Mohsen Hariri

Highlights

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Research interests

Machine Learning, Quantum Computing

Education

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

Research Focus: "Mathematical interpretation of deep networks", Supervisor: Prof. Vipin Chaudhary

B.Sc. 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

Research experience

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

Authors: M Hariri, A Samandar, M Hinczewski, V Chaudhary

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

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

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

DFloat11 compresses LLM weights losslessly by ~30% using dynamic-length floats, cutting GPU memory use without any accuracy loss. Supervisors: Vipin Chaudhary, Anshumali Shrivastava, Xia Hu

Quantize What Counts: More for Keys, Less for Values

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

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). Supervisor: Vipin Chaudhary

K^4: Online Log Anomaly Detection Via Unsupervised Typicality Learning

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

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. Supervisor: Vipin Chaudhary

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

Authors: 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

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. Supervisors: Vipin Chaudhary, Xia Hu

Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: Application to renal cancer CT scans

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

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

Masked-speech recognition using human and synthetic cloned speech

Authors: L Calandruccio, M Hariri, E Buss, V Chaudhary

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

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

Authors: 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

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. Supervisor: Satish Viswanath

Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2

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

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. Supervisor: Vipin Chaudhary, Xiaojuan Li

Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn’s Disease

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

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. Supervisor: Satish Viswanath

Spatial attention wavelon network (SpAWN) for survival-based risk stratification in kidney cancers via CT

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

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. Supervisor: Satish Viswanath

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

Authors: 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

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

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

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

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. Supervisor: Satish Viswanath

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

Authors: 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

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. Supervisor: Satish Viswanath

Virtual Reality as an Acute Pain Reliever During Laceration Repair in Emergency Departments: A Randomized Controlled Trial

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

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

Professional experience

Academic

AI Scientist • Department of Computer and Data Sciences, CWRU

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

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