Find Academic Supervisors

Ewan DunbarView profile

Assistant Professor

• Computational Linguistics
• Speech Perception
• Unsupervised Learning
• +3 more

Ewan Dunbar is an Assistant Professor of Computational Linguistics at the University of Toronto (downtown campus), affiliated with St. Michael's College. He holds appointments in the Department of French, Department of Linguistics, and Department of Computer Science. His research explores human speech perception, technological advancements in speech processing, and related areas such as unsupervised learning and language acquisition. Educational Background: While specific degrees aren't listed, his research and appointments indicate advanced training in linguistics and computational sciences. Research Interests: Dunbar focuses on how humans perceive speech, developing computational models of speech perception, and advancing unsupervised learning techniques. He leads the Perceptimat Research Group and organizes the Zero Resource Speech Challenge, a machine learning competition promoting unsupervised approaches to speech processing. Awards: He has received notable grants including the 2028 NSERC Discovery Grant, 2028 NSERC Discovery Launch Supplement, and 2023 Connaught New Researcher Award, all supporting his work on computational psycholinguistics and speech perception models. Advising & Grants: While specific student names aren't listed, his grants indicate involvement in mentoring undergraduates (e.g., 2024 NSERC Undergrad Studentship Grant). His labs focus on creating speech perception tools and benchmarks like Perceptimatic. Labs/Teams: Director of the Perceptimat Research Group, organizer of the Zero Resource Speech Challenge.

Kaspar RiesenView profile

Researcher

• Pattern Recognition
• Machine Learning
• Computer Vision
• +4 more

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

Farzad FarnoudView profile

Assistant Professor

• Information and Coding Theory
• Machine Learning
• Bioinformatics
• +3 more

Farzad Farnoud is an Assistant Professor in the Electrical and Computer Engineering and Computer Science Departments at the University of Virginia. His research focuses on information theory, coding theory, computational biology, and machine learning, with applications in DNA storage and genomic data analysis. He holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois (2013) and postdoctoral experience at Caltech. Education: B.S. in Electrical Engineering (Sharif University of Technology, 2006) M.S. in ECE (University of Toronto, 2008) M.S. in Mathematics (UIUC, 2012) Ph.D. in ECE (UIUC, 2013) Postdoc at Caltech (2013–2016) His research interests span information-theoretic analysis of biological datasets, coding for DNA storage, stochastic modeling of genomic mutations, and rank aggregation algorithms. Notable contributions include error-correcting codes for duplication channels and frameworks for metagenomic data analysis. Recent work includes studies on short-duplication error correction (2023), active ranking algorithms (2022), and heterogeneous rank aggregation (2020). He has received the Robert T. Chien Memorial Award (2013) and the IEEE Data Storage Best Student Paper Award (2014). Teaching includes courses on computational biology, statistical learning, and graphical models. He leads the Information Processing + Storage Lab, focusing on interdisciplinary challenges in data storage and analysis.

Josie ClowneyView profile

Associate Professor

• Neural Circuits
• Developmental Neuroscience
• Chemosensory Systems
• +3 more

Josie Clowney is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she has held faculty position since 2017. Her research investigates the genomic algorithms that construct neural circuits during development, using Drosophila as a model to study how chemosensory systems drive both instinctual behaviors and learning. She teaches Bio 172 and an upper-level seminar on cellular diversity and scientific writing, and mentors graduate students through MCDB, CMB, NGP, and BIOINF PhD programs. Her educational background includes: Ph.D. in Biomedical Sciences (2012) from the University of California, San Francisco B.S. in Cellular and Molecular Biology (2005) from the University of Michigan, where she conducted research with Cunming Duan Clowney's research centers on understanding how definitive neuronal parameters are encoded in genomic information and translated into cellular architectures. Her lab hypothesizes that developmental algorithms for learning circuits versus instinctual circuits differ fundamentally in their genomic requirements, with chemosensory circuits serving as key models. Using fruit flies for their tractable brain organization, her work bridges computational principles and biological implementation to uncover universal brain organization rules. Analysis of her 15 most recent publications (2016-2025) reveals consistent focus on Drosophila mushroom body development, neural sexual differentiation, and spatial constraints in circuit formation. Key themes include non-deterministic mechanisms diversifying cell surface expression, chromatin dynamics in circadian regulation, and how input density tunes sensory responses. Her work integrates genomics, neuroanatomy, and behavior to model how compact genomic information generates complex neural architectures. No scientific awards were mentioned in the provided text. Dr. Clowney advises graduate students through multiple PhD programs at the University of Michigan, though specific student names and grant details are not provided in the source material. Her teaching includes foundational undergraduate coursework and advanced seminars emphasizing scientific writing. The active publication record spanning 2016-2025 indicates sustained research funding supporting her lab's investigations into neural circuit development. The Clowney Lab, housed in the Biological Sciences Building (4218 BSB), employs Drosophila genetics and neuroanatomical techniques to dissect developmental algorithms of brain wiring. Current projects explore how spatial constraints structure learning circuits, mechanisms of neural sexual differentiation, and the genomic encoding of circuit diversity. The lab collaborates within Michigan's neuroscience community through the Program in Biology and participates in interdisciplinary initiatives studying brain evolution and function.

Manos KapritsosView profile

Associate Professor

• Distributed Systems
• Formal Verification
• Fault-Tolerant Replication
• +3 more

Manos Kapritsos is an Associate Professor in the Department of Computer Science & Engineering at the University of Michigan, Ann Arbor. His research focuses on increasing the reliability of distributed systems through formal verification, fault-tolerant replication, and automated proof techniques. He has contributed to projects such as Aegean (replication beyond client-server models), I4 (automated inductive invariant inference), and Armada (concurrent code verification). His work emphasizes practical applications of formal methods to ensure system correctness and performance. Education details are not explicitly provided in the text, but his career trajectory suggests advanced academic training in computer science. His research interests include distributed systems, formal verification of protocols, and high-performance systems. Notable awards include the NSF CAREER Award (2021), Google Faculty Award (2017), and Distinguished Paper Awards at PLDI 2020 and USENIX Security 2017. Teaching includes courses on formal verification, operating systems, and distributed systems, reflecting his expertise in systems software. He has advised students through his GLaDOS research group, though specific advisee names are not listed. Key grants include NSF FMitF and Large grants to advance verification techniques for distributed systems. Recent articles focus on automating proofs for undecidable protocols (Basilisk), efficient replication communication (Scrooge), and formal latency analysis (Performal). His work bridges theory and practice, aiming to simplify end-to-end verification of complex systems.

Stephanie RièsView profile

Associate Professor

• Cognitive Neuroscience
• Psycholinguistics
• Speech and Language Disorders
• +4 more

Stephanie Riès is an Associate Professor in the Department of Speech, Language, and Hearing Sciences at San Diego State University, where she directs the Laboratory for the Brain Dynamics of Language. Her research specializes in the cognitive neuroscience of human language, examining brain dynamics in language production, control processes, and neural reorganization following brain disorders. Education: BS in Biology from University of Rennes 1 MS in Cognitive Neuroscience from Aix-Marseille University MS in Cellular Biology and Animal Physiology from University of Rennes 1 PhD in Cognitive Neuroscience from Aix-Marseille University Her work integrates electrophysiology (EEG, EMG) and neuroimaging to study language production mechanisms and compensatory plasticity in conditions like stroke, epilepsy, and brain tumors. Key research areas include bilingual interference resolution, speech monitoring mechanisms, and neural reorganization patterns. Recent publications focus on neural hierarchies in speech production, neuroplasticity in language recovery, and advanced brain mapping techniques. Article analyses reveal strong emphasis on electrophysiological methods (sEEG, ERP), bilingual cognition, and clinical translation of neuroscience findings. Dr. Riès leads significant research initiatives through her laboratory, investigating brain-behavior relationships using intracranial recordings and developing novel approaches for speech activity detection across neurological populations.

Cláudia ReisView profile

Assistant Professor

• Coastal Engineering
• Structural Engineering
• Natural Hazards Engineering
• +5 more

Cláudia Reis is an Assistant Professor at Lehigh University with a joint appointment in the Department of Civil & Environmental Engineering and the Institute for Cyber-Physical Infrastructure and Energy (I-CPIE) within the P.C. Rossin College of Engineering and Applied Science. She teaches courses in Coastal and Offshore Infrastructure Engineering, Structural Analysis I, and Finite Element Method in Structural Engineering. Her research focuses on fluids-solids interaction in coastal and offshore engineering, multi-risk management, and multi-scale/multi-physics modeling of critical infrastructure in multi-hazard regions. Dr. Reis leads the NHERI Lehigh Experimental Facility as a Co-Principal Investigator and chairs the Advanced Technology for Large Structural Systems (ATLSS) Engineering Research Center. She has secured grants including a PITA Grant ('Resiliency of Coastal Infrastructure') and a PEER Transportation Systems Research Program project ('Cascading Seismic and Tsunami Loads for the Design of Open Wharves'). Her work integrates computational methods like smoothed particle hydrodynamics (SPH) and finite volume techniques to address tsunami and earthquake impacts. She contributes to national/international committees such as NHERI and FIB, and serves on editorial boards and grant review panels. Dr. Reis' research emphasizes infrastructure resilience, cascading hazards, and exascale computing applications for natural hazard mitigation.

Kazuya KoyamaView profile

Professor

• Theoretical Cosmology
• Dark Energy
• Modified Gravity
• +4 more

Professor Kazuya Koyama is a leading scholar in theoretical cosmology at the University of Portsmouth's Institute of Cosmology and Gravitation (ICG), part of the Faculty of Technology. His research focuses on understanding the origin of cosmic structure and the late-time acceleration of the Universe, with a particular emphasis on modified gravity theories as alternatives to dark energy. He has held prestigious awards including the Philippe Leverhulme Prize (2009) and the Young Scientist Award from the Physical Society of Japan (2010). His work is funded by major grants like the ERC Consolidator Grant 'Cosmological Tests of Gravity' and previously the ERC Starting Grant 'Modified Gravity Models as an Alternative to Dark Energy'. Education: PhD in Theoretical Physics from Kyoto University (2002). Key collaborations include the Euclid space mission and joint projects with Kyoto University, recognized by the Daiwa-Adrian Prize (2010). His research integrates advanced numerical simulations (e.g., MGCAMB, COLA methods) and observational analysis for cosmological probes like the Euclid telescope and SDSS surveys. Key themes include testing general relativity on cosmological scales, emulating gravity models, and analyzing large-scale structure data. Research Interests: Structure formation, cosmic acceleration, modified gravity, dark energy, stochastic inflation, and cosmological parameter inference. Current projects emphasize Euclid mission preparations, nonlinear structure simulations, and constraining gravity theories using redshift-space distortions and weak lensing. Grants & Collaborations: ERC Consolidator Grant (2014–present), STFC support, Royal Society International Joint Project with Kyoto University. Active in international initiatives like the Novel Probes Project and Euclid Consortium, focusing on gravity tests and cosmological parameter estimation. Labs/Teams: Leads research groups at ICG on cosmological tests of gravity, large-scale structure analysis, and modified gravity simulations. Collaborates with global teams on Euclid mission data exploitation and N-body simulation development.

• Computer Operating Systems
• Computer Systems Analysis
• Distributed Systems
• +5 more

Liuba Shrira is a Professor of Computer Science at Brandeis University, affiliated with the Michtom School of Computer Science and the Benjamin and Mae Volen National Center for Complex Systems. Her research focuses on distributed systems, storage systems, blockchain technology, concurrent programming, and system architectures. She holds a Ph.D., M.S., and B.S. from the Technion – Israel Institute of Technology. Her work emphasizes reliable and highly available systems, including innovations in snapshot management, transactional memory, and adversarial cross-chain commerce. She has been recognized with awards such as the ACM Distinguished Scientist (2009), Lady Davis Fellowship (2010-2011), and a Best Paper Award (2020). Her research has been supported by grants from the National Science Foundation and other institutions. Recent publications highlight advancements in optimistic concurrency control, blockchain interoperability, and modular past-state systems. Shrira has also contributed to middleware design and distributed computing frameworks, with applications in both academic and industry settings.

• Information Theory
• Active Learning
• Decentralized Learning
• +4 more

Tara Javidi holds the Jerzy (George) Lewak Endowed Chair and is a Professor in the Department of Electrical and Computer Engineering and Halicioglu Data Science at the University of California San Diego (UCSD). She leads multiple initiatives, including serving as Founding CTO of KavAI, Co-Director of the Center for Machine Intelligence, Computing and Security, and Co-Principal Investigator (CoPI) of the NSF AI Institute TILOS. Her research focuses on stochastic analysis, design, and control of information systems, emphasizing active learning, decentralized optimization, and wireless networks. Key areas include information acquisition/utilization, stochastic control, and AI-driven communication solutions. Her work bridges theoretical foundations and practical implementations, such as drone systems for information gathering (via detecdrone.ucsd.edu) and optical data center networking. Notable contributions include end-to-end scheduling for all-optical data centers and hybrid wireless-optical architectures. Javidi is an IEEE Fellow and has received significant grants, including leading UCSD’s Schmidt AI in Science Postdoctoral Fellowship program. She actively collaborates with industry and academia, with a focus on next-generation wireless networks and decentralized systems. Education: Ph.D. in Electrical Engineering (implied from title). Affiliations: IEEE Journal of Selected Areas in Information Theory (Editor-in-Chief), CALIT2, CNS, and TILOS. Grants: NSF AI Institute TILOS ($20M over 5 years), Schmidt AI Fellowship program. Her research group emphasizes both theoretical rigor (e.g., sequential hypothesis testing) and practical testing, with applications in service drones, cognitive networks, and federated learning. Recent articles highlight advancements in optical networking, secure communication, and distributed learning protocols. Awards: IEEE Fellow, Jerzy Lewak Chair. Labs/Teams: Center for Machine Intelligence, TILOS Institute, KavAI, and UCSD’s AI in Science initiatives.

• Computer Vision
• Machine Learning
• Image and Video Processing
• +3 more

Fouad Khelifi is an Associate Professor in the Department of Computer and Information Sciences at Northumbria University. His research focuses on computer vision, machine learning, image/video processing, biometrics, multimedia forensics, and medical image analysis. He obtained his PhD in Computing Science from Queen's University Belfast (2007) and held prior research roles at the University of Bradford (2007–2009) before joining Northumbria in 2010. He supervises PhD students in cybersecurity applications and palm-vein recognition systems. Education: PhD in Computing Science, Queen's University Belfast (2004–2007) Fellow of the Higher Education Academy (FHEA, 2014) Research Interests: Khelifi’s work spans advanced deep learning techniques for medical imaging (e.g., cancer detection, retinal disease analysis), source camera identification in digital forensics, and biometric authentication systems. He develops novel algorithms for feature extraction, fusion networks, and transformer-based models in healthcare and multimedia security. Advising: Supervising Egallekanda Perera (PhD, 2019–2025): Efficient Keypoint-based Palm-vein Recognition Co-supervising Ikechukwu Ikpeama (PhD, 2024–): Cybersecurity for Industrial Control Systems Labs/Teams: Active in Northumbria’s Digital Media and Systems Research groups, contributing to interdisciplinary projects in AI-driven medical imaging and multimedia forensics.

Colleen BaileyView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

Rosanne RademakerView profile

Research Professor

• Perception
• Working Memory
• Attention
• +3 more

Dr. Rosanne Rademaker is a Research Professor and Group Leader at the Rademaker Lab, part of the Ernst Strüngmann Institute (ESI) in Frankfurt, Germany, affiliated with Goethe University’s Department of Psychology. Her research focuses on understanding how sensation and cognition interact to shape human perception, particularly in visual working memory, attention, and physiological arousal states. Her lab employs behavioral, computational, and neuroimaging techniques (fMRI, M/EEG) to explore how the brain balances perceptual input with stored memories. In addition to foundational work on memory and attention, the lab investigates context effects on perception, motor-output impacts on visual processing, and computational neural principles. Rosanne emphasizes collaborative, fun science, fostering an inclusive environment through outreach and international collaborations. Key recent work includes studies on categorical representations in the visual hierarchy and neural dynamics during memory recall. Lab Members: Giuliana Giorjiani (PhD), Noa Noelle Krause (MSc), Amit Rawal (PhD), Maria Servetnik (PhD), Nursima Ünver Aydingül (PhD). Grants & Collaborations: Mishal Qubad’s “Junior Clinician Scientist” grant on schizophrenia visual maps, international collaborations with Toronto and the Max Planck School of Cognition. Teaching: Lectures on “Introduction to Cognitive Psychology” at Goethe University. Publications highlight her work in Nature Neuroscience , eLife , and Journal of Cognitive Neuroscience , with over 30 peer-reviewed articles. The lab actively engages in conferences (VSS, ECVP) and hosts annual retreats to promote scientific exchange.

Wenwen WangView profile

Associate Professor

• Computer Systems
• Compiler Design
• Computer Architecture
• +4 more

Wenwen Wang is an Associate Professor in the School of Computing at the University of Georgia's Franklin College of Arts & Sciences. His research focuses on computer systems, compiler design, and embedded systems security. He holds a Ph.D. in Computer Science from the University of Chinese Academy of Sciences (2014). Education: Ph.D., Computer Science, University of Chinese Academy of Sciences, 2014 His research emphasizes dynamic binary translation, compiler optimization, and secure embedded systems. Notable contributions include frameworks like JavART (JIT compiler optimization) and BSan (memory error detection). He received the 2021 M. G. Michael Award for Sciences from the Franklin College. Wang has secured two NSF grants totaling $1.2 million, including CSR: Small grants for FALCON (2023–2027) and Modernizing Dynamic Binary Translation Systems (2023–2027). He advises three graduate students: Ruili Fang, Yage Hu, and Boyang Yi. His work addresses challenges in cross-architecture virtualization, GPU-based graph computing, and hardware-triggered security mechanisms. Recent projects include Liberator (GPU graph processing) and InvisiGuard (embedded device integrity).

Jennifer TangView profile

Research Fellow

• information theory
• prediction and estimation
• quantization and data compression
• +4 more

Jennifer Tang is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), holding dual appointments in the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). She conducts her research under Professor Ali Jadbabaie, focusing on interdisciplinary problems at the intersection of information theory, network science, and social dynamics. Her position is temporary as she actively seeks a permanent academic role through the 2025 job market. Her academic credentials include: Ph.D. in Electrical Engineering and Computer Science from MIT, advised by Professor Yury Polyanskiy Bachelor of Science in Engineering (B.S.E.) in Electrical Engineering from Princeton University, with independent work supervised by Paul Cuff Dr. Tang's research program centers on theoretical and applied aspects of information theory, including channel capacity, quantization, and data compression. She investigates prediction and estimation in high-dimensional settings, data analytics for complex systems, and mathematical modeling of social dynamics and inference in multi-agent networks. Her work employs tools from statistics, optimization, and network theory to address challenges in communication, decision-making, and societal systems, with particular emphasis on opinion dynamics under social pressure and efficient representation of probability distributions. Analysis of her publication record reveals consistent contributions to information-theoretic limits, social network modeling, and compression techniques. Her works frequently appear in top venues like IEEE Transactions on Information Theory and major conferences (ISIT, CDC, ACC), demonstrating expertise in bridging theoretical foundations with real-world applications in networked systems and societal challenges. Her scientific achievements have been recognized with: Best Student Paper Award at IEEE International Symposium on Information Theory (ISIT) 2022 Best Student Paper Award at IEEE Machine Learning for Signal Processing (MLSP) 2022 Student Competition Winner at the Shannon Centennial Celebration Dr. Tang maintains an active teaching portfolio, having served as instructor for MIT 1.022: Introduction to Network Models (Spring 2025) and teaching assistant for multiple core courses including 6.008 (Introduction to Inference), 6.041/6.431 (Probabilistic Systems Analysis), 6.437 (Inference and Information), and 6.439 (Statistics, Computation and Applications). She also contributed to the MIT Women's Technology Program as a Mathematics Instructor during summer 2017. Her research is embedded within MIT's Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems, and Society (IDSS), two premier interdisciplinary laboratories fostering collaboration on data-driven decision-making, societal challenges, and foundational theory in information and systems.