Find Academic Supervisors

Behnaam AazhangView profile

Professor

• Signal and Data Processing
• Information Theory
• Neuroengineering
• +4 more

Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.

Monica LamView profile

Professor

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +4 more

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

Mohammed AledhariView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .

Ellie PavlickView profile

Assistant Professor

• Language Acquisition
• Grounded Language Learning
• Neural Language Models
• +7 more

Ellie Pavlick is an Assistant Professor of Computer Science and Linguistics at Brown University, where she leads the Language Understanding and Representation (LUNAR) Lab . Her work bridges computational linguistics and cognitive science, focusing on grounded language learning and the structural dynamics of neural language models. PhD from University of Pennsylvania (2017), specializing in paraphrasing and lexical semantics Collaborates with Brown's Robotics, Visual Computing labs, and Cognitive, Linguistic, and Psychological Sciences department Research interests center on cognitively-inspired approaches to language acquisition, analyzing how LLMs and humans encode abstract reasoning and compositional structures. Her lab explores: Grounded language learning in multimodal systems Emergence of compositional behavior in neural networks Interpretability frameworks for black-box AI Key publication areas include: Transformer mechanisms and cognitive modeling Formality and complexity in language systems Relational reasoning and multimodal integration Ellie teaches courses on computational linguistics and human-machine language processing, with a focus on synergies between AI and psychological science.

Shungeng ZhangView profile

Assistant Professor

• Distributed Systems
• Cloud Computing
• Cybersecurity
• +4 more

Shungeng Zhang serves as an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Louisiana State University (2021) and a B.E. in Computer Engineering from Huazhong University of Science and Technology (2014). His educational background includes: Ph.D. in Computer Science, Louisiana State University, 2021 B.E. in Computer Engineering, Huazhong University of Science and Technology, 2014 Dr. Zhang's research spans distributed systems, cloud computing, and cybersecurity with a focus on enhancing performance and scalability of web applications and IoT stream processing in cloud environments. He employs sophisticated timeline analysis and fine-grained monitoring to identify transient bottlenecks causing long-tail latency problems, addressing propagation effects in complex dependency chains among application components. His publication record (2017-2022) reveals consistent contributions to cloud systems performance, particularly in n-tier architectures, concurrency control, and latency optimization. Key themes include stream processing synchronization, fanout query performance, adaptive concurrency for SLO compliance, and mitigation of transient resource contention attacks across distributed systems. No scientific awards were mentioned in the provided information. Dr. Zhang actively seeks graduate students with strong computer systems backgrounds for research collaboration. His departmental service includes Faculty Assembly participation (2021-2022) and faculty interviewing duties. Professionally, he serves as a reviewer for ACM SoCC'23, ACM TOIT, SmartCom 2023, The Journal of Supercomputing, and ACM SoCC'22. Teaching responsibilities include AIST 4720 (Enterprise System Architectures), CSCI 1200 (Introduction to Computers and Programming), and graduate courses such as CSCI 8940 (Dissertation Research) and AIST 3310 (Advanced Networking). No dedicated research labs or teams were specified in the available documentation.

• Software Engineering
• Data Analytics
• Mining Software Repositories
• +5 more

Ahmed E. Hassan is a Professor and Canada Research Chair in Software Analytics at the School of Computing, Queen's University. He serves as NSERC RIM Industrial Research Chair and leads the Software Analysis and Intelligence Lab (SAIL). Dr. Hassan pioneered the Mining Software Repositories (MSR) conference and co-edited special issues in the IEEE Transactions on Software Engineering and the Journal of Empirical Software Engineering on MSR topics. Education Ph.D. in Computer Science, University of Waterloo (2005) MMath in Computer Science, University of Waterloo BMath in Computer Science, University of Waterloo His research focuses on software analytics, mining software repositories, and systems engineering. Projects at SAIL include analyzing version control systems, predicting software defects, and improving software quality through empirical methods. The lab's work spans software evolution, architecture analysis, and debugging of distributed systems. Dr. Hassan has taught courses like CISC 322: Software Architecture and CISC 880: Mining Software Engineering Data at Queen's University, University of Victoria, and University of Waterloo. His teaching philosophy emphasizes hands-on learning with tools like WEKA and R, and includes project-based assignments using MSR Challenge datasets. Scientific Awards NSERC RIM Industrial Research Chair in Software Engineering Canada Research Chair in Software Analytics He has industrial experience from RIM (Blackberry platform), IBM Research (Almaden Lab), and Nortel Networks. Dr. Hassan is a named inventor on patents in the US, Europe, Canada, Japan, and India. SAIL lab is funded by NSERC, ORF, CFI, and industry partners.

Silvia VignoliniView profile

Professor

• Biomaterials
• Sustainable Materials
• Biophysics
• +6 more

Prof. Silvia Vignolini is a leading researcher in sustainable and bio-inspired materials. Since January 2023, she has served as Director at the Max Planck Institute of Colloids and Interfaces , where she leads the department of Sustainable and Bio-inspired Materials . Her academic career includes a Lecturer in Physics at University College London (2013-2017) and a Professor of Biomaterials and Sustainability at the University of Cambridge (2020-2022). Education: University of Florence (Physics, PhD) Postdoctoral Research: University of Florence , University of Cambridge Her research bridges chemistry , soft matter physics , optics , and biology , focusing on the self-assembly of natural materials into functional architectures. She pioneered work on cellulose-based photonic materials with applications in displays, pigments, and radiative cooling. The selected articles highlight her work on cellulose nanocrystals , structural coloration , and sustainable fabrication techniques . Key trends include mechanochromic hydroxypropyl cellulose systems, bio-inspired light management, and applications in microalgae growth and bacterial symbiosis. Scientific Awards : Philip Leverhululme Prize (2019) ACS Lectureship in Sustainable Chemistry (2018) Ipazia Prize for Women in Science (2012) PhD Thesis Award (University of Florence, 2009) At the Max Planck Institute, her interdisciplinary research group explores bio-inspired design principles for sustainable materials, combining experimental and computational approaches to create functional materials from natural resources.

Yuxin ChenView profile

Professor

• statistics
• optimization
• reinforcement learning
• +5 more

Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .

Emmett WitchelView profile

Professor

• Computer Architecture
• Systems and Networking
• Security and Privacy
• +2 more

Emmett Witchel is a Professor of Computer Science at the University of Texas at Austin , with research spanning computer architecture, systems, networking, security, and privacy . His work focuses on low-level systems optimization and secure concurrent execution. Research Interests: Concurrent systems, secure execution environments, GPU integration, distributed systems. Teaching: CS 380L (Advanced Operating Systems), CS 371M (Mobile Computing). Scientific Awards: Runner-up Best Paper, ASPLOS 2013 Runner-up Award for Outstanding Research, USENIX Symposium 2012 IEEE Micro Top Pick Award 2007 ACM Honorable Mention 2004 George M. Sprowls Award, MIT EECS 2004 Recent Publications demonstrate leadership in CXL pod databases ( Tigon ), stateful serverless computing ( Boki ), SmartNIC-accelerated file systems ( LineFS ), and GPU security ( Telekine ). His work bridges hardware-software co-design and practical systems implementation.

Sarah ShirazyanView profile

Lecturer

• Technology Law and Policy
• Misinformation Governance
• Responsible AI Development
• +3 more

Sarah Shirazyan is a Lecturer in Law at Stanford Law School , where she designed the Interpol-Stanford Policy Lab. She also serves as Director and Head of Meta's GenAI Product Policy work, overseeing responsible AI development across Meta's platforms. Doctor of Juridical Sciences (J.S.D.) from Stanford Law School LL.M. from SOAS/UCL, University of London Her research focuses on the intersection of national security , public policy , and international law , particularly examining: Effectiveness of UN Security Council responses to WMD terrorism Framework for regional organization collaboration in WMD prevention Platform content policy development and misinformation governance Responsible AI implementation under legal frameworks Key publication themes include: Platform algorithmic accountability Media capture in digital domains Content policy development during crises UN Security Council institutional limitations Regional organization synergies in security Recipient of Gerald J. Lieberman Award for research, teaching, and community service Professional affiliations: Stanford Law School Stanford Center for International Security and Cooperation (CISAC) INTERPOL European Court of Human Rights Council of Europe United Nations

Paul Pu LiangView profile

Assistant Professor

• Multimodal Machine Learning
• Human-AI Interaction
• Generative AI
• +3 more

Paul Pu Liang is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Media Lab and Department of Electrical Engineering and Computer Science (EECS). He directs the Multisensory Intelligence research group, focusing on building AI systems that integrate diverse sensory inputs to enhance human-AI symbiosis. His work spans theoretical foundations, large-scale resources, and neural architectures for multisensory learning. Education: PhD in Machine Learning (Carnegie Mellon University), MS in Machine Learning (Carnegie Mellon), BS with University Honors in Computer Science and Neural Computation (Carnegie Mellon) Research Interests: Multimodal machine learning, human-AI interaction, clinical AI, generative models, and responsible deployment of AI systems Key Contributions: MultiBench, HEMM evaluation framework, CLIMB clinical data foundations, and multimodal transformer architectures Recent publications emphasize multimodal foundation models , clinical applications , and socially responsible AI . His work has been recognized with multiple best paper awards and fellowships from Siebel, Facebook, and other institutions. Scientific Awards Siebel Scholars Award Waibel Presidential Fellowship Facebook PhD Fellowship Center for ML and Health Fellowship Rising Stars in Data Science Four best paper awards Paul teaches courses on machine learning and multimodal AI at MIT and CMU. He mentors students across multiple programs including Media Arts & Sciences, EECS, and IDSS, with former advisees now at institutions like OpenAI, UC Berkeley, and Princeton.

Giorgio AscoliView profile

Professor

• Computational Neuroanatomy
• Neuroinformatics
• Neuronal Morphology
• +5 more

Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.

Thomas PeyrinView profile

Professor

• Symmetric Cryptography
• Cryptanalysis
• Lightweight Cryptography
• +5 more

Thomas Peyrin is a Full Professor at Nanyang Technological University (NTU) in Singapore, leading the Symmetric Key and Lightweight Cryptography Lab (SyLLab) since 2012. He co-founded TT-logic.ai, focusing on cryptographic innovations. His academic journey includes a Ph.D. in Cryptology from Orange Labs and University of Versailles (2008), a Master in Computer Science from MPRI (2005), and engineering training at CPE Lyon (2004). Education : Ph.D. (2008), MPRI Master (2005), EPFL Exchange (2003-2004), CPE Lyon (1999-2004) Career : Full Professor at NTU (2022-present), Associate Professor (2017-2022), Nanyang Assistant Professor and NRF Fellow (2012-2017), Research Fellow at NTU (2010-2012), Cryptography Expert at Ingenico (2008-2010) Thomas specializes in symmetric cryptography, cryptanalysis of hash functions and block ciphers, and machine learning applications to security. His work includes groundbreaking research on SHA-1 collisions, lightweight cryptographic primitives (e.g., GIFT, PHOTON), and automated cryptanalysis frameworks. Recent projects explore integrating AI with cryptographic verification and fault attack resistance. His publications span top conferences like CRYPTO, EUROCRYPT, and CHES, with awards including the FSE Test of Time Award (2025), FSE Best Paper (2023), and NRF Investigatorship (2022). He has advised numerous students in cryptographic research and co-founded SyLLab, a leading lab for symmetric cryptography innovation. Scientific Awards : Test of Time Award at FSE 2025 Best Paper Award at FSE 2023/ToSC NRF Investigatorship (2022) Lee Kuan Yew Postdoctoral Fellowship Best Paper Awards at FSE 2012 and ASIACRYPT 2007

Gianni FranchiView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Uncertainty Quantification
• +2 more

Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.

Leslie ValiantView profile

Professor

• Complexity Theory
• Machine Learning
• Parallel Computation
• +4 more

Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in Harvard University's School of Engineering and Applied Sciences, where he has held a faculty position since 1982. A foundational figure in theoretical computer science, his work bridges artificial and natural computational phenomena across multiple disciplines. His academic background includes education at: King's College, Cambridge Imperial College, London Ph.D. in Computer Science from Warwick University (1974) Valiant's research spans computational complexity , machine learning theory , parallel systems , and computational neuroscience . He pioneered the PAC (Probably Approximately Correct) learning framework that established computational learning theory as a rigorous field. His holographic algorithms work revealed deep connections between computational complexity and statistical physics, while his neuroidal model and evolvability theory provide computational explanations for cognitive processes and biological evolution. Current investigations focus on cortical computation primitives and knowledge infusion architectures. His publication trends show increasing integration of neuroscience with computational theory since 2010, with dominant themes in holographic computation (2006-2018), cortical modeling (2012-2018), and evolvability (2009-2017). The work consistently applies computational complexity analysis to biological and cognitive systems. Major recognitions include: Nevanlinna Prize (1986) for mathematical aspects of computer science Knuth Award (1997) for foundational algorithms contributions EATCS Award (2008) for theoretical computer science impact Turing Award (2010) for computational learning theory and complexity Fellowship in the Royal Society and National Academy of Sciences Valiant's research has been supported by NSF and international grants enabling cross-disciplinary work in computational neuroscience and evolutionary algorithms. While specific advisees aren't documented in source materials, his theoretical frameworks have shaped generations of researchers in machine learning and complexity theory. His current research group explores neuroidal architectures for cognitive computation, investigating how cortical circuits achieve robust information processing through in-circuit testing methodologies. Ongoing projects aim to identify fundamental computational primitives in neural systems and develop biologically inspired AI frameworks.