Applied Researcher | ex-Adobe Firefly | Multimodal Generative Models
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A machine learning engineer with a mix of academic and industry research experience. Specialized in machine learning, computer vision, and NLP. A keen interest in end to end development of data products.
Stanford University
Independent Coursework, Artificial Intelligence
January 1, 2016 – Present
Vanderbilt University
Doctor of Philosophy (PhD), Computer Science
January 1, 2006 – January 1, 2012
Chalmers University of Technology
MS, Dependable Computer Systems
N/A – Present
Devi Ahilya Vishwavidyalaya
Bachelor's degree, Computer Science
N/A – Present
BlueDot Impact
Technical AI Safety Fellow
May 1, 2026 – Present
Altimate AI
Research Consultant
April 1, 2025 – October 1, 2025
Adobe
Staff Machine Learning Engineer (Adobe Firefly)
January 1, 2023 – January 1, 2025
Adobe
Senior Data Scientist (Adobe I/O)
January 1, 2018 – August 1, 2018
Adobe
Staff Machine Learning Engineer (Adobe Sensei)
January 1, 2018 – January 1, 2023
Accenture
R&D Technology Associate Principal
November 1, 2016 – January 1, 2018
San Francisco Bay Area
Idibon
Senior Machine Learning Engineer
January 1, 2015 – January 1, 2016
San Francisco Bay Area
Townsquared
Data Scientist
January 1, 2014 – January 1, 2015
San Francisco Bay Area
Scala SAT Solver
January 1, 2014 – Present
Technologies: Scala, Eclipse Implemented a basic boolean satisfiability (SAT) solver in Scala, based on DPLL algorithm. Given a formula in conjunctive normal form, the solver returns whether the formula is satisfiable for any assignment of the variables. The solver uses unit propagation and pure literal elimination to speed up the solution. The intent of this project was to learn the basics of Scala. The implementation uses Lists, case classes, typed classes, pattern matching and higher order functions.
Concurrent Sudoku Solver
March 1, 2013 – Present
Technologies: Java, Eclipse, Java Concurrent Library Designed and implemented a parallel Sudoku solver that allows a set of threads to work on smaller search spaces in parallel and thereby minimizes the execution time, leading to a solution faster. Implemented two strategies to parallelize the solver. First stategy consists of a global bounded buffer, which consists of partially solved instances of the puzzle and is shared by all solvers instances that consume from and produce instances to the buffer. Second strategy uses individual buffers which can be used only by a single thread, but a pair of threads can share the workload. Used semaphores, locks, monitors, message passing and futures to allow a set of threads to work on smaller search spaces in parallel.
A Generic Framework for Design Space Exploration
August 1, 2012 – Present
Tools/Technologies: C++, Java, Generic Modeling Environment, Genetic Algorithms, Combinatorial optimization solvers. Designed and implemented a framework to model and solve combinatorial search problems in model-based design. Designed and implemented a generic framework to automate solution of combinatorial optimization problems that occur during Model Based Design (MBD). By default combinatoral problems in MBD like resource allocation, placement, routing and configuration problems are solved using off-the shelf solvers or customized algorithms. This method is extremely resource intensive especially for small problem instances. The generic framework reduces development costs involved in solving small instances of combinatorial problems by providing a generic set of generic intermediate formats and translators. • Designed a UML profile that included entities to represent key aspects of a search problem. Im- plemented an easy to use GUI using MFC to help the user configure the framework. • Designed a textual constraint specification language for specifying the constraints that valid solu- tions should satisfy. Implemented a parser for the language in Java using ANTLR. • Implemented a set of model compilers in C++ to translate a graphical of the search problem to a textual mathematical model that is solved using off the shelf constraint solvers or genetic algorithms.
Expression Evaluator using GoF Design Patterns
August 1, 2009 – Present
Tools/Technologies: C++, Eclipse, Valgrind Designed and implemented an expression evaluator using linked lists, array, queues, dynamic memory management, and Singleton, Adapter, Strategy, Bridge, Abstract Factory, Factory Method, Iterator, Visitor, Composite, Interpreter and Builder design patterns, and C++ concepts of STL iterators, idioms (RAII, CRTP, etc) and strong exception safety.
Tool Neutral State Machine
August 1, 2004 – Present
Tools and technologies: C++, Graph Rewriting and Transformation Tool. Implemented a model compiler for UML statecharts that generates a state chart simulator in C++. The implementation decouples the structure of the state-charts from execution semantics, allowing the user to select the semantic variations at run-time. The implementation is based on the work by Dr. Daniel Balasubramanian and Carolyn Duby.
Intro to Data Ethics
University of San Francisco
June 24, 2026 – Present
Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on AI/ML, moving from Data Scientist to Senior ML Engineer and Staff ML Engineer roles at prominent companies like Adobe and Accenture. The personal projects demonstrate a proactive learning attitude and interest in core computer science principles. The recent part-time roles as 'Technical AI Safety Fellow' and 'Research Consultant' in AI further align with a forward-thinking, research-oriented culture. However, the project descriptions are primarily technical and do not offer much insight into collaborative or team-oriented contributions, which limits the assessment of cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a structured approach to complex technical challenges. Experience in research and development roles suggests an ability to innovate and adapt. However, without specific behavioral assessment data, it is difficult to fully assess soft skills like teamwork, leadership, or stress handling.