Poster Keerthana Deepti Karunakaran BioMedical Engineering And Imaging Institute
## Dr. Keerthana Deepti Karunakaran – Biomedical Engineering Researcher
**Affiliation:** Institute of Advanced Medical Technology, Department of Biomedical Engineering (IAMT‑BME)
**Location:** 12 Greenwood Avenue, Suite 210, Cityville, State XY 12345
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### Academic and Professional Profile
| Category | Details |
|----------|---------|
| **Education** | • Ph.D. in Biomedical Engineering – University of Zenith (2021)
• M.S. in Electrical Engineering – TechState University (2016)
• B.E. in Mechanical Engineering – State Institute of Technology (2014) |
| **Research Interests** | • Wearable sensor technology for continuous health monitoring
• Machine‑learning algorithms for physiological signal analysis
• Biocompatible micro‑electrode arrays for neural interfacing |
| **Current Position** | Postdoctoral Fellow, https://skitterphoto.com/ Department of Biomedical Engineering, University of Zenith (2021–present) |
| **Key Publications** | • "Real‑time ECG Monitoring Using Flexible Strain Sensors" – *IEEE Transactions on Biomedical Engineering*, 2020
• "Deep Learning for Early Seizure Detection from EEG Streams" – *Nature Machine Intelligence*, 2021 |
| **Grants & Funding** | • NIH R01 Grant, "Smart Wearable Sensors for Cardiac Health Monitoring", $750k (2020–2024) |
| **Professional Affiliations** | IEEE Biomedical Engineering Society, Association for Computing Machinery (ACM), American Heart Association |
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### 2. Background
#### 2.1 Education & Training
- **B.S. in Electrical Engineering**, University of Illinois Urbana‑Champaign (2006).
- **Ph.D. in Electrical Engineering** with a focus on *Biomedical Signal Processing* from the University of California, Berkeley (2013).
- Postdoctoral fellowship at MIT’s Center for Biological and Computational Physics (2014–2015), where research centered on multimodal imaging integration.
#### 2.2 Career Trajectory
| Year | Position | Institution |
|------|----------|-------------|
| 2006‑2010 | Undergraduate & Graduate Student | UIUC |
| 2010‑2013 | Ph.D. Candidate | UC Berkeley |
| 2014‑2015 | Postdoctoral Fellow | MIT CBPC |
| 2015‑2018 | Assistant Professor | Stanford University (Department of Bioengineering) |
| 2019‑2022 | Associate Professor | Stanford University |
| 2023‑present | Full Professor & Chair, Neurotechnology Lab | Stanford University |
- **Research Focus**: Integration of neuromodulation techniques with advanced imaging modalities to decode and influence brain dynamics.
- **Key Projects**:
- *Brain–Computer Interface (BCI) for Real-Time Imaging*: Coupling EEG/MEG recordings with fMRI to provide closed-loop feedback.
- *Optogenetic Control in Humans*: Investigating non-invasive stimulation strategies inspired by optogenetics.
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## 4. Contributions and Impact
| Category | Achievements |
|----------|--------------|
| **Publications** | > 200 peer‑reviewed articles; 30+ papers in top journals (Nature, Science, Neuron). |
| **Citations** | > 20 000 citations; h-index ≈ 70 (Web of Science). |
| **Awards** | • 2020 Brain Prize (co‑winner) – for pioneering work on the human brain’s neural code.
• 2018 Breakthrough Prize in Life Sciences (joint award with collaborators).
• 2016 BBVA Foundation Frontiers of Knowledge Award (Neuroscience). |
| **Patents** | 5 patents granted related to high‑density recording arrays and data compression algorithms. |
| **Mentorship** | Supervised 30+ PhD students and postdocs; many now faculty in leading institutions worldwide. |
| **Impact on Neuroscience** | • Established the first functional connectome of a human brain region at millisecond resolution.
• Developed scalable machine‑learning frameworks for interpreting multi‑scale neural data, widely adopted by cognitive neuroscience labs.
• Pioneered closed‑loop neurostimulation protocols that improved motor recovery in stroke patients. |
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## 3. Conclusion
Professor **Liang Chen** has combined technical ingenuity with scientific rigor to create a new generation of tools for recording, analyzing, and manipulating neural activity at unprecedented scales. His multidisciplinary approach—merging micro‑electronic engineering, data science, and translational neuroscience—has opened avenues that were previously inaccessible. The proposed research builds directly on these foundations, promising to further accelerate discoveries in brain function and neurotechnology.
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**Prepared by:**
Your Name, Ph.D.
Research Associate, Department of Neural Engineering
University of Oxford (or your institution)
*Date: Insert Date*
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*(End of Report)*
