Hopkins Engineering Applications & Research Tutorials (HEART) and Hopkins Engineering Research-Opened Investigation Courses (HEROIC)

This course provides a general introduction into the science behind design of materials and structures being used in extreme applications spanning from protective equipment for soldiers to deep space craft such as SpaceX’s starship. The course provides theoretical background from first-principle approach behind these exciting applications.

Section, Date, and Time: EN.500.111.01 / Mon / 9:00 to 10:15 am

Instructor: Piyush Wanchoo

Piyush Wanchoo has a Ph.D. in mechanical engineering and is currently serving as a postdoctoral fellow at the Hopkins Extreme Materials Institute (HEMI), JHU. His research focuses on developing advanced automated AI assisted research facilities to enable rapid exploration of material space for extreme loading applications.

Integration of renewable energy sources poses both challenges and opportunities for efficient, reliable, and sustainable power system operations. This course offers students a unique perspective into addressing these challenges through various applications of machine learning algorithms. Providing a hands-on and interactive learning experience, the course explores how machine learning can enhance current decision-making processes in power systems and market operations, all within the context of rapid sustainable energy advances.

Section, Date, and Time:

EN.500.111.02 / Mon / 10:30 to 11:45 am

Instructor: Zhirui Liang

Zhirui Liang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at Johns Hopkins University. Her research interests lie at the intersection of electrical engineering, optimization, machine learning, and power system economics. Zhirui earned her M.S. degree from Xi’an Jiaotong University and her B.E. degree from North China Electric Power University. She was a student summer intern at the Electric Power Research Institute (EPRI) in 2022 and at the National Renewable Energy Laboratory (NREL) in 2023.

Important knowledge in medicine and the life sciences has been created using mathematical models that help predict and interpret what we observe, and in this course will introduce models of various organs and body systems using elementary ideas from physics. Several important physiological systems (heart and circulation, kidney, etc.) will be covered while introducing many of the essential terms and ideas used in modeling the physical processes of flow, transport, etc. The goal is to introduce students to the use of mathematical methods in modeling biological systems by studying simple models that have been used decisively in understanding how these systems work.

Sections, Dates, and Times:

EN.500.111.03 / Mon / 8:30 to 9:45 am

Instructor: Zan Ahmad

Zan Ahmad is an Applied Mathematics and Statistics Ph.D. student, advised by Professor Natalia Trayanova in the Computational Cardiology Lab. His research focuses on the development and application of mathematical models and machine learning methods for stroke risk prediction in atrial fibrillation. He is a predoctoral fellow of the American Heart Association and works on left atrial hemodynamics simulations, statistical shape analysis and neural operators for learning the governing partial differential equations for fluid dynamics and solid mechanics of the heart.

Real-world systems are often complex and dynamic, necessitating innovative approaches to understand their multifaceted nature, including human behaviors, socio-environmental conditions, interactions and feedback loops. In this class, we will learn how to use a novel computational simulation method – agent-based modeling (ABM) – to simulate and dissect these complexities. This practical introductory-level course requires no coding background, covering topics ranging from fundamental ABM principles to hands-on modeling activities with simple real-world case studies such as crowd evacuation, urban design, and transportation planning.

Section, Date, and Time:

EN.500.111.04 / Mon / 5:30 to 6:45 pm

Instructor: Tingting Ji

Tingting Ji is a postdoctoral fellow in the Department of Civil and Systems Engineering at Johns Hopkins University, advised by Prof. Takeru Igusa. She is an early-career researcher specializing in complex systems modeling and simulation to support data-driven policy making, particularly focusing on improving health outcomes for vulnerable populations. She is currently working on simulating the effectiveness of Service Delivery Redesign for reducing maternal mortality, a multi-faceted intervention that is being deployed in Kenya with sponsorship by the Bill and Melinda Gates Foundation and the World Bank. Prior to her postdoc program, Tingting demonstrated expertise in applying advanced modeling techniques to various social and engineering topics spanning from people-centric smart cities to disaster resilience assessment and optimization, resulting in multiple publications in esteemed journals. She earned her Ph.D. from Hong Kong Polytechnic University in 2022 and B.S. from Chongqing University in 2017.

Can One Hear the Shape of a Drum? Inverse problems are concerned with finding information about an unknown object of interest from given indirect measurements. In this course we will study a wide range of inverse problems with applications in science and engineering. We will investigate some elementary inverse problems making use of simple algebra, analytic geometry, and trigonometry. Part of the class will be spent in presenting how the recent progress in the development of deep learning can be used to solve such inverse problems.

Section, Date, and Time:

EN.500.111.05 / Tue / 1:30 to 2:45 pm

Instructor: Aseel Titi

Aseel Titi is a postdoctoral fellow in the department of Applied Mathematics and Statistics at Johns Hopkins University mentored by Professor Fadil Santosa. Her research interests lie in the field of inverse problems for partial differential equations. In particular, Aseel worked on an inverse problem in electrical impedance tomography with minimal measurements and lately is interested in an inverse source problem for Maxwell’s equations. Aseel completed her Ph.D. in applied mathematics in 2021 where she studied the inverse problem of gravimetry when the number of measurements is limited. Aseel graduated with a master degree in mathematics in 2014 and taught different courses in mathematics and statistics for undergraduate students at Birzeit University in Palestine between 2014 and 2016.

In the fields of biomedical engineering and mathematical medicine, the application of modeling and machine learning plays a pivotal role in dissecting physical processes and optimizing treatment strategies. The primary objective of this course is to cultivate an understanding of computational modeling and machine learning applications in clinical settings. This course intends to lay the groundwork for mathematical modeling and offers a high-level perspective on how these novel tools can be utilized to tailor treatments to individual patients, with a special focus on neurosurgery.

Sections, Dates, and Times:

EN.500.111.06 / Tue / 4:30 to 5:45 pm

Instructor: Avisha Kumar

Avisha is a third year PhD student in the department of Electrical and Computer Engineering at Johns Hopkins. Before joining Hopkins, she completed her undergraduate and master’s education at Cornell University. Her research is focused on ultrasound simulations and deep learning in the context of neurosurgery. She is interested in optimizing focused ultrasound therapy with acoustic wave modeling and physics informed neural operators.

This course explores the design principles of biological circuits to understand how molecular networks process information and dynamically affect cell behavior. Students will develop an intuitive understanding of biological network behavior by exploring how these evolved biochemical circuits process information, perform robust computations, and impact cell fate decisions. This course will integrate lectures, discussion, interactive exploration of computational models (no programming experience required), and student presentations connecting principles of network biology to broader biomedical topics.

Section, Date, and Time:

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

Instructor: Amy Gill

Amy Gill (they/she) is a PhD candidate in Biomedical Engineering at Johns Hopkins University. Amy received their BA in Biological Sciences and Chemistry and MS in Cancer Biology at University of Chicago, where they became fascinated with biological networks. As a wet lab biologist, Amy studied cell stress responses in cancer, explored regulation of limb development, and genetically manipulated cell signaling circuits in mouse models of leukemia. Among their many career adventures, Amy also received an MAT in Secondary Education at National Louis University, coordinated labs and student research opportunities in a high school science department, and designed online data science and bioinformatics courses at HarvardX. Now a computational biologist, Amy’s doctoral research uses mechanistic computational models and simulations to analyze protein trafficking and explore how the composition of heterogeneous cellular communities influences signaling network behavior.

This hands-on course introduces fundamental hardware and software skills in neuroengineering device design. Students will learn and be able to apply skills such as microcontroller fundamentals, printed circuit board (PCB) design and assembly, and CAD modeling with 3D printing. Using these techniques, students will work in small groups to design, build, and demo their own functional neural stimulator prototypes.

Sections, Dates, and Times:

EN.500.111.08 / Tue / 7:00 to 8:15 pm

Instructors: Celia Fernandez Brillet and Kiara Quinn

Celia Fernandez Brillet is a “la Caixa” fellow and a fourth-year Ph.D. Candidate in the Department of Biomedical Engineering at Johns Hopkins University. She earned her B.S. in Biomedical Engineering with honors from Universidad Carlos III de Madrid (Spain) in 2020. Celia is passionate about bridging the gap between medicine and engineering. She is especially interested in the design of medical devices to interface with the nervous system, with an emphasis on vestibular implants. Under the guidance of Dr. Charles Della Santina and Dr. Gene Fridman, she contributes to the Johns Hopkins Multichannel Vestibular Implant Early Feasibility Study. Her research aims to gain insight into the performance of vestibular implants in patients with bilateral vestibular loss. Additionally, she explores ways to extend the functionality of vestibular implants, including investigating the safety of chronic direct current stimulation and researching methods to restore the sensation of gravitoinertial forces.

Kiara Quinn is a Biomedical Engineering Ph.D. candidate, Distinguished Kavli NDI Graduate Fellow, and NSF Graduate Research Fellow advised by Dr. Nitish Thakor. Her current research focuses on interfacing with reinnervated muscle after an amputation to improve motor control and sensory feedback to and from upper limb prosthetic devices. Before starting at JHU, Kiara received her B.S. in Neuroscience from the University of California, Los Angeles and gained research experience in a range of neuroengineering areas including spatial learning and memory, electrode fabrication, motor disorders such as Amyotrophic Lateral Sclerosis (ALS), and spinal cord injuries.

To predict the course of climate change and extreme weather events, scientists have increasingly turned to machine learning methods that rely on large datasets that ignore the underlying physics relationships. By incorporating the laws of physics into our models with a technique called equivariant machine learning, we can learn more efficient and more accurate models. The first half of the course will focus on key concepts from mathematics including group theory, equivariance, tensor algebra, and convolutions, while the second half will focus on the computer science tools and include some hands-on Python coding.

Sections, Dates, and Times:

EN.500.111.09 / Wed / 9:00 to 10:15 am

Instructor: Wilson Gregory

Wilson Gregory is a 4th year Ph.D. student in Applied Mathematics and Statistics working with advisor Soledad Villar. Wilson is primarily interested equivariant machine learning– machine learning to tackle problems that behave predictably under group transformations. Many applications in physics, chemistry, and other physical sciences fall into this broad category of problems. Prior to starting his Ph.D., Wilson worked in Silicon Valley as a Full Stack Developer. Wilson received his B.S. in Computer Science and Mathematics from Rensselaer Polytechnic Institute in 2018.

In this introduction course, students will learn about the basic energy knowledge along with the evolution of energy system, from the combustion of fossil fuels for heat and power, to the diverse use of energy resources in hydropower, wind, solar, and energy storage. The course will elaborate the scientific knowledge, engineering design, and environmental impacts for each energy genre by topic. With international case studies in the energy industry, it will also introduce some relevant economic fundamentals in typical energy market, including coal, oil and gas, electricity, and renewable energy.

Sections, Dates, and Times:

EN.500.111.10 / Wed / 10:30 to 11:45 am

Instructors: Ziting Huang

Ziting Huang is a Ph.D. student in Geography and Environmental Engineering. She is working with Prof Scot Miller in the greenhouse gas lab as well as with Dr Benjamin Hobbs in the environment decisions group. Her primary research focus is to enhance the sustainability of energy system through scientific and quantitative policy analysis, including greenhouse gas emissions tracking through satellites, power system planning optimization, and economic power market operation. Prior to her Ph.D. study, Ziting worked on energy system planning projects for African countries at the World Bank.

Unlock the enabling technology advancements of 3D printing.  Students will learn about cutting-edge 3D printing technologies for materials, including polymers, metals, and ceramics. Class topics will include 3D modeling basics, design adjustments for 3D printing, basics of product design, and a deep dive into extrusion-based 3D printing.

Section, Date, and Time:

EN.500.111.11 / Wed / 12:00 to 1:15 pm

Instructor: Nathan Brown

Nathan C. Brown received the B.S. and M.S. degrees in mechanical engineering from Brigham Young University (BYU), where he studied compliant mechanisms and origami-inspired deployable structures.  His work has impacted the design of CUBESAT deployable antennas and foldable solar arrays.  He is currently pursuing his Ph.D. at Johns Hopkins University. His current research interests include multi-material additive manufacturing techniques and advanced 3D printhead design

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

Sections, Dates, and Times:

EN.500.111.12 / Wed / 4:30 to 5:45 pm

Instructor: Manav Jain

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

Introduction to foundational principles that govern optical lens systems and microscopy design. Students will cover topics including Snell’s law, the paraxial approximation, optical aberration, and polarization. Each lecture will be complimented with a hands-on lab or practical demonstration, including building their own basic compound microscopy system, to bridge the gap between theory and application.

Section, Date, and Time:

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

Instructors: Dominique Meyer and Marisa Morakis

Dominique Meyer is a 4th year Ph.D. student in the biomedical engineering program and is advised by Dr. Ji Yi. Her research focuses on designing and implementing novel high-speed large-FOV fluorescent microscopy systems to study dynamic calcium activity for neuroscience imaging applications. Dominique previously earned her Bachelor of Science in biomedical engineering from Washington University in St. Louis.

Marisa Morakis is a 5th year Ph.D. student in Nick Durr’s Computational Biophotonics lab. Her undergraduate studies were in Biomedical Engineering at Bucknell University, where she also played varsity field hockey. At Johns Hopkins, her research focuses on using novel optical imaging techniques for non-invasive blood analysis and slide-free pathology, especially for global health applications. 

This course gives a peek into the research area of regression and inference, which is engineer-speak for “connecting dots with a line in a principled way.” Alternating between theory and practice, students will explore both basic and fancier techniques for finding patterns in periodic and oscillatory data, such as blood and brain pressure waves. Through hands-on coding activities and classroom discussions, students will experience for themselves the deep intersection between statistics and computing, pushing them to seek out more new and interesting knowledge during their remaining semesters at JHU.

Section, Date, and Time:

EN.500.111.14 / Thu / 1:30 to 2:45 pm

Instructor: Josiah Lim

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

This course introduces engineering research on both human and robotic arm movement. It covers two main topics: 1) how external robotic devices and interfaces, such as virtual reality, can assist individuals with neurological disorders in controlling their arm and 2) how understanding human arm control can provide insights to improve robotic arm design and functionality. This course aims to build intuition and inspiration between human arm movement control and robotic arm design.

Section, Date, and Time:

EN.500.111.15/ T / 11:00 am to 12:15 pm

Instructor: Di Cao

Di Cao is a Mechanical Engineering Ph.D. student at Johns Hopkins University, advised by Prof. Noah J. Cowan and co-advised by Profs. Amy J. Bastian and James S. Freudenberg. Her research focuses on modeling human brain function to understand arm movement control. This includes investigating how the human brain controls arm movements and enhancing movement control for individuals with neurological motor disorders (cerebellar ataxia) by modifying visual perception; studying how the brain compensates for sensory feedback delays using internal models; examining how humans learn and enhance motor skills through reinforcement learning. She obtained the M.S. in Robotics from Johns Hopkins University and B.S. in Engineering Mechanics from Peking University. She received Creel Family Teaching Assistant Award, Society for the Neural Control of Movement (NCM) Scholarship and has previously interned with Meta on neuromotor interface.

Engineering is intertwined with some of the most pressing problems in society, including environmental sustainability, public health, social justice, and the protection of democracy. Each week of this course will feature a different sociotechnical system, including modeling for epidemic response and other high-stakes decision making, social media algorithms, the promise and perils of large language models, and climate change technologies. Students will practice decision-making with case studies, wrestle with ethical questions, do a deep dive on a sociotechnical system of their interest, and reflect on their own values and ambitions as an engineer.

Sections, Dates, and Times:

EN.500.111.16 / Thu / 5:30 to 6:45 pm

Instructor: Kristen Nixon

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

The course will be set up to serve as a high-level tour of modern techniques used at the intersection of optimization and statistics. After building some fundamentals, we will discuss techniques for learning low-dimensional representations of data such as PCA to Diffusion Maps, and how one may use these learned representations via graph based and Machine Learning techniques. The course will primarily serve as an accessible introduction to modern tools used within data science, but along the way we will highlight applications such as image compression to finding structure and patterns in high dimensional biological data.

Sections, Dates, and Times:

EN.500.111.17 / Fri / 10:30 to 11:45 am

Instructor: Ian McPherson

Ian McPherson is a third year Ph.D. student in the Applied Mathematics and Statistics Department advised by Mauro Maggioni. His research focuses on problems at the mathematical foundations of Data Science, motivated by the desire to exploit data for building generalizable and interpretable models. Inspired by a variety of ideas and domains, Ian’s research focuses on the development and analysis of scalable algorithms for representing and analyzing high-dimensional data sets. Prior to beginning his Ph.D., Ian received his M.Sc. in Mathematics at Tufts University and worked at MIT’s Lincoln Laboratories on signal processing problems. In 2024, he received the Teaching Assistant Award from the Whiting School of Engineering.

Designed for those passionate about understanding the science behind light-matter interactions, this course will enrich your understanding of emerging optical spectroscopic techniques such as absorption, fluorescence, Raman, and infrared spectroscopy, with a keen focus on their biomedical research applications.

Sections, Dates, and Times:

EN.500.111.18 / Fri / 12:00 to 1:15 pm

Instructor: Swati Tanwar

Swati Tanwar, Ph.D., is a postdoctoral fellow at Johns Hopkins University in the Mechanical Engineering department, mentored by Prof. Ishan Barman. Her Ph.D. from the Indian Institute of Science Education and Research was centered on crafting plasmonic nanoantennas via DNA nanotechnology for enhanced single-molecule spectroscopy. Currently, her research pivots on developing sophisticated nanodevices that integrate DNA and peptides for cutting-edge photonic and biomedical applications.

Graph theory is the study of mathematical objects called “vertices,” and “edges” connecting those vertices, and a function is a description of how one set of objects is mapped to another. A young subfield combining these two mathematical notions––functional graph theory––utilizes insights from both areas to understand and tackle graph theory problems. This course will be a gentle introduction to foundational ideas in functional graph theory and their applications in graph theory problems, including the graceful tree conjecture.

Section, Date, and Time:

EN.500.111.19 / Mon / 10:30 to 11:45 am

Instructor: Kailee Lin

Kailee Lin is a fourth year Ph.D. candidate at the Department of Applied Mathematics and Statistics at Johns Hopkins University. She became interested in combinatorics and graph theory primarily after attending Budapest Semesters in Math during her junior year as an undergraduate. Currently, her research interest spans topics in functional graph theory under the supervision of Dr. Edinah Gnang. Kailee is interested in pursuing a career in education. Besides academics, Kailee greatly enjoys ballet, baking, and being outdoors.

Computational modeling of biophysical systems serves as a powerful tool in our efforts to predict and understand various diseases. In this course we will discuss the theories and methods used to model complex biopysicial systems, specifically cardiology, without the need of prior mathematical and coding skill. Students will gain practical and hands-on skills for a career in computational research.

Section, Date, and Time:

EN.500.111.20 / Mon / 1:30 to 2:45 pm

Instructor: Shane Loeffler

Shane was appointed as a postdoctoral student at Johns Hopkins in the Biomedical Engineering department starting in January 2022. Before becoming a postdoc, Shane was a doctoral researcher at the University of North Carolina at Greensboro in the Nanoscience department in the Nano-Biophysics lab. His research interests include solving biophysical problems using computational methods, artificial intelligence, and applied mathematics. Shane is interested in translational research to bring the lab to the clinic to provide better healthcare for people suffering from cardiac diseases.

Biomolecular Design Lab: From Theory to Practice is an advanced laboratory course that explores the principles and applications of biomolecular design. The students will gain hands-on experience in designing, synthesizing, and characterizing biomolecules for various applications, including drug delivery, diagnostics, and materials science.

Section, Date, and Time:

EN.500.312.01 / MW / 10:00 to 111:15 am

Instructor: Ece Özdemir

Ece Özdemir started her Ph.D. in 2020 in Hristova Lab and currently, she is a 4th year Ph.D. Candidate working on Receptor Tyrosine Kinases. She is quantifying heterointeraction-induced activation between heteroreceptors using plasma membrane-derived vesicles as well as ligand bias. In the summer of 2019, she interned at Korea Advanced Institute of Science and Technology (KAIST) in South Korea which gave her a chance to experience different research topics and lab environment and she decided to pursue Ph.D. degree. She had a B. Sc. in Materials Science and Nanoengineering from Sabancı University in Turkey. Her involvement in research started with the Program for Undergraduate Research (PURE) at the end of her freshman year.

Explore the intersection of theory and practice in materials science through our college-level course. Learn how to employ LAMMPS for materials simulation, harnessing its power to model atomic behavior and predict material properties with precision. Utilize Python for data analysis, empowering yourself with a versatile tool applicable across a spectrum of scientific endeavors.

Section, Date, and Time:

EN.500.312.02 / MW / 10:00 to 11:15 am

Instructor: Spencer Fajardo

Starting with an undergraduate degree in applied mathematics, Spencer is currently pursuing a Ph.D. in materials science and engineering, focusing specifically on materials simulation. This research journey has allowed him to dive deeper into the realm of computational modeling and simulation, developing expertise in predicting material properties and understanding their fundamental behavior. Spencer’s passion for teaching emerged early on during his undergraduate years when he worked as a math tutor. He found joy in helping students grasp complex mathematical concepts and guiding them towards academic success. Furthermore, Spencer had the opportunity to work as a substitute high school teacher, where he honed his pedagogical skills and adapted to diverse learning environments. As a teaching professional, Spencer aims to create an engaging and inclusive classroom environment that encourages critical thinking, collaboration, and exploration. He is excited to bring his interdisciplinary background, hands-on experience, and enthusiasm for materials simulation to empower students to become skilled problem solvers and innovative thinkers in the field.

This course is an advanced introduction to the research topic of Computational Cardiology. Students will learn the mathematical foundations of machine learning and cardiovascular modeling (fluid dynamics and electrophysiology) and develop an understanding of how computational tools can be used in clinical workflow for patient-specific diagnosis and treatment of adverse cardiac events. Students will have the opportunity to engage with the material with hands-on workshops where they can run their own simulations and visualize results with sample code provided by the instructor.

Section, Date, and Time:

EN.500.312.03 / MW / 4:30 to 5:45 pm

Instructor: Zan Ahmad

Zan Ahmad is an Applied Mathematics and Statistics Ph.D. student, advised by Professor Natalia Trayanova in the Computational Cardiology Lab. His research focuses on the development and application of mathematical models and machine learning methods for stroke risk prediction in atrial fibrillation. He is a predoctoral fellow of the American Heart Association and works on left atrial hemodynamics simulations, statistical shape analysis and neural operators for learning the governing partial differential equations for fluid dynamics and solid mechanics of the heart.

This course seeks to provide students with critical thinking and biological reasoning skills needed to understand and rigorously analyze scientific articles. Each week during class we, as a group, will discuss one or two seminal papers, assigned the week prior, to develop quantitative biological reasoning skills as well as dive into the scientific methodologies associated with these papers to build the students’ scientific technique toolbox. Students will be encouraged to participate meaningfully during each class as participation will be a majority of their grade, and ultimately use the skills they have learned in the course to prepare a chalk talk presentation (in a group of 2-3) of an assigned scientific technique in a method paper.

Section, Date, and Time:

EN.500.312.04 / MW / 5:30 to 6:45 pm

Instructor: Elise Gray-Gaillard

Elise Gray-Gaillard is a Ph.D. Candidate in Biomedical Engineering at Johns Hopkins University, co-advised by Dr. Jennifer Elisseeff and Dr. Drew Pardoll. Her research aims to understand how preexisting conditions and comorbidities, specifically chronic injuries, can be responsible for the heterogeneity in cancer immunotherapy treatment in order to identify novel biomarkers and targets for response. Elise graduated from the University of Virginia in 2017 with a B.S. in Biomedical Engineering and a B.S. in Mathematics. Upon graduation in 2017, she received a Whitaker Fellowship for international research study at the Ludwig Institute for Cancer Research in Lausanne, Switzerland. In 2020, she graduated from the University of Lausanne with an M.Sc. in Biomedical Sciences with a specialization in Immunology and Oncology.

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

Section, Date, and Time:

EN.500.312.05 / MW/ 5:00 to 6:15 pm

Instructor: Nikita Sivakumar

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

Through a mix of lectures, discussions, and hands-on labs, this course will explore advancements in neural recording and human-machine interfaces that enable the control of computer cursors, prosthetic limbs, and other robotic devices for those with movement disabilities. Students will gain hands-on experience in using innovative hardware and software tools to record, process, and analyze their own muscle signals. Advanced research topics such as machine learning methods, implantable recording interfaces, and challenges/limitations of existing devices will also be discussed.

Section, Date, and Time:

EN.500.312.06 / TTh / 5:30 to 6:45 pm

Instructor: Kiara Quinn

Kiara Quinn is a Biomedical Engineering Ph.D. candidate, Distinguished Kavli NDI Graduate Fellow, and NSF Graduate Research Fellow advised by Dr. Nitish Thakor. Her current research focuses on interfacing with reinnervated muscle after an amputation to improve motor control and sensory feedback to and from upper limb prosthetic devices. Before starting at JHU, Kiara received her B.S. in Neuroscience from the University of California, Los Angeles and gained research experience in a range of neuroengineering areas including spatial learning and memory, electrode fabrication, motor disorders such as Amyotrophic Lateral Sclerosis (ALS), and spinal cord injuries.

This course will expose students to a wide range of cutting-edge spectroscopic techniques based on advanced nanophotonic and quantum optical effects. Starting with fundamental concepts in nanophotonics, quantum optics, and basic scientific principles of optical sensing, we will focus on various state-of-the-art quantum sensing techniques and their applications in physics, chemistry, biology, and medicine. Besides lectures, lab demonstrations and live simulations will also be conducted to illustrate key concepts and introduce important applications.

Section, Date, and Time:

EN.500.312.07 / F / 12:00 to 2:30 pm

Instructor: Peng Zheng

Peng Zheng is an Assistant Research Scientist at Johns Hopkins University with a joint appointment as a Guest Researcher at the National Institute of Standards and Technology, Gaithersburg, MD. With broad research interests in the fields of plasmonics, nanophotonics, quantum optics, optical sensing, and micro/nano systems, Peng strives to innovate optical metamaterials and harness nanophotonic and quantum optical effects through combined experimental and computational approaches to develop advanced diagnostic tools to safeguard human health. Prior to joining Johns Hopkins, Peng obtained his PhD degree at West Virginia University with a dissertation that earned him the National Award for Outstanding Self-Financed Chinese Students Overseas.