The Hopkins Engineering Applications & Research Tutorials (HEART) program provides new undergraduate students with a window on cutting-edge engineering research and its applications to society. These small classes are taught by advanced graduate students and postdoctoral fellows. Students will be introduced to cutting-edge engineering research and learn how that research impacts society. These tutorials will be useful to students as they evaluate their potential role in research projects. To ensure these courses are accessible to entering freshmen (who have priority on registration) they have no prerequisites. The tutorials will be kept small so students will have ample time to interact with their instructor and each other.

The Hopkins Engineering Research-Opened Investigation Courses (HEROIC) program provides upper-division undergraduates with a chance to learn about the frontiers of research being explored in Hopkins laboratories. These small classes are taught by advanced PhD students and postdoctoral fellows working on engineering-related projects across the institution who have distinguished themselves as exemplary instructors in the HEART program. Like HEART courses, HEROIC courses are kept small—with a limit of about 12 in each section—so students will have ample time to interact with their instructor and each other.

Detailed information about the tutorials and instructors is available below. Alternatively, you can view the General Engineering course listings for information about the tutorials being taught this semester, including the day and time for each section.

Incoming first-year students can enroll in one of the tutorials when course registration begins on July 21, 2025. You may also register for the HEROIC course with permission.

Sophomores, juniors, and seniors can register for the tutorials beginning August 1, 2025.

The courses have no prerequisites and are open to all JHU undergraduates in both KSAS and WSE. Registration is done through SIS.

2025 HEART Courses

This course gives a peek into the research area of regression and inference, which is engineer-speak for “connecting dots with a line in a principled way.” Alternating between theory and practice, students will explore both foundational and modern techniques for finding patterns in data. Through hands-on coding activities and classroom discussions, students will experience for themselves the deep intersection between real world data, mathematical statistics, and computing, pushing them to seek out even more new and interesting knowledge during their remaining semesters at JHU. The course will emphasize key paradigms that appear often in applied mathematics, allowing students to apply their knowledge beyond medical applications and to areas such as economics, biology, and computer science.

Section, Date, and Time:

EN.500.111.01 / Mon / 1:30-2:45 pm

Instructor: Josiah Lim

Josiah Lim is a Ph.D. student in the Department of Applied Mathematics and Statistics, working with Dr. Tamás Budavári to analyze large-scale medical data using interpretable machine learning techniques. His research is supported by an NSF Graduate Research Fellowship. Besides research, he is invested in teaching and mentoring. He received the Professor Joel Dean teaching award for his teaching in Introduction to Statistics and Introduction to Data Science. On the side, he organizes the JHU Directed Reading Program for undergraduates, jointly run with the Department of Mathematics. In his free time, Josiah likes cooking, gardening, pickup soccer, reading, KonMari-ing and listening to music at the Baltimore Symphony Orchestra.

Cell and gene therapies are transformative therapeutic modalities, highlighted by recent FDA approvals or CAR-T and CRISPR-Cas9 based therapies. Given that these therapies are relatively new, there is still a lot that we don’t know, and many challenges left to solve regarding efficacy and translation. This course will introduce students to cell and gene therapies through a mixture of lectures covering core scientific concepts, engaging debate-style activities, student research projects, and talks from experts in the field.

Section, Date, and Time:

EN.500.111.02 / Wed / 4:30-5:45 pm

Instructor: Manav Jain

Manav Jain is a 2nd year Biomedical Engineering Ph.D. student at Johns Hopkins, where he is co-advised by Dr. Jordan Green and Dr. Jonathan Schneck. His research focuses on design different nano- and micro-scale biomaterials for gene delivery to and immuno-modulation of T cells in vivo. These research interests to design biomaterials that enhance mRNA delivery and CAR-T therapy align closely with many of the core concepts covered in Intro to Cell and Gene Therapies. Manav is a National Science Foundation Graduate Research Fellow recipient. He received his Bachelor of Science in Biomedical Engineering with a minor in Materials Science and Engineering from Georgia Tech.

Deep learning (DL) has become a widely recognized and powerful approach in machine learning, excelling in supervised tasks by learning complex patterns within the data. In this course, students will be introduced to fundamental and advanced DL frameworks and explore how these techniques can be applied to spatial transcriptomics (ST) – a cutting-edge field that integrates gene expression data with spatial information to provide deeper insights into tissue architecture and function. Through a mix of lectures, discussions, and hands-on Python coding exercises, students will gain practical experience and apply a DL technique to a real-world spatial transcriptomics dataset in their final project.

Sections, Dates, and Times:

EN.500.111.03 / Mon / 10:30 to 11:45 am

Instructor: Caleb Hallinan

Caleb is a Ph.D. student in Biomedical Engineering (BME) and is advised by Dr. Jean Fan. His research focuses on the relationship between spatial gene expression and tissue architecture in 2D, with the goal of extending these insights to 3D applications. Caleb will soon serve as co-President of the BME Student Council and continually seeks teaching opportunities, aiming to become a Teaching Professor. Prior to his Ph.D., he obtained a B.A. in Biology and Statistics at the University of Virginia and spent two years as a research assistant in Boston. In his free time, he enjoys playing and watching sports, spending time with friends, and reading fantasy novels.

This course will provide a general introduction to the field of Artificial Intelligence (AI) driven computational mechanics and explore various applications of this research in material design. Students will be familiarized with various methods of bridging AI and computational mechanics using state-of-the-art models through hands-on experience.

Section, Date, and Time:

EN.500.111.04 / Thu / 1:30 to 2:45 pm

Instructor: Indrashish Saha

Indrashish Saha is a Ph.D. candidate in the Department of Civil and Systems Engineering at Johns Hopkins University. His current research is in the area of AI driven computational models of materials under extreme environments. Indrashish completed his M.Tech (Research) degree from the Indian Institute of Science in 2021.

This course aims to provide an overview of protein engineering and synthetic biology techniques for applications in molecular immunotherapy design. Students will gain a foundational understanding of immunology and common disease targets for immunotherapy, as well as delving into the engineering and design principles behind the creation of novel therapeutic molecules and microbes. This course will also introduce students to current molecular immunotherapies and their applications, with a particular focus on the most relevant and popular research areas in the field, including bispecific antibodies, fusion proteins, and oncolytic viruses.

Section, Date, and Time:

EN.500.111.05 / Mon / 6:00 to 7:15 pm

Instructor: Emily Ariail

Emily Ariail is currently a Ph.D. candidate in the Biomedical Engineering Department where she is co-advised by Dr. Jamie Spangler and Dr. Jonathan Schneck. Her research interests lie at the interface of protein engineering and immunology, and she leverages techniques from both fields to design and characterize novel molecular immunotherapies. Ultimately, the goal of her thesis work is to engineer a cytokine/antibody fusion protein, or immunocytokine, to improve adoptive T cell therapies for cancer treatment.

2025 HEROIC Courses

Data science is intertwined with some of the most pressing problems in society, including environmental sustainability, public health, social justice, and the preservation of democracy. Each week of this course will feature a different case study, including modeling for high-stakes decision making, the impact of social media algorithms on political discourse and mental health, the promise and perils of large language models, and the future impact of artificial general intelligence. Students will wrestle with ethical questions, practice responsible decision-making, do a deep dive on a case study of their interest, and reflect on their own values and ambitions as an engineer.

Section, Date, and Time:

EN.500.312.01 / MW / 6:00 to 7:15 pm

Instructor: Kristen Nixon

Kristen is a Ph.D. student in the Center for Systems Science and Engineering (CSSE) and is advised by Lauren Gardner. Her research is focused on translating data and modeling tools to be useful for public health decision-makers and studying the spread of vaccine misinformation. Kristen works in the JHU writing center and is passionate about inclusive pedagogy. Prior to her Ph.D., she obtained a B.S. in Chemical & Biomolecular Engineering from JHU. She enjoys hiking, rock climbing, and audiobooks.

Trends in many real-world systems are often complex and difficult to extract through experimentation alone— for example, when controlling disease spread on a college campus what combination of in-person, online, or hybrid class schedules would maximize safe social interactions, while preventing a surge in infection? In this class we will learn how we can integrate data science principles and computational simulations to predict how interventions affect emergent outcomes in these complex, multidisciplinary systems. Along the way, we will learn practical skills that allow us to analyze large datasets, build computational models, and effectively communicate findings from these models in a research setting.

Section, Date, and Time:

EN.500.312.02 / TTh/ 4:30 to 5:45 pm

Instructor: Nikita Sivakumar

Nikita Sivakumar is a Ph.D. student in the Johns Hopkins biomedical engineering department. Her research focuses on applying data-driven approaches to high dimensional biological datasets to understand how aging affects critical immune cell behaviors and interactions. Her work also applies computational simulation to explore how dysregulation of these immune cellular interactions can lead to emergent dysfunction in the body and to test how various interventions can improve aging outcomes. Nikita is a national science foundation graduate research fellow and previously received her Bachelor of Science degree in biomedical engineering from the University of Virginia.

This course equips students with the skills to analyze and interpret neural data using advanced computational techniques. Students will work with real-world neural data to extract scientific insights, while focusing on computational methods such as machine learning, dimensionality reduction, regression models, and dynamical systems. Prior knowledge of Python programming and familiarity with linear algebra is expected. FYI – there will be a 15-minute break.

Section, Date, and Time:

EN.500.312.03 / Th / 4:30 to 7:15 pm

Instructor: Noga Mudrik

Noga Mudrik is a fourth-year Ph.D. candidate in Biomedical Engineering, advised by Dr. Adam Charles. Her research aims to understand whole-brain interactions under time-varying conditions by developing new computational tools. Specifically, she develops new machine learning and dynamical systems methods to analyze complex, high-dimensional neural data, which advance our understanding of brain function. Noga is a Kavli NeuroData Discovery Institute fellow and was also supported by the Kavli Foundation NeuroData Discovery Award during her Ph.D.