CS Seminars

Abstract:

A long-standing vision in computer science has been to evolve computing devices into proactive assistants that enhance our productivity, health and wellness, and many other facets of our lives. User digitization is crucial in achieving this vision as it allows computers to intimately understand their users, capturing activity, pose, routine, and behavior. Today’s consumer devices – like smartphones and smartwatches – provide a glimpse of this potential, offering coarse digital representations of users with metrics such as step count, heart rate, and a handful of human activities like running and biking. Even these very low-dimensional representations are already bringing value to millions of people’s lives, but there is significant potential for improvement. In my research, I develop new algorithms and methods that allow consumer devices to capture rich, continuous representations of their users. Armed with such knowledge, our future devices could offer longitudinal health tracking, more productive work environments, full-body avatars in extended reality, and embodied telepresence experiences, to name just a few domains. Critically, these advances cannot come at the expense of user practicality, meaning my work must be strategic in developing new sensors and making use of existing sensors and edge computation.

Biography:

Karan is a Ph.D. candidate at the School of Computer Science at Carnegie Mellon University, specializing in novel sensing and interaction techniques. In his thesis work, Karan focused on increasing the fidelity of user digitization technologies while retaining or improving user practicality, opening new paradigms in augmented and virtual reality, health monitoring, natural user interfaces, and context-aware computing. Many of his research projects have been open-sourced, deployed in-the-wild, licensed by tech companies, and even shipped as a product feature. To date, Karan has published over 25 papers at top venues. He is a Siebel Fellow and the Editor-in-Chief of ACM Crossroads (XRDS). His research has been widely covered in the media, including NBC Nightly News, Today Show, CNN, TechCrunch, Engadget, NPR, Fast Company, and Gizmodo among others.

CS Department Events

IDEAL Workshop on Machine Learning, Interpretability, and Logic

Other Events & Opportunities

Thought Leader Dialogue on AI Education

Artificial intelligence (AI) is becoming increasingly integrated across our technology ecosystem, yet public understanding of AI is limited. AI education initiatives are needed to aid people in making informed decisions about their personal technology use, AI-related policy decisions, and the role AI should play in their schools and workplaces. In addition, widespread AI literacy has the potential to equip the public with skills needed to collaborate and create with rapidly changing AI technologies. Join Northwestern’s Center for Human-Computer Interaction + Design  for an engaging conversation about fostering public AI literacy through initiatives such as incorporating AI in K-12 education, designing novel and creative AI learning experiences, and engaging adults in learning and decision-making about AI in their communities. This panel will explore best practices for designing AI learning experiences, how to ensure access to AI education is inclusive and equitable, and what AI education may look like across different populations. Each speaker will give a short presentation on their research related to AI education.

Speakers include Dr. Cynthia Breazeal (MIT), Dr. Christina Gardner-McCune (University of Florida), and Dr. Ken Holstein (Carnegie Mellon). Presentations will be followed by a panel discussion moderated by Dr. Duri Long, Northwestern’s Center for Human Computer Interaction + Design.

WildHacks Mentorship Application

WildHacks: Intro to Fullstack Development with Python (hosted by NU RAISO)

Data Science Challenges in Gravitational Wave Surveys

Gravitational wave (GW) astronomy has transformed from a niche area of speculative research to an invaluable component of the astronomer's toolkit. As access to the GW spectrum broadens, so too must the computational approaches used to extract information from the data. The Laser Interferometer Space Antenna (LISA) is an ESA-led mission to survey the mHz regime of the GW spectrum richly populated with a variety of galactic, extragalactic, and cosmological sources. LISA will also be a multimessenger power house, with copious sources expected to produce counterparts covering the electromagnetic spectrum. Fully realizing the science capabilities of LISA demands new data science approaches for GW signal extraction to cope with the numerous overlapping sources simultaneously present in the data. These include high- and trans-dimensional optimization algorithms for Bayesian model selection, machine learning algorithms for compactly representing multidimensional probability distributions, and hardware acceleration with GPUs to improve computational and energy efficiency for the pipelines. This talk will summarize the LISA mission, its science objectives, and take an in-depth look at the nuances, computational challenges, and current state of the art for processing and interpreting the LISA data.

Building Hippo: A Healthcare Price Comparison Web App from Idea to Launch (hosted by Felix Haba)

Intro to Web Dev (hosted by NU Emerging Coders)

NU-TAU Nano-Research Exchange Opportunity

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