Aaksha Meghawat
Machine Learning Engineer at Apple
Machine Learning @ Apple
I measure my life by the impact I make. I believe that data and scale mean power. Which is why I plunged into the cerebral world of algorithms and the human-machine frontier at CMU. It is my vision to combine the capabilities of technology with the complexities of people. Data and lots of it, viewed/ transformed into the right perspective can enhance human decisions for speed, ease and quality. It is my objective to build systems and products that make this happen.

As a graduate research assistant at CMU I worked on some exciting cutting-edge research in ML and AI, acquiring a rigorous academic perspective. I have spent the rest of my career building impactful & most importantly safe (unbiased, privacy conscious) ML products. It is always my goal to continuously combine the academic & industry perspectives to create amazing cutting-edge technology products that have a positive impact on society!
Apple
https://www.linkedin.com/company/apple
􀁸 Tech Lead - Global Expansion of hyper-local Language Models (GeoLM):

o DRI (Directly Responsible Individual) for developing location based speech recognition models (ASR) for the 10 largest iPhone markets servicing 3mn+ location related Siri queries daily
o Led direction, key design & pipeline implementation for 13 languages (morphological diversity), 10 countries (geographical diversity)
o Featured in TechCrunch! Apple’s ML Spotlight! (Links below)

􀁸 My team’s work on Knowledge Graphs for ASR was published at Interspeech 2021 (nominated for best student paper award): “A Discriminative Entity-Aware Language Model for Virtual Assistants”

􀁸 Selected (1 of 24 from 10k+ Apple employees) to present graduate research at Apple ML Summit 2018,
Title: “Representation Learning for Small Datasets” attended by 1000+ Apple employees (at the Steve
Jobs Theatre).

􀁸 Tech Lead – Named Entity Analysis Tool
o Developed a tool for systematically monitoring & analysing performance of ML models for highly
complex low data languages (e.g. Italian, Korean, Russian, Turkish)
o Helped surface the highest priority issues leading to >30% improvement in Named Entity ASR
o Drove adoption across 10+ globally located language engineers

Tech: Python, Tensorflow, NLP, Distributed Computing (PySpark, Hadoop, Docker, Kubernetes), C++
San Francisco Bay Area
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Machine Learning Engineer
Carnegie Mellon University
https://www.linkedin.com/company/carnegie-mellon-university
Pragmatics
􀁸 Developed a new ML algorithm for Representation Learning in small datasets & Identity Bias mitigation.
o Benchmarked on Sentiment Analysis for Youtube videos with 8-12% increase in accuracy establishing new SoTA. Published in ICME ’17 (w/ Prof. Eric Xing & LP Morency): poster, 10 min. PPT, paper

􀁸 Computationally measure Openness, Alignment & Topic development in Github discussions
o Developed model to predict acceptance/rejection of pull requests
o Achieved F1 score of 58.04, a 65% inc. over baseline
o Key Results ⬇️  5 min. read

Semantics
􀁸 StackOverflow answers search & retrieval
o Compared GloVe & an early version of customized ranking embeddings (StarSpace framework)
o Improvement of 60% on Hits@5 and DCG@5 metrics
o Integrated into the NLU component of a dialog agent assisting developer productivity

􀁸 Trained Bidirectional LSTM on MS COCO dataset captions for caption generation conditioned on Inception V3 image features.

􀁸 Compared Autoencoder, Variational Autoencoder (VAE), Conditional VAE & Deep Boltzmann Machines to model generative distribution of handwritten digits. (code)

Syntax & Morphology
􀁸 Trained bidirectional RNNs to predict POS tags on Brown Corpus, 96% accuracy, reproduced SoTA.
􀁸 Implemented Unlexicalized Parsing & discriminative re-ranking to learn Probabilistic Context Free
Grammar Constituency trees over Penn Treebank corpus. (code)

Special Projects
􀁸 Trained a bidirectional GRU encoder/decoder model for symbolic/arithmetic reasoning of addition, subtraction. Achieved a MAE of 475 over 1300 test math problems.
􀁸 Experimented with Bayesian Logistic Regression to build credible intervals for odds ratios of income
hypotheses on UCI Adult dataset.
􀁸 Compared Deep Deterministic Policy Gradients & Normalized Advantage Functions to teach DeepMind
MuJoCo’s ‘Half-Cheetah-v1’ agent navigation of a test obstacle course by training only for walking and
jumping in simpler environments. (Report: Skill Trees for Hierarchical RL)
Greater Pittsburgh Area
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Graduate Research Assistant
Sutherland Global Services
https://www.linkedin.com/company/sutherland-global
 Designed ML pipelines for identification of customer issues from call centre chats & social media for
major US telecom providers, healthcare chains (~2 million chats processed monthly)
 Developed a sentiment analysis toolkit adopted across the company to streamline/improve textual
sentiment analysis.
 Geospatial Network optimization for improved logistics (R, GoogleMaps API)
Mumbai Area, India
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Data Scientist, Analytics
Opera Solutions
https://www.linkedin.com/company/operasolutions
Developed a unified in-house platform for mathematical and statistical analysis of large datasets (Java)
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Software Specialist
Morgan Stanley
https://www.linkedin.com/company/morgan-stanley
► Developed automation software for testing risk management applications. (Java)
► The automation software was able to process and verify 11000+ test cases in 1 day covering all possible combinations of parameter values (compared to 800 test cases in 8 days previously).
► Efficiency gain of 8 work days, increase in accuracy and robustness of testing
Mumbai Area, India
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Technology Analyst (Intern)
London Business School
https://www.linkedin.com/company/london-business-school
► Redesigned ‘Hedge Fund Management Game’, a business simulation game to train 60-100 students of the elective ‘Hedge Funds’ for the LBS MBA program ranked 1 globally. (Python, PostgreSQL,  R and Django web framework)
► Teaching Assistant in the course ‘Equity Investment Management’.
London, United Kingdom
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Research Assistant
Brevan Howard
https://www.linkedin.com/company/brevan-howard
► Studied fundamentals of bonds, credit derivative swaps, credit derivative indices, specifically calibration of default probabilities, basic CDS pricing and credit curves.
https://media-exp1.licdn.com/dms/image/C4D0BAQGly1nAGSWOJw/company-logo_100_100/0/1619803934051?e=1655942400&v=beta&t=85Onpac8QQ8tbz9VvNKi4C84hY0iYImtsk49HAQzv9E
Research Intern
Mentored-Research
https://www.linkedin.com/company/www-mentored-research-com
► Formulated an investment thesis on a publicly-traded company and communicated the opinion through an equity research report.
www.mentored-research.com
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Intern- Equity Research
Center for Entrepreneurial Leadership, BITS Pilani
https://www.linkedin.com/company/center-for-entrepreneurial-leadership-bits-pilani
► Led a team of 40 people to organize the Out-of-Box Conference, a conclave of technology geeks, scientists, creative geniuses and business rebels. 
► Based on the TED format
► Instrumental in getting revolutionaries like Alphonse Kannanthanam, Vasu Dixit and Dr. P Namperumalsamy as speakers. 
► The event was part of the international business plan competition, Conquest BITS, Pilani 2011.
BITS Pilani
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Coordinator, Out-of-Box Conference
Birla Institute of Technology and Science Pilani
https://www.linkedin.com/company/birla-institute-of-technology-and-science-pilani
► Led a team of 20 student athletes to participate in BOSM 2011 and won the gold cup for the best athletics team of the tournament.
BITS Pilani
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Captain, Athletics Team
Birla Institute of Technology and Science Pilani
https://www.linkedin.com/company/birla-institute-of-technology-and-science-pilani
► Prepared curriculum assignments and designed evaluation material
► Assisted and supervised students in lab sessions for the course (With excellent recommendation)
BITS Pilani
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Professional Assistant, Computer Programming II
WATConsult - Social Media Agency
https://www.linkedin.com/company/watconsult
► Analyzed technology and business trends in the web,social media, online services and software industry and prepared articles for WATBlog readers, followers and subscribers
► Conducted interviews with business and industry professionals on behalf of the company for analysis and research
Mumbai Area, India
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Blogger, Social Media
Trusted Shares and Investments
► Led a team of 2 programmers to develop a suite of software programs in C++ to simulate and optimize the Turtle Trading model by Richard Dennis, enabling statistical and heuristic analysis of large amounts of financial data.
Mumbai Area, India
Software Programmer (Intern)
Masters
Awarded Graduate Research Fellowship
Selected for research fellowship under the NSF BIGDATA initiative which sponsors graduate tuition, research work, living expenses and a monthly stipend

James R. Swartz Entrepreneurial Fellow
This initiative fast-tracks the careers of Carnegie Mellon University's graduate students who are passionate about entrepreneurship in the technology arena.
Language Technologies Institute
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Carnegie Mellon University
https://www.linkedin.com/school/carnegie-mellon-university/
B.E.(Hons.) Computer Science, Masters in Mathematics
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Birla Institute of Technology and Science, Pilani
https://www.linkedin.com/school/birla-institute-of-technology-and-science-pilani/
European Philosophy
Philosophy
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Stanford Continuing Studies
https://www.linkedin.com/school/stanford-continuing-studies/
CISCE Grade XII
Science
Lilavatibai Podar Senior Secondary High School
ICSE Grade X
Sahyadri School, Krishnamurti Foundation India
High-quality automatic speech recognition (ASR) is essential for virtual assistants (VAs) to work well. However, ASR often performs poorly on VA requests containing named entities. In this work, we start from the observation that many ASR errors on named entities are inconsistent with real-world knowledge. We extend previous discriminative n-gram language modeling approaches to incorporate real-world knowledge from a Knowledge Graph (KG), using features that capture entity type-entity and entity-entity relationships. We apply our model through an efficient lattice rescoring process, achieving relative sentence error rate reductions of more than 25% on some synthesized test sets covering less popular entities, with minimal degradation on a uniformly sampled VA test set.
A Discriminative Entity-Aware Language Model for Virtual Assistants
Interspeech 2021 (Nominated for Best Paper Award)
https://arxiv.org/pdf/2106.11292.pdf
Multimodal sentiment analysis has only a few high-quality data sets annotated for training machine learning algorithms. These limited resources restrict the generalizability of models, where, for example, the unique characteristics of a few speakers (e.g., wearing glasses) may become a confounding factor for the sentiment classification task. In this paper, we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained neural networks for multimodal sentiment analysis.
Select Additive Learning: Improving Generalization in Low Data Settings
ICME
https://arxiv.org/pdf/1609.05244.pdf
This initiative fast-tracks the careers of Carnegie Mellon University's graduate students who are passionate about entrepreneurship in the technology arena.The highly selective program develops each Fellow's entrepreneurial potential and leadership skills through hands-on experiences, networking, mentoring and courses in entrepreneurship.
Swartz Center for Entrepreneurship, CMU
James R. Swartz Entrepreneurial Fellow
Selected for research fellowhsip under the NSF BIGDATA initiative which sponsors graduate tuition, research work, living expenses and a monthly stipend
NSF via CMU
Graduate Research Fellowship
For outstanding achievement in areas of leadership, with a particular focus on outstanding achievement in a chosen category, and consistent demonstration of all-round leadership potential.
BITS Alumni Association
Mantra Award for Outstanding Leadership
3000m Long Distance Race
BITS Open Sports Meet 2010
Silver Medal, Athletics
Among Top 5 Rank Holders in National Board ISC XII
Lilavatibai Podar School
Merit Student Award
Rank 1st in National Board ICSE Exams
Sahyadri School KFI
Merit Student Award
The limited number of high-quality multimodal sentiment data samples introduce the problem of the sentiment being dependent on the individual specific features in the dataset. This results in a lack of generalizability of the trained models for classification on larger online platforms. We propose a Select Additive Learning (SAL) procedure that improves the generalizability of trained discriminative neural networks.
Select-Additive Learning: Improving Cross-individual Generalization in Multimodal Sentiment Analysis
https://arxiv.org/pdf/1609.05244v1.pdf
Hedge funds as an asset class has gained considerable popularity and a number of individuals look forward to starting their own hedge fund or working in a hedge fund start up. Since the credit crisis of 2007-08, and the subsequent changes in regulation of investment products, the regulatory environment and business landscape has changed considerably and there is a crucial need as well as demand to train individuals by exposing them to the challenges involved in running a start-up hedge fund. The best way to do this is by allowing them to operate in a simulated yet realistic virtual environment and hence developing a software simulation tool for the same.
Hedge Fund Management Game 2012, London Business School
Implemented compiler logic in C for an experimental programming language.
Compiler Construction
Trinity College London
501/1953/9
Grade 1 Music (Guitar) with Distinction
Coursera
www.coursera.org
R Programming
https://www.coursera.org/course/rprog
Coursera
www.coursera.org
The Data Scientist’s Toolbox
https://www.coursera.org/course/datascitoolbox
Coursera
www.coursera.org
Machine Learning
https://www.coursera.org/course/ml
https://www.linkedin.com/in/snehaprunwal
Cupertino, CA
Sneha Runwal
Manager, Machine Learning at Apple
https://www.linkedin.com/in/koundinyanidumolu
San Francisco Bay Area
Koundinya Nidumolu
Machine Learning Engineer at Apple
https://www.linkedin.com/in/charankilari
Cupertino, CA
Charan Kilari
Machine Learning Engineer at Apple
https://www.linkedin.com/in/ayushpareek
San Francisco Bay Area
Ayush Pareek
Machine Learning Engineer at Apple |  Data Scientist Intern at Microsoft | Research Grad at CMU, Yale
https://www.linkedin.com/in/shah-saumya
Seattle, WA
Saumya Shah
Machine Learning Engineer at Apple
https://in.linkedin.com/in/prateep-mukherjee
Hyderabad
Prateep Mukherjee
Machine Learning Engineer at Apple
https://www.linkedin.com/in/poorvarane
San Jose, CA
Poorva Rane
Machine Learning @ Apple, Siri | Carnegie Mellon University
https://www.linkedin.com/in/anushreeapk
Cupertino, CA
Anushree Prasanna Kumar
Machine Learning | Apple | Carnegie Mellon University
https://www.linkedin.com/in/vinayapolamreddi
Stanford, CA
Vinaya Polamreddi
Machine Learning Engineer @ Apple | Stanford AI grad
https://www.linkedin.com/in/rewa-sood-634214a3
Stanford, CA
Rewa Sood
Machine Learning Engineer at Apple
https://www.linkedin.com/in/muralidharandeepak
Cupertino, CA
Deepak Muralidharan
AI/ML at Apple
https://www.linkedin.com/in/pranika-seth-733b4573
Cupertino, CA
Pranika Seth
Software Engineer at Apple
https://www.linkedin.com/in/nikitajuneja
Cupertino, CA
Nikita Juneja
Software Engineer at Apple
https://www.linkedin.com/in/malvika-pandey
Cupertino, CA
Malvika Pandey
Software Engineer at Apple
https://www.linkedin.com/in/akanksha-bindal
San Francisco Bay Area
Akanksha Bindal
Software Engineer, Machine Learning
https://www.linkedin.com/in/siminkarvigh
San Francisco Bay Area
Simin Karvigh, PhD
Senior Machine Learning Engineer at Meta | Ex-Apple
https://www.linkedin.com/in/atharva-gupta
Austin, TX
Atharva Gupta
Machine Learning Engineer at Apple | Python | Ex-Computer Vision Intern @ Lenovo | MS in Electrical Engineering
https://www.linkedin.com/in/krishnavishal
Redmond, WA
Vishal Krishna
Machine Learning Engineer at Twitter
Brijesh Bharadwaj
      
          



            
              
        
              
  

    
        Aaksha is an absolute delight to work with, she sets herself the highest standards of professionalism and almost always manages to out do herself, she is ice cool under pressure and that very quality helps her lead teams and execute projects effortlessly. Any organization would be lucky to have her. Arun Aravind
      
          



            
              
        
              
  

    
        I am very glad to be writing this recommendation for Aaksha, I oversaw Aaksha's research on Arch Coal, right from the beginning i noticed that she had a very clear way of presenting her analysis, While the research and analysis required a deep understanding of the Coal mining industry, she was able to quickly grasp the core underlying factors that are important to the industry. I can see Aaksha being a very valuable asset to any organization, she chooses to be a part of.
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It’s gut-wrenching to see the people of Ukraine suffering so much. The world feels especially heavy right now, with each day bringing news of…
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YourStory Media is an outlier. The first digital media platform in India to focus on startups, YourStory was bringing us the real stories of…
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#breakthebias So many biases, still so prevalent. One of the biases that goes across ethnicity, sexual orientation, and socio-economic background is…
Big Ideas Book Club
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https://www.linkedin.com/groups/13992188
CODiE Award Judges
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https://www.linkedin.com/groups/8140533
Women Impact Tech - San Francisco Alumni
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