Find Academic Supervisors

• Algorithm fairness
• Explainable AI
• Trustworthy AI
• +7 more

Eliana Pastor is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . She teaches courses including Explainable and Trustworthy AI (Computer Engineering) and Business Intelligence for Big Data (Management Engineering) across academic years 2023-2025. Scientific branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC sectors: Algorithms, Artificial Intelligence, Machine Learning, Software Engineering, Web Systems Her research focuses on Algorithm Fairness , Explainable AI , and Trustworthy AI , with applications in speech processing, computer vision, and ethical AI systems. She leads the commercial research project Root Cause Analysis in Mechatronic Systems via Pattern Recognition and Causal AI (2024-2025) and supervises PhD students Eleonora Poeta (Safe and Trustworthy AI) and Alkis Koudounas (Speech Foundation Models). Recent publications address fairness in speech models, video LLMs for zero-shot summarization, and Kolmogorov-Arnold Networks for language understanding Collaborates with DBDM - Database and Data Mining Group (DAUIN) on AI ethics and data science projects

Jyothi VeduradaView profile

Assistant Professor

• Compilers
• Program Analysis
• High-Performance Computing
• +3 more

Jyothi Vedurada is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Her research focuses on compilers, program analysis, high-performance computing (HPC), and software engineering. Prior to joining IIT Hyderabad, she was a postdoctoral researcher at Microsoft Research Lab in Bangalore and worked as a Software Engineer at Hewlett Packard in Chennai. Her research spans four major areas: Compiler Optimizations : Developing global compiler optimizations for CPU-GPU heterogeneous systems to enhance performance across varied hardware environments. ML for PL : Leveraging AI to overcome scalability and semantic challenges in program analysis, with applications in bug detection, API misuse recommendation, and algorithm description generation for Jupyter notebooks. Parallelization : Designing efficient GPU algorithms for tasks like Approximate Nearest Neighbour search and subgraph isomorphism optimization, alongside developing the Tensor Transposition Library for GPUs (TTLG). Concurrency Testing : Creating systematic testing frameworks for concurrent systems, including language-agnostic libraries and mock storage systems to validate weak isolation levels in databases. Scientific Awards & Recognition : Distinguished Artifact Award at ECOOP 2025 Google Research exploreCSR Award (2021) Tata Consultancy Services PhD Fellowship ACM SIGAI, SIGPLAN, and IEEE Travel Grants Advising & Collaborations : Supervising 11 PhD and MTech students, including Soumik Kumar Basu and Karthik V, with projects spanning compiler optimizations, HPC, and AI-driven software analysis. Collaborating with institutions like Microsoft Research and IIT Madras.

Nathan UrbanView profile

Professor

• Neuroscience
• Olfactory system
• Neuronal diversity
• +4 more

Nathan Urban is a Professor and currently serves as the Provost and Senior Vice President for Academic Affairs at Lehigh University. His research focuses on understanding the physiological and circuit mechanisms underlying the functional and computational properties of brain neuronal networks, with particular emphasis on the olfactory system. Dr. Urban's research interests span multiple areas of neuroscience including: Neuronal diversity and functional differences between cell types Olfactory navigation and algorithms used by animals to solve odor-based navigation problems Neuronal modeling using both statistical and mechanistic approaches Neuronal reliability and the role of variability in neural processing Olfactory plasticity and experience-dependent changes in neural circuitry His lab has made significant contributions to understanding how the olfactory system processes information, with research spanning from detailed anatomical and physiological properties of cells and synapses to computational models of neural circuits. Dr. Urban leads the Urban Lab, which has established resources like NeuroElectro.org, a public database of neuronal properties. Through his extensive publication record spanning decades, Dr. Urban has explored questions about cell types, diversity within cell populations, and the mechanisms underlying olfactory navigation. His recent work has increasingly focused on understanding neural circuit mechanisms in the context of neurodevelopmental disorders, particularly autism spectrum disorders, as evidenced by his publications examining sensory processing abnormalities in ASD models. Dr. Urban has mentored numerous students and postdocs, providing an environment where they can develop technical and intellectual skills to become successful scientists. His research has been supported by various funding sources including NSF, and he continues to actively contribute to the field with publications in top neuroscience journals through 2024.

Andrea BrunelloView profile

Assistant Professor

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

Andrea Brunello serves as an Assistant Professor (tenure-track) at the Department of Humanities and Cultural Heritage (DIUM) of the University of Udine, while maintaining a dual affiliation with the Department of Mathematics, Computer Science and Physics (DMIF) where he participates in the Data Science and Automatic Verification Laboratory. He holds a prominent position on the board of directors for the AI4CH Initiative, reflecting his unique bridging of technical AI development and humanities scholarship. His educational foundation includes a Computer Science degree earned through an international program between the University of Udine and Austria's University of Klagenfurt, followed by a PhD in Computer Science, Mathematics, and Physics from Udine. His doctoral research included a significant international component at the University of Western Australia, where he specialized in temporal logic and formal methods. Dr. Brunello's research program demonstrates remarkable interdisciplinary reach: Technical AI Development: Specializing in machine/deep learning architectures with emphasis on model interpretability (XAI), neural network applications, and temporal data processing Domain Applications: Medical diagnostics, intelligent sensor systems, localization technologies, and digital humanities projects Data Infrastructure: Expertise spanning relational and NoSQL databases, data warehousing, and decision support systems Theoretical Contributions: Work on symbolic/subsymbolic AI integration and spatio-temporal data modeling His professional engagements extend beyond academia through substantial collaborations with industry partners including u-blox, SAL Silicon Austria Labs, GAP srlu, and beanTech. He regularly conducts specialized training for non-academic audiences on data integration, decision support systems, and explainable AI implementation. As an active PeerJ Editor & Reviewer with over 900 contribution points, he maintains strong connections with the international computer science research community. Dr. Brunello directs his efforts through the Data Science and Automatic Verification Laboratory at DMIF and the interdisciplinary AI4CH Initiative, creating pathways for meaningful technology transfer between humanities scholarship and advanced computational methods.

• Neurotoxicology
• Developmental Toxicology
• Nanotoxicology
• +3 more

Dr. Julia Liebing serves as a Researcher in the Neurotoxicology and Chemosensation group at the Leibniz Research Centre for Working Environment and Human Factors (IfADo), affiliated with Technical University of Dortmund. Her work focuses on advanced in vitro methodologies for assessing neurotoxic and developmental effects of chemicals, with significant contributions to nanosafety data standardization and transcriptome-based toxicology. Her primary research areas include Neurotoxicology (particularly mechanisms of organophosphate and acrylamide neurotoxicity), Developmental Toxicology (stem cell-based assays for chemical safety evaluation), and Nanotoxicology (metadata frameworks for reproducible nanosafety studies). She employs cutting-edge techniques such as micropatterned neuronal networks, transcriptome analysis, and microfluidic co-culture systems to investigate chemical impacts on neuronal function and development. Analysis of her 14 publications (2010-2022) reveals evolving expertise from foundational neurotoxicity screening of vehicle emissions toward sophisticated nanosafety data infrastructure and transcriptome-based classifiers for developmental toxicants. Her work consistently bridges toxicology with data science and bioengineering, emphasizing reproducibility through standardized metadata and modular assay design. As part of Prof. Dr. Jan G. Hengstler's research team, Dr. Liebing contributes to IfADo's mission of addressing occupational and environmental health risks through interdisciplinary collaboration. The Neurotoxicology and Chemosensation group utilizes specialized in vitro platforms including stem cell-derived neuronal models and microfluidic systems for comprehensive neurotoxicity assessment.

• Big Data
• Data Mining
• Machine Learning
• +2 more

Xiang Zhang is an Associate Professor at the College of Information Sciences and Technology, The Pennsylvania State University, focusing on big data, data mining, machine learning, and biomedical informatics. His work develops algorithms for analyzing large-scale social, biological, and medical datasets using graph-based methods. Education: Ph.D. in Computer Science (2011), University of North Carolina at Chapel Hill Research Interests: Xiang Zhang's research spans graph neural networks, dynamic graph analysis, and biomedical data mining. He specializes in explainable AI, robust learning, and topology-aware models for imbalanced data. His group investigates security in graph models, synthetic data generation, and cross-domain adaptation. Publications Trends: Recent work emphasizes explainable dynamic graph neural networks, adversarial attacks on LLMs, and robust contrastive learning. His team explores topology-level imbalances, prompt attacks, and causal relation modeling in temporal systems. Scientific Awards: Best Research Paper Award at SIGKDD 2008 Best Student Paper Award at ICDE 2008 Best Paper Candidate at ICDM 2018 and ICDM 2012 SIGKDD Dissertation Award Honorable Mention (2012) Advising: He mentors a group of Ph.D. students including Zongyu Wu, Minghua Lin, and Junjie Xu, with alumni securing faculty positions at Florida International University and North Carolina State University.

Michael T. JohnsonView profile

Professor

• Speech and Signal Processing
• Machine Learning
• Bioacoustics
• +4 more

Dr. Michael T. Johnson is a Professor and Associate Dean for Undergraduate Education and Student Success at the University of Kentucky College of Engineering . Previously, he served as Professor and Chair at UKY (2016–present), Full Professor at Marquette University (2013–2016), and in progressive academic roles from Assistant to Associate Professor at Marquette (2000–2013). PhD in Electrical and Computer Engineering, Purdue University (2000) M.S. in Electrical Engineering, University of Texas at San Antonio (1994) B.S. in Engineering (EE concentration) and Computer Science, LeTourneau University (1989–1990) His research spans speech processing , machine learning , bioacoustics , and dynamical systems modeling . Key projects include electromagnetic articulography (EMA) for language learning, acoustic-to-articulatory inversion , and bioacoustic analysis of animal vocalizations (tigers, belugas, elephants). Collaborative work with institutions like Tsinghua University and the Georgia Aquarium highlights his interdisciplinary focus. Recent publications emphasize deep learning for speech enhancement (CNN-based temporal envelope reconstruction), dysarthric speech synthesis , and cross-lingual speaker identification . His 15 most recent articles (2016–2024) span machine learning, signal processing, and bioacoustics, with subfields including neural networks, articulatory modeling, and pandemic preparedness analysis. Scientific awards include Eta Kappa Nu (HKN) Teaching Awards (2005, 2014). He has mentored numerous PhD/Masters students at University of Kentucky and Marquette University, with theses on topics like acoustic-to-articulatory inversion , EMA databases , and bioacoustic classification . His work also involves virtual reality welding systems and nonlinear speech enhancement via phase space reconstruction.

• Machine Learning
• Deep Neural Networks
• Video Analytics
• +4 more

Daniel Kang is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois. His research focuses on machine learning systems, cybersecurity for AI models, and database optimization for unstructured data. He specializes in developing robust systems for large language models (LLMs), including defenses against adversarial attacks and benchmarking frameworks for AI agents. His work bridges machine learning and systems research, addressing challenges such as prompt injection vulnerabilities, zero-day exploit mitigation, and privacy-preserving inference via zero-knowledge proofs. He has contributed to tools like LEAP for processing unstructured data and AIDB for ML-driven databases. Key collaborations include studies on AI safety, vulnerability exploitation, and ethical AI evaluation. His research outputs emphasize practical applications of ML systems in cybersecurity, data engineering, and interdisciplinary domains like social science analytics.

• Database Systems
• Distributed Systems
• Formal Verification
• +3 more

Dr. Si Liu is a researcher at ETH Zürich's Institut für Informationssicherheit, specializing in distributed systems and database verification. His research focuses on developing formal methods to ensure consistency and isolation guarantees in database transactions, DNS security, and neural network verification. His work bridges theoretical computer science with practical system design, creating tools like Plume for black-box isolation checking and techniques for compositional DNS attack analysis. Recent projects include NOC-NOC for optimal distributed transactions and abstraction-based DNN verification. Dr. Liu has contributed significantly to protocol design for internet architectures and database concurrency models, with publications spanning top conferences including EuroSys, OSDI, and USENIX Security.

Maitreyee RoyView profile

Associate Professor

• Optical Physics
• Quantum Dots
• Contact Lens
• +6 more

Associate Professor Maitreyee Roy is a researcher at the School of Optometry and Vision Science, University of New South Wales (UNSW Sydney). She specializes in Optical Physics, Quantum Dots, 3D Optical Imaging, and Artificial Neural Networks with applications in biomedical instrumentation, nanophotonics, and optometry/ophthalmology. Her research includes: Developing novel imaging technologies like Full-Field Optical Coherence Microscopy Investigating blue-blocking lens effects on visual/non-visual systems Creating AI-driven ocular disease detection systems Visualizing tear film dynamics using non-toxic quantum dots 3D reconstruction of OCT images for glaucoma diagnosis Recent publications focus on machine learning applications for glaucoma staging, quantum dot tear film imaging, and 3D medical visualization techniques. She collaborates with international labs at UC Berkeley, Tokyo Tech, and Mexico's INAOE. Awards: NMI World Metrology Day Outstanding Achievement (2013, 2011) ProSciTech Trans-Tasman Award (2006) Fellow of Optical Society of America JSPS Invitation Fellowship (2004) She supervises postgraduate and undergraduate students in projects related to optical coherence tomography, artificial intelligence applications, and quantum dot bioimaging. Professional memberships include Optical Society of America, SPIE, and Australian Standards Committee for eye protection.

• Information Retrieval
• Data Analytics
• Machine Learning
• +9 more

Shane Culpepper is an Adjunct Professor in the School of Computing Technologies at RMIT University, where he also serves as Director of the Centre for Information Discovery and Data Analytics and a Vice-Chancellor's Principal Research Fellow. His research focuses on building efficient search systems, measuring answer quality, and human-driven data science transformation of heterogeneous data. Key areas include algorithms, machine learning, distributed computing, and statistical modeling. Teaching interests span space-efficient data structures, data compression, bioinformatics, natural language processing, and combinatorics. His research interests include information retrieval, artificial intelligence, and computational theory. Supervision projects include topics like query performance prediction, inferential risk measures, and efficient cascade ranking. Over 100 research outputs are listed, with recent work on query optimization, multimodal feature extraction, and trajectory data analytics. Collaborations and a personal website ( https://www.culpepper.io/ ) highlight active engagement in the academic community.

Ullrich MartinView profile

Professor

• Railway Engineering
• Transport Systems
• Fault Diagnosis
• +7 more

Ullrich Martin is a Professor and Head of the Institute of Railway and Transport Engineering at the University of Stuttgart, Germany. His work spans railway systems, infrastructure modeling, and transport policy. Key areas include fault diagnosis, simulation-based optimization, and capacity evaluation. Contact: Pfaffenwaldring 7, 70569 Stuttgart, Germany Office Hours: By agreement Research Trends: Recent publications focus on GAN-based data augmentation for railway fault detection, high-speed maglev systems optimization, and early instability detection in ballast tracks. His team explores discrete element modeling, operational risk analysis, and machine learning applications for track condition monitoring. Infrastructure Innovation: He drives digitalization in railway planning through graph-based knowledge databases and automated compliance tools. Projects address dwell time forecasting, electromagnetic suspension design, and accessibility solutions like the Sinn²-App for visually impaired passengers. Operational Modeling: His work includes tabu search algorithms for dispatching, reinforcement learning for timetable calibration, and multi-scale simulation frameworks. He investigates interactions between track irregularities and vehicle dynamics, with a focus on safety and efficiency.

Dag Trygve Truslew HaugView profile

Professor

• Syntax
• Formal Semantics
• Computational Linguistics
• +5 more

Dag Trygve Truslew Haug is a Professor at the Department of Classics at the University of Oslo, specializing in Greek and Latin linguistics with a focus on syntax and computational approaches. His research bridges classical philology with modern computational linguistics, particularly through treebank development and semantic analysis. His primary research interests include: Greek and Latin grammatical structures Lexical-Functional Grammar and formal semantics Computational linguistics and treebank development Historical linguistics and syntactic change Semantic annotation frameworks Morphological tagging systems His recent publications demonstrate a strong focus on computational linguistics applications for classical languages, semantic parsing methodologies, and the development of linguistic resources for Norwegian. The work shows consistent innovation in formal semantics, corpus linguistics, and interdisciplinary applications in computational biology. He leads the DAMOS project (Database of Mycenaean at Oslo) and participates in multiple research groups including Digital Humanities, Grammar and Meaning, Language Change, Linguistics and Poetics, Pragmatics: Theory & Experiments, and Super Linguistics.

• Secure Computation
• Privacy-Preserving Machine Learning
• Homomorphic Encryption
• +4 more

M. Sadegh Riazi is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. He completed his PhD at UCSD in 2020 with a dissertation titled 'Towards A Private New World: Algorithm, Protocol, and Hardware Co-Design for Large-Scale Secure Computation.' Dr. Riazi's research focuses on the critical intersection of cryptography, machine learning, and hardware security, with particular emphasis on making secure computation practical for real-world applications. His work spans secure multi-party computation, homomorphic encryption, privacy-preserving machine learning, and hardware acceleration for cryptographic protocols. He has made significant contributions to optimizing secure computation frameworks for deep learning applications, developing techniques that balance security guarantees with computational efficiency. His publication record shows a clear progression from foundational secure computation techniques to increasingly sophisticated applications in privacy-preserving AI. Notably, his work on HEAX (Homomorphic Encryption Acceleration) and XONN (XNOR-based Oblivious Neural Network) demonstrates practical approaches to making encrypted deep learning feasible. The research trends in his publications indicate a consistent focus on bridging theoretical cryptographic security with practical system implementations. Dr. Riazi has established a strong collaborative network, most prominently with Professor Farinaz Koushanfar's research group at UCSD, with whom he has co-authored numerous papers across multiple domains including biometric security, secure hardware design, and privacy-preserving machine learning. His research has been published in top-tier venues including IEEE Security & Privacy, USENIX Security, ASPLOS, and CCS, reflecting the high impact and quality of his work in both the security and systems communities.

Jonathan MaceView profile

Assistant Professor

• Distributed systems
• Cloud computing
• System observability
• +3 more

Jonathan Mace is a tenure-track faculty member heading the Cloud Software Systems Group at the Max Planck Institute for Software Systems and University of Saarland. His research develops systems for operating cloud infrastructure, distributed systems, and serverless architectures. Key projects include Blueprint for reconfigurable microservices, Hindsight for distributed tracing of edge cases, and Clockwork for predictable DNN serving. His work addresses observability, performance predictability, and resource management in cloud environments. Mace has received the Distinguished Artifact Award at OSDI, Facebook PhD Fellowship, and SOSP Best Paper Award. He teaches courses on distributed systems and cloud computing and has supervised multiple PhD students.