Find Academic Supervisors

Kuan-Hao HuangView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Multimodal Learning
• +3 more

Kuan-Hao Huang is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University , part of the College of Engineering . He leads the FLAIR Lab , focusing on developing trustworthy and generalizable language AI systems. Previously, he was a Postdoctoral Researcher at the University of Illinois Urbana-Champaign and earned his Ph.D. from UCLA. Education : Ph.D., Computer Science, UCLA (2023) M.S., National Taiwan University (2016) B.S., National Taiwan University (2014) Research Interests : His work centers on robustness of large language models (LLMs), multimodal concept learning, multilingual text understanding, and data-driven knowledge extraction. He emphasizes systems that are adaptable to new domains, languages, and modalities. Recent Trends in Publications : His recent work explores personalized AI agents, data augmentation techniques, event extraction benchmarks, and ethical AI alignment. The articles reflect a focus on bridging theory and application in NLP and multimodal systems. Awards : ACL 2023 Area Chair Award KDD Cup 2013/2015 Top Placements Taiwanese AI Thesis Honorable Mention (2016) Advising & Labs : He mentors a growing team at the FLAIR Lab, including PhD/MS students like Oscar Chew and Saransh Agrawal. His lab actively seeks motivated researchers in LLMs, multimodal learning, and multilingual NLP.

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +9 more

Ioannis Mitliagkas is an Associate Professor at the University of Montréal's Department of Computer Science and Operations Research, affiliated with the Faculty of Arts and Science. He is a core faculty member at Mila and a staff research scientist at Google DeepMind. His research focuses on machine learning, deep learning, optimization algorithms, and generative models. He holds the Canada CIFAR AI Chair and has led numerous projects on distributed training, causal discovery, and robust generalization. Education: While specific academic degrees are not explicitly listed in the text, his roles imply a PhD in Computer Science. His work bridges theoretical foundations and applied machine learning. Research Interests: Machine learning theory, optimization dynamics, generative models, out-of-distribution generalization, and causal inference. His work emphasizes scalable algorithms for deep learning and distributed systems. Awards: Canada CIFAR AI Chair (2018). Grants & Projects: Lead researcher on over 50 projects funded by CRSNG, MITACS, and others, including grants on optimization dynamics, causal discovery, and industrial applications (e.g., healthcare, energy, robotics). Students: Advised PhD and Master's students on topics like distributional shifts, generative models, and performative prediction.

• Cyber-security
• Internet of Things
• Machine Learning
• +3 more

Professor Amir Hussain is a faculty member at Edinburgh Napier University's School of Computing Engineering and the Built Environment. His expertise spans Cyber-security, Internet of Things (IoT), Machine Learning, and Medical Informatics. He is associated with the Centre for Cybersecurity, IoT and Cyberphysical Systems and has led projects such as a stretchable piezoionic sensor system for electromechanical sensing. His research integrates AI-driven solutions for healthcare, IoT security, and network optimization. Key projects include developing secure edge computing frameworks and post-quantum cryptographic systems. Collaborations involve interdisciplinary work on biomedical sensor networks and federated learning applications. His publications address challenges in face forgery detection, sepsis transcriptomics, and energy-efficient wireless networks. He contributes to both computational and medical domains, emphasizing privacy preservation and secure AI implementations.

• Machine Learning
• Privacy-Enhancing Technologies
• Biometrics
• +3 more

Dr. Mahshid Sadeghpour is a Lecturer in the School of Science at RMIT University, Australia. She specializes in Machine Unlearning, Explainable AI, Privacy-Enhancing Technologies, Biometrics, and Federated Learning. Her research focuses on security and privacy in biometric systems, particularly retinal recognition and template protection. She is open to supervising Masters and PhD students in topics like echo chamber detection using sentiment analysis. She has contributed to projects such as the GRETINA dataset for retinal image security assessments and has published on topics ranging from generative AI in academic writing to cryptanalysis of digital signature schemes. Her ORCID identifier is 0000-0002-7964-329X .

Tianlong ChenView profile

Assistant Professor

• Machine Learning
• Sparsity
• Robustness
• +5 more

Tianlong Chen is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he established the UNITES Lab (University of North Carolina, AI Trustworthiness, Efficiency, and for Science Group). He joined UNC in Fall 2024 after completing his Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin (2023) and serving as a postdoctoral researcher at MIT (CSAIL), Harvard University (BMI), and the Broad Institute of MIT & Harvard. His research focuses on building accurate, trustworthy, and efficient machine learning systems , with emphasis on: Sparsity and model compression techniques Robustness against adversarial challenges Learning-to-optimize methodologies Graph representation learning Diffusion models and generative AI Quantum computing applications Bioengineering and healthcare AI solutions His publications demonstrate strong trends in large language model optimization , efficient multimodal systems , and trustworthy AI frameworks , with frequent appearances at top venues (NeurIPS, ICML, CVPR, ICLR). Significant scientific honors include: IBM and Adobe Ph.D. Fellowships Best Paper Award at Learning on Graphs Conference 2022 AdvML Rising Star (2023) Amazon Research Award (2024) Two UNC Accelerating AI Awards (2025) NAIRR Pilot Award (2025) He leads 19+ advisees in the UNITES Lab and has secured research funding from Cisco, Amazon, and institutional grants. Current projects explore sparsity-aware architectures, LLM alignment, and efficient multimodal learning systems.

• Phonetics
• Phonology
• Morphology
• +7 more

Ruben van de Vijver is a Professor of Phonetics and Phonology at the Heinrich-Heine University Düsseldorf, Germany, within the Faculty of Arts and Humanities and Department of Linguistics. His office is located in Building 23.21, Room 04.048, with office hours held on Wednesdays from 15:00 to 16:00 and by appointment. His research centers on the sound structures of language, with primary interests in phonetics, phonology, morphology, and their interfaces. Key areas include laboratory phonology, metrical phonology, morphophonology, prosodic morphology, sociophonetics, and language acquisition. He investigates how phonetic detail influences phonological representations and how morphological knowledge interacts with phonological processes, particularly through experimental and computational approaches. Van de Vijver's recent publications (2023-2024) reveal a strong emphasis on discriminative learning models applied to linguistic phenomena, especially in Maltese and Kinyarwanda. His work bridges phonetics, phonology, and morphology using interdisciplinary methods from cognitive science and neuroscience, exploring word recognition, morphological acquisition, intonation variation, and neural correlates of language processing. He actively participates in professional communities as a member of the Deutsche Gesellschaft für Sprachwissenschaft (German Society for Linguistics), the Association for Laboratory Phonology, and GĦILM (International Association of Maltese Linguistics), reflecting his engagement with both theoretical frameworks and language-specific research.

Qing QuView profile

Assistant Professor

• Signal Processing
• Data Science
• Machine Learning
• +4 more

Qing Qu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan – Ann Arbor, with affiliations to the Michigan Institute for Data Science (MIDAS), the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM), and the Michigan Institute for Computational Discovery and Engineering (MICDE). He earned a B.E. from Tsinghua University (2011), a M.Sc. from Johns Hopkins University (2012), and a Ph.D. from Columbia University (2018) under the supervision of John Wright. From 2018–2020, he was a Moore-Sloan Fellow at NYU Center for Data Science. Research Focus: At the intersection of signal processing, data science, machine learning, and numerical optimization, with emphasis on deep representation learning and diffusion models. Awards: Microsoft Ph.D. Fellowship (2016), NSF Career Award (2022), Amazon AWS AI Award (2023), UM CHS Junior Faculty Award (2025), Google Research Scholar Award (2025), and Best Paper at NeurIPS’23 Workshop on Diffusion Models. Publications: Recent work explores generalization in diffusion models via Probability Flow Distance , low-dimensional subspace clustering, controllable image editing, and watermarking techniques. His research also investigates neural collapse theory and optimization landscapes in deep learning. Collaborations: Co-organizer of ICCV’25 and ICML’25 tutorials with experts like Yi Ma, Yuxin Chen, and Liyue Shen. Former students include Peng Wang, Yifu Lu, Jinfan Zhou, and Siyi Chen, who have joined institutions like Princeton, UChicago, Duke, and University of Macau. Grants: Funded by NSF, ONR, ARO, Amazon Research, KLA Corporation, MICDE, and MIDAS.

Adel BibiView profile

Research Fellow

• Machine Learning
• Computer Vision
• AI Safety
• +4 more

Adel Bibi is a prominent Research Fellow at the University of Oxford 's Department of Engineering Science, with concurrent roles at Kellogg College and the ELLIS Society . He serves as R&D Distinguished Advisor for Softserve , specializing in AI Safety through robustness certification and optimization. Academic Background: PhD in Electrical Engineering (4.0/4.0), KAUST (2020) MSc in Electrical Engineering (4.0/4.0), KAUST (2016) BSc in Electrical Engineering (3.99/4.0), Kuwait University (2014) His research focuses on Trustworthy AI through three main pillars: AI Safety : Specializing in robustness certification, alignment, and security of foundational models in vision/language Continual Learning : Developing efficient frameworks for model updates and domain adaptation Optimization : Creating novel approaches for training stability and resource allocation Recent publications demonstrate expertise in large language model security , interpretable architectures , and robust training , with a particular focus on mitigating risks in agentic systems. Scientific Recognition: Systemic AI Safety Grant (~$250,000) - UK AI Security Institute (2025) Google Gemma 2 Academic Program - $10,000 GCP Credit (2024) Amazon Research Award - Machine Learning Algorithms & Theory (2022) 4 Best Paper Awards (NeurIPS23, ICML23, CVPR22, 2018 Optimization & Big Data) Notable Area Chair Award - NeurIPS23 (top 8.1%) He actively mentors through: 13 PhD/MSc students at Oxford (including 4 graduated PhDs) 3 Unofficial PhD Mentees at KAUST/MIT Supervision of 6 Graduated MSc Students

Isabelle GuyonView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +3 more

Isabelle Guyon is a Full Professor at University Paris-Saclay and a Research Scientist at Google DeepMind since 2022. She is also a community lead for the open-source Codalab competitions project and President of ChaLearn, a non-profit organizing machine learning challenges. Her career spans academia, industry, and non-profit leadership, with affiliations at INRIA and the LISN laboratory. PhD in Paris under Gerard Dreyfus and Leon Personnaz Bell Labs researcher (1990s) Graduate of ESPCI Her research focuses on machine learning challenges and benchmarks across diverse domains including causality , computer vision , deep learning , and high energy physics . Recent projects explore power grid optimization , LLM-assisted paper reviewing , and systematic bias mitigation in AI applications. Key awards include the BBVA Frontier in Knowledge Award (2020), ICMI Ten-Year Impact Award (2023), and IEEE Best Paper Award (2023). She is also an ELLISS fellow and AMIA fellow .

Sanjeev AroraView profile

Professor

• Machine Learning Theory
• Natural Language Processing
• Theoretical Computer Science
• +4 more

Sanjeev Arora is the Charles C. Fitzmorris Professor in the Department of Computer Science at Princeton University, where he has been since 1994. He earned his Ph.D. from UC Berkeley in 1994 and holds a B.S. in Math with Computer Science from MIT. His research focuses on Machine Learning Theory , Natural Language Processing , and Computational Complexity . Education SB in Math with Computer Science, MIT (1990) PhD in Computer Science, UC Berkeley (1994) Recent research explores emergence of skill compositionality in LLMs, context-enhanced learning , and provably safe AI techniques . His lab has published 15 papers since 2023 on topics like automated theorem proving , visual reasoning , and mathematical reasoning in AI. Key scientific awards include: Fulkerson Prize (2012) ACM Prize in Computing (2011) Twin Gödel Prizes (2001, 2010) Packard Fellowship (1997) ACM Doctoral Dissertation Award (1995) He has advised numerous Ph.D. students including Subhash Khot and Tengyu Ma, and led the NSF-funded Center for Computational Intractability (2008-13). His work spans approximation algorithms , probabilistically checkable proofs , and AI model interpretability .

• theoretical aspects of cryptography
• computational assumptions
• theoretical aspects of machine learning
• +6 more

Mohammad Mahmoody is an Associate Professor in the Computer Science Department at the University of Virginia (UVA), where he conducts theoretical research at the intersection of cryptography and machine learning. His research focuses on understanding fundamental limitations in cryptography and machine learning through the lens of lower bounds, impossibility results, and security barriers. Key areas include cryptographic protocols, adversarial robustness in learning systems, and computational complexity. His recent publications show a strong trend toward bridging cryptographic techniques with machine learning security, particularly in understanding data poisoning attacks, robust learning under adversarial conditions, and the computational foundations of security guarantees. His work spans both theoretical computer science venues (CRYPTO, EUROCRYPT, TCC) and machine learning conferences (NeurIPS, ICML). He actively advises graduate students and postdocs, with a track record of mentoring PhD candidates through completion of their degrees. His service to the academic community includes extensive program committee work for major conferences in both cryptography and machine learning. Dr. Mahmoody teaches advanced courses including Theory of Computation, Algorithms, and Cryptography at both undergraduate and graduate levels, demonstrating his commitment to educating the next generation of theoretical computer scientists.

Julia HockenmaierView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Grounded Language Processing
• +4 more

Julia Hockenmaier is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Her research focuses on Natural Language Processing (NLP), particularly in grounded language processing, collaborative dialogue systems, and vision-language interfaces. She leads a research group exploring linguistically expressive yet computationally tractable grammars for wide-coverage parsing. Willett Faculty Scholar award recipient Published extensively in ACL, EMNLP, and JAIR venues Active in interdisciplinary research connecting NLP with computer vision and robotics Her recent work investigates the limitations of large language models in code understanding and the role of grounded dialogue systems in Minecraft environments. She has advised numerous PhD and MS students, including Alice Lai, Yonatan Bisk, and Rajarshi Haldar. Key article trends include semantic code search, multi-modal NLP, and grammar induction. Her scientific contributions span from computational linguistics to protein folding analysis with awards recognizing her faculty excellence.

• Data Mining
• Large Language Models (LLMs)
• Knowledge Graphs
• +7 more

Jing Gao is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University , West Lafayette, Indiana. She serves as the Director of Graduate Admissions and focuses on data mining, machine learning, and their applications in healthcare, biology, and societal systems. Research Interests Data veracity analysis and multi-source data integration Knowledge graphs and large language models Fairness, interpretability, and efficiency in AI Federated learning and transfer learning Applications in healthcare, education, and criminal justice Recent Article Trends Her work emphasizes large language models (LLMs) with innovations in knowledge editing, federated learning, and fairness-aware systems. Key themes include: Improving LLM efficiency via sparse adaptation and quantization Advancing privacy-preserving federated learning architectures Developing fairness-aware algorithms through counterfactual reasoning Integrating knowledge graphs for factuality validation Optimizing multi-agent reasoning and self-reflective confidence Technical Areas Her research spans computer engineering domains including: Signal & Image Processing Communications & Networking AI/ML Model Efficiency

Aditi RaghunathanView profile

Assistant Professor

• Machine Learning
• AI Safety
• Reliability of Large Language Models
• +7 more

Aditi Raghunathan is an Assistant Professor in the Computer Science Department at Carnegie Mellon University , with affiliations to the Machine Learning Department . She holds a PhD from Stanford University (2021) , advised by Percy Liang , and a B.Tech in Computer Science from IIT Madras (2016) . Her research focuses on advancing the scientific understanding of frontier models , addressing their reliability, safety, and unlearning challenges. Education PhD, Stanford University (2021) B.Tech, IIT Madras (2016) Research Themes Overtraining effects in LLMs Memorization sinks for unlearning Algorithmic creativity beyond next-token prediction Scalable frameworks for AI safety Her work at ICML 2025 revealed critical limitations in LLM pre-training, including catastrophic overtraining and imperfect unlearning due to entangled memorization-generalization circuits. She has developed seed-conditioning and multi-token learning to enhance structured diversity and creativity in models. Scientific Recognition includes: NSF CAREER Award Arthur Samuel Best Thesis Award at Stanford Google Research Scholar Forbes 30 Under 30 in Science She supervises PhD and Master’s students such as Jacob Mitchell Springer , Christina Baek , and Taeyoun Kim , and actively collaborates with CMU graduate students. Her teaching includes courses on Trustworthy AI and Graduate Artificial Intelligence .

Qin HuView profile

Assistant Professor

• Security and Privacy
• Blockchain
• Edge Computing
• +3 more

Dr. Qin Hu is an Assistant Professor in the Department of Computer Science at Georgia State University (GSU), with prior experience as an Assistant Professor at Indiana University–Purdue University Indianapolis (IUPUI). She holds a Ph.D. in Computer Science from The George Washington University (2019). Her research focuses on security and privacy, blockchain, edge computing, federated learning, and IoT, with a goal to secure interactive computing in wireless systems. She has received multiple accolades, including the Best Paper Award at WASA 2020 and NSF CRII funding for her work on blockchain-based distributed machine learning. Education: Ph.D., Computer Science, The George Washington University (2019) Affiliations: Georgia State University, Indiana University–Purdue University Indianapolis Her research interests emphasize securing mobile computing through mechanisms like federated learning and blockchain. She has contributed to journals such as IEEE Transactions on Mobile Computing and IEEE Transactions on Information Forensics and Security, and serves on editorial boards for Journal of Network and Computer Applications and High-Confidence Computing . She also chairs workshops and conferences like IEEE EUC 2022 and serves on TPC for IEEE INFOCOM and IEEE Blockchain. Dr. Hu has advised numerous students, including PhD candidates at GSU and Purdue University Indianapolis. Her NSF-funded project integrates blockchain with federated learning to enhance privacy in mobile crowd sensing. She has also held teaching roles, including the prestigious Indiana University Trustees Teaching Award in 2024.