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 22, 2021.

Sophomores, juniors, and seniors can register for the tutorials beginning August 1, 2021.

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

2021 HEART Courses

The subject of this course is epigenetics, but the intended lesson is how to navigate reams of scientific literature without losing your way. We will help you “switch scales” between fine-grained technical information (how does DNA methylation prevent proteins from binding DNA?) and a broad goal or agenda (What controls the behavior of pancreatic cancer?). This course is recommended for people who want to learn about basic research but get distracted or have trouble focusing on a goal when reading about a new topic.

Section, Date, and Time: EN.500.111.01 / Mon / 8:30 to 9:45 a.m.

Instructor: Eric Kernfeld

Eric Kernfeld is a PhD student jointly advised by Alexis Battle and Patrick Cahan. His career focuses on embedded bioinformatics, which is a subfield at the intersection of genomics and applied statistics. Embedded bioinformatics is distinguished by a primary interest in data analysis rather than software development, as well as a deep engagement with biologists — in Eric’s case, stem cell biologists. Before starting at JHU, Eric worked for the René Maehr lab, a stem cell group working towards in vitro models for the most essential process in human adaptive immunity: T cell selection.

Nanoparticles (particles of the size ~ 1nm) when mixed with polymers form a hybrid material known as polymer-nanocomposites (PNC). They are seen to offer unexpected improvements in properties such as conductivity, strength, stability, well above the native polymer while maintaining the manufacturing benefits of such materials. The demand for their superior properties is expected to raise the market value to about $11,549 million by the year 2022. This course would discuss the fundamental physics of polymers and polymer-nanocomposites and their physical behaviors, while covering the latest research developments and breakthroughs in the areas pertaining to energy storage such as secondary lithium-ion batteries, polymer electrolyte membranes and fuel cells; in environment remediation such as air filtration and waste-water management; in the areas of biomedical research including drug delivery, complex coacervates, sensors etc.; and in smart materials. Beside lectures on the topics, we will have fun activities based on what has been covered. Additionally, we will have guest lectures showing experimental demos and lab tours.

Section, Date, and Time: EN.500.111.02 / Mon / 8:30 to 9:45 a.m.

Instructor: Rituparna Samanta

Rituparna recently graduated from the Department of Chemical Engineering at University of Texas, Austin. During graduate school, she worked with Prof. Venkat Ganesan to develop computational methods to study the behavior of charged nanoparticles in charged polymer solutions pertaining to applications in the food industry and biosensor applications. Rituparna is currently a postdoctoral student at the Chemical and Biomolecular Engineering department, JHU and work with Prof. Jeffrey Gray. Her current research focuses on developing tools to study membrane protein structure predictions and use machine learning tools for antibody loop design.

This course serves as an introduction to the mathematical modelling of shapes and images, and to numerical algorithms 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 basic shape analysis frameworks for performing shape registration and shape clustering. Applications to computer vision will be discussed throughout the course.

Sections, Dates, and Times:

  • EN.500.111.03 / Mon / 8:30 to 9:45 a.m.
  • EN.500.111.23 / Wed / 8:30 to 9:45 a.m.

Instructor: Yashil Sukurdeep

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

This course will introduce modeling in nonlinear dynamical systems that can exhibit chaotic behavior. The class will include mathematical formulations to describe the physics as well as visualization via computer demonstrations in SCIGMA. Lastly, a data-driven approach will be introduced. Examples include the butterfly effect, synchronization in nature, turbulence, vortex and mixing, and instabilities and pattern formation.

Section, Date, and Time: EN.500.111.04 / Mon / 4:00 to 5:15 p.m.

Instructor: Cristina Martin Linares

Cristina Martin Linares is a PhD candidate in the Department of Mechanical Engineering at Johns Hopkins University. Cristina’s research focuses on nonlinear dynamics and machine learning for modeling mechanistic multiscale problems. She has worked on modeling closures in fluid mechanics and nonlinear viscoelastic constitutive modeling in liquid crystal elastomers. She is also interested in applying these modeling techniques to problems in biology. She contributed to the development of a mechanistic toroidal ventilator to help during COVID-19, which was also selected as a semifinalist to the MIT competition. She also has research mentoring and teaching experience as a lecturer. During her PhD, she was awarded the “la Caixa” fellowship and Mechanical Engineering Departmental fellowship. She has also been a finalist to the science prize “Andaluces del Futuro”. Her research has been featured in the journal Soft Matter, and her artwork selected for the main front cover, featuring the connection between the sciences and art in her research in liquid crystal elastomers.

Through lectures, discussion, group projects, and lab tour, this course will expose the students to a range of advanced optical techniques that have revolutionized science. With a carefully balanced intellectual depth and width, this course is divided into three modules, including nanophotonics, plasmonics, and recently emerging nanoscopy methods. Each module covers fundamental science and technological innovations for a particular class of optical tools and how they are empowered with unparalleled capability to attack complex problems in biology and medicine.

Section, Date, and Time: EN.500.111. / Mon / 8:30 to 9:45 a.m.

Instructor:Peng Zheng

Peng Zheng is a postdoctoral fellow in the Department of Mechanical Engineering at Johns Hopkins University. His research in Prof. Ishan Barman’s group is directed toward studying coherent light-matter interactions that can be harnessed for developing advanced optical tools, such as nanoscale laser probes, super-resolution techniques, and optical sensing platforms, to tackle difficult questions in biomedicine. Prior to joining Hopkins, Peng graduated from West Virginia University where he conducted doctoral studies that earned him the prestigious National Award for Outstanding Self-Financed Chinese Students Studying Overseas.

This research seminar will introduce students to the steps involved in creating new therapies, medical procedures, and diagnostic tools for neurological disorders. The course will focus on three of the most common major neurological disorders: Epilepsy, Parkinson’s Disease, and Depression. In addition to being particularly debilitating, these neurological disorders are special in that they are affected by electrical stimulation and are subject to neuromodulation. In recent years, there has been a boom in the development of invasive and non-invasive neural stimulation techniques. This course will provide an overview of these neurological disorders and their treatments, after which it will dive into the current state of translational research, examine the use of novel analytic tools (i.e., machine learning, network analysis, fMRI), and explore closed-loop approaches.

Sections, Dates, and Times:

  • EN.500.111.06 / Mon / 4:00 to 5:15 p.m.
  • EN.500.111.13 / Tue / 4:00 to 5:15 p.m.

Instructor: Daniel Ehrens

Daniel Ehrens is a PhD candidate in biomedical engineering and is the recipient of an HHMI Gilliam Fellowship for Advanced Study. He researches the development of strategies for seizure detection, prediction and control at different levels of translational research at the Neuromedical Control Systems Lab, where he is advised by Dr. Sridevi Sarma.

This course aims to introduce magnetic systems and their potential usage in surgical operations.  Magnetic fields allow rapid force and torque transfer on rigid magnetic bodies. These magnetic bodies become wireless end effectors of the electromagnetic system. Automation is a crucial component to precisely and safely move these end effectors. For a reliable autonomous surgical task, two subalgorithms are crucial: (1) an accurate localization/sensing of the state of a magnetic robot, and (2) a control algorithm for calculating the necessary forces and torques to accomplish the task. In this course, we will study establishing a simulation environment for magnetic field generation, calculation of magnetic forces and torques on the robots, and implementing dynamics to generate motions on magnetic particles. This simulation environment will provide a basis to implement control algorithms for autonomously control position and orientation of the robots. Last section of the course will cover magnetic actuation with a teleoperated fashion in real experimental system.

Section, Date, and Time: EN.500.111.07 / Mon / 4:00 to 5:15 pm

Instructor: Onder Erin

Onder Erin received his BS degree in Mechatronics Engineering from Sabanci University, Istanbul, Turkey, in 2014. He received his MSc and PhD 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 PhD 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.

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.500.111.08 / Mon / 5:00 to 6:15 p.m.
  • EN.500.111.27 / Wed / 5:00 to 6:15 p.m.

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 BS 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 is designed for the fresh undergraduate who has no background on geometry processing with a focus on polygon meshes. Geometry processing is a research area that combines mathematics and computer science to invent and develop efficient algorithms for manipulating digitized 3D objects such as surface reconstructions, animations, shapes matching, etc., which is useful in domains varying from computer games, physics simulations, to medicine. This course will provide an introduction to the fundamental mathematics behind the scene, the computer sciences background, and the applications.

Sections, Dates, and Times:

  • EN.500.111.09 / Mon / 6:00 to 7:15 p.m.
  • EN.500.111.29 / Wed / 6:00 to 7:15 p.m.

Instructor: Sing Chun Lee

Sing Chun LEE is a computer science PhD candidate at Johns Hopkins University. He graduated from the Chinese University of Hong Kong in Mathematics and Information Engineering double degree program and received his master’s degree in Biomedical Computing from Technical University of Munich. He is interested in bringing mathematical theories to practice, in particular, geometry processing and augmented reality. Recently, he is interested in Geometric Algebra which is a unified mathematical language to manipulate geometric object algebraically.

This course explores the structure of the power grid and the options and challenges of integrating sustainable energy sources.  We will cover current renewable energy research topics including energy storage and wind energy, which are in the process of grid integration. Apart from learning real-world applications, we will also touch on research skills such as data assimilation and simulation.

Sections, Dates, and Times:

  • EN.500.111.10 / Mon / 6:00 to 7:15 p.m.
  • EN.500.111.30 / Wed / 6:00 to 7:15 p.m.

Instructors: Genevieve Starke; Rajni Kant Bansal

Genevieve Starke is a PhD candidate in the labs of Dr. Charles Meneveau and Dr. Dennice Gayme in the Department of Mechanical Engineering. Her research project is on wind farm modeling and control, which is an interdisciplinary project that combines the complex fluid dynamics in the wind farm with active control challenges. She is also interested in using reduced-order modeling to represent complex systems, data estimation and assimilation, and graph theory. She completed her bachelor’s degree in Aerospace Engineering from Syracuse University and also holds a master’s degree from Johns Hopkins.

Rajni Bansal is a PhD candidate co-advised by Dr. Dennice Gayme in the Department of Mechanical Engineering and Dr. Enrique Mallada in the Department of Electrical and Computer Engineering. His research lies in the interdisciplinary field of power systems and economics of the electricity market, with a focus on the control and optimization of energy storage systems. Before joining the PhD program at Johns Hopkins University, he earned his bachelor’s degree from the Indian Institute of Technology, Kanpur, India.

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.

Section, Date, and Time: EN.500.111.11 / Wed / 5:00 to 6:15 p.m.

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 in the Translational Tissue Engineering Center 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 covers the basic of renewable energies, with a focus on solar cells and nanomaterials for solar energy applications. We will look at the limitations of current common rooftop solar cells and potential means to overcome some of the limitations. This course delves into materials such as colloidal quantum dots (CQDs) and transitional metal dichalcogenide (TMD) nanoflakes as novel materials to advance the field of solar energy.

Sections, Dates, and Times:

  • EN.500.111.12 / Mon / 5:00 to 6:15 p.m.
  • EN.500.111.16 / Tue / 5:00 to 6:15 p.m.

Instructor: Arlene Chiu

Arlene Chiu is a PhD Candidate in the Department of Electrical and Computer Engineering under the mentorship of Prof. Susanna Thon at Johns Hopkins University. She received her a BS in Electrical Engineering from the University of Maryland College Park. Her broad range of research interests include photovoltaics, nanomaterials, semiconductor physics, photonic crystals, and renewable energy.

This course will introduce students to speech processing and the interdisciplinary research area of speech translation. We will study the basics of phonetics and machine translation, in order to understand the challenges specific to speech translation, and we’ll focus on methods that can generalize across linguistically diverse languages and language pairs. At the end of the course, students will be able to reason about linguistic phenomena and diversity, and understand how current speech translation systems work.

Section, Date, and Time: EN.500.111.14 / Tue / 4:00 to 5:15 p.m.

Instructor: Elizabeth Salesky

Elizabeth Salesky is a Computer Science PhD student at the Center for Language and Speech Processing, where her research focuses on different aspects of speech and text translation. She received her BA in Linguistics and Mathematics from Dartmouth College and MS from the Language Technologies Institute at Carnegie Mellon. She previously worked on machine translation and language learning applications at MIT Lincoln Labs.

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.

Section, Date, and Time:

  • EN.500.111.15 / Tue / 4:00 to 5:15 p.m.
  • EN.500.111.34 / Thurs / 4:00 to 5:15 p.m.

Instructor: John Rattray

John Rattray is a PhD 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 BS in Computer Engineering and received his MSE 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. John is an avid soccer player, digital artist, and innovator. Additionally, 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.

This course will provide the students with a rich understanding of spatial computing as the next wave of computing after personal and mobile computing and belongs to the devices that can sense the space or are “spatially” aware. It further discusses human interaction with a machine in which the machine retains and manipulates referents to real objects and spaces. Students will explore the practical implication of this subject in healthcare, industry, and society through weekly readings.

Section, Date, and Time: EN.500.111.17 / Tue / 5:00 to 6:15 p.m.

Instructor: Ehsan Azimi

Ehsan Azimi is a Provost’s Postdoctoral Fellow at the Johns Hopkins University. He completed his PhD in Computer Science at the Johns Hopkins University. Ehsan is passionate about the applications of technology in healthcare and surgery. His research focuses on mixed reality and medical robotics. He was named a Siebel Scholar. He has developed novel display calibration methods and new user interaction modalities for head-mounted displays that improve surgical navigation and training of medical procedures. The work is covered in the Engineering Magazine as well as the other media. He also implemented techniques for robot-assisted cochlear implant placement, intraocular robotic snake, and needle steering. Before joining Johns Hopkins, he worked at Harvard Medical School where he innovated a method that improves the resolution and dynamic range of a medical imaging system. Mr. Azimi holds multiple patents and his work has led to over 20 peer-reviewed articles in journals and conferences. He was awarded the Link Fellowship. Ehsan served as a mentor for several students and scholars in their projects and studies.

This course introduces students to engineering materials that are designed for extreme conditions and the fundamental concepts required for tailoring material properties for such harsh environments. Lecture topics include an overview of extreme conditions, relevant material properties, and the correlations between the atomic structure of materials and their characteristics; followed by a discussion on 3 classes of engineering materials used in aerospace (such as AlON, which just recently was suggested as a candidate for the large upper windows of the Starship spacecraft from SpaceX), defense (such as boron carbide and other ultrahard ceramics), and energy (such as metallic glasses and nanocomposites) applications. This introductory course is a gateway into the fields of Materials Science and Mechanical Engineering and welcomes all undergraduates with a basic knowledge of general physics, chemistry, and mathematics.

Sections, Dates, and Times:

  • EN.500.111.18 / Tue / 5:00 to 6:15 p.m.
  • EN.500.111.35 / Thurs / 5:00 to 6:15 p.m.

Instructor: Arezoo Zare

Arezoo is a postdoctoral fellow in the Ramesh Lab at the Hopkins Extreme Materials Institute (HEMI). She completed her master’s degree in Materials Science at Sharif University of Technology in Iran, followed by a PhD in Mechanical Engineering at Oklahoma State University. Her research primarily focuses on understanding the relationships between the mechanical properties of materials and their atomic structure, with the goal of designing materials with improved mechanical performance under extreme conditions. At JHU, Arezoo is working on experimental characterization of mechanical and structural properties of boron carbide, which is an ultrahard ceramic used as a protective material against extreme impact conditions.

In this course we will walk through the clinical and technical requirements associated with the design and development of surgical robotic systems, based on my 12 years’ experience in step by step development of surgical robots.  There would be lots of discussions on technological and clinical challenges during the development process and how to address them in the design. We will also talk about the set of skills required for a future research or career in robotics, and more specifically, surgical robotics.

Sections, Dates, and Times:

  • EN.500.111.19 / Tue / 5:00 to 6:15 p.m.
  • EN.500.111.37 / Thurs / 5:00 to 6:15 p.m.

Instructor: Alireza Alamdar

Alireza Alamdar is a postdoctoral fellow at the Laboratory for Computational Sensing and Robotics (LCSR) since 2020. He received his PhD degree in Mechanical Engineering from Sharif University of Technology in 2020. His research focuses on design and development of surgical robotic systems, from bench to bedside, and he has been in this field for 12 years. His expertise lies in design and fabrication as well as modeling and optimization.

The course provides an overview of the engineering research into modern neurorehabilitation techniques for patients with neurologic disorders such as stroke and Parkinson. It will emphasize the role of engineers in the development of treatments that will be used in hospitals and clinics. Classes include lectures and a hands-on project, where students will design and pilot test their own rehabilitation intervention.

Sections, Dates, and Times:

  • EN.500.111.21 / Tue / 6:00 to 7:15 p.m.
  • EN.500.111.39 / Thurs / 6:00 to 7:15 p.m.

Instructor: Cristina Rossi

Cristina is a Biomedical Engineering PhD student in Dr Amy Bastian’s lab. Her research aims to understand how people learn new movements, with a focus on improving rehabilitation therapies for people with motor disorders, such as stroke survivors. Cristina has received a MEng in Biomedical Engineering from Imperial College London.

An introduction to imaging techniques used in the biomedical field. Topics include image acquisition, processing, and analysis methods. We will discuss various imaging methods for biomedical applications in the class.

Section, Date, and Time: EN.500.111.22 / Tue / 7:00 to 8:15 p.m.

Instructor: Jeong Hee (Jenn) Kim

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 biochemical features of cells and tissues for clinical applications. Her work involves obtaining microscopic and spectroscopic data on biological samples and analyzing them using image reconstruction and machine learning methods. She received a BS in mechanical engineering from Georgia Institute of Technology and worked in the industry before joining Johns Hopkins.

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!

Section, Date, and Time: EN.500.111.24 / Wed / 8:30 to 9:45 a.m.

Instructor: Anna Liebhoff

Anna 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.”

The recent technological advances in systems neuroscience have empowered neuroscientists to address scientific questions with unprecedented scale and resolution. The goal of this research seminar is to introduce students to the most advanced technologies for studying the brain circuits at the intersection of neuroscience, electronics, and optics. In addition to navigating the core concepts behind the technologies, the course will also highlight the underpinning scientific motivations behind the technological advances, as well as their translational applications in medicine.

Section, Date, and Time: EN.500.111.25 / Wed / 4:00 to 5:15 p.m.

Instructor: Jung Yoon (Clare) Choi

Jung Yoon (Clare) is a postdoctoral researcher in Richard Huganir’s lab, in the department of neuroscience. Her research interests include neuromodulation, short-term memory, and the prefrontal cortex. Currently, she is investigating the interplay between synaptic and neural memory traces in the prefrontal cortex. Jung Yoon received her bachelor’s and PhD degrees in Psychology and Neuroscience from Princeton University.

Can we build reliable, secure computer systems that ensure user privacy? We’ll look at three key areas of cutting-edge research — censorship resistance, the Internet-of-Things, and electronic voting — and see if we can start to answer this question! Every class, lecture material will be accompanied by a hands-on component designed to provide first-hand experience in computer security and privacy.

Sections, Dates, and Times:

  • EN.500.111.26 / Wed / 4:00 to 5:15 p.m.
  • EN.500.111.33 / Thurs / 4:00 to 5:15 p.m.

Instructor: Tushar Jois

Tushar Jois is a fourth-year PhD candidate at Johns Hopkins University, studying computer security under his advisor, Dr. Avi Rubin. He received his BS and MSE degrees in computer science from Johns Hopkins. His primary research interests are in systems, software, and network security, with emphasis on security and privacy for personal devices: protecting users and their everyday data from prying eyes.

How can we turn stem cells into cardiomyocytes, hepatocytes, or neurons in the lab? This course will introduce experimental and computational strategies employed in stem cell engineering and give students hands-on experience with computational stem cell biology tools. Pivotal studies regarding cell identity, pluripotency, gene regulatory network reconstruction, and the development of cell fate engineering protocols will drive discussion on the challenges of understanding and manipulating cell fate, and the impact on the future of science and regenerative medicine.

Sections, Dates, and Times:

  • EN.500.111.28 / Wed / 5:00 to 6:15 p.m.
  • EN.500.111.43 / Mon / 5:00 to 6:15 p.m.

Instructor: Emily Su

Emily Su is a PhD candidate in the Department of Biomedical Engineering. She completed her bachelor’s degrees in Biomedical Engineering and Applied Mathematics and Statistics from Johns Hopkins University. She currently works in the lab of Dr. Patrick Cahan at the Institute for Cell Engineering at the School of Medicine. Her research focuses on the development of computational tools to elucidate the transcriptional regulation underlying cell fate decisions, such as germ layer specification in embryogenesis, and the application of network science in aiding manipulation of cell fate in vitro.

In this course, first, basic concepts of Neural Networks will be covered. This will be followed by Convolution Neural Networks and Recurrent Neural Networks (applications of Long Short-Term Memory (LSTM) will be demonstrated) with a specific focus on the application of Bi-directional LSTM (used in Amazon Alexa and Google Voice). Typical applications will include a basic demonstration of biological/ non-biological geometries and shapes, which will be followed by the classification, regression and prediction from videos and speech data.

Sections, Dates, and Times:

  • EN.500.111.31 / Wed / 7:00 to 8:15 p.m.
  • EN.500.111.40 / Thurs / 7:00 to 8:15 p.m.

Instructor: Debonil Maity

Debonil is a postdoctoral fellow in the Department of Biomedical Engineering and Institute for Nanobiotechnology (INBT). His research experience is broad, ranging from theoretical modelling, computational analysis to experimental projects in Mechanobiology. He brings theoretical insights into experiments and vice versa. He received his PhD in Chemical and Biomolecular Engineering where he studied cell motility. Currently, he is working on implementing Artificial Intelligence to unveil deeper insights into ageing.

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.

Sections, Dates, and Times:

  • EN.500.111.32 / Thurs / 8:30 to 9:45 a.m.
  • EN.500.111.38 / Thurs / 6:00 to 7:15 p.m.

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.

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 or citation graphs. 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 Python or R. A primary theme of the course will be in exploring how network-specific problems such as community detection can be recast as classical statistical problems through dimensionality reduction.

Section, Date, and Time: EN.500.111.36 / Thurs / 5:00 to 6:15 p.m.

Instructor: Joshua Agterberg

Joshua Agterberg is a PhD student in Applied Mathematics and Statistics advised by Professor Carey Priebe. His research interests are in the mathematical and statistical foundations of data science, particularly as they pertain to the theoretical analysis of matrix and graph data. As of Spring 2021 he is an AMS Teaching Fellow, and previously his research has been supported through a Counselman Fellowship and fellowships from the Mathematical Institute for Data Science (MINDS). Joshua graduated from the University of Wisconsin-Madison in 2017 with a Bachelor of Business Administration in Actuarial Science and Mathematics.

Supported by multidisciplinary approaches and collaborations, modern brain research has made unprecedented progress in our understanding of brain function and dysfunction in health and disease. This course aims to introduce students to the multidisciplinary world of modern brain research, with a particular focus on cutting-edge research techniques that stem from a diverse array of engineering disciplines. Two lectures are organized in a pair to focus on each different discipline of engineering, in which one lecture explains the fundamental principles of key techniques and the other introduces real-world research application of those techniques.

Section, Date, and Time: EN.500.111.41 / Thurs / 7:00 to 8:15 p.m.

Instructor: Ho Namkung

Ho Namkung is a PhD candidate in Biomedical Engineering at Johns Hopkins University. His research goal is to understand neural circuit mechanisms of higher cognitive functions that are impaired across a wide array of psychiatric and neurological disorders. Supported by multi-disciplinary approaches spanning optogenetics/chemogenetics, in vivo neural activity imaging, electrophysiology, computational modeling, animal behavior, and genetic engineering, he currently investigates how a neural circuit centered on the insular cortex orchestrates emotional learning and memory.

The goal of this course is to give students an understanding of the physical principles behind ultrasound and photoacoustic imaging. Students will also learn the fundamentals of machine learning and how these techniques can be paired with ultrasound and photoacoustic imaging to improve image quality. The course will culminate with a hands-on project where students will use MATLAB (no prior experience needed) to apply principles of ultrasound and photoacoustic imaging and machine learning.

Section, Date, and Time: EN.500.111.42 / Fri / 8:30 to 9:45 a.m.

Instructor: Alycen Wiacek

Alycen Wiacek is a PhD candidate in electrical and computer engineering at Johns Hopkins University in the Photoacoustic and Ultrasonic Systems Engineering (PULSE) Lab under the direction of Dr. Muyinatu Bell. Her research applies advanced signal processing and machine learning techniques to breast ultrasound in order to develop new algorithms that will improve the diagnosis of breast cancer.