About me
Ravi Kushawaha is a highly skilled professional with a Bachelor's degree in Electrical Engineering from IIT Bombay, where he maintained an impressive academic record with a GPA of 8.59. Ravi's educational background equipped him with a strong foundation in critical thinking, coding, research, mathematics, independent learning, and data analysis. With a passion for continuous improvement and a knack for problem-solving, Ravi excels in crafting innovative solutions through his deep analytical skills and attention to detail. Having served as an AI Scientist at Qure.ai, Ravi has hands-on experience in training deep learning models, developing production-ready code, and publishing research papers. His role at Qure.ai honed his expertise in cutting-edge technologies and reinforced his strengths in research, analysis, and coding. Outside of work, Ravi finds joy in various activities including cycling, volunteering for community service, hiking, traveling, and painting. These diverse interests reflect his adventurous spirit and his commitment to both personal growth and community engagement.
Strengths
Critical thinking
Coding
Research
Mathematics
Independent learning
Data analysis
Weaknesses
Trouble with long-term planning
Too much detail - focused
Difficulty saying no
Likes
Cycling
Volunteering for community service
Going hiking or camping
Traveling to new places
Painting
Dislikes
Long lectures
Excessive work
Lack of practicality
Qualifications
Graduate(UG)
Electrical Engineering
Percentage/Grade: 8.59
Experiences
AI Scientist
1. Working on pre-emptive resource allocation to dynamically manage CPU/GPU usage prior to running ML models 2. Reducing turn-around-time by parallelizing tasks 3. Optimizing post-processing to reduce memory consumption 4. Wrote production-level code for three releases and deployed through docker
Quantitative Researcher
1. Developed medium frequency alphas to predict inter-day price dynamics 2. Traded strategies worth $100K+ in my portfolio generating > 8 % returns with more than 20 live strategies 3. Explored a wide range of financial datasets to develop signals for systematic quantitative strategies
