Hopkins Engineering Applications & Research Tutorials (HEART)

In this dynamic college-level course on materials simulation, students will embark on an exciting journey into the world of computational modeling and its applications in materials science and engineering. Through a combination of theoretical foundations and practical hands-on experience, students will learn how to simulate and predict material properties, understand their behavior at the atomic level, and gain insights into the design and optimization of advanced materials. This course offers a unique opportunity for students to harness the power of simulation tools and develop essential skills for tackling real-world materials challenges.

Section, Date, and Time:

EN.500.111.01 / Mon / 8:30 to 9:45 am

EN.500.111.21 / Wed / 8:30 to 9:45 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, his aim is to create an engaging and inclusive classroom environment that encourages critical thinking, collaboration, and exploration. Spencer 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 10-week undergraduate course provides an understanding of biomolecular materials and the methods used to characterize them. Students will learn about biomolecular materials design principles and real-world applications and explore future directions in biomolecular materials research and development. By the end of the course, students will gain a comprehensive understanding of biomolecular materials and their potential applications in biotechnology, as well as the tools and techniques used to study them.

Section, Date, and Time:

EN.500.111.02 / Mon / 8:30 to 9:45 am

EN.500.111.12 / Tue / 9:00 to 10:15 am

Instructor: Ece Özdemir

Ece Özdemir started her PhD in 2020 in Hristova Lab and currently she is a third-year PhD student working on the activation of different receptor tyrosine kinases by using a new method in plasma membrane derived vesicles. In the summer of 2019, she interned in Korea Advanced Institute of Science and Technology (KAIST) in South Korea in Functional Thin Films Lab (FTFL) which gave her a chance to experience a different research topics and lab environment and she decided pursuing PhD degree. She finished her undergraduate in Sabancı University. Her involvement in research started with Program for Undergraduate Research (PURE) at the end of her freshman year to her senior year when she joined Microfluidics Lab in Mechatronics Engineering and Polymer CVD Lab in Materials Science and Nanoengineering.

This practical course provides hands-on training in implementing machine learning algorithms using Python. Students will explore real-life examples from authentication and attack detection research, gaining a comprehensive understanding of machine learning concepts through coding exercises and a collaborative group project. Additionally, they will develop an authentication system using passwords and biometric signature comparisons while analyzing user login patterns to detect security vulnerabilities such as spoofing, brute force attacks, and benign errors.

Sections, Dates, and Times:

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

EN.500.111.22 / Wed / 8:30 to 9:45 am

Instructor: Thomas Thebaud

Dr. Thomas Thebaud holds a Ph.D. in speech biometry from the University of Le Mans. With a specialization in spoofing and anonymization techniques for speech and handwriting, his research has focused on mitigating vulnerabilities in authentication systems. Previously, Dr. Thebaud contributed his expertise to the Authentication team at Orange telecommunication company. Currently, he is a post-doctoral fellow specializing in studying various attacks against speech systems.

This course reviews the challenges surrounding the public acceptance of autonomous vehicles (AVs) and the development of effective policies. Through a combination of theoretical discussions and practical simulations, students will gain the necessary skills to assess the impact of AV technology on public health and understand the importance of building public confidence. By the end of the course, students will have the ability to simulate policy-driven transportation scenarios while considering the social benefits and potential risks associated with their implementation.

Section, Date, and Time:

EN.500.111.04 / Mon / 4:30 to 5:45 pm

Instructor: Zhixi Chen

Zhixi Chen is a Ph.D. student in the Department of Civil and Systems Engineering at Johns Hopkins University. Her primary research interests are in the systems approach to community health and health equity with a particular emphasis on the integration of system models with machine learning to explore and bring new insights to public health applications. The objectives of her research are to decipher the complexity of health systems at the community level within an interdisciplinary holistic framework, then apply this understanding to design and evaluate interventions that improve health and health equity and use a participatory approach to help communities deploy system models based on iterative learning and modification. Zhixi holds dual bachelor’s degrees in Management and Law from Tianjin University. Then, she obtained her master’s degree in Industrial and Operations Engineering from University of Michigan.

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

Section, Date, and Time:

EN.500.111.05 / Mon / 4:30 to 5:45 pm

EN.500.111.15 / Tue / 4:30 to 5:45 pm

Instructor: Nikita Sivakumar

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

This course will introduce students to the fundamentals of mathematical modeling and computer simulations for describing physical processes and contextualize how these tools can be leveraged for diagnosing and treating adverse cardiac events. In-class coding workshops and demonstrations will provide students with a hands-on learning opportunity to interact with the visual graphics produced by the mathematical descriptions and formalisms discussed in the lecture component. Additionally, machine learning (ML) and its applications in cardiovascular medicine will be covered with the intention of demystifying what goes into the “black box”, by developing basic theoretical intuition for some of the mathematical and statistical principles at play.

Sections, Dates, and Times:

EN.500.111.06 / Mon / 4:00 to 5:15 pm

EN.500.111.26 / Wed / 4:00 to 5:15 pm

Instructor: Zan Ahmad

Zan Ahmad is a Ph.D. student in the Applied Mathematics and Statistics Department working in Dr. Natalia Trayanova’s Computational Cardiology Lab. His current projects focus on quantifying stroke risk in atrial fibrillation patients with mechanistic computational modeling and machine learning. While studying Mathematics at New York University, Zan worked with Dr. Charles Peskin on constructing mathematical models for optimizing surgical interventions for congenital heart defects as well as for predicting circulatory response to dynamic perturbations such as exercise and changes in gravitational acceleration during spaceflight launch. Prior to beginning his Ph.D., Zan worked at Yale School of Medicine’s Neurology Department as a computational neuroscience research associate investigating impaired consciousness in temporal lobe epilepsy.

The Nanotechnology for Biomedical Applications course explores the intersection of nanotechnology and biomedical sciences. Topics covered include drug delivery systems, medical imaging enhancements, diagnostic tools, and therapeutic interventions. By the course’s end, students will possess a comprehensive understanding of current and prospective nanotechnology applications in biomedicine, encompassing both FDA-approved treatments and ongoing clinical trials.

Section, Date, and Time:

EN.500.111.07 / Mon / 4:00 to 5:15 pm

Instructor: Swati Tanwar

Swati Tanwar, Ph.D., is a postdoctoral fellow in mechanical engineering at Johns Hopkins University, working with Prof. Ishan Barman. Her research focuses on engineering smart nanodevices using DNA and peptides for photonics and biological applications. She obtained her Ph.D. in nanoscience from the Indian Institute of Science Education and Research, where she specialized in designing plasmonic nanoantennas using DNA nanotechnology for single molecule spectroscopic applications.

This course focuses on how programming language design choices lead to potential security vulnerabilities. The course will provide background knowledge in programming language concepts and well-known security vulnerabilities and their counter measures, and the current research in vulnerability detection and security type systems.

Sections, Dates, and Times:

EN.500.111.08 / Mon / 5:30 to 6:45 pm

EN.500.111.29 / Wed / 5:30 to 6:45 pm

Instructor: Logan Kostick

Logan is a Ph.D student in the Department of Computer Science under the guidance of Avi Rubin. He earned his B.S. from the University of Wisconsin – Madison in Computer Engineering and M.S. Security Informatics from Johns Hopkins University.  His research is focused on bringing theoretical programming language concepts into practical security applications.

Healthcare systems entail entities providing care including hospitals, primary cares, labs, and pharmacies. Healthcare systems engineering is an emerging field that combines industrial engineering, operations research, management science, artificial intelligence, and statistics to study and improve these complex systems. The course focuses on explaining healthcare system concepts and key components using engineering techniques like data sciences, optimization, and statistical analysis, with an emphasis on active discussions and activities based on novel research.

Sections, Dates, and Times:

EN.500.111.09 / Mon / 5:00 to 6:15 pm

Instructor: Farzin Ahmadi

Farzin is a Ph.D. candidate at the Center for Systems Science and Engineering and Malone Center for Engineering in Healthcare.  His research is focused on data-driven constrained inference models and their applications in healthcare, optimization for healthcare operations, and healthcare decision making. He has been involved with guest lectureships and assistantships at Johns Hopkins University, being the principal teaching assistant for the core courses of Artificial Intelligence at the Carey Business School and Operations Research at the Johns Hopkins Whiting School of Engineering. In 2022, he received the Teaching Assistant Award from the Whiting School of Engineering.

Novel methods of 3D-printing can be used to create all manner of cool things, like bio-inspired structures, circuits, and lattices. How can this be done? Through printing multiple materials like soft silicones, ceramics, and even liquid metals in a single part. This course will introduce students to 3D-printing methods, printable materials design, and exciting applications for multi-material printed parts.

Sections, Dates, and Times:

EN.500.111.10 / Mon / 5:00 to 6:15 pm

EN.500.111.30 / Wed / 5:00 to 6:15 pm

Instructors: Daniel Ames

Daniel Ames is an engineering Ph.D. candidate researching new multi-material 3D-printing methods advised by Dr. Jochen Müller. His research encompasses many aspects of the 3D-printing process, with the goal of creating new ways to fabricate complex printed parts and machines. Daniel graduated with his master’s in mechanical engineering from BYU where he published and presented novel ways to create compliant mechanisms. When not printing cool new stuff, he really enjoys playing the synthesizer and traveling.

The coursework will give students a detailed background in optical spectroscopy. It will then introduce them to chemometrics and machine learning methods to analyze real-world spectral data collected on biologically relevant samples. No coding background is required. Our hands-on session will cover the fundamentals of programming.

Section, Date, and Time:

EN.500.111.11 / Mon / 6:00 to 7:15 pm

EN.500.111.31 / Wed / 6:00 to 7:15 pm

Instructor: Piyush Raj

Piyush Raj is a 4th year Ph.D. student at Johns Hopkins University. He obtained his bachelor’s degree in mechanical engineering from the Indian Institute of Technology (IIT) Guwahati. Thereafter, he briefly worked as a mechanical workshop supervisor at L&T Heavy Engineering before enrolling in the Nanoscience master’s program at Indian Institute of Science (IISc) Bangalore. He was awarded Institute Medal in the master’s program for scoring the highest GPA. Upon completing his master’s degree, he embarked on his current journey at JHU, focusing on various areas, including Raman cell and tumor imaging, SORS, quantitative phase imaging, and plasmonics.

This course will introduce students to the emerging field of bioelectronic medicine and electroceuticals which uses stimulation of the nervous system to offer a promising alternative to classic pharmaceutical drugs to treat chronic conditions such as rheumatoid arthritis, diabetes, and paralysis. In this course, students will learn the fundamental neuroscience and engineering principles of electrical stimulation and explore current state-of-the-art applications in academia and industry, allowing them to explore possible research/internship opportunities and career pathways beyond their undergraduate education. Students will also engage in discussions about biomaterial considerations, healthcare and societal impacts, and possible ethical issues of bioelectronics.

Sections, Dates, and Times:

EN.500.111.13 / Tue / 9:00 to 10:15 am

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

Section, Date, and Time:

EN.500.111.14 / Tue / 4:30 to 5:45 pm

EN.500.111.24 / Wed / 4:30 to 5:45 pm

Instructor: Saikat Dan

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

Speech Technologies in AI smart assistants like Alexa, Siri, Hey Google, etc. have transformed everyday life. In this course, the students will learn the basics of Speech Processing along with hands-on Python Jupyter notebook-based real-world applications of Speech Systems [Automatic Speech Recognition (speech-to-text), Speaker Recognition, Emotion Recognition, Language Recognition, etc.]. The course will also cover Security Threats and Countermeasures against Speech Systems [Spoofing, Adversarial attacks, and Poisoning attacks].

Section, Date, and Time:

EN.500.111.16 / Tue / 4:30 to 5:45 pm

Instructor: Sonal Joshi

Sonal Joshi is a Ph.D. student in the Electrical & Computer Engineering Department and Center for Language and Speech Processing. She is advised by Prof. Najim Dehak, Prof. Jesús Villalba, and Prof. Laureano Moro-Velázquez. Sonal’s research interest is developing countermeasures against adversarial attacks on speech technologies. She is also interested in tackling challenges in medical applications of speech technologies. Earlier, Sonal received her master’s degree in Electrical Engineering from the Indian Institute of Technology Jodhpur and worked at TCS Research and Innovation. In her free time, she loves to travel, cook and paint (though not necessarily in that order!).

This course is taught online and 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.500.111.17 / Tue / 4:30 to 5:45 pm

EN.500.111.27 / Wed / 4:30 to 5:45 pm

Instructor: John Rattray

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

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

Sections, Dates, and Times:

EN.500.111.18 / Tue / 5:30 to 6:45 pm

EN.500.111.32 / Thu / 5:30 to 6:45 pm

Instructor: Eric Kernfeld

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

AI systems are becoming increasingly prevalent in the real world, and it is important to understand the limitations of such useful tools. This course will explore topics in AI Safety and Security, with a focus on interacting with and probing publicly released models like ChatGPT. Lectures will draw on high level material to encourage accessibility and will include topics like data collection and data poisoning, privacy aware machine learning, human alignment, large language models, and plagiarism/memorization.

Sections, Dates, and Times:

EN.500.111.19 / Tue / 5:00 to 6:15 pm

EN.500.111.33 / Thu / 5:00 to 6:15 pm

Instructor: Marc Marone

Marc Marone is a Ph.D. student in Computer Science advised by Benjamin VanDurme. He works on topics around large language models and datasets. His research interests include analyzing there liability of NLP systems, machine translation, and large scale data curation. Prior to starting at JHU, Marc worked at the Microsoft Machine Translation research group and obtained a B.S. in Computer Science from Georgia Tech. Outside of work he enjoys climbing, hiking, and swimming.

Please be aware that this course will be taught online for the first 6 weeks. The instructor will provide the link before classes start. The first in-person class will begin the week of October 9. This course provides a comprehensive overview of AI in medical imaging, covering classification, segmentation, and the use of different data types and modalities. It explores deep learning techniques with PyTorch, algorithms for medical segmentation, preprocessing and data augmentation, synthetic data generation, domain adaptation, bias and uncertainty handling, limited supervision learning, emerging architectures, and the applications of clinical AI in research and practice, including enhancing diagnosis, treatment, and patient outcomes.

Sections, Dates, and Times:

EN.500.111.20 / Tue / 6:30 to 7:45 pm

EN.500.111.35 / Thu / 7:30 to 8:45 pm

Instructor: Aimon Rahman

Aimon Rahman is a Ph.D. student at the Vision and Image Understanding Lab (VIU) in the ECE department, supervised by Dr. Vishal M Patel. Her research interests encompass computer vision and medical image analysis, with a specific focus on leveraging deep learning techniques to enhance healthcare accessibility and affordability worldwide. Aimon has publication history as a first author in renowned conferences like MICCAI and CVPR during her time at VIU Lab. Before joining her Ph.D. program, she demonstrated expertise in applying deep learning to medical imaging in resource-limited environments, resulting in multiple publications in esteemed journals. More at https://aimansnigdha.github.io/.

The exponential growth of biological sequences and the increasing need for sophisticated sequence-matching solutions have led to the development of efficient algorithms in recent years. In this course, I will cover fundamental concepts in sequence search, including widely used algorithms applicable in various fields such as information retrieval. Topics will include famous exact pattern matching methods (e.g. Boyer-Moore) as well as approximate methods (e.g. alignment algorithms) for identifying the most similar sequence in a database.

Section, Date, and Time:

EN.500.111.23 / Wed / 8:30 to 9:45 am

Instructor: Moshen Zakeri

Mohsen Zakeri is a postdoctoral fellow at Johns Hopkins University’s computer science department, working in the esteemed Langmead lab. His research primarily focuses on designing and developing efficient algorithms and data structures for preprocessing and analyzing high-throughput sequencing data. Currently, Mohsen is actively engaged in developing an ultra-fast approach for adaptive sampling of long reads. He earned his Ph.D. from the computer science department at the University of Maryland, where he was mentored by Prof. Rob Patro.

This course will introduce students to the basic idea of contact mechanics and its various applications. Topics include kinematics of contacts, line loading, point loading, Hertz theory, frictional contact models, and rolling resistance models. Throughout the course, the emphasis will be on engineering applications. The mathematical formulation of contact models will be introduced, but details of their derivation will not be given in this course.

Section, Date, and Time:

EN.500.111.25 / Wed / 4:30 to 5:45 pm

Instructor: Kwangmin Lee

Kwangmin Lee is a Ph.D. student in the Department of Mechanical Engineering at Johns Hopkins University, advised by Dr. Ryan Hurley. His research focuses on establishing the first principles of particle rearrangements in granular materials and developing numerical implementation techniques for simulating material contact. Before joining Johns Hopkins University, Kwangmin received his B.S. in Mathematics and Mechanical Engineering and M.S. in Mechanical Engineering at Sogang University and worked in industry.

This course will introduce students to large language models (LLMs) and information retrieval (IR) systems that are used for knowledge-intensive settings such as chatbots, question answering, and fact checking. The class will include hands-on interaction with new technology as well as group discussions to illustrate the progress and shortfalls of the current approaches (such as ChatGPT, GPT-4 and Bard) and their impact on future AI research and society.

Section, Date, and Time:

EN.500.111.28 / Wed / 4:30 to 5:45 pm

Instructor: Orion Weller

Orion Weller is a third-year Ph.D. student at the Center for Language and Speech Processing at Johns Hopkins University. Advised by Benjamin VanDurme and Dawn Lawrie, he focuses on the intersection of natural language processing (NLP) and information retrieval (IR). Orion’s research aims to enhance NLP and IR model’s abilities to find and comprehend knowledge, particularly for improving knowledge-intensive tasks requiring reasoning, handling noisy data, and improving factuality and attribution. He is supported by a National Science Foundation Graduate Research Fellowship and has previously interned with Apple AI/ML and the Allen Institute for Artificial Intelligence.

In this course, students will learn about the state-of-the-art technologies of the emerging field of micro/nanorobotics with focus on biomedical and surgical applications. Topics cover microengineering concepts from design, manufacturing, robot motion control perspective and their applications.

Section, Date, and Time:

EN.500.111.34 / Thu / 5:00 to 6:15 pm

Instructor: Mohammad Salehizadeh

Mohammad Salehizadeh is currently pursuing his NSERC fellowship in medical robotics and imaging at Johns Hopkins University in Prof. Russell Taylor’s lab. Prior to that, he did a two-year postdoc at Harvard Medical School and Brigham and Women’s Hospital. Mohammad Salehizadeh obtained his Ph.D. degree from University of Toronto in Mechatronics and Robotics in 2020. He got his MSc. degree from Concordia University and his Undergraduate degree from Isfahan University of Technology. His main research interest is in surgical robotics and medical imaging as well as microrobotics.

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.500.111.36 / Fri / 8:30 to 9:45 am

Instructor: Aseel Titi

Aseel Titi is a postdoctoral fellow in the department of applied mathematics and statistics at the Whiting School of Engineering. Currently, Aseel collaborates with Professor Fadil Santosa on an inverse problem in electrical impedance tomography. Aseel completed my Ph.D. in applied mathematics in 2021 where I studied the inverse problem of gravimetry when the number of measurements is limited. I graduated with a master’s degree in mathematics in 2014 and I taught different courses in mathematics and statistics for undergraduate students at Birzeit University in Palestine between 2014 and 2016.

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.500.111.37 / Mon / 5:00 to 6:45 pm

EN.500.111.38 / Wed / 5:00 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.

This course provides a practical, hands-on introduction to computer graphics, serving as an inspiring stepping-stone towards the advanced 3 credit course in Computer Graphics. Central to this study is the fundamental concept of “the Laplacian”, an important topic, but not addressed in the 3-credit course. This technology has many practical applications, including the modeling of computer games, creation of animations, and production of visual effects in movies.

Section, Date, and Time:

EN.500.111.39 / Tue / 4:30 to 5:45 pm

EN.500.111.40 / Thu / 9:00 to 10:15 am

Instructor: Crane He Chen

Crane is an engineering Ph.D. candidate researching 3D-shape reconstruction, advised by Dr. Misha Kazhdan. She’s also received insightful input from the co-advisor, Dr. Noah Cowan. Her research touches on several areas, including implicit surface reconstruction, geometric data processing, and machine learning. She is passionate about maintaining geometric details and combating over-smoothing in the process of surface reconstruction. To pursue this goal, she developed a software program tool named TotalCurvatureCalculator which processes geometric data. The tool is applicable to both point clouds and triangle meshes. Apart from research and programming, she has given considerable thought to education and developed her teaching philosophy. She believes that insights are difficult to document and transmit, so they sometimes fade away. An effective educator combats the overwhelming complexity and abstraction of knowledge, conveying the intuitions to students. Crane holds a master’s degree in computer science from Hopkins, and she has spent a year at Apple Inc. There, she pushed the boundaries of spatial computing techniques.