Small Introductions to Big Ideas

In the HEART program’s tutorials, which are taught by advanced graduate students and postdoctoral fellows, undergraduates are introduced to cutting-edge engineering research and learn how that research impacts society. The program provides valuable information for students as they consider the roles they can play in research projects at JHU, and the small size of the tutorial groups helps ensure that students have ample time to interact with their instructors and with each other. To ensure these courses are accessible to entering first-year students (who have priority on registration) they have no prerequisites. 

Incoming first-year students can enroll in one of the tutorials when course registration begins on July 14, 2020.

Sophomores, juniors, and seniors can register for the tutorials beginning August 1, 2020.

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

2020 HEART Courses

Traumatic Brain Injuries (TBIs) are one of the most common yet least understood injuries to the body, and are typically experienced in sports, automotive crashes, and military environments. The HEART course will cover the biomechanics of traumatic brain injury (TBI), delving more specifically into blunt impacts in sports and vehicle crashes that cause mild-to-moderate TBI. The course will cover an overview of research in injury biomechanics, a historical review of TBI research using animal models, and current state-of-the-art computational models that are used to TBI prediction and mitigation.


EN.500.111.13 / Tues / 5:00 to 6:15 p.m.

EN.500.111.23 / Wed / 5:00 to 6:15 p.m.


Ahmed Alshareef

Bio: Ahmed is a postdoctoral researcher in the labs of Dr. Prince (ECE) and Dr. Ramesh (ME). His research interests are in injury and sports biomechanics, computational modeling, and traumatic brain injury (TBI). At JHU, he is working on a study investigating the deformation of the human brain in volunteers subjected to mild impact using magnetic resonance imaging techniques, with the goal of developing biofidelic computational models of the human brain for TBI research. Ahmed received his bachelor’s degree from Duke University, followed by a PhD at the University of Virginia.

The goal of this course is to explore the evolution of Brain-Computer Interfaces (BCIs), technologies designed to aid those with neurological disorders by transforming signals from the brain to control external devices such as robotic arms. We will cover BCIs through lectures and discussions of topics including the history of BCIs from their inception in popular culture to realization through the BRAIN Initiative launched by President Obama, the fundamental knowledge of neuroscience and engineering, the ethical issues that should be considered, all of which will lead to discussions about their limitations and the next research advancements needed to develop and distribute this technology. Students will leave this course with an understanding of the multiple facets of BCIs, which will prepare them for life in a technologically advanced future centered around the interaction between man and machine.


EN.500.111.10 / Tues / 8:30 to 9:45 a.m.

EN.500.111.41 / Wed / 8:30 to 9:45 a.m.


Macauley Breault

Bio: Macauley Breault is a PhD candidate in biomedical engineering at Johns Hopkins University in Neuromedical Control Systems Lab under Dr. Sridevi Sarma. Her research interests lie at the intersection of data science, neuroscience and sensorimotor control. Currently, she is studying how past actions and performance influence future movements and how this is represented in the human brain.

This course covers the sources and distribution of greenhouse gases, a heated environmental topic that connects the atmosphere, biosphere, hydrology as well as human activities. This course mainly focuses on (1) a description and discussion of the natural and anthropogenic sources from Carbon Dioxide, Methane and Nitrous Oxide, (2) heated topics and debates about greenhouse gas emissions in this century, and (3) potential mitigation strategies towards an effective reduction in greenhouse gas emissions.


EN.500.111.07 / Mon / 4:00 to 5:15 p.m.


Zichong Chen

Bio: Zichong Chen is currently a postdoctoral fellow in the Department of Environmental Health and Engineering. He achieved his Ph.D. degree in the University of Minnesota. Throughout his Ph.D. and postdoc career, he is devoting himself in quantifying regional- to global-scale greenhouse gas emissions and constraining the relationships between these emissions and underlying natural or anthropogenic factors.

This course will introduce the pipeline of single-cell transcriptomic analysis and a range of emerging applications in the fields of biology and medicine. The course will begin with building computational foundations and introducing basic principles of cell and molecular biology. The latter half of the course will explore specific examples in diverse fields of how the single-cell transcriptomic analysis has enabled scientists to solve complex biomedical problems.


EN.500.111.31 / Thur / 4:00 to 5:15 p.m.


Ray Cheng

Bio: Ray Cheng is a PhD candidate advised by Dr. Patrick Cahan in the Department of Molecular Biology and Genetics at Johns Hopkins University. He specializes in computational single-cell genomics. He is especially interested in applying single-cell transcriptomics to understand the cell identity and cell dynamics in adult stem cells. Before entering the Ph.D. program at Johns Hopkins University, he received his medical degree from National Yang-Ming University, Taiwan in 2016.

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.


EN.500.111.16 / Tues / 6:00 to 7:15 p.m.

EN.500.111.40 / Mon / 6:00 to 7:15 p.m.


Daniel Ehrens

Bio: 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.

Can you really see atoms in an Aluminum foil? Do you know how Coronovirus is imaged? From transistors in phones to proteins in drug delivery, biologists and engineers strive to design innovative and exciting materials by controlling things down to the atomic level. In this course, students will explore light and electron microscopy techniques (SEM & TEM) used by a wide variety of researchers to visualize and analyze materials at different length scales. Note that this class will not include long one-sided lectures but will consist of fun class activities, short demos using simulation tutorials, and exciting tours to different labs in the Homewood campus to acclimate students to some of the cutting-edge research tools on campus.


EN.500.111.37 / Fri / 4:00 to 5:15 p.m.

EN.500.111.38 / Fri / 6:00 to 7:15 p.m.


Suhas Eswarappa Prameela

Bio: Suhas Prameela is currently a graduate student pursuing a PhD in the Department of Materials Science and Engineering (DMSE), Johns Hopkins University. He completed his BS in Mechanical Engineering with Summa Cum Laude from RV College of Engineering, Bangalore, and MS from Arizona State University, Tempe. He was also a research fellow at IIT, Ropar, as part of the Indian Academy of Sciences’ research fellowship. He is currently working on improving the dynamic performance of Magnesium alloys to build lightweight and robust body armor, a project with the Hopkins Extreme Materials Institute (HEMI) and Army Research Lab (ARL). He was also recently awarded the Engaged Scholar Graduate Student Award from the JHU Center for social concern for teaching and undergraduate mentoring.

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.


EN.500.111.17 / Tues / 6:00 to 7:15 p.m.

EN.500.111.26 / Wed / 6:00 to 7:15 p.m.


Mark Iskarous

Bio: 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. 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.

I plan to cover topics commonly discussed in an introductory physics class, but I will show students how to draw on their own personal experiences to make the problems relevant and easy to understand. We will still work with some basic equations, but the emphasis will be on understanding cause and reaction and order of magnitudes of the results rather than getting bogged down in the math.


EN.500.111.24 / Wed / 5:00 to 6:15 p.m.


Kelly Karl

Bio: Kelly Karl is a graduate student in the Molecular Biophysics Program. Her research focuses on protein-protein interactions and their roles in in embryonic development. Kelly is a unique student as she worked as an engineer in industry for a number of years before returning to pursue her doctoral degree. In her free time, Kelly likes to spend time with her dog, 2 cats, and husband, and she enjoys doing anything outside when it’s warm.

An introduction to imaging techniques used in the biomedical field. Topics include image acquisition, processing, and analysis methods. Various imaging methods for biomedical applications are discussed.


EN.500.111.07 / Mon / 7:00 to 8:15 p.m.

EN.500.111.18 / Tue / 7:00 to 8:15 p.m.


Jenn (Jeong Hee) Kim

Bio: Jenn Kim is a PhD student in mechanical engineering at Johns Hopkins University. She focuses on 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.

Graphs are a mathematical structure used to represent relationships between objects, and they can be found throughout the sciences as well as in the computer and phone applications people use every day. From modeling friendships in social networks, to giving the best directions on a map, to assembling genomes, there is a never-ending range of applications for graphs and the algorithms used to process them. In this course we will explore some of the real-world applications of graphs which drive the need for efficient algorithms.


EN.500.111.09 / Tue / 8:30 to 9:45 a.m.

EN.500.111.20 / Wed / 8:30 to 9:45 a.m.


Melanie Kirsche

Bio: Melanie Kirsche is a computer science PhD student in Mike Schatz’s lab. Her research focuses on designing efficient computational methods to study structural genomic variation between individuals and populations. She obtained her MS in Computer Science from the University of Central Florida with a focus on algorithms and data structures.

While Linear Algebra manipulates one-dimensional vectors using only algebraic expressions – matrix, Geometric Algebra extends this algebraic manipulation concept for higher-dimensional objects (called multivectors) using Geometric product.  With this extension, Geometric Algebra provides a unified mathematical language to model various kinds of geometric problems in different domains – physics (special and general relativity), computer sciences (computer graphics), engineering (robotics), etc.  In this course, we are going to explore Geometric Algebra, starting with 2D Euclidean geometry, then extending it to Compass Ruler Algebra, and finally studying applications in robot kinematics, computer vision, and computer graphics using Geometric Algebra.


EN.500.111.05 / Mon / 5:00 to 6:15 p.m.

EN.500.111.39 / Mon / 6:00 to 7:15 p.m.


Sing Chun Lee

Bio: Sing Chun Lee is a PhD candidate in computer science at Johns Hopkins University. He graduated from the Chinese University of Hong Kong in Mathematics and Information Engineering double degree program. Then he received his master’s degree in Biomedical Computing from the Technical University of Munich. He likes to bring 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 objects algebraically.

Medical robotics is a multidisciplinary field dedicated to providing enhanced information and assistance to clinicians and to produce better healthcare. This course will be a primer on the field of medical robotics and an introduction to the recent advance in the robotic technologies developed for medical applications. This course will introduce the fundamental knowledge of medical robotics, including robot kinematics, mechanical design considerations, control paradigms, sensing, and medical image guidance.


EN.500.111.21 / Wed / 4:00 to 5:15 p.m.


Gang Li

Bio: Gang Li is a postdoctoral fellow at the Laboratory for Computational Sensing and Robotics (LCSR) since 2018. He was a contract Researcher at General Motors before joining Hopkins. He received a Ph.D. degree in Mechanical Engineering from Worcester Polytechnic Institute in 2016. His research focuses on leveraging mechatronics, robotics, and medicine in the application to medical robotic systems.

This course will introduce mathematical modeling for physics phenomena including elementary to advanced mathematics and data-driven approach. Then, this class will introduce how to simplify models without losing key physics and how to interpret models from a data-driven approach. Examples of physical phenomena include fluids, structures, robotics, and astronomy.


EN.500.111.06 / Mon / 5:00 to 6:15 p.m.

EN.500.111.34 / Thurs / 5:00 to 6:15 p.m.


Chang Liu

Bio: Chang Liu is a Ph.D. candidate in the Department of Mechanical Engineering in the lab of Prof. Dennice Gayme, Johns Hopkins University. Chang’s research focuses on input-output based approach for modeling, analysis, and control of shear flows. He is also interested in control theory, nonlinear dynamics, state estimation, fluid-structure interactions, geophysical fluid dynamics, and celestial mechanics. His work has been published in the Journal of Fluid Mechanics, Journal of Fluids and Structures and he is serving as a reviewer for Marine Structures. He completed his bachelor’s degree in Shanghai Jiao Tong University major in Naval Architecture and Ocean Engineering with a minor in Computer Technology and its Application. He was awarded Mechanical Creel Family Engineering Teaching Assistant Award at Johns Hopkins University, Top 1% Bachelor thesis in Shanghai Jiao Tong University, and Outstanding Contribution Award of Chun-Tsung Foundation.

The goal of this course is to explain at a high level a set of machine learning algorithms that have been shown to be useful in medicine with an emphasis on intuitive explanations for why and how they work. We will look at linear methods and decision trees, dimension reduction via PCA and nonlinear methods, and basic optimization. Then we will move to a quick overview of unsupervised methods and finally deep learning in broad terms with an introduction to the common frameworks – there will be interactive Python labs in class and applications to medicine throughout.


EN.500.111.15 / Tue / 5:00 to 6:15 p.m.


Jason Miller

Bio: Jason Miller is pursuing a Ph.D. in applied mathematics and statistics advised by Bloomberg Distinguished Professor Mauro Maggioni. His research focuses on the design and analysis of machine learning algorithms for high-dimensional data sets, most recently on nonparametric inference of dynamical systems. He is especially interested in data coming from medicine and physics. He attended The University of Virginia where he earned a bachelor’s and a master’s in mathematics in 2014 and 2015, respectively, and then worked as a quantitative research analyst from 2015 to 2017 before entering the Ph.D. program at Johns Hopkins University.

Discover the impact that cyber attacks have on varying stakeholders such as you, the average user, corporations/organizations, government, and countries. This course will introduce both offensive and defensive approaches, as well as an understanding of threat actors’ perspective, to the current topics of cybersecurity literacy. Students will gain an appreciation of how digital hygiene, a routine that dictates how technology is used, managed, and maintained, has the power to transform the likelihood of becoming a victim to cybercrime. In this course, students will participate in class activities including role-playing, discussions, case studies, and research.


EN.500.111.04 / Mon / 4:00 to 5:15 p.m.

EN.500.111.11 / Tue / 4:00 to 5:15 p.m.


Karol Pierre

Bio: Karol Pierre is a current GEM Fellow and Intel Scholar pursuing her M.S. degree in Cybersecurity at Johns Hopkins University. She holds a B.S. in Computer Engineering with minors in Computer Science and Cybersecurity from Villanova University. Her current research areas include Cyber Literacy & Awareness, Malware Analysis & Detection, and Digital Forensics.

Electrets are insulating materials that store electrical charges for an extended period of time and are used in applications ranging from motion sensors and microphones to air filters and ultrasonic transducers. This course will provide an overview of classifications (i.e. piezoelectric, space-charge) and types (i.e. polymers, ceramics, biomaterials) of electret materials, as well as an introduction into the fabrication, characterization, and applications of electrets. Students will participate in hands-on activities to understand the use of electrets in common devices and to characterize the electrical response of a widely used electret material, polyvinylidene fluoride.


EN.500.111.08 / Tue / 8:30 to 9:45 a.m.

EN.500.111.19 / Wed / 8:30 to 9:45 a.m.


Valerie Rennoll

Bio: Valerie Rennoll is a graduate student in the Electrical and Computer Engineering Department. She earned Bachelor of Science degrees in Physics and Audio Technology from American University in 2016. As part of the West Research group, Valerie’s research focuses on developing transducers that are optimized for sound propagation through the skin and water. She is currently investigating new materials that can be used to make transducers with tunable impedance, flexibility, and increased sensitivity.

The course provides an overview of the engineering research into modern neurorehabilitation techniques for patients with motor, cognitive and psychiatric disorders. It will emphasize the role of engineers in the development of devices and in the investigation of therapy treatments that will be used in hospitals and clinics. Students will learn the material through lectures, interaction with patients, and hands-on laboratory activities.


EN.500.111.27 / Wed / 6:00 to 7:15 p.m.

EN.500.111.35 / Thurs / 6:00 to 7:15 p.m.


Cristina Rossi

Bio: Cristina is a Biomedical Engineering Ph.D. 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.

After a drug molecule has been shown to be effective in the lab, finding the best route of administration for that drug becomes essential for its performance in the clinic. In this course, we examine the biological barriers that must be overcome in order for drugs delivered via traditional routes (oral, intravenous, etc.) to be effective. Students will be introduced to the basics of pharmacokinetics, bioavailability, and drug clearance. We will also discuss next generation drug delivery systems, including pain-free, controlled release formulations as well as new classes of therapeutic molecules and their unique delivery challenges.


EN.500.111.01 / Mon / 8:30 to 9:45 a.m.


Yuan Rui

Bio: Yuan is a Biomedical Engineering Ph.D. student in Dr. Jordan Green’s lab. She is interested in designing biomaterials to facilitate gene therapies for cancer treatment and tissue regeneration. Outside the lab, she enjoys trying out new recipes in the kitchen and hanging out with her cats.

This course will introduce the research area of machine translation and recent advances in the field. We’ll focus on models that can translate multiple languages simultaneously, which play an increasingly important role in commercial systems. At the end of the course, students will be able to reason about big data as well as cross-lingual linguistic phenomena and understand how existing machine translation systems work.


EN.500.111.02 / Mon / 8:30 to 9:45 a.m.

EN.500.111.29 / Thurs / 8:30 to 9:45 a.m.


Pamela Shapiro

Bio: Pamela is a 5th-year Ph.D. student in the Computer Science department working with Kevin Duh in natural language processing and machine translation. She has spent time studying Arabic, which informs her research interests.

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.


EN.500.111.30 / Thurs / 8:30 to 9:45 a.m.

EN.500.111.36 / Thurs / 6:00 to 7:15 p.m.


Srujan Singh

Bio: Srujan 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.

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.


EN.500.111.25 / Wed / 5:00 to 6:15 p.m.

EN.500.111.33 / Thurs / 5:00 to 6:15 p.m.


Emily Su

Bio: Emily Su is a Ph.D. 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.

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 techniques for solving image processing problems (such as contrast enhancement, edge detection, image denoising and deblurring), as well as basic techniques for solving shape analysis problems (such as shape registration and shape clustering). Applications to computer vision will be mentioned throughout the course.


EN.500.111.12 / Tue / 4:00 to 5:15 p.m.

EN.500.111.22 / Wed / 4:00 to 5:15 p.m.


Yashil Sukurdeep

Bio: 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, 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 B.S. in Mathematics and a M.S. in Applied Mathematics.

How can we use nanotechnology to deliver therapeutic peptides, nucleic acids, and proteins? This course will provide the necessary background on the field of biologics therapy, examine common difficulties encountered during the translational process, and explore nanomedicine design principles. At the end of the course, students will have an understanding of major nano-carrier systems and critically examine nanomedicine formulations.


EN.500.111.14 / Tue / 5:00 to 6:15 p.m.


Tony Wu

Bio: Tony Wu is a Biomedical Engineering Ph.D. candidate under Dr. Rangaramanujam Kannan at the Center for Nanomedicine in the Johns Hopkins School of Medicine. He received his B.S. and M.S.E. in Biomedical Engineering from the Johns Hopkins University. His current research focuses on the synthesis and application of novel dendrimer-biologic conjugates to treat ocular and neurodegenerative diseases.

The world has over 7000 documented languages; until recently, researchers have focused on only a small percentage of these. This course will introduce the field of natural language processing, focusing on the multilingual aspects of NLP. We will explore tasks and algorithms that involve and exploit multiple languages and realize the challenges computers face in understanding and translating languages around the world.


EN.500.111.32 / Thurs / 5:00 to 6:15 p.m.


Winston Wu

Bio: Winston Wu is a Ph.D. student in Computer Science and the Center for Language and Speech Processing, where he is advised by David Yarowsky. He works on multilingual natural language processing, with a focus on low-resource languages. He received undergraduate degrees in computer science and Latin from UT Austin.

Through lectures, discussion, and lab tour, this course will expose the students to a range of advanced microscopic and spectroscopic techniques that have revolutionized biomedical research. With a carefully balanced intellectual depth and width, this course is divided into three modules, including microscopy methods, surface-enhanced spectroscopy, 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 an unparalleled capability to attack complex problems in biology and medicine.


EN.500.111.28 / Wed / 7:00 to 8:15 p.m.


Peng Zheng

Bio: 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 developing advanced optical tools, such as nanoscale laser probes, super-resolution techniques, and nano-optical sensing platforms, to answer 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.

Eyes to medicine, nanomaterial assisted non-invasive medical imaging is steering diagnostics and therapeutics towards precision, individuality and safety. This course will survey the salient theories and applications of modern non-invasive medical imaging platforms, the specific design considerations of nanomaterials as imaging probes, issues that are present and how they are circumvented, along with modern technologies on nanomaterial synthesis and characterization. Upon establishing basic knowledge in physics, chemistry, biology as well as exploring current advanced researches, students will engage in brainstorm sessions to develop and engineer next generation imaging probes, and culminating with an arranged visit to the imaging facility.


EN.500.111.42 / Monday / 6:00 to 7:15 p.m.

EN.500.111.43 / Tuesday / 6:00 to 7:15 p.m.


Ge Si

Bio:Ge Si is a Ph.D. candidate in the Department of Chemical and Biomolecular Engineering and is working under the mentorship of Dr. Dmitri Artemov in the Department of Radiology and Radiological Science, the Johns Hopkins School of Medicine. She received her M.Sc. in Chemical Engineering from Columbia University in the City of New York and B.Sc. in Chemistry with a minor in Applied Psychology from Nankai University. Her broad range of research experiences involve organic synthesis, liposome-based drug delivery, self-assembled nanomaterials, and biosensor engineering. Her research goal is to develop iron oxide-based multimodal imaging probes towards image-guided precision medicine.