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.

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 freshmen can enroll in one of the tutorials when course registration begins on July 25, 2022.

Sophomores, juniors, and seniors can register for the tutorials beginning August 1, 2022.

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

2022 HEART Courses

How can we image biological samples, often live and transparent, at various scales? This course introduces the fundamentals of the image acquisition process. We will discuss processing and analysis methods to understand biological processes, which ultimately link to clinical diagnoses.

Section, Date, and Time:

EN.500.111.01 / Mon / 9:00 to 10:15 am

Instructor:Jeong Hee (Jenn) Kim

Jeong Hee (Jenn) Kim is a PhD student in mechanical engineering at Johns Hopkins University, advised by Professor Ishan Barman. Her research interest lies in studying the molecular features of cells and tissues using microscopic and spectroscopic tools in combination with machine learning approaches for analysis. She received a BS in mechanical engineering from Georgia Institute of Technology and worked in industry before joining Johns Hopkins.

Can you imagine a future where novels, news articles, and comedy were written by a program? This course will introduce students to text generation from the field of natural language processing in which we will discuss the methods, their applications and ethical implications. The class will include hands-on interaction with new technology to aid in the understanding of its capabilities and limitations.

Section, Date, and Time:

EN.500.111.02 / Mon / 9:00 to 10:15 am

EN.500.111.05 / Mon / 4:00 to 5:15 pm

Instructor: Isabel Cachola

Isabel Cachola is a Computer Science PhD student in the Center for Speech and Language Processing. Her research interests include text generation, summarization, question answering, and computational social science. Previously she worked on automatic summarization and scientific document accessibility at the Allen Institute for AI. She earned her B.S. in Mathematics at the University of Texas at Austin.

This course will introduce students to the role mathematical optimization plays in solving challenging problems across engineering, operations research and machine learning disciplines. From tasks like image denoising and classification to designing energy grids and supply chain logistics, new optimization methods and algorithms are constantly being designed and improved to meet modern challenges.  The topics covered will be intended to give students a “bird’s eye view” understanding of how optimization plays a key role across many disciplines as well as the types of ideas that are involved in the field.

Sections, Dates, and Times:

  • EN.500.111.03 / Mon / 9:00 to 10:15 am

Instructor: Phillip Kerger

Phillip Kerger is a fourth year PhD candidate in the JHU Applied Math & Statistics department interested in optimization working with Dr. Amitabh Basu. He is working on both the theoretical and applied side of optimization, studying algorithmic complexity questions as well as making use of optimization techniques and cutting-edge quantum annealing hardware for image applications. Phillip has received various teaching awards during my time at JHU and also bring with me practical experience from research internships in industry. Outside of math and teaching, he enjoys playing guitar, bass, soccer, and chess.

This is a class about using computers to gain insight from large numbers of measurements (mostly measurements of genes). In stark contrast to most such classes, this class puts “why” first and “how” second. Amidst the ever-growing jungle of quantitative methods, we will apply principles that help decide what methods to use.

Section, Date, and Time:

EN.500.111.04 / Mon / 4:00 to 5:15 pm

EN.500.111.07 / Mon / 5:30 to 6:45 pm

Instructor: Eric Kernfeld

Eric Kernfeld is a JHU BME PhD candidate jointly advised by Alexis Battle and Patrick Cahan. His work focuses on genomic data analysis, and it is driven by deep engagement with biologists — so far, mostly stem cell biologists. Before starting at JHU, Eric received an M.S. in Statistics at the University of Washington, and he analyzed data for René Maehr’s lab at the University of Massachusetts Medical School, which studies the development of adaptive immunity. His doctoral work asks what switches each gene on and off. He aims to understand the extent to which modern or near-future genome measurement technologies can detemine these on/off switches en masse.

This course will provide an introduction into the latest technologies that allow researchers to visualize the structure and function of whole-brain networks at single-cell resolution. Class discussions will be focused on how researchers transform massive biological datasets into tangible observations using engineering tools, starting with the design of transgenic animals using CRISPR gene editing, and culminating in hands-on light-sheet microscopy and deep learning demonstrations. To provide a practical foundation, the class lectures, demos, and lab tour, will all revolve around a central research hypothesis that is undergoing active investigation, providing students with the opportunity to critically participate in the scientific process.

Section, Date, and Time:

EN.500.111.06 / Mon / 5:00 to 6:15 pm

Instructor: Yu Kang (Tiger) Xu

Tiger Xu is a Neuroscience PhD candidate and Distinguished Kavli NDI Graduate Fellow jointly advised by Dr. Dwight Bergles and Dr. Jeremias Sulam. His research is focused on understanding the complex patterned connections formed between different types of cells in the brain, and how this web of interactions supports higher order cognition and healthy aging. Tiger graduated from McGill University in 2018 with a BS in Physiology and completed his honors thesis in Dr. Jack Antel’s lab where he designed a high throughput screening method to identify potential therapeutics for multiple sclerosis.

This course will introduce the research area of Machine Translation and contextualize its place within the larger field of Natural Language Processing (NLP). We will cover the basics of linguistic diversity and how it makes this problem interesting. We will introduce the modern approaches and advancements made by academia and industry. We will also discuss alternatives to the traditional Machine Translation paradigms and what motivates research into new directions.

Sections, Dates, and Times:

  • EN.500.111.08 / Mon / 6:00 to 7:15 pm
  • EN.500.111.17 / Wed / 6:00 to 7:15 pm

Instructor: Rachel Wicks

Rachel Wicks is a Computer Science PhD student co-advised by Matt Post and Philipp Koehn. She works out of the Center of Language and Speech Processing (CLSP) focusing on machine translation and natural language processing applications in many languages. She received her BA in Computer Science and Linguistics from the University of Virginia and her MSE in Computer Science from Johns Hopkins.

This introductory course will provide an exposure to the world of computer simulations as a tool to obtain knowledge of complex physical phenomena. It will build on high school mathematical concepts and provide necessary tools to solve complex problems with the use of active computer programming. No prerequisites are required but active participation in class will be highly encouraged.

Section, Date, and Time:

EN.500.111.09 / Mon / 6:00 to 7:15 pm

EN.500.111.18 / Wed / 6:00 to 7:15 pm

Instructor: Saikat Dan

Saikat Dan is a Graduate Research Assistant in the Department of Civil & Systems Engineering. He holds a masters’ degree from the Department of Mechanical Engineering at JHU. His current research comprises of performing computer simulations of cracks propagating in material systems using a combination of applied mechanics and computational methods. Prior to joining JHU, he was actively involved in industrial research, working with numerical modeling of large offshore and shipping vessels.

This course will give a broad overview of the applications of biomaterials to enhance cancer treatments through harnessing the body’s immune system. Students will learn the fundamentals of cancer biology and immunology as well as the limitations of treatment paradigms currently implemented in the clinic. Following this, topics on basic materials structure, properties, and performance will be presented with special attention given to biomaterials used in various cancer immunotherapy applications – particular focus will be placed on highly relevant and popular research topics in this field: cancer vaccines, drug delivery, and engineered cell therapies.

Sections, Dates, and Times:

  • EN.500.111.10 / Tue / 5:00 to 6:15 pm
  • EN.500.111.15 / Wed / 5:00 to 6:15 pm

Instructor: Joseph Choy

Joseph Choy is a PhD student in Materials Science and Engineering co-advised by Dr. Hai-Quan Mao and Dr. Jonathan Schneck at Johns Hopkins University. His research studies T cells and their interactions with biomaterials in order to develop better cell-based cancer therapies. He completed his Bachelor of Applied Science degree in Materials Science and Engineering, with a minor in Bioengineering at the University of Toronto in 2020.

This course will focus on the field of neural prostheses and will cover the basic neuroscience and physiology of the nervous system, the design and function of neural technology, the interface between the nervous system and neural prostheses, the roadblocks and future directions of this field, the various applications of neural prostheses, and the societal impact of these technologies. Students will engage in these topics through lectures, short readings, class discussion and a final presentation. By the end of this course, you will be literate in the field of neural prostheses and be able to understand their design, applications, and impact in a meaningful way.

Sections, Dates, and Times:

  • EN.500.111.11 / Tue / 5:00 to 6:15 pm
  • EN.500.111.16 / Wed / 5:00 to 6:15 pm

Instructor: Mark Iskarous

Mark Iskarous is currently pursuing a PhD degree in biomedical engineering at Johns Hopkins University in the Neuroengineering and Biomedical Instrumentation Laboratory under the mentorship of Dr. Nitish Thakor. His research interests include sensory feedback for upper limb prostheses, neuromorphic models of tactile sensory information, and neuromorphic computing. Previously, he received a B.S. degree in electrical engineering and computer science from the University of California at Berkeley in 2015. From 2015 to 2017, he was a Hardware Development Engineer at Amazon Lab126 working on consumer electronic devices.

This course highlights two research endeavors in the area of neuromorphic engineering – a branch of engineering focused on utilizing biological sensor design as inspiration for electrical sensor design. The course will cover event-based sensing, its application in predicting health outcomes in a clinical setting, and its application in determining visual saliency for the purposes of aiding autonomous vehicles.

Sections, Dates, and Times:

  • EN.500.111.12 / Tue / 5:00 to 6:15 pm
  • EN.500.111.25 / Thu / 5:00 to 6:15 pm

Instructors: John Rattray

John Rattray is a Ph.D. GEM fellow from Baltimore, MD pursuing his doctorate in Electrical and Computer Engineering. In addition to being a doctoral student, John is also the founder and current CEO of Sparkwear Inc, a company that uses wearable technology to increase safe interpersonal interaction and engagement in physical spaces. John graduated from the University of Maryland, Baltimore County (UMBC), as a Meyerhoff Scholar, in 2015 with a B.S. in Computer Engineering and received his M.S.E in Electrical and Computer Engineering in 2017 from The Johns Hopkins University. John’s passion for technology coupled with his entrepreneurial drive has exposed him to opportunities both in academia and in industry to work with organizations such as DARPA, Cisco, and Norwegian Cruise Lines. When he is not actively pursuing his passion in technology, John enjoys sharing his experiences and insight with others, especially STEM students, as a public speaker, workshop instructor, tutor, and mentor. John is an avid soccer player, digital artist, and innovator.

A vector is a geometric object with a direction and magnitude, which we can visualize using an arrow, while a vector field is a function that takes a point in the space and returns a vector, which we can visualize by drawing arrows at each point over the space. Using vector fields, we can model fields and flows such as the gravitational field, optical flow, velocity, etc. In the physical world, these fields/flows are continuous; however, in the computer world, space is discrete. So, we need to represent and compute vector fields discretely. This course will teach you how to represent surfaces in a computer, compute scalars and vectors directly on a discrete surface, and efficiently in its texture domain.

Section, Date, and Time:

EN.500.111.13 / Tue / 6:00 to 7:15 pm

EN.500.111.26 / Thu / 6:00 to 7:15 pm

Instructor: Sing Chun Lee

Sing Chun is a computer science PhD candidate at Johns Hopkins University. He graduated from the Chinese University of Hong Kong in Mathematics and Information Engineering and received his master’s degree in Biomedical Computing from the Technical University of Munich. He wants to bring mathematical theories to practice, particularly geometry processing and augmented reality and his research interest is geometry processing.

This course focuses on a special category of medical robotics; the robots manipulated and controlled by magnetic fields. The course will cover high level concepts of magnetic fields, examples in the real world, and research questions related to the magnetic robots and their biomedical applications.

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Sections, Dates, and Times:

  • EN.500.111.14 / Wed / 5:00 to 6:15 pm

Instructor: Onder Erin

Onder Erin received his B.S. degree in mechatronics engineering from Sabanci University, Istanbul, Turkey, in 2014. He received his M.Sc and Ph.D. degrees in mechanical engineering from Carnegie Mellon University, Pittsburgh, PA, in 2020. During his studies, Onder converted a clinical MRI scanner into a remote magnetic manipulation platform. He received Max-Planck-Society Ph.D. Fellowship in 2016 and conducted some of his research activities in Max Planck Institute, Physical Intelligence Department until 2020. He is currently working as a postdoctoral fellow at Johns Hopkins University since 2021 January. His research interests include medical robotics, mobile and soft robotics, and medical applications of robotic systems under clinically relevant settings.

The course intends to touch upon all the key aspects of “tissue engineering” technology –biomaterials, stem cell biology, tissue fabrication, advanced bio-fabrication techniques, etc. Topics like growing blood vessels, cartilage repair, cardiac tissue engineering, cranio-facial reconstruction, etc. would be discussed along with a few clinical case studies. Innovative fabrication techniques like electrospinning, bio-printing, etc. which are indispensable to the field of tissue engineering would be introduced.

Section, Date, and Time:

EN.500.111.19 / Wed / 6:00 to 7:15 pm

Instructor: Srujan (Sami) Singh

Srujan (Sami) Singh is a graduate student at the Lab for Craniofacial and Orthopedic Tissue Engineering (LabCOTE) headed by Dr. Warren Grayson from the Department of Biomedical Engineering at Johns Hopkins University. His research project mainly focuses on, however, is not limited to fabricating protease sensitive biomaterials which could then be 3D printed into implants for treating craniofacial bone defects in a clinical setting. His expertise lies in biomaterials, bioconjugation chemistry, additive manufacturing (3D printing), pre-clinical large animal studies, etc. He has a background in Chemical engineering and a strong liking for regenerative medicine.

The course will give an introduction to brain and neural computations by combining a systems neuroscience perspective with theories of control and dynamical systems. Lectures will have examples from artificial machines (robotics, autonomous cars, aerospace etc.) to build intuition for the conceptual topics on theory of control and dynamical systems. This intuition will form a scaffold for explaining concepts in neuroscience and covering the emerging role of control and dynamical systems in neural computations.

Section, Date, and Time:

  • EN.500.111.20 / Wed / 7:00 to 8:15 pm

Instructor: Gorkem Secer

Gorkem Secer is a postdoctoral fellow in the Noah Cowan’s lab at the Whiting School of Engineering and Jim Knierim’s lab at the Mind Brain Institute. He completed his MSc and PhD in electrical engineering and computer science with a focus on legged locomotion of bipedal robots. His current research interests are neurophysiology and computational modeling of hippocampal formation.

The objective of this course is to explore the field of Augmented Reality (AR) as well as Virtual Reality (VR), and to understand human’s interaction with those AR/VR systems. This course will introduce basic definitions, principles, and applications of the immersive technologies. Students will also study how to design experiments to study interaction between human and the machine.

Section, Date, and Time:

EN.500.111.21 / Wed / 7:00 to 8:15 pm

Instructor: Dayeon Kim

Dayeon Kim is a Ph.D. candidate in the Department of Computer Science. She is a member of the Sensing, Manipulation, and Real-Time Systems (SMARTS) laboratory led by Peter Kazanzides, and the Intuitive Computing Lab led by Chien-Ming Huang. Her research focuses on developing immersive technologies (AR/VR/MR) to better deliver healthcare services, which include control of prostheses, mental therapy, and surgical team training.

This course serves as an introduction to the mathematical modelling of shapes and images, and to numerical algo-rithms for image processing and shape analysis. We will survey image processing techniques for performing contrast enhancement, edge detection, image denoising and deblurring, as well as shape analysis frameworks for performing shape registration and statistical shape analysis. Applications to computer vision and medical imaging will be discussed throughout the course.

Sections, Dates, and Times:

  • EN.500.111.22 / Thu / 5:00 to 6:15 pm
  • EN.500.111.28 / Thu / 6:30 to 7:45 pm

Instructor: Yashil Sukurdeep

Yashil Sukurdeep is a Ph.D. Candidate in Applied Mathematics and Statistics at Johns Hopkins University, advised by Professor Nicolas Charon. His research interests lie in shape analysis, image analysis, optimization, and machine learning. More specifically, he develops mathematical models and numerical algorithms for shape registration, statistical shape analysis and image processing, leading to applications in computer vision, medical imaging and astronomy. Yashil graduated from Brown University in 2018 with a B.S. in Mathematics and a M.S. in Applied Mathematics.

This course gives an introduction to the challenges of software development with examples of biomedical applications. We cover modern software architectures, agile development methods and the specialties to bio-medical applications as they are developed in academic settings. We will have some hands-on experience with case studies, coding examples and SCRUM-style meetings. Coding experience is not required but helps!

Sections, Dates, and Times:

  • EN.500.111.23 / Thurs / 5:00 to 6:15 pm
  • EN.500.111.29 / Thurs / 6:30 to 7:45 pm

Instructor: Anna Liebhoff

Anna Liebhoff is a postdoctoral fellow in the lab of Ben Langmead in the Department of Computer Science at the Johns Hopkins University. Currently, she is working in a collaboration with Ben Larman (Pathology Department in the School of Medicine) on the PhipSeq technology in the field of computational immunology.  She received her PhD in computational biology in a collaboration of the Medical Center Hamburg-Eppendorf (UKE) and the Hamburg University of Technology where she built and published an online database system with various analysis functionalities for small RNA. Before entering the bio-medical realm she received her BSc and MSc in Computer Science, worked for Airbus and as a freelancer in the start-up world until she got fascinated by the human body and brain and its still un-decoded “computing powers”.

Recent development in organic semiconducting materials and synthesis techniques combined with the advancement in fundamental understanding, have led to creation of devices with a variety of functionalities and performance, some of which are on par with the current inorganic technologies. This innovative course covers recent advances on the synthesis, characterizations, applications and challenges of organic semiconductors and devices. Main topics include organic thermoelectrics, optoelectronics, sensors, transistors, mixed ion-electron conductors, stretchable and flexible electronics, OFETs, OLEDs, etc.

Sections, Dates, and Times:

  • EN.500.111.24 / Thurs / 5:00 to 6:15 pm

Instructor: Nan (Louise) Chen

Nan (Louise) Chen is currently a second-year PhD student from Dr. Howard Katz’s Lab at the Department of Materials Science and Engineering. She received her bachelor’s and master’s degrees in Chemical Engineering from New York University in 2019 and in 2020. Her research focuses on designing and creating organic semiconducting based hybrid devices for thermoelectric rachet and vapor sensing applications. She is also performing the fundamental studies on ionic polymers and understand the ion movement kinetically and thermodynamically through thermoelectric characterizations. She is also the Intercampus Chair of Graduate Representative Organization (GRO) and the International Representative of Materials Graduate Society (MGS).

This course will introduce students to the ethics of AI in the healthcare space. The course will introduce multiple components of AI, including transparency and explainability, algorithmic bias, privacy, and other challenges. At the end of the course, students will be able to understand principles and possible pitfalls of AI applications, and reason about ethical considerations for specific use cases in healthcare applications.

Section, Date, and Time:

EN.500.111.27 / Thu / 6:00 to 7:15 pm

Instructor: Carlos Aguirre

Carlos Aguirre is a PhD student affiliated with the Center for Language and Speech Processing (CLSP) and the Computer Science department at Johns Hopkins University, working with Dr. Mark Dredze. His research investigates the fairness of natural language technologies used in applications for healthcare.

This course will introduce students to the exciting world of granular matter: sand, snow, and moondust! These materials are ubiquitous in nature, among the most frequently manipulated in industrial settings, and share one common feature: they are composed of many individual, macroscopic particles ranging in size from microns to decimeters. In this course, students will learn about the numerous applications of granular matter; approaches for modeling its behavior; state-of-the-art computer simulation techniques; and have the opportunity to directly play with, model, and simulate this uniquely complex material.

Section, Date, and Time:

EN.500.111.30 / Fri / 2:00 to 3:15 pm

EN.500.111.32 / Fri / 3:30 to 4:45 pm

Instructor: Aaron Baumgarten

Aaron is a postdoctoral fellow with the Hopkins Extreme Materials Institute (HEMI) at Johns Hopkins University. His research with Professor K.T. Ramesh focuses on developing models that describe the behavior of rocks and sediments during impact events — e.g., the collision of a meteorite with the Earth’s surface. Prior to joining HEMI, Aaron received his PhD from the Massachusetts Institute of Technology, where he studied mechanics, computational modeling, and aerospace engineering.

In this class we will survey how to use modern data science techniques to extract statistical insights from network data sets, such as social networks. Along the way, we will also introduce some methods in data science as needed, such as dimensionality reduction and clustering, and we will also cover implementations of these methods in R. A primary theme of the course will be in exploring how dimensionality reduction can be used to uncover underlying network information.

Section, Date, and Time:

EN.500.111.31 / Fri / 2:00 to 3:15 pm

EN.500.111.33 / Fri / 3:30 to 4:45 pm

Instructor: Joshua Agterberg

Joshua Agterberg is a PhD student in Applied Mathematics and Statistics at Johns Hopkins University, advised by Carey Priebe. His research interests include statistical network analysis, spectral methods, and high-dimensional statistics, with an emphasis on addressing theoretical problems in the mathematical and statistical foundations of data science. His work has been supported through a Mathematical Institute of Data Science (MINDS) Fellowship and a Counselman Fellowship, and he is an Applied Mathematics and Statistics Teaching Fellow. In 2021 he received the Institute of Mathematical Statistics Hannan Award and a best presentation award for his talk and paper at the Joint Statistical Meetings student competition in nonparametric statistics.