WIECON-ECE 2019 Speaker Profiles
Bibliographic record
Abstract
Ms. Vidya Hungud is a passionate technologist and a product leader with extensive experience in building highly scalable, resilient E-commerce and enterprise applicaons that are SaaS, IaaS and PaaS based. Aer having graduated with Master's Degree from San Jose State University (SJSU, CA) with Major in Client Server Compung, Vidya worked in United States for large size tech company Sun Microsystems on Identy Access Management, mid-size product-based company Intuit on flagship products: TurboTax, QuickBooks, and startup company Soware Tree on object-relaonal mapping soware. Vidya has worked on AgriNova, an SMS based soluon to help farmers sell their produce at a fair market price that gave an immense sasfacon of having put principles of Design Thinking into pracce from ground-up. As an Innovaon catalyst, Vidya has coached several startups at NSRCEL, IIM Bangalore in partnership with Pensaar Inc and at GHC on Design for Delight and Customer Driven Innovaon. Currently, Vidya Hungud is driving part of digital transformaon journey of India through Reliance, leading Jio Cloud plaorm and Site Reliability Engineering that entails design through deployment, post-producon. As part of paying forward, Vidya has visited universies and educated on AI/ML, mentored women techies through HerSecondInnings.com, speaker in BOAST19 conference, open stack summit, GHCI, and panelist at Cisco, Ericsson, Pensaar Inc. Vidya connues to drive charter for Tech Women at Reliance Jio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.040 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".