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My professional curriculum vitae including education, experience, and research interests in AI Safety, AI for Science, Multimodal Learning, and NeuroAI.
Basics
| Name | Mallikarjuna Tupakula |
| Label | ML Researcher | AI Safety & Science |
| tmallikarjuna1111@gmail.com | |
| Url | https://malli7622.github.io/ |
| Summary | Independent student researcher pursuing MS in Computer Science at Rochester Institute of Technology with full graduate scholarship. Focused on AI Safety, AI for Science, Multimodal Learning, and NeuroAI. Published sole-author work at NeurIPS'25 FM4LS workshop. |
Work
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2024.09 - Present Rochester, NY, USA
Graduate Research Assistant | ML Research
Rochester Institute of Technology
Independent student researcher working on AI Safety, AI for Science, Multimodal Learning, and NeuroAI. Advisor: Prof. Ashique KhudaBukhsh
- Sole-authored publication at NeurIPS 2025 (FM4LS) on 'Thin Bridges for Drug Text Alignment: Lightweight Contrastive Learning for Target Specific Drug Retrieval' - achieving Recall@1 = 0.762 on ChEMBL and 3× scaffold-split gain over baselines
- Co-authored publication in INFORMS Journal on Computing: developed multimodal machine-comprehension framework evaluating 48,956 YouTube Kids videos; achieved 77.5% accuracy on ScienceQA and F1 = 0.82 on CVQA dataset
- Developing diffusion-based brain decoding pipelines using fMRI data and image reconstruction for multimodal perception tasks with Stable Diffusion and CLIP embeddings
- Developing large-scale empathetic dialogue systems using CANDOR dataset (1 TB video/audio/text) and studying LLM neuron interpretability to align multimodal agents with human values for safer reasoning in medical and financial domains
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2020.12 - 2024.08 Hyderabad, India
Research Assistant | Pre-doctoral Program
Indian School of Business
Research Assistant in Pre-doctoral Program working on AI for Social Good and AI Safety problems on social media platforms. Advisor: Prof. Sumeet Kumar
- Worked on AI for Social Good and AI Safety problems on social media platforms by processing large multimodal datasets (YouTube, Amazon, Twitter) at TeraBytes scale, leading to publications at AAAI, ASONAM, INFORMS, and ACL
- Applied PyTorch, OpenCV, and CUDA to accelerate large-scale computer vision pipelines, optimizing training and inference speed for terabyte-scale datasets
- Publications at top-tier venues: NeurIPS, ACL, ASONAM, INFORMS Journal on Computing, INFORMS on Data Science, and AAAI
- Awarded prestigious Kathuria Pre-Doctoral Scholarship from Shri Nihal Chand Kathuria Education Trust
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2020.08 - 2020.11 Stockholm (Remote)
Research Intern | Core ML Team
Spacept
Research Intern on Core ML Team developing deep learning solutions for satellite imagery analysis. Advisor: Sergiu Iliev (Founder)
- Developed a custom Deep Learning model for oil spill classification using Google Earth Engine and Sentinel satellite data of the Mauritius oil spill (July 2020), engineering scalable pipelines and augmentations
- Improved accuracy from 65% to 78%, demonstrating transferability to real-world autonomous sensing applications
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2019.12 - 2020.03 Chennai, India
Research Intern | Neuromotor Team
Indian Institute of Technology Madras
Research Intern on Neuromotor Team working on medical imaging and CT reconstruction. Advisor: Prof. Srinivasa Chakravarthy
- Implemented Elman-Jordan-based CT reconstruction halving projections (360 → 180) for efficient imaging AI techniques extendable to on-device simulation
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2019.05 - 2019.07 Bangalore, India
Research Intern | Research and Development Team
Indian Institute of Management Bangalore
Research Intern on R&D Team analyzing real-time sales data and marketing analytics. Advisor: Prof. Trilochan Sastry
- Analyzed real-time data sales from Farmveda and predicted future sales, even further improving the sales by being actively involved in the Digital Marketing team
Education
Skills
| Machine Learning | |
| Computer Vision | |
| Deep Learning | |
| Probabilistic Modeling | |
| Neural Networks |
| AI Research | |
| AI Safety | |
| AI for Science | |
| Multimodal Learning | |
| NeuroAI |
| Programming | |
| Python | |
| PyTorch | |
| C/C++ | |
| Java | |
| MATLAB |
| Data Science | |
| Data Analysis | |
| Visualization | |
| Statistical Modeling |
Languages
| English | |
| Fluent |
| Telugu | |
| Native |
| Hindi | |
| Fluent |
Interests
| AI Safety | |||
| Safe AI Systems | |||
| Interpretability | |||
| AI for Science | ||||
| Drug Discovery | ||||
| Foundation Models | ||||
| NeuroAI | ||||
| Multimodal Learning | |||
| Cross-modal Learning | |||
| Vision-Language Models | |||
References
| Professor Ashique KhudaBukhsh | |
| Rochester Institute of Technology, Advisor |
| Professor Sumeet Kumar | |
| Indian School of Business, Advisor |
Awards
- 2025.05.13
CVPR'25 Broadening Participation Participant Award
IEEE/CVF
Award for participating in CVPR 2025 Broadening Participation program
- 2024.11.26
NeurIPS 2024 Reviewer Registration Support
NeurIPS 2024
Received registration support for serving as a reviewer at the Machine Learning and the Physical Sciences Workshop at NeurIPS 2024
- 2024.09.08
PyTorch 2024 Conference Travel Support
Linux Foundation
Received funding from Linux Foundation for attending PyTorch 2024 conference at San Francisco
- 2024.04.15
Full Merit Scholarship for Graduate Studies
Rochester Institute of Technology
Awarded full merit scholarship for graduate studies at Rochester Institute of Technology
- 2023.01.21
Kathuria Pre-Doctoral Scholarship
Shri Nihal Chand Kathuria Education Trust
Awarded prestigious Kathuria Pre-Doctoral Scholarship for showing excellence in pre-doctorship at Indian School of Business
- 2020.06.20
Full Scholarship for Undergraduate Studies
Indian Academic Institution
Awarded full scholarship for undergraduate studies in lieu of exceptional performance in entrance examination and prior academic record
- 2019.09.13
Publications
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2025 Do Vision Transformers and Convolutional Neural Networks fit together?
Under Conference Submission (Rejected from CVPR 2025)
Research exploring the integration of Vision Transformers and CNNs for improved computer vision tasks.
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2025 Gender Biases and Stereotyping in Children's Videos: A Study on 100 Popular Kids Channels on YouTube
Submitted to Nature Human Behaviour (previously rejected from PNAS)
Comprehensive study analyzing gender stereotyping across 100 popular YouTube channels for children.
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2025 Large Language Models for Rating the Language of Children's Videos on YouTube
Submitted to IEEE Transactions on Knowledge and Data Engineering
Co-authored research on using LLMs for language rating and quality assessment of children's educational content on YouTube.
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2025 Quantifying the academic quality of children's videos using machine comprehension
INFORMS Journal on Computing
Developed multimodal machine-comprehension framework evaluating 48,956 YouTube Kids videos; achieved 77.5% accuracy on ScienceQA and F1 = 0.82 on CVQA dataset.
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2025 Thin Bridges for Drug Text Alignment: Lightweight Contrastive Learning for Target Specific Drug Retrieval
Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: 2nd Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences
Sole-authored publication developing lightweight contrastive learning for drug-text alignment. Achieved Recall@1 = 0.762 on ChEMBL and 3× scaffold-split gain over baselines.
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2024 Anonymous Dissent in the Digital Age: A YouTube Dislikes Dataset
International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Developed YouTube Dislikes dataset for studying anonymous dissent and user engagement patterns on social media platforms.
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2023 [SRW] Gender Stereotyping in Popular Children's Videos
The 61st Annual Meeting Of The Association For Computational Linguistics (ACL)
Student research workshop paper on analyzing gender stereotyping in children's video content.