Looking for opportunities to support data-centric teams and solutions and artistic collaborations. If you’re interested in what you see please send me a note below or at manan.kocher@gmail.com.

I look forward to hearing from you!

CONTACT:

Multi-instrument observations of an Interplanetary Coronal Mass Ejection disturbance detected in situ by ACE/SWICS at 17:51 UT, 2011 August 5 (a) STEREO B/EUVI 304 Å; (b) SDO/AIA 304 Å; (c) STEREO A/EUVI 304 Å; (d) STEREO B/COR1; (e) LASCO/C3; (f) STEREO A/COR1; (g) STEREO B/COR2; (h) LASCO/C2; (i) STEREO A/COR2. [Kocher et al. 2018]

ACKNOWLEDGEMENTS:

I’d like to offer my deepest gratitude to individuals who have provided their invaluable mentorship in various stages of my professional career: Dr. S. Lepri, Dr. E. Landi, Dr. D. Ozturk, Dr. K. Goodman, Dr. H. Moss, Dr. A. Swagman, H. Kalvaria, Dr. A. Torres, D. Connolly, K. Sands, H. Fevrier, Dr. P. Brendel, S. Deshpande, Dr. S. Baxi, Dr. G. Maro, Dr. F. Shihadeh, Dr. B. Moy, Dr. R. Patel, & Dr. J. Fischer. I’d also like to offer my deepest appreciation for the talent, collaboration, and spirit of L. Wiener, M. Trivedi, Dr. S. Kauwe, Dr. I. Chin, J. Peavey, Dr. P. Jin, Dr. C. Pfiefer; who I had the privilege of managing at Verana Health.

KEY REFERENCES:

1. Broussard, M. (2019). Artificial Unintelligence: How Computers Misunderstand the World. MIT Press. https://doi.org/10.7551/mitpress/11022.003.0020

2. COBBE, J. (n.d.). TECHNOCHAUVINISM. Sciences Po. Retrieved January 24, 2024, from https://www.sciencespo.fr/public/chaire-numerique/wp-content/uploads/2022/06/website-.pdf

3. Cutter, G. (2003). Effect of relapses on development of residual deficit in multiple sclerosis. PubMed. Retrieved January 6, 2024, from https://pubmed.ncbi.nlm.nih.gov/14663037/

4. Howarth, J. (2023, November 3). Startup Failure Rate Statistics (2024). Exploding Topics. Retrieved January 21, 2024, from https://explodingtopics.com/blog/startup-failure-stats

5. Jacka, J. M., & Keller, P. J. (2009). Business Process Mapping: Improving Customer Satisfaction. Wiley.

6. Kamińska, J. (2022, May 18). 8 types of data bias that can wreck your machine learning models. Statice. Retrieved December 20, 2023, from https://www.statice.ai/post/data-bias-types

7. Najibi, A. (2020, October 24). Racial Discrimination in Face Recognition Technology. Science in the News. Retrieved January 24, 2024, from https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/

8. National Institute of Science and Technology. (2022, March). Towards a Standard for Identifying and Managing Bias in Artificial Intelligence. Towards a Standard for Identifying and Managing Bias in Artificial Intelligence. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf

9. Robertson, D., & Moreo, N. (2016). Disease-Modifying Therapies in Multiple Sclerosis: Overview and Treatment Considerations. NCBI. Retrieved January 6, 2024, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366576/

10. Unmasking AI harms and biases. (n.d.). Algorithmic Justice League. Retrieved January 21, 2024, from https://www.ajl.org/

11. Wankhede, C. (2023, August 22). 6 alternatives to lithium-ion batteries: What's the future of energy storage? Android Authority. Retrieved December 15, 2023, from https://www.androidauthority.com/lithium-ion-battery-alternatives-3356834/

12. Kocher, M. et al 2018 ApJ 860 51DOI 10.3847/1538-4357/aac5f9

13. Kocher, M. et al 2017 ApJ 834 147 DOI 10.3847/1538-4357/834/2/147

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15. Verana Health. from https://veranahealth.com/solutions/neurology-qdata/

16. Hastie, Tibshirani and Friedman (2009). Springer-Verlag. The Elements of Statistical Learning (2nd Edition)

17. Firas Bayram, Bestoun S. Ahmed, Andreas Kassler, From concept drift to model degradation: An overview on performance-aware drift detectors, Knowledge-Based Systems, Volume 245, 2022, 108632, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2022.108632.

18. Ma, LL., Wang, YY., Yang, ZH. et al. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better?. Military Med Res 7, 7 (2020). https://doi.org/10.1186/s40779-020-00238-8

19. Sylolypavan A, Sleeman D, Wu H, Sim M. The impact of inconsistent human annotations on AI driven clinical decision making. NPJ Digit Med. 2023 Feb 21;6(1):26. doi: 10.1038/s41746-023-00773-3. PMID: 36810915; PMCID: PMC9944930.

20. National Institutes of Health. "NIH Strategic Plan for Data Science." 2018. Web. https://datascience.nih.gov/sites/default/files/NIH_Strategic_Plan_for_Data_Science_Final_508.pdf.

21. ODSC. "The Comprehensive Guide to Model Validation Framework: What is a Robust Machine Learning Model?" Medium, 2 Nov. 2020, https://odsc.medium.com/the-comprehensive-guide-to-model-validation-framework-what-is-a-robust-machine-learning-model-7bdbc41c1702.

22. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095. PMID: 34286183; PMCID: PMC8285156.

23. Palaniappan K, Lin EYT, Vogel S. Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector. Healthcare (Basel). 2024 Feb 28;12(5):562. doi: 10.3390/healthcare12050562. PMID: 38470673; PMCID: PMC10930608.

24. HealthIT.gov. Data Quality. In Health IT Playbook. Retrieved from https://www.healthit.gov/playbook/pddq-framework/data-quality/data-quality/

25. Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2023). Human-AI Ensembles: When Can They Work? Journal of Management, 0(0). https://doi.org/10.1177/01492063231194968

26. Wiens J, Saria S, Sendak M, Ghassemi M, Liu V, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M, et al. Do no harm: A roadmap for responsible machine learning for healthcare. Nature Medicine. 2019;25 (10) :1337-1340.