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Bhiman's Website 
Bhiman Kumar Baghel
I'm a 3rd Year Ph.D. student in Computer Science at the University of Pittsburgh advised by Prof. Xiang (Lorraine) Li.
My research focuses on interpretable, parameter-efficient reasoning in large language models.
I developed a plug-and-play editing framework that improves editing performance by 38 points over prior SOTA. This was enabled by formalizing key failure modes in model editing—UnderEdit (failure to inject knowledge) and OverEdit (unintended side effects)—and identifying their root causes through empirical and representational analysis.
More recently, I've been exploring how parameter-efficient tuning methods, like LoRA, affect reasoning in legal domains, using data Shapley values and training dynamics to understand and improve generalization. My broader goal is to steer foundation models toward more reliable, trustworthy reasoning in high-stakes applications.
My research focuses on interpretable, parameter-efficient reasoning in large language models.
I developed a plug-and-play editing framework that improves editing performance by 38 points over prior SOTA. This was enabled by formalizing key failure modes in model editing—UnderEdit (failure to inject knowledge) and OverEdit (unintended side effects)—and identifying their root causes through empirical and representational analysis.
More recently, I've been exploring how parameter-efficient tuning methods, like LoRA, affect reasoning in legal domains, using data Shapley values and training dynamics to understand and improve generalization. My broader goal is to steer foundation models toward more reliable, trustworthy reasoning in high-stakes applications.
Publications and Patents
Research:
Bhiman Kumar Baghel, Scott M. Jordan, Zheyuan Ryan Shi, Xiang Lorraine Li
EMNLP 2025 • Findings
We propose techniques to systematically resolve UnderEdit and OverEdit issues in model editing, improving both precision and generalization.
Bhiman Kumar Baghel, Arun Balajiee Lekshmi Narayanan, Michael Miller Yoder
GeBNLP Workshop, ACL 2024 • Workshop
We investigate biases in human vs AI-generated student summaries, proposing fairness metrics and improving reflection generation systems.
Yang Zhong, Bhiman Kumar Baghel
MULA Workshop, CVPR 2024 • Workshop
We propose methods for fair interpretation of memes by jointly modeling image and text, focusing on bias mitigation across sensitive attributes.
Niraj Kumar, Bhiman Kumar Baghel
IEEE Access, 2021 • Journal
We propose intent-focused semantic parsing and zero-shot out-of-domain detection strategies to enhance the robustness of spoken language understanding systems.
Niraj Kumar, Bhiman Kumar Baghel
IEEE Access, 2021 • Journal
We introduce a smart stacking approach for intent-slot extraction in multi-intent spoken language understanding tasks, improving extraction granularity.
Sourabh Tiwari, Bhiman Kumar Baghel, Jalaj Sharma, Manish Chauhan, Boddu Venkata Krishna Vinay, Syed Khaja Moinuddin
Samsung Electronics Co Ltd • Patent • App No: WO2025018568A1
Venkata Krishna Boddu Vinay, Bhiman Kumar Baghel, Gorang Maniar, Syed Khaja Moinuddin, Sudhansu Ranjan Acharya
Samsung Electronics Co Ltd • Patent • App No: US 18517995
Niraj Kumar, Bhiman Kumar Baghel
Samsung Electronics Co Ltd • Patent • App No: US 18202687
Niraj Kumar, Bhiman Kumar Baghel
Samsung Electronics Co Ltd • Patent • App No: US 17835387
News
Sept 2024
👨🔬 Started working as Graduate Research Assistant @ SCI, UPitt
Feb 2024
🤩 Attended Google Research Week
Jan 2024
👨🏫 Started working as Teaching Assistant - Intro to NLP @ SCI, UPitt
Sep 2023
👨🏫 Started working as Teaching Assistant - Operating Systems @ SCI, UPitt
Aug 2023
🎓 Started CS PhD @ University of Pittsburgh, PA, USA
Mar 2023
🆙 Promoted to Lead Engineer - NLP @ SRIB
Mar 2023
🏆 Received Samsung Excellence Award @ SRIB
Mar 2023
🌟 Received MBO High Performance Bonus @ SRIB
Sep 2022–Dec 2022
✈️ Business trip to Samsung HQ, South Korea
Projects
LoRA Fine-tuning Framework
A modular and extensible LoRA fine-tuning framework for question-answering tasks with PEFT integration. Demonstrates parameter-efficient training with configurable LoRA parameters and structured evaluation metrics.
Tools: PyTorch, Transformers, PEFT, LoRA, Datasets, Pandas
Chat-Enabled AI Web Agent
Designed modular prompting strategies enabling the agent to reason over multi-step flight search actions based on dynamic browser observations and user goals, enhancing the agent's temporal and spatial reasoning capabilities.
Tools: BrowserGym, Gradio, OpenAI GPT-4o, PyTorch
Chat-Enabled AI Web Agent
Automatic Concept Map Generation
Built a pipeline to generate and visualize concept maps from Wikipedia by extracting entities and semantic relations using entity linking, word embeddings, and syntactic parsing.
Tools: PySpotlight, FastText, Stanford CoreNLP
Automatic Concept Map Generation
AI Text Completion App
Built a Streamlit web application for AI-powered text completion using Meta's Llama-3.2-1B model with automatic GPU/CPU detection and intuitive interface.
Tools: Streamlit, PyTorch, Transformers, Meta Llama-3.2-1B
AI Text Completion App
Twitter Sentiment Analysis
Developed a web application that fetches real-time tweets based on user queries and classifies their sentiment (positive, negative, neutral) using a Naive Bayes classifier.
Tools: Tweepy, NumPy, Scikit-learn, Flask
Twitter Sentiment Analysis
LoRA Fine-tuning Framework
A modular and extensible LoRA fine-tuning framework for question-answering tasks with PEFT integration. Demonstrates parameter-efficient training with configurable LoRA parameters and structured evaluation metrics.
Tools: PyTorch, Transformers, PEFT, LoRA, Datasets, Pandas
LoRA Fine-tuning Framework
Chat-Enabled AI Web Agent
Designed modular prompting strategies enabling the agent to reason over multi-step flight search actions based on dynamic browser observations and user goals, enhancing the agent's temporal and spatial reasoning capabilities.
Tools: BrowserGym, Gradio, OpenAI GPT-4o, PyTorch
Chat-Enabled AI Web Agent
Education
University Of Pittsburgh, PA, USA
PhD in Computer Science
GPA: 3.5/5
August 2023 - April 2027
Indian Institute of Technology (IIT), Kharagpur, India
M.Tech in Computer Science
GPA: 8.82/10
July 2017 - May 2019
National Institute Of Technology (NIT), Jalandhar, India
B.Tech in Computer Science
GPA: 6.82/10
June 2013 - June 2017
Professional Experience
Amazon
Applied Scientist Intern
September 2025 – Present
Seattle, WA, USA
- •Selected for a competitive internship with the People eXperience and Technology Central Science (PXTCS) team.
- •Internship will focus on Generative AI applications and infrastructure with emphasis on enterprise knowledge integration, while ensuring fairness and privacy are maintained.
University of Pittsburgh
Graduate Research Assistant
August 2024 – Present
PA, USA
- •Engineered a plug-and-play iterative editing pipeline that enhanced edit-success rate by 38 percentage points over prior SOTA on LLaMA-3/2 and GPT-J, enabling rapid knowledge updates without full-model fine-tuning
- •Developed a Shapley- and cartography-based framework to identify influential training examples, revealing key differences in generalization behavior of LoRA on legal reasoning tasks compared to other tuning methods
- •Conducted a gender-bias audit of GPT-3.5 and BART summaries over 19,579 student reflections; used Jensen–Shannon divergence to reveal a 10% male-topic skew and uncovered under-represented female topics
- •Built a 2,900-meme multimodal dataset; manual audit revealed stereotype bias in 40% of LLaVA and MiniGPT-4 explanations, traced to visual/named-entity stereotypes, and text–image representation imbalance
Samsung Research
Lead NLP Engineer
June 2019 – August 2023
Bangalore, India
- •Spearheaded CoSMIC, a BERT-based multi-intent NLU engine for SmartThings; shipped to 100M+ devices, reaching 96% intent accuracy and cutting live NLU errors by 67%
- •Localized and scaled CoSMIC for the Korean market, mentoring a cross-site team and re-engineering tokenization to lift intent-slot F₁ by 25%
- •Architected production conversational-AI models (intent, slot, OOD) that raised multi-intent F₁ from 87% → 92% and achieved 90% OOD recall across all public benchmarks
IBM
Machine Learning Intern
May 2018 – July 2018
Bangalore, India
- •Prototyped an LSTM-based anomaly-prediction engine that monitors 33 infrastructure health metrics and launches auto-remediation scripts, forecasting critical failures with 97% precision
Honors and Awards
2023
Samsung High Performance Bonus (3×)
Samsung Research -- Bangalore, India
2023
Samsung Excellence Award (5×)
Samsung Research -- Bangalore, India
Recognized for SmartThings CLab innovation finalist and 4 US A1 patent filings.
2018
2nd Runner-Up, Audience Poll
IBM Extreme Blue Expo -- Bangalore, India
Voted top-3 of 24 projects by 100+ expo attendees.