazza for social bookmarking, enabling students to explore and share applications of data analytics that aligned with their interests.
Implementation
These methods were introduced gradually, with careful consideration of student feedback and learning needs. For instance, in the flipped classroom model, I provided the presentation of the on-coming lecture and used class time for hands-on problem-solving and group discussions. Adjustments were made based on student feedback gathered through a pre-lecture survey, a mid-term survey, and communication during office hours. For example, more structured guidance was added for complex topics, and flexible office hours were introduced to better accommodate diverse schedules. This iterative approach ensured that the methods were accessible and effective for all learners.
Measures of Effectiveness
Student feedback from CTECs highlighted increased confidence in applying course concepts to real-world problems, and several students published their course projects in top journals or presented them at conferences like the Transportation Research Board Annual Meeting. These outcomes demonstrate the success of these methods in fostering deeper understanding and practical application of knowledge.
New Projects
Project Descriptions: I have introduced several new projects and experiments in my courses to align with learning outcomes and industry trends. For example, in Data Science for Urban Systems, students worked on large-scale urban data problems, applying algorithms like k-means clustering and DBSCAN to analyze real-world datasets. In Uncertainty Analysis, I redesigned the curriculum to include a semester-long project where students collected, analyzed, and visualized data to solve engineering problems, integrating traditional probability concepts with modern data science techniques.
Impact: These projects have had a significant impact on student learning and engagement. Students reported a deeper understanding of course material and its practical applications, with many expressing enthusiasm for tackling complex, real-world challenges. For instance, in Data Science for Urban Systems, 90% of students achieved proficiency in using data engineering tools, and several went on to pursue internships or research opportunities in data science. The success of these projects is further supported by the fact that student work has been published in peer-reviewed journals and presented at national conferences, showcasing their ability to apply knowledge effectively.
Mentoring and Coaching Activities
My mentoring philosophy is grounded in the belief that each student is unique, with distinct goals, interests, and potential. I prioritize getting to know each student individually, understanding their aspirations, and helping them chart a path forward in their academic and professional journeys. I believe in fostering an environment where individuals feel supported, encouraged, and inspired to reach their fullest potential. Here’s how I approach mentoring:
o Empathy and Active Listening: I prioritize understanding everyone’s unique challenges, aspirations, and learning styles. By actively listening and empathizing with them, I provide personalized guidance and foster a safe, inclusive space where students feel comfortable sharing their thoughts and ideas. During my initial meeting with a student, I ask questions such as: Do you have a project idea or a general area of interest? What are your plans for the next steps? Based on their feedback, I tailor my guidance accordingly. For example, if a student plans to pursue a higher degree, I help them identify a research topic by discussing current trends and aligning their interest with feasible research questions. If they plan to enter the workforce, I guide them in developing a practical project that showcases their skills to potential.
o Goal-Oriented Coaching: Setting clear, achievable goals is key to the mentoring process. I work with students to define both short-term and long-term goals, then help them break down the steps necessary to reach those goals. For instance, if a student is motivated to prepare a paper, I base it on the timeline of the submission deadline for a suitable conference or journal. We establish a long-term goal, such as submitting the paper by a specific date, and then break it into manageable steps, like cleaning the data within two weeks. This structured approach ensures steady progress and keeps students focused and motivated.
o Building Confidence and Independence: My goal is not just to teach but to empower mentees to think independently and take ownership of their growth. I encourage them to step out of their comfort zones and explore new areas of learning, while providing a safety net to ensure they feel supported when facing challenges. One of my students worked with me on multiple projects, but none progressed to the stage of a paper submission. Despite her strong motivation to publish, she struggled to identify meaningful findings and felt stuck. Rather than giving direct answers, I posed guiding questions to help her critically evaluate results and refine her approach. When she encountered setbacks, I reassured her that mistakes were part of the learning process, helping her build resilience. Over time, she gained confidence in her research, and ultimately, she successfully published a paper.
o Fostering a Collaborative Environment: I encourage collaboration between students and their peers, as I believe learning is a dynamic, shared process. This helps build a sense of community, fosters diverse perspectives, and cultivates a growth-oriented network. In one of my courses, students were working on a project analyzing taxi demand patterns using data from the City of Chicago data portal. Instead of having them work individually, I grouped students with diverse backgrounds—some with strong statistical skills and others with expertise in urban planning or programming. One group initially struggled with integrating their different strengths. Rather than intervening with solutions, I encouraged them to communicate their thought processes and learn from one another. Over time, the students began leveraging each other's expertise—one student took the lead in coding, another focused on interpreting results in a transportation context, and a third ensured clear visual storytelling. By the end of the project, they not only produced a strong analysis but also gained appreciation for collaborative problem-solving. Several of them continued working together beyond the course, demonstrating how peer collaboration fosters both learning and lasting professional connections.
In Spring 2017, a structural engineering student expressed interest in my "Data Analytics for Transportation and Urban Infrastructure Applications" course and research. I advised him in two research initiatives at the Northwestern University Transportation Center (NUTC), guiding him to analyze Chicago Taxi Data using Python for network analyses and to explore taxi demand patterns. I also encouraged him to investigate the relationship between taxis and public transit using the Google Distance Matrix API to collect data for comparing travel times. When he expanded into structural health monitoring, I advised him on applying unsupervised clustering algorithms to bridge strain data.
Through regular discussions and feedback, I helped him refine his critical thinking and research communication skills, leading to two publications in the Transportation Research Record (TRR). I supported his Ph.D. application in Computer Science, providing feedback on materials and mock interview sessions. He was accepted into Vanderbilt University and completed his Ph.D. successfully. His journey continued as he built a career in Computer Science, ultimately rising to the role of Chief Technology Officer at Mobius AI.
His transition from structural engineering to a tech leadership role exemplifies the transformative impact of personalized mentoring and tailored academic guidance. By guiding this research, refining his analytical skills, and supporting his Ph.D. application, I helped him navigate key milestones in his academic and professional journey.
Future Plans
The impact of my innovations and mentoring activities on my teaching practice and student outcomes has been substantial. By integrating real-world applications and interdisciplinary approaches, I have observed a marked increase in student engagement and understanding. The case of the student who transitioned from structural engineering to a successful career in Computer Science highlights how tailored mentoring can guide students in navigating complex academic and career pathways. I fostered an environment where he felt empowered to explore new disciplines and apply his learning to practical challenges, guiding him through each stage of his journey.
Through these mentoring activities, I have learned the value of adaptability and personalized support. Every student brings a unique set of experiences and aspirations, and addressing these individual needs has enriched my teaching practice. The collaborative nature of research projects has not only enhanced students’ technical skills but also their ability to work in diverse teams, an essential competency in today’s global workforce. These experiences have reinforced my commitment to providing a dynamic and inclusive learning environment that nurtures both academic and personal growth.
Looking ahead, I plan to further innovate my teaching through the development of new courses, and mentoring practices through research projects. One of my upcoming initiatives is the launch of a mentoring program that pairs undergraduate students with graduate researchers through structured research apprenticeships. This program will provide undergraduates with early exposure to research, allowing them to develop critical thinking skills and an appreciation for interdisciplinary collaboration. By creating a structured pathway for research engagement, I anticipate an increase in student confidence and a stronger foundation for pursuing advanced studies. Additionally, I aim to incorporate more problem-based learning (PBL) and flipped classroom models into CIV_ENV 306: Uncertainty Analysis, ensuring students gain hands-on experience with real-world challenges while fostering independent learning.