Introduction to Computing Research

Established in 2021, the Introduction to Computing Research (ICR) is a program with the mission to introduce undergraduate students to various areas of computing research and career options in those areas. Our goal is to provide equal access to exploratory research opportunities. ICR consists of two main components: (a) virtual workshops and (b) research internships.

Virtual Workshops

The ICR program offers a series of workshops to introduce undergraduates to various areas and career options in computing research. The workshop talks will have three main types:

Area overview talks: These talks introduce an area or a sub-area of computing research and research opportunities in that area.
Example: a broad introduction to cloud computing and open research questions related to it.

Lab overview talks: In these talks, leading researchers introduce the research in their own labs.
Example: an overview of the research on theoretical computer science at Hopkins.

Career talks: These talks give advice about applying to grad school, life as a grad student, joining industry after a PhD in computer science, etc.

The first round of ICR'21 workshops will be held virtually.

ICR'21 schedules:

Register to Attend ICR'21 Virtual Workshops!

Research Internships

ICR offers an eight-week internship program that provides an opportunity for undergraduate students to gain hands-on research experience in computing while conducting research under the guidance of faculty members, postdoctoral fellows, and advanced graduate students.

As an ICR research intern, you will receive:
  • Advising by a faculty member
  • A postdoctoral fellow or advanced graduate student mentor
  • Hands-on research experience in computing research or an interdisciplinary area
  • Potential opportunity to contribute to and co-author a scientific paper and/or build a software system
  • 1:1 career counseling
  • A bi-weekly stipend
To be eligible, you must attend one of the virtual workshops.
Important dates:
  • Summer 2021 Application Deadline: 6/25/2021
  • Summer 2021 Internship: 7/5/2021 - 8/30/2021
Email us for questions, concerns, or to serve as a faculty advisor.
Acknowledgment: ICR'21 is partially funded by an unrestricted gift from Google.
We are thrilled to partner with the following researchers (alphabetical order):

Archana Venkataraman

Archana_Venkataranam Prof. Venkataraman is a John C. Malone Assistant Professor of Electrical and Computer Engineering at Johns Hopkins. She develops new mathematical models to characterize complex processes within the brain. She is core faculty in the Malone Center for Engineering in Healthcare, which aims to improve the quality and efficacy of clinical interventions, and she is affiliated with the Mathematical Institute for Data Science. Venkataraman's lab, the Neural Systems Analysis Laboratory (NSA Lab), concentrates on building a comprehensive and system-level understanding of the brain by strategically integrating computational methods, such as machine learning, signal processing and network theory, with application-driven hypotheses about brain functionality. Based on this approach, Venkataraman and her team aim toward a greater understanding of debilitating neurological disorders, with the long-term goal of improving patient care.

  • Area: Functional Neuroimaging (fMRI, EEG), Machine Learning & Probabilistic Inference, Network Modeling of the Brain, Integration of Imaging, Genetics and Behavioral Data
  • Talk title: Engineering Solutions for Brain Dysfunction
  • Talk type: Lab overview
  • Research internship opening!
  • Link to the talk: TBD

Chien Ming Huang

chien_ming_huang Prof. Huang is a John C. Malone Assistant Professor in the Department of Computer Science, studies human-machine teaming and creates innovative, intuitive, personalized technologies to provide social, physical, and behavioral support for people with a variety of abilities and characteristics, including children with autism spectrum disorders. Huang directs Johns Hopkins' interdisciplinary Intuitive Computing Laboratory and is a member of JHU's Malone Center for Engineering in Healthcare and the Laboratory for Computational Sensing and Robotics. An expert in human-robot and human-computer interaction, Huang is particularly passionate about using novel technologies to help special-needs populations. Drawing on human-computer interaction (HCI), robotics, and artificial intelligence (AI), Huang's research has significant applications in healthcare, education, and manufacturing.

  • Area: Human-Robot Interaction, Human-Computer Interaction, Robotics
  • Talk title: Designing Robotic Technology for People
  • Talk type: Lab overview, Career talk
  • Research internship opening!
  • Link to the talk: TBD

Jan Wassenberg

jan_wassenberg Dr. Wassenberg works at Google Research. He received his PhD in algorithms for efficient image analysis in 2011. He is expertised in SIMD and compression and, in addition to publishing research papers, is focused on open sourcing code for reproducibility. His areas of interest include distributed systems and parallel computing, hardware and architecture, software engineering, and image compression.

  • Area: algorithms and theory, distributed systems and parallel computing, machine perception, security, privacy, and abuse prevention
  • Talk title: Thoughts on CS research, career
  • Talk type: Career talk
  • Research internship opening!
  • Link to the talk: TBD

Jean Fan

jean_fan Prof. Fan is interested in understanding the spatial-contextual and other regulatory mechanisms that shape cellular identity and heterogeneity. We are particularly interested in characterizing heterogeneity in the context of cancer and understanding how this heterogeneity shapes tumor progression, therapeutic resistance, and ultimately clinical outcomes. While heterogeneity within cellular systems has long been widely recognized, only recently have technological advances enabled measurements to be made on a single cell level. Applying traditional bulk analysis methods on single cells has met with varied degrees of success due to the high levels of technical as well as biological stochasticity and noise inherent in single cell measurements. Therefore, we develop machine learning and other statistical methods to harness the power of these new large-scale multi-omic single cell resolution data in addressing basic science and translational research questions. Our methods are available as open-source computational software and accessible to the broader scientific community.

  • Area: Biomedical Data Science, Computational Medicine, Genomics and Systems Biology
  • Talk title: Interactive analysis and visualization of spatial transcriptomics data
  • Talk type: Lab overview
  • Potential research internship opening!
  • Link to the talk: TBD

Mathias Unberath

matthias_unberath Prof. Unberath is an Assistant Professor in the Department of Computer Science at Johns Hopkins University with affiliations to the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. He has created and is leading the Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab that conducts research at the intersection of computer vision, machine learning, augmented reality, robotics, and medical imaging to develop collaborative systems that assist clinical professionals across the healthcare spectrum. Previously, Mathias was an Assistant Research Professor in Computer Science and postdoctoral fellow in the Laboratory for Computational Sensing and Robotics at Hopkins, and completed his PhD in Computer Science at the Friedrich-Alexander-Universitat Erlangen-Nurnberg from which he graduated summa cum laude in 2017. While completing a Bachelor's in Physics and Master's in Optical Technologies at FAU Erlangen, Mathias studied at the University of Eastern Finland as ERASMUS scholar in 2011 and joined Stanford University as DAAD fellow throughout 2014. He has published more than 75 journal and conference articles, and has received numerous awards, grants, and fellowships, including the NIH NIBIB R21 Trailblazer Award.

  • Area: Imaging Systems, Machine Learning, Human-AI Interaction, Augmented Reality
  • Talk title: Advancing Healthcare with Artificial Intelligence: The Good, the Bad, and the Ugly
  • Talk type: Area overview, Lab overview
  • Potential research internship opening!
  • Link to the talk: TBD

Michael Dinitz

Dinitz Prof. Dinitz works in theoretical computer science, with a focus on approximation algorithms, online algorithms, distributed algorithms, and hardness of approximation. He is also interested in applications of theory, particularly to computer networking and distributed systems. He obtained his Ph.D. from Carnegie Mellon University in 2010 and his A.B. from Princeton University in 2005. Before coming to Johns Hopkins he was a postdoctoral fellow at the Weizmann Institute of Science in Rehovot, Israel.

  • Area: Theoretical computer science
  • Talk title: Theory of Network Design Problems
  • Talk type: Area overview, Lab overview
  • Potential research internship opening!
  • Link to the talk: TBD

Yashar Ganjali

yashar_ganjali Prof. Ganjali is a member of Computer Systems and Networks Group at the University of Toronto. He received his BSc in Computer Engineering from Sharif University of Technology, and his MSc in Computer Science from University of Waterloo. He completed his PhD in Electrical Engineering at Stanford University. His PhD dissertation studied the buffer sizing problem in Internet core routers, and showed the possibility of reducing buffer sizes from millions of packets to just a few packets in Internet core routers.

  • Area: Systems and networking
  • Talk title: Essentialism in Graduate School
  • Talk type: Career talk
  • Link to the talk: TBD