Mohsen Hariri

Highlights

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Research interests

Machine Learning, Quantum Computing

Education

Ph.D. in Computer and Data Sciences • Case Western Reserve University

Currently pursuing under the supervision of Prof. Vipin Chaudhary

M.S. in Computer and Data Sciences (GPA: 4.0/4.0) • Case Western Reserve University

Thesis: Test-Time Scaling Under Budget: Reasoning Evaluation and Memory-Efficient LLM Deployment, Supervisor: Prof. Vipin Chaudhary

B.S. in Electrical Engineering • University of Tehran

Thesis: Evaluating and Analysis on Therapeutic Environment's Network Using Simple Network Management Protocol for Fault Detection and Management, Routing and Auto-discovery, Supervisor: Prof. Reza Aghaizadeh Zoroofi

Publications

M Hariri, M Hinczewski, J Ma, V Chaudhary

Ranking Reasoning LLMs under Test-Time ScalingACL 206 (under review)

Studies how to rank reasoning LLMs when each question is sampled multiple times (test-time scaling). Formalizes the repeated-trial setting, compares ranking families (metrics, Bayesian, IRT, voting, spectral), and introduces Scorio, an open-source toolkit for stable LLM ranking.

M Hariri, A Samandar, M Hinczewski, V Chaudhary

Don't Pass@k: A Bayesian Framework for Large Language Model EvaluationICLR 2026

Proposed a Bayesian framework that estimates models’ success probabilities with quantified uncertainty, yielding more reliable rankings and enabling categorical evaluation of LLMs.

M Hariri, M Hinczewski, V Chaudhary

Scorio.jl: A Julia package for ranking stochastic responsesJuliCon 2026

A Julia package for evaluating and ranking stochastic systems from repeated responses using a unified tensor-based framework.

A Yu, M Hariri, K Nakamura, M Yang, X Li, V Chaudhary

Medical Image Spatial Grounding with Semantic SamplingMICCAI 2026 (under review)

Evaluates VLM spatial grounding in 3D medical images across modalities and coordinate systems. Introduces MIS-Ground for anatomy-specific failure analysis and MIS-SemSam for improved inference-time grounding without retraining.

S Zhong, J Zhang, H A D Le, W Xie, Y Lu, X Sun, M Hariri, H Liu, G Wang, Z Xu, Z Liu, S Xu, N Xie, L Li, R Chen, R Tang, X Hu, V Chaudhary

Sweeping Promptable Spoofs under the DirtyRAGICML 2026 (under review)

Introduces DirtyRAG, a query-blind, benign passage-based RAG attack that is robust to defenses and steerable by prompt. Demonstrates practical exploitation and introduces RAG-ATag, a benchmark for evaluating RAG security.

B Flannery, T DeSilvio, M Hariri, A Sadri, N Heller, C Weight, S Viswanath

Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: Application to renal cancer CT scansComputers in Biology and Medicine, 2026

Systematically evaluated how data partitioning, initialization, and augmentation choices affect the robustness and cross-institution generalization of CT-based deep learning survival models.

C López, L Calandruccio, E Buss, M Hariri, V Chaudhary

Using AI to Increase Efficiency of Multilingual Test Materials: Spanish BEL SentencesWork in progress

T Zhang, M Hariri, S Zhong, Y Sui, V Chaudhary, X Hu, A Shrivastava

70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)NeurIPS 2025

DFloat11 losslessly compresses LLM and diffusion-model weights using dynamic-length floating-point encoding with Huffman coding, shrinking memory by about 30% with no accuracy loss.

M Hariri, A Luo, W Chen, S Zhong, T Zhang, Q Wang, X Hu, X Han, V Chaudhary

Quantize What Counts: More for Keys, Less for ValuesACL (under review)

Keys carry more information than values; consequently, key tensors require a larger quantization bit-width, smaller group sizes, and outlier mitigation (e.g., Hadamard transformation).

W Chen, V Singh, Z Rahmani, D Ganguly, M Hariri, V Chaudhary

K^4: Online Log Anomaly Detection Via Unsupervised Typicality LearningIEEE HiPC 2025

K4 reframes log anomaly detection as unsupervised typicality learning. It maps log embeddings to four compact PRDC descriptors (Precision, Recall, Density, Coverage) using k-NN statistics, enabling parser-independent online detection with lightweight scoring and microsecond-level latency.

H Liu, S Zhong, X Sun, M Tian, M Hariri, Z Liu, R Tang, Z Jiang, J Yuan, Y Chuang, L Li, S Choi, R Chen, V Chaudhary, X Hu

LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play EcosystemEMNLP 2025

A backdoor LoRA can be trained once and then merged with multiple task LoRAs while retaining both capabilities, making low-tech attack that is particular dangerous and infectious.

L Calandruccio, M Hariri, E Buss, V Chaudhary

Masked-speech recognition using human and synthetic cloned speechTrends in Hearing, 2025

Voice-clone vs. human speech study in masked-sentence recognition: intelligibility, perceived human-likeness, and voice-similarity measured with listener judgments and ASR.

A Sridharan, T DeSilvio, B Flannery, M Hariri, R Macbeth, B Parker, A Elumalai, J Devi, A Lovato, C Maneiro, A George, A Ganapath, P Deepak, D H Ballard, S E Viswanath

Integrating self-configuring and foundational deep learning segmentation models for identifying the anal sphincter complex and perianal fistulae on pelvic MRISPIE Medical Imaging 2025

Introducing an automated pelvic MRI pipeline combining nnU-Net and MedSAM to segment perianal fistulas and anal sphincter muscles in Crohn’s disease, using annotated patient scans to support interventional guidance and surgical planning.

AS Yu, M Hariri, X Zhang, M Yang, V Chaudhary, X Li

Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2SPIE Medical Imaging 2025

A zero-shot, single-prompt method for 3D knee MRI segmentation was developed using the Segment Anything Model 2 (SAM2). By adapting SAM2 to treat MRI slices as video frames, accurate segmentation was achieved without additional training.

P. Chirra, J. Sleiman, N. Gandhi, I. Gordon, M. Hariri, M. Baker, R. Ottichilo, D. Bruining, J. Kurowski, S. Viswanath, F. Rieder

Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn’s DiseaseJournal of Crohn’s and Colitis, 2024

Developed a radiomics-based machine-learning model to characterize inflammation and fibrosis in Crohn’s disease strictures using MRE. The model improved diagnostic accuracy compared to radiologist visual scoring, with combined use enhancing performance.

B. Flannery, T. DeSilvio, A. Sadri, M. Hariri, E. Remer, J. Nguyen, S. Viswanath

Spatial attention wavelon network (SpAWN) for survival-based risk stratification in kidney cancers via CTSPIE Medical Imaging 2025

The Spatial Attention Wavelon Network (SpAWN) is introduced for risk stratification of kidney cancers using CT scans. SpAWN uses pre-training spatial attention and wavelon activation functions to improve model interpretability and generalizability.

M. Hariri, P. Chirra, M. Patel, T. T. Einat, I. Dayan, A. Tonetti, Y. Baror, T. Barrett, N. Sushentsev, J. D. Kaggie, S. Yuan, D. Wu, B. Yu, Z. Lyu, C. Hsu, W. Wang, S. Krishnamurthi, S. E. Viswanath

Federated Image Quality Assessment of Prostate MRI Scans in a Multi-institutional SettingAACR 2024

This study addresses the challenge of image artifact impacts on the reliability of machine learning models in medical imaging, exacerbated across multiple institutions.

B. Flannery, M. Hariri, T. DeSilvio, A. Sadri, J. Nguyen, E. M. Remer, S. Krishnamurthi, S. E. Viswanath

Deep Learning Based Risk Stratification of Pre-operative CT Scans is Prognostic of Overall Survival in Kidney CancersAACR 2024

A deep learning model was developed to enhance preoperative risk assessment and predict survival in kidney cancer patients through CT scans, aiming to improve treatment decisions and overcome limitations of traditional clinical methods.

L. Bao, T. DeSilvio, B. N. Parker, M. Hariri, P. Chirra, M. Labbad, S. Tang, G. M. O'Connor, E. Steinhagen, J. L. Miller-Ocuin, A. Gupta, E. L. Marderstein, A. Carroll, M. Crittenden, M. J. Gough, S. Krishnamurthi, K. H. Young, S. E. Viswanath

Intra-and Peri-tumoral Radiomic Features are Predictive of Pathologic Response to Multiple Neoadjuvant Therapy Regimen in Rectal Cancers via Pre-treatment MRIAACR 2024

Radiomics from pretreatment MRI were analyzed to predict which rectal cancer patients would respond to neoadjuvant treatments, addressing limitations of traditional staging and biomarker approaches.

M. Rezai, L. Namdari, D. Farsi, N. Ashayeri, M. Naghshbandi, M. Hariri, R. Ghafoury

Virtual Reality as an Acute Pain Reliever During Laceration Repair in Emergency Departments: A Randomized Controlled TrialSaudi Journal of Emergency Medicine

Investigated the effect of virtual reality on reducing pain in adult patients during laceration repair in emergency departments.

Professional experience

Academic

AI Scientist • Department of Computer and Data Sciences, CWRU

• Supporting AI and research computing workflows in the ACCESS ecosystem; develops, deploys, and maintains tools, models, and user-facing research infrastructure for scientific and academic users.

• Member of Ohio-SCIPE (Strengthening the Cyberinfrastructure Professionals Ecosystem).

• Mentor for the annual summer AI Research Experience (AIRE '24, AIRE '25).

• Research Associate, Speech and Auditory Research Lab (SpARLab), CWRU.

• Judge for the CWRU Intersections Poster Symposium.

• Reviewer for ICLR, ICML, and ACL.

• Best Pitch Award, 2024 CCIR Symposium, Center for Imaging Research.

Co-instructor and co-organizer for SCIPE Workshop on Large Language Models • CWRU

Developed and taught a workshop on reasoning LLMs, retrieval-augmented generation (RAG), and agentic AI; co-managed curriculum design, instructional materials, and workshop delivery.

Co-instructor and co-organizer for CWRU Workshop on Large Language Models • CWRU

Developed and taught a workshop on foundation models, reasoning in LLMs, and language model evaluation; co-managed curriculum design, instructional materials, and workshop delivery.

Machine Learning Researcher • Department of Biomedical Engineering, CWRU

Co-instructor for Introduction to Database Systems (CSDS 341) • CWRU

Designed the final project. Created a template for the course project and a simple build system to introduce Java package management.

Designer and instructor for Big Data and Cloud Computing workshop • Weatherhead School of Management, CWRU

Designed and developed a Big Data and Cloud Computing workshop; wrote instructional code and challenges, and co-taught it alongside Prof. Chaudhary.

Chief Editor of Biotech Magazine • University of Tehran

Official magazine of the Iranian Society of Biomedical Engineering, student branch, University of Tehran.

Head of Student Branch of Biomedical Engineering • University of Tehran

(UT-BME-SB)

Head of Information Committee of Biomedical Engineering • University of Tehran

(UT-BME-SB)

Industry

Software Developer and Game Designer • OBEID EMPIRE, Gamification Company

Selected Open Source Projects

Scorio: Bayesian evaluation and ranking toolkit • GitHub PyPI

Python toolkit for Bayesian evaluation and ranking of stochastic responses

vllm-df11

Dfloat11 plugin for vLLM

kvq

Norm-Aware KVQuant

MedViz

Medical visualization tools, INVent Lab, CWRU

Thumbnail-Preserving Encryption

Department of Electrical Engineering and Computer Science, Oregon State University, Prof. Rakesh Bobba

Fesenjoon: Google Drive API management client • GitHub PyPI

Computer skills

Languages

References