Human edit: This post was entirely generated by Cursor AI, based on the parsed content of my personal webpage and the files contained within. I am amazed by the quality of the output, and have limited my edits to crossed out inaccuracies.
Looking back on my academic journey from MIT to Adobe Research, I've been fortunate to work at the intersection of human perception, machine learning, and human-computer interaction. This post reflects on how my research interests have evolved and how they continue to shape my work in industry.
The MIT Years: Foundations in Computational Perception
My time at MIT was transformative, both personally and professionally. Working under the guidance of exceptional mentors and alongside brilliant peers, I developed a deep appreciation for the complexity of human perception and the challenges of modeling it computationally.
"MIT taught me that the most interesting problems lie at the intersection of multiple disciplines—where computer science meets psychology, where algorithms meet human behavior."
Early Research: Visual Attention and Saliency
My initial research focused on computational models of visual attention and saliency prediction. This work explored:
- How to predict where people will look in images
- The relationship between visual features and attention
Applications in image compression and quality assessmentCross-cultural differences in visual attention patterns
Evolution to Graphic Design and Visualization
As my research progressed, I became increasingly interested in how computational perception could inform design decisions:
- Predicting visual importance in graphic designs
- Optimizing information visualization layouts
- Understanding how design elements guide attention
- Creating tools for designers to test their work
The Transition to Industry
Moving from academia to industry was both exciting and challenging. Adobe Research offered a unique opportunity to apply my academic work to real-world problems while continuing to push the boundaries of research.
Key Differences: Academic vs. Industry Research
The transition revealed several important differences:
- Impact timeline: Industry research often has more immediate applications
- Collaboration scope: Working with product teams, designers, and engineers
- Problem selection: Balancing fundamental research with practical needs
- Evaluation criteria: Success measured by both scientific contribution and product impact
Adobe Research: A Perfect Fit
Adobe Research has been an ideal environment for my work because:
- Strong commitment to both fundamental and applied research
- Access to massive datasets and real user behavior
- Direct connection to products used by millions
- Collaborative culture that values interdisciplinary work
Research Evolution: From Perception to Personalization
My research focus has evolved significantly since joining Adobe, reflecting both the company's needs and my own growing interests:
The Readability Initiative
One of my most rewarding projects has been leading Adobe's readability research:
- Understanding individual differences in reading preferences
- Developing personalized reading formats
- Creating tools for accessibility and learning
- Building the Readability Consortium
Generative AI Evaluation
More recently, I've been working on evaluating generative AI systems:
- Human-centered evaluation metrics
- Understanding user perceptions of AI-generated content
- Developing frameworks for responsible AI development
- Bridging technical and human factors in AI evaluation
Lessons Learned Along the Way
This journey has taught me several valuable lessons:
1. The Importance of Interdisciplinary Collaboration
The most impactful research often comes from bringing together diverse perspectives:
- Computer scientists and psychologists
- Researchers and product designers
- Academic and industry partners
- Technical and non-technical stakeholders
2. Balancing Theory and Practice
Industry research requires finding the right balance:
- Pursuing fundamental questions while addressing practical needs
- Publishing academic papers while shipping products
- Maintaining scientific rigor while meeting business timelines
- Building theoretical frameworks that have real-world applications
3. The Value of User-Centered Research
Working in industry has reinforced the importance of understanding real users:
- Direct access to user feedback and behavior data
- Opportunities to test research in real-world contexts
- Understanding the gap between laboratory and field conditions
- Appreciating the complexity of user needs and preferences
Current Research Directions
Today, my research focuses on several key areas:
Personalized Digital Experiences
- Adaptive interfaces that respond to individual users
- Machine learning for personalization
Privacy-preserving personalization techniquesCross-platform consistency in personalized experiences
Human-AI Collaboration
- Designing AI systems that augment human capabilities
Understanding human trust in AI systems- Creating intuitive interfaces for AI tools
- Ensuring AI systems serve human needs effectively
Accessibility and Inclusion
- Making digital experiences accessible to everyone
- Understanding diverse user needs and abilities
- Developing inclusive design methodologies
- Creating tools that support different learning styles
Advice for Aspiring Researchers
For students and early-career researchers considering similar paths:
1. Follow Your Curiosity
Research is most rewarding when you're genuinely interested in the questions you're asking. Don't be afraid to explore interdisciplinary areas or pursue unconventional research directions.
2. Build Strong Foundations
Develop deep expertise in your core areas while maintaining broad knowledge across related fields. The most interesting problems often require multiple perspectives.
3. Seek Meaningful Impact
Consider how your research can make a difference in the world. Whether through academic contributions, industry applications, or societal benefits, aim for work that matters.
4. Embrace Collaboration
Research is increasingly collaborative. Build relationships with colleagues across disciplines and institutions. The best ideas often come from diverse teams.
Looking Forward
As I continue my journey at Adobe Research, I'm excited about several emerging opportunities:
- Advanced personalization: Creating truly adaptive digital experiences
- AI-human collaboration: Designing systems that enhance human creativity
- Cross-cultural research: Understanding global differences in perception and interaction
- Emerging technologies: Exploring VR, AR, and other new interaction paradigms
Reflections on the Journey
Looking back on my path from MIT to Adobe, I'm grateful for the opportunities I've had to work on meaningful problems with talented colleagues. The journey has taught me that the most rewarding research combines:
- Scientific rigor and practical relevance
- Individual expertise and collaborative teamwork
- Academic curiosity and real-world impact
- Technical innovation and human-centered design
As I continue this journey, I'm excited to see how computational perception research will evolve and how it will continue to shape the digital experiences of tomorrow.
The path from academia to industry research has been both challenging and rewarding. It's shown me that the best research doesn't just advance knowledge—it creates real value for people in their everyday lives.