Kritika Sharma

As a value-driven product manager, I have 8 years of experience leading global cross-functional teams to successfully deliver innovative software and hardware products in leading companies such as Ericsson and Brightly, A Siemens Company.

I completed my Master's from Johns Hopkins University in Systems Engineering and Bachelor's in Electronics and Communications Engineering from Mody University.

My key strengths lie in my relentless curiosity, systems thinking, and ability to break down ambiguous and complex problems. I thrive on taking action and am often the go-to person for driving projects forward and aligning multi-disciplinary teams.

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A Product Manager at Ericsson for Ericsson Operations Engine and at Brightly, A Siemens Company for SmartGov

I actively contributed to the NPD/NPI process, starting with competitive intelligence. I identified ways to strengthen the product, particularly focusing on reducing the time to value (TTV) for SmartGov. Through stakeholder interviews, I defined internal efficiency needs and translated them into detailed product specifications. I led cross-functional sprint reviews and collaborated with UX/design and product teams to enhance the user experience. To address product priorities, I documented the implementation journey, challenges, and proposed steps forward. I focused on reducing TTV through recommendations such as streamlining configurations, utilizing external tools, and improving the discovery process. These changes aimed to improve sales win-rate, reduce churn rate, and expedite lead time. I also proposed creating standardized practices, including preconfigured databases, reports, and a streamlined data migration option, to further reduce TTV. By emphasizing SmartGov's highly standardized yet customizable nature, through my recommendations, SmartGov was able to achieve business goals. Throughout my tenure, I conducted additional discovery and scoping work to refine product approach and align it with company objectives.

Ericsson Operations Engine (EOE) is a product and service offering by Ericsson that optimizes the operations and management of network environments for telecom operators and service providers. As a Product Manager for EOE, I drove product success, aligned business objectives, and delivered a valuable and competitive solution in the market. My responsibilities encompassed the complete product lifecycle, from strategy and roadmap to customer engagement, launch, and ongoing performance analysis. I leveraged advanced technologies like AI, ML, and automation to automate tasks, enhance network performance, and enable efficient service delivery to deliver proactive and intelligent operations management.

Supplement

For this project at Cedar Electronics, I had the opportunity to work on several exciting projects. During my time at Cedar Electronics, I recommended new advanced warning and blind spot monitoring features for the radar detector software re-launch. I collaborated closely with the product team, conducting market research and competitor analysis to identify industry trends and ensured proposed features aligned with market demands. Working alongside the engineering team, I helped define the technical requirements and specifications for the new software features. By implementing strategic initiatives such as bottleneck analysis and user experience enhancements, I was able to improve loading times by and app responsiveness. Throughout the process, I worked closely with the development & implemention team while monitoring the impact on performance metrics. Furthermore, I took on the challenge of creating a business case for accelerometer module integration for car safety features (module now in production)

Kritika Sharma, Neil Fendley, Haolin Yuan

Advisor: Anqi Liu

Currently working on combining filter avoidance prompts with adversarial optimized payloads \& using generative AI techniques to search for avoidance prompts to prevent people from accessing malicious content.

Kritika Sharma, Nikhil Sharma, Ammar Latheef

Advisor: Anqi Liu

To expand the capabilities of superhuman imitation learning (IL) to include bipedal walking. Focussed on incorporating superhuman autonomy objectives into the IL framework. Proposed a methodology that leverages the Minimum Suboptimal Inverse Optimal Control (MinSub IOC) function. This methodology aims to enhance performance beyond the highest level achieved by humans for each component of the cost function. The implementation was within the context of the bipedal walking task in the OpenAI Gym environment, serving as a testing platform. Employed a gradual data augmentation technique to enhance the system's performance, given the constraints of a limited dataset in a low-resource setting.

Optimization Analyst

Utilized machine learning prediction models to recommend enhancements to the current routes and operational efficiency of the Charm City Circulator, aiming to expand transit access for key equity zones within the city and improve connectivity to job centers that are currently underserved.

Kritika Sharma, Natalie Wang, Gwenyth Portillo Wightman, Niharika Desaraju

Advisor: Mathias Unberath

To identify misinformation in medical and health forums on Reddit the Reddit Health Advice Checker Bot (HACKBot) was developed. It automates fact-checking of claims related to public health by generating predictions for the truthfulness of comments. Additionally, a model was created to match 'advice' in comments against known fact-checking data, using tokenization and KNN for binary classification. The system increases transparency by highlighting the most similar statements to the input. To provide an interactive experience for user study, a web application was designed using REST API for false claim evaluation.

Kritika Sharma, Thaissa Peixoto, Xinrui Zou

Advisor: Chien-Ming Huang

Conceptualized design for mobile application to revolutionize online shopping by integrating multiple brand websites, virtual fitting rooms, and seamless communication, replicating the interactive in-store shopping experience for enhanced user satisfaction.

Kritika Sharma, Natalie Wang, Elahe Mohammadi Siahroodi

Advisor: Lauren Gardner

Created a novel drug-drug-ADE network and trained accurate machine learning classifiers to predict drug-ADE links based on network metrics. The project investigates drug-protein-ADE relations to identify similar drugs based on the proteins they target and how they are associated with the drugs' reported ADEs. We aim to explore newer drug compared to existing drugs with well-known ADEs by using target protein commonalities to predict likely new drug-ADE relationships. Our method can aid ADE detection in the clinical trial stage or before, thus averting severe and often fatal adverse effects that are experienced by users when drugs are approved prematurely.

IoT-Accelerator Consultant

Implemented an NB-IoT-based sensor network to monitor air pollution in Delhi as part of the Digital India Initiative, focusing on effective communication with stakeholders during critical air quality events. Improved alert accuracy by training the system to recognize air pollution patterns using machine learning algorithms. Achieved faster response times through real-time data processing techniques, utilizing distributed computing and edge computing. Collaborated with cross-functional teams, conducted user research, delivered the system on time, and ensured seamless connectivity and reliability.

IoT-Accelerator Consultant

Project aimed to leverage IoT technology to optimize resource usage, improve crop yields, and enhance sustainability. Created interactive data visualization dashboards for performance analysis. Affirmed compatibility with the data management system and quantified metrics such as data accuracy, dashboard responsiveness, system availability, and algorithm accuracy. Monitored the system's performance by analyzing metrics like crop yield, water usage, energy consumption, and fish growth rates, tracked operational challenges faced by stakeholders, and gathered stakeholder feedback on system performance and usability.

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