“Rohit has been both a student and then a Teaching Assistant for two semesters in my graduate-level Project Management class in the Pratt School of Engineering at Duke University. Rohit was an exemplary PM student and now for the last two semesters has performed the role of Teaching Assistant with passion and excellent performance. Rohit enjoys interacting with the students and gives very mature team-activity and assignment guidance to individual students and our project teams. Rohit has also taken on more technical TA tasks when managing program simulations and student input surveys for the class. His use of Microsoft Excel when organizing and analyzing survey data has been very valuable. With proper guidance Rohit will be a valuable addition to an organization's PMO or working as an entry-level or Assistant Project Manager.”
About
Experience & Education
Licenses & Certifications
Volunteer Experience
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Project Innovator - StudentU Project
Duke Interdisciplinary Social Innovators (DISI)
- 4 months
Education
Patents
Projects
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ASA Datafest 2018 (Indeed.com Challenge)
See projectAnalyzed 17 million rows of data related to job listings on Indeed.com to find interesting business insights and presented the findings to the client Indeed.com, Team finished in top 15%.
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Building a bankruptcy prediction model for global public companies by considering their financial attributes
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See projectI used S&P dataset having the financial attributes of more than 400 global companies to build the prediction model using Logistics Regression and Decision Tree classifiers in scikit package.
- The model predicted the bankruptcy with 73% accuracy on test dataset with logistics regression and 72% accuracy on test dataset with decision tree classifier.
I tuned the hyperparameters (Penalty and Cp for Logistics Regression, Max_features, Min_Sample_split, Min_samples_leaf for Decision…I used S&P dataset having the financial attributes of more than 400 global companies to build the prediction model using Logistics Regression and Decision Tree classifiers in scikit package.
- The model predicted the bankruptcy with 73% accuracy on test dataset with logistics regression and 72% accuracy on test dataset with decision tree classifier.
I tuned the hyperparameters (Penalty and Cp for Logistics Regression, Max_features, Min_Sample_split, Min_samples_leaf for Decision Tree) for improving the model accuracy.
- After hyperparameter tuning, the model predicted the bankruptcy with 80% accuracy on test dataset with logistics regression and 79% accuracy on test dataset with decision tree classifier.
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Titanic: Machine Learning from Disaster
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See projectSuccessfully completed a Kaggle project for predicting survival rates on Titanic using Python.
• Used the survivor data, determined the likelihood of survival using logistic regression
• The model successfully predicted the likelihood with 75.59% accuracy. -
Energy Storage Systems Market and Competitive Analysis
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See projectProject Manager - Duke MEMP Energy Club (Nikola Power)
- Led a diverse team of three to analyze Energy Storage Systems (ESS) market and competitors in this arena to recommend the go-to-market strategy for an energy storage system startup
- Developed a decision tree to analyze the consequences of following different strategies to the ESS market -
Millennials : A destination for all financial needs
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See projectOrganisation: IXL Center
Competition: Innovation Olympics
Role: Innovation Associate Consultant Intern
• Collaborated with a team of 5 members to develop the marketing strategy for a $4 trillion asset management company to expand into a new consumer segment: millennials
• Conducted primary and secondary research and analyzed the primary data to identify the target customer segment, the market size, and their unmet needs
• Crafted prioritization matrix to expedite resolution…Organisation: IXL Center
Competition: Innovation Olympics
Role: Innovation Associate Consultant Intern
• Collaborated with a team of 5 members to develop the marketing strategy for a $4 trillion asset management company to expand into a new consumer segment: millennials
• Conducted primary and secondary research and analyzed the primary data to identify the target customer segment, the market size, and their unmet needs
• Crafted prioritization matrix to expedite resolution of client-generated acquisition problem by system analysis
• Recommended a break-through business case, go-to-market strategy, revenue model to acquire millennials.
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Go-to-Market Strategy for AI-powered Cyber Intelligence Products
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See projectProduct Manager Cloudadic (A Public Cloud Startup) - Duke MEMPDC Consulting Club
- Collaborated with a team of 5 to develop the go-to-market strategy and product roadmap for an AI-powered cyber intelligence product startup
- Performed A/B testing for the web products in an effort to create the best conversion-optimized products -
B.Tech [Major Project] : Analysis of RC4 algorithm
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The project analyzed performance of RC4 and its variant RC4 ICCSAM in term of time and energy it utilized for 10 million bytes data. The project also include randomness analysis for RC4 and RC4 ICCSAM using frequency test, frequency block test, run test, test for longest run of ones in a block and overlapping template matching test.
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B.Tech [Minor Project] : Real Time Video Surveillance using Motion Detection
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The project detects motion in a live video feed and is based on MATLAB. The motion is detected using 2D Cross Correlation Algorithm and Sum of Absolute Differences (SAD) algorithm. The results are shown on GUI in the form of log file, movie and frame number versus variance value.
Honors & Awards
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First Place - Design Thinking Product Management Case Competition
Duke University
Test Scores
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AIEEE
Score: 232
Percentile: 99.9927
The All India Engineering Entrance Examination (AIEEE), is an examination organized by the CBSE. Introduced in the year 2002, this national level competitive test is for admission to various under-graduate engineering and architecture courses in institutes accepting the AIEEE score, mainly 30 (NITs) and 5 (IIITs). -
IITJEE
Score: 168
Percentile: 99.97
The Indian Institutes of Technology-Joint Entrance Examination (IIT-JEE) is an annual engineering college entrance examination in India. It is conducted by one of the seven zonal IITs (IIT Roorkee, IIT Kharagpur, IIT Delhi, IIT Kanpur, IIT Bombay, IIT Madras, and IIT Guwhati ) under guidance of the Joint Admission Board(JAB). It is used as the sole admission test by the 23 Indian Institute of Technology (IITs). Other universities like the Rajiv Gandhi Institute of Petroleum…Percentile: 99.97
The Indian Institutes of Technology-Joint Entrance Examination (IIT-JEE) is an annual engineering college entrance examination in India. It is conducted by one of the seven zonal IITs (IIT Roorkee, IIT Kharagpur, IIT Delhi, IIT Kanpur, IIT Bombay, IIT Madras, and IIT Guwhati ) under guidance of the Joint Admission Board(JAB). It is used as the sole admission test by the 23 Indian Institute of Technology (IITs). Other universities like the Rajiv Gandhi Institute of Petroleum Technology, Indian Institute of Science Education and Research (IISERs), and the premier Indian Institute of Science (IISc) also use the score obtained in JEE Advanced as the basis for admission.
Languages
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Hindi
Native or bilingual proficiency
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English
Native or bilingual proficiency
Recommendations received
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Divyan Gupta
75 of 89 US H-1B employment markets declined. I expected the national number to be interesting when we started looking at the latest US Department of Labor data. It turned out to be one of the less interesting numbers in the dataset. Employer-declared new H-1B employment positions fell 7.5% nationally between April-June 2025 and the same period in 2026. Across the six Dallas-Fort Worth cities we have been tracking, the fall was 33.4%. Then you look across individual US markets and the picture breaks apart. San Francisco was up 86.7%. San Jose was up 58.7%. Raleigh was down 52.0% and Edison, New Jersey was down 62.6%. The extremes are also a useful warning about how this data should be read. San Diego rose 228.3% from a base of 893 positions, while Redwood Shores fell 74.2% from 551. Those city-level numbers can be heavily affected by a small number of large employers, so treating either as evidence of a broad local boom or collapse would be a stretch. That left us with a more interesting real estate question. If H-1B employment activity changes sharply in a particular city, when does that actually begin to affect housing? A visa filing does not move a home price. But a household losing a job, relocating, choosing to sell rather than hold, converting a home into a rental, or deciding not to move into a market can. If enough of those decisions happen in the same place, and equivalent demand does not replace them, the employment signal can eventually reach housing. There was another finding I did not expect. In several markets, the number of workers employers requested moved very differently from the number of certified filings. San Jose had 2.1% fewer certified filings but 58.7% more requested new-employment positions. In Raleigh, filings fell 13.6%, while requested positions fell 52.0%. We have published the full analysis covering 89 US city-state markets, the policy changes around them, the limitations in the data, and the housing indicators we would watch next. Read the analysis: https://lnkd.in/dtvCE9us
3 Comments -
Hima Bindhu Busireddy
🇺🇸 Understanding H-1B Grace Period After a Layoff: What Really Happens Behind the Scenes One of the most confusing times for professionals on an H-1B visa is right after a layoff — especially when trying to understand when the 60-day grace period actually begins. Let’s break it down clearly 👇 💡 What Is the H-1B Grace Period? When your employment on an H-1B ends unexpectedly (layoff, termination, etc.), U.S. immigration law grants a 60-day grace period (or until your I-94 expiry, whichever is sooner). During this window, you can: Find a new employer willing to file an H-1B transfer, or File a change of status (to B-1/B-2, F-1, dependent, etc.), or Make arrangements to leave the U.S. gracefully. 🕒 When Does the 60 Days Actually Start? This is where most confusion happens — and it’s not always the same as your last working day. Here are the possible scenarios: If you’re still on payroll (notice period): Your grace period does not start until your last day on payroll — i.e., when your salary officially stops. ➤ Example: If your company gives you a 2-week notice or pays you for that duration, your 60 days begin after that period ends. If you receive severance pay (lump sum or continued): USCIS generally looks at the actual last date of employment, not the severance period — unless the employer reports otherwise. ➤ So, if you’re on paid administrative leave, your clock might not start yet. But if severance is just compensation after employment ends, your clock has already started. If the employer updates USCIS early: Once the employer withdraws your H-1B petition, USCIS treats that as the end of employment. ➤ But remember — it’s often the last date you were paid that counts most, especially if the employer maintains payroll compliance until that date. ⚠️ The Most Common Confusion People often think: “My company gave me a month of notice, so my 60 days start later.” ✅ True if you’re still being paid. “My severance counts as employment.” ❌ Not necessarily. “My last working day and grace period start date are the same.” ⚠️ Only if your employer ends payroll that day. 🧭 Key Takeaways for H-1B Employees Always confirm your last paycheck date with HR or payroll. Keep proof of pay stubs or HR email confirming your end date. Use this 60-day window wisely: line up interviews, request transfers, or explore alternate visa options. Don’t panic — but don’t delay either. Immigration timelines are firm, not flexible. 🎯 Career Coach’s Note: If you’ve recently been impacted by a layoff, take this time to plan wisely. I offer a free 30-minute discovery call to help you strategize your next move — both career-wise and immigration-wise. You’re not alone — clarity is the first step toward stability. 💪 #Layoff #H1B #GracePeriod #Termination #NoticePeriod #LastDayAtWork #Severance #NonImmigrant #JobHunt #Strategy #VisaTimelines #BounceBack
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