How can AI make people more creative, intelligent, and innovative? My research focuses on making sense of overwhelming information to accelerate decision-making and innovation. I combine AI, cognitive science, social computing, visualization, and new interaction techniques to build systems that augment human thinking and creativity.
Read about us in the press and want to find out more?
See our project page on Supercharging Innovation
Professor | HCI Institute | School of Computer Science | Carnegie Mellon
I help organizations build new products and services using AI. Over the past 5 years I've trained 20+ teams and advised senior leadership for clients including Optum, WNS, PPG, Mahindra, Progressive, Vapotherm and more. You can see my short talk at IMPACT AI to get a sense of AI case studies I use from our research. Send me an email to discuss more.
If you're interested in getting involved with our research, get on our mailing list where I'll occasionally send updates on what we're working on and thinking about. If you care deeply about making the world smarter and more creative, join the community! There'll be many opportunities to get involved.
I highly value real-world impact, and we've had great multi-year collaborative projects with Google, Microsoft, Bosch, Toyota, AI2, Wikimedia, ConservationX, and more that have influenced products used by millions (e.g., Semantic Reader, Google Shopping, Wikipedia). We also work with VCs to spin out technology, for example intuitive data visualization or solving browser tab overload.
Our work has been written up in venues ranging from the Economist to TechCrunch, and on podcasts ranging from NPR to Geekwire. If you're interested in writing about our work or an interview, contact myself or Aaron Aupperlee at SCS Media Relations.
Introducing LLM-Augmented Cognition at IMPACT AI
While generative AI has the potential to help people be smarter and more creative, current approaches often risk hallucinating, missing crucial context, or homogenizing creative work. Even if successful, LLM systems risk atrophying the critical thinking and creative processes we value as humans. Our research explores a future of composite cognition, in which humans, LLMs, and other computational agents work together in partnership, each augmenting the other.
Instead of replacing the deep contextual nuances of human cognition, diverse computational elements, and rich visual interfaces with one-size-fits-all LLMs and chat, we instead aim to augment cognition, computation, and interfaces with diverse LLMs and agents. In doing so we are unlocking new ways of combining human and LLM intelligence resulting in better decisions and more creativity than using either humans or LLMs alone. Some recent examples of our work include:
🐢BioSpark (CHI2025)
Using LLMs to help people find analogical inspirations from nature and adapt them to solve complex design and engineering problems
[coming soon]
✏️InkSpire (CHI2025)
Using multimodal LLMs to power an interactive analogical sketching interface that increases designers' agency and expressiveness
[paper][website]
🌐Selenite (CHI2024)
Augmenting sensemaking on the web with LLMs by helping people build personalized overviews of unfamiliar topics while avoiding hallucinations
[paper][video]
🔬Synergi (UIST2023)
Combining graph mining and LLMs to help people explore threads of scientific literature instead of just individual papers.
[paper]
We spend 1 trillion hours a year making sense of the web -- about two Wikipedias worth of work each hour. In our lab and with our partners we build systems to help individuals collect, organize, and make decisions with online information, and to help others with similar needs build on their work instead of starting from scratch. Some of our work includes:
Selenite (CHI2024)
The Semantic Reader Project (CACM2024)
Synergi (CHI2023)
Wigglite (UIST2022)
Threddy (UIST2022)
Fuse (UIST2022)
Crystalline (CHI2022)
Strata (CSCW2021)🏆
Tabs.do (UIST2021)
When the tab comes due (CHI2021)
Mesh (UIST2020)
Unakite (UIST2019)🏆
SearchLens (IUI2019)
Bento Browser (CHI2018)
Intentionally Uncertain Highlighting (UIST 2016)
The Knowledge Accelerator (CHI2016)🏆
Alloy (CHI2016)🏆
Crowdlines (HCOMP2015)
Kinetica (CHI2014)🏆
CrowdSynthesis (CSCW2014
Standing on the Schemas of Giants (CSCW2014)
Costs and Benefits of Structured Information Foraging (CHI2013)
Distributed Sensemaking (CHI2012)🏆
CrowdScape (UIST2012)🏆
CrowdWeaver (CSCW2012)
CrowdForge (UIST2011)
Apolo (CHI2011)
The Cognitive Atlas (Frontiers in Neuroinformatics 2011)
2010 and earlier...
Scaling up Serendipity at the National Academy of Sciences
Some of the greatest innovations in history have been made by finding analogical inspirations across distant fields. However, the explosion of information means that today it is difficult to explore a single field, let alone find useful connections across fields. We build AI and human partnerships to accelerate analogical innovation in science, technology, and design.
Summarizes multiple threads of our work using crowdsourcing and AI to scale up the search for distant inspirations that can increase creative innovation
Introduces an approach to using RNNs to extract vector representations of purposes and mechanisms of products at scale, enabling finding analogical inspirations that increase creative ideation by ~3x
Decomposes analogical innovation into parts that can be distributed across multiple minds to make idea generation more efficient and effective.
How can we use crowdsourcing to accomplish complex and creative tasks such as writing, synthesizing knowledge, or journalism?
Selected papers:
The Future of Crowd Work
Crowdsourcing User Studies with Mechanical Turk
Distributed Sensemaking
Alloy
Crowdforge
CrowdWeaver
Knowledge Accelerator
CrowdScape
Task Fingerprinting
Crowd Synthesis
CrowdLines
How can we create sociotechnical architectures that motivate and coordinate thousands of individuals doing complex work?
Selected papers:
Harnessing the Wisdom of Crowds in Wikipedia
The Polymath Project
Collaborative Problem Solving on MathOverflow
He says, She says: Conflict and Coordination in Wikipedia
WikiDashboard
Can You Ever Trust a Wiki?
Effectiveness of Shared Leadership in Online Communities
Your Process is Showing
A Market in Your Social Network
The Cognitive Atlas
Power of the Few vs. Wisdom of the Crowd
Aniket "Niki" Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His research on AI-augmented cognition looks at how we can accelerate knowledge acquisition and innovation by partnering human and machine intelligence. He has authored and co-authored over 100 papers, 17 of which have received best paper awards or honorable mentions. Dr. Kittur is a Kavli fellow, has been inducted into the CHI Academy, has received an NSF CAREER award, the Allen Newell Award for Research Excellence, major research grants from NSF, NIH, Google, Microsoft, Bosch, and Toyota, and his work has been reported in venues including Nature News, The Economist, The Wall Street Journal, NPR, TechCrunch, and the Chronicle of Higher Education. He received a BA in Psychology and Computer Science at Princeton, and a PhD in Cognitive Psychology from UCLA.
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