Find Academic Supervisors

Nirav AjmeriView profile

Senior Lecturer

• Artificial Intelligence
• Multiagent Systems
• Intelligent Autonomous Systems
• +7 more

Dr. Nirav Ajmeri is a Senior Lecturer in Artificial Intelligence at the University of Bristol's School of Computer Science. He holds a PhD and MS from North Carolina State University and a BE from Sardar Vallabhbhai Patel Institute of Technology. His work focuses on socially intelligent multiagent systems, ethics in AI, privacy-preserving technologies, and socio-technical systems design. Education: PhD and MS in Computer Science, North Carolina State University B.E. in Computer Engineering, Sardar Vallabhbhai Patel Institute of Technology Research Interests: Dr. Ajmeri explores ethical AI frameworks, normative multiagent systems, privacy in socio-technical environments, and human-agent collaboration. His work bridges technical innovation with societal impacts, focusing on fairness, accountability, and transparency in autonomous systems. Recent Trends in Publications: Recent work emphasizes ethical governance in AI (Rawlsian fairness, macro ethics), graph-based social network modeling, and multiagent simulations of polarization. He also investigates practical applications like misuse audits in mobile apps and cybersecurity hygiene promotion through normative systems. Awards: Best Blue Sky Paper Award at AAMAS 2020 Most Influential Paper Award (2024) for 2013 JSS publication on agile requirements Advising & Grants: Supervises PhD students in AI ethics, interactive AI, and cybersecurity. Co-developed the UKRI AI for Collective Intelligence Hub and contributed to national AI strategy frameworks. Engaged in tool development (e.g., Coco ASP implementation for norm reasoning). Labs & Teams: Active in Bristol's Interactive AI research group, leading projects on ethical AI design and multiagent system dynamics. Collaborates with interdisciplinary teams across computer science, social sciences, and policy domains.

Risi KondorView profile

Associate Professor

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +6 more

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

Farshid HajatiView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +1 more

Farshid Hajati is a Lecturer in Data Science at the University of New England's School of Science and Technology. He holds a PhD from Western Sydney University and has industry experience as a Senior Data Scientist at Australian government health agencies. His expertise spans machine learning, medical AI, and computer vision. Dr. Hajati's research develops deep learning solutions for medical applications including retinal disease detection, cardiac arrhythmia classification, and fungal infection diagnosis. He has secured significant funding including $433,000 for an intracranial pressure assessment device and $100,000 from Google Research. His publications demonstrate consistent innovation in multimodal medical AI, with recent advances in interpretable graph networks for biomedical data and handheld retinal imaging. Earlier foundational work established methods for 3D face recognition and dynamic texture analysis.

Ivano BilenchiView profile

Researcher

• Artificial Intelligence
• Semantic Web
• Internet of Things (IoT)
• +4 more

Ivano Bilenchi is a postdoctoral researcher at the Polytechnic University of Bari's Information Systems Laboratory (SisInf Lab). He holds a Master's in Computer Science Engineering (2020) and a Ph.D. in Electrical and Information Engineering (2024) from the same institution. His research focuses on AI, Semantic Web technologies, edge computing, and IoT applications, with notable contributions to embedded OWL reasoners and cloud-edge intelligence frameworks. He teaches courses such as Formal Languages and Compilers, Secure Programming, and Information Systems Security. His work bridges academic research with practical applications, including iCleaner (iOS system cleaner), Tiny-ME (Semantic Web reasoner), and AI-LMD (fleet optimization tool). He actively participates in conferences like ICWE and I-CiTies, and has contributed to initiatives like the sustainable development project HowtUyoga. His awards include a First Prize at the Sustainable Development Festival (2018). Research highlights include developing Cowl (lightweight OWL library for edge devices) and proposing innovative architectures for cloud-edge AI in sensor networks. Collaborations span semantic blockchain marketplaces (RideMATCHain) and UAV autonomy using knowledge representation.

Ali GhorbaniView profile

Professor

• Cybersecurity
• Machine Learning
• Network Security
• +3 more

Dr. Ali Ghorbani is a Professor and Tier 1 Canada Research Chair in Cybersecurity at the University of New Brunswick's Faculty of Computer Science, where he served as Dean from 2008-2017. He is the founding Director of the Canadian Institute for Cybersecurity (CIC), established in 2016. Ghorbani holds BSc (Tehran), MSc (George Washington University), and PhD (UNB) degrees. With 42+ years in academia, his research spans cybersecurity, machine learning, adaptive systems, and critical infrastructure protection. Ghorbani has co-founded three cybersecurity startups (Sentrant Security, EyesOver Technologies, Cydarien Security) and co-invented four awarded patents. He has published 300+ peer-reviewed papers and supervised 250+ researchers. Professional roles include co-founding the National Cybersecurity Consortium (NCC) and serving as co-editor-in-chief of Computational Intelligence journal. Awards & Honors: Canada Research Chair in Cybersecurity Startup Canada Senior Entrepreneur Award (2017) RBC Top 25 Canadian Immigrants (2019) CAIAC Lifetime Achievement Award (2024)

Hy Truong SonView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Drug Discovery
• +6 more

Dr. Hy Truong Son is an Assistant Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. He holds a Ph.D. in Computer Science from the University of Chicago and has prior experience as a Lecturer and Postdoctoral Fellow at the Halicioglu Data Science Institute, UC San Diego. His research focuses on AI-driven solutions for science and engineering, particularly deep learning applications in drug discovery, repurposing, and biomedical problem-solving through his HySonLab group. Dr. Son’s educational background includes a Ph.D. from the University of Chicago, emphasizing foundational training in computer science. His postdoctoral work at UC San Diego further solidified his expertise in data science and interdisciplinary AI applications. Research interests span AI for drug discovery, generative AI, multimodal learning, and healthcare technologies. His lab’s work integrates AI with molecular biology, medical imaging, and wearable sensor data to address challenges in precision medicine and environmental science. Notable projects include DrugPipe for drug repurposing and SilVar-Med for explainable medical imaging analysis. His recent publications highlight advancements in generative models, speech synthesis, protein design, and scalable graph neural networks. These works reflect a focus on bridging AI with real-world biomedical and engineering applications. While no awards are explicitly listed, his prolific publication record and active lab indicate significant contributions to the field. He advises students and engineers in his team, fostering collaborative research environments. Ongoing projects include wearable device datasets for mental health and multimodal biomedical knowledge graph development. Dr. Son’s HySonLab group emphasizes translational research, aiming to deploy AI solutions in clinical and scientific settings. Current efforts include optimizing molecular interactions via large language models and enhancing drug discovery pipelines through interdisciplinary computational methods.

Cuneyt Gurcan AkcoraView profile

Associate Professor

• Data Science on Complex Networks
• Graph Mining
• Topological Data Analysis
• +3 more

Cuneyt Gurcan Akcora is an Associate Professor of Computer Science at the University of Central Florida (UCF) and an Adjunct Professor in Computer Science and Statistics at the University of Manitoba, Canada. He holds a Ph.D. from Università degli Studi dell’Insubria (Italy) and an M.S. from SUNY Buffalo (USA) as a Fulbright Scholar. His research focuses on data science applied to complex networks and graph mining, particularly in blockchain and online social networks. He has collaborated with institutions like Yahoo! Research Barcelona, Qatar Computing Research Institute, and Huawei. Education: PhD in Computer Science, 2014 – Università degli Studi dell’Insubria, Italy MEng in Computer Science, 2010 – SUNY Buffalo, USA BSc in Electrical & Electronics Engineering, 2008 – Karadeniz Technical University, Turkey Research Interests: Explainable AI and Graph Machine Learning Topological Data Analysis (TDA) for Blockchain Networks Large-Scale Graph Analysis and Anomaly Detection Nonparametric Statistical Methods Key Contributions: His work includes developing chainlet theory for Bitcoin price prediction, scalable TDA algorithms for blockchain forensics, and interdisciplinary grants exploring blockchain applications in insurance. Notable projects include the Chartalist Dataset for blockchain network analysis and ChainNet , a topological graph learning framework. Awards & Grants: NSERC Discovery Grant (2020) UCF Interdisciplinary Grant for Blockchain in Insurance (2020) Fulbright Scholarship (M.S., SUNY Buffalo) Labs & Teams: Active in UCF’s FData Lab and leads blockchain data analytics initiatives. His work bridges academia and industry through collaborations with Huawei, QCRI, and others.

Carson LeungView profile

Professor

• Big Data Science
• Data Mining
• Machine Learning
• +5 more

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).

Michael A. BenderView profile

Professor

• Algorithms
• Data Structures
• Cache and I/O-Efficient Computing
• +7 more

Michael A. Bender is the John L. Hennessy Chaired Professor of Computer Science at Stony Brook University. His work spans both theoretical and applied domains in algorithms, data structures, and storage systems. He co-founded Tokutek, Inc., a database company acquired by Percona in 2015, and has held visiting positions at MIT and King's College London. Education: PhD in Computer Science from Harvard University (1998), DEA and Magistère in Computer Science from École Normale Supérieure de Lyon (1993), BA in Applied Mathematics from Harvard (1992). His research focuses on cache-oblivious algorithms , I/O-efficient computing , parallel systems , and scheduling . His work addresses scalability in large-scale data management and computational geometry, with applications in bioinformatics and robotics. The 15 most recent publications highlight advancements in list labeling , hash table design , graph algorithms , and memory optimization . These papers reflect his commitment to solving practical problems with rigorous algorithmic approaches. Awards include: PODS Best Paper Award (2024) ASPLOS Distinguished Paper Award (2023) USENIX FAST Best Paper Award (2016) Chancellor's Award for Excellence in Teaching (2015) R&D 100 Award (2006) He has led the Stony Brook Computer Science Honors Program and advised graduate students in algorithmic research. His grants portfolio includes 39 funded projects, emphasizing algorithm engineering and systems optimization.

Björn SchemberaView profile

Researcher

• Research Data Management
• Metadata
• Semantics
• +4 more

Björn Schembera is a Researcher at the Institute of Applied Analysis and Numerical Simulation (IANS) at the University of Stuttgart, focusing on Research Data Management (RDM), metadata standards, and ontologies. He holds a Dr.-Ing. (Engineering Doctorate) and a Diplom-Informatik (Computer Science) degree, with interdisciplinary training in philosophy. His current work centers on FAIR principles, knowledge graphs, and dark data in computational and mathematical sciences, particularly within the MaRDI (Mathematical Research Data Initiative) project under NFDI funding. Education and Professional Background: After completing his doctoral studies on dark data in simulations at HLRS (2011–2022), he joined IANS in 2022. His research bridges technical RDM challenges with societal implications of information technology. Research Interests: Schembera’s work emphasizes metadata models (e.g., EngMeta), ontology development for mathematics and engineering, and ethical aspects of data stewardship. He contributes to projects like bwDataArchive and NFDI4Cat, addressing interoperability and standardization in research data infrastructures. Teaching: He leads the course 'Schlüsselqualifikation Forschungsdatenmanagement' since 2023/24, promoting open science and RDM practices. Key Achievements: His 2023 Best Paper Award (MTSR Conference) recognizes contributions to metadata and semantic research. Over 50 publications span journals like Energies, IEEE Transactions, and conference proceedings on metadata, ontologies, and simulation workflows. Projects: He collaborates on MaRDI (mathematical data infrastructure), NFDI4Cat (catalysis sciences), and bwDataArchive (long-term storage).

Igor MolybogView profile

Assistant Professor

• Artificial Intelligence
• Large Language Models
• Multimodal Modeling
• +5 more

Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Masoud Makrehchi is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, part of the Faculty of Engineering and Applied Science. His research focuses on Natural Language Processing, Artificial Intelligence, Machine Learning, and Social Computing, with a strong emphasis on applications in network science and moral AI. He holds a PhD from the University of Waterloo (2007), along with earlier degrees from Shiraz University and Iran University of Science and Technology. Prior to academia, he worked as a Senior Research Scientist at Thomson Reuters (2008–2012) and completed a postdoctoral fellowship at the University of Waterloo (2007–2008). His research expertise includes text mining, social network analysis, and network science, addressing challenges such as signed social network analysis, document classification, and crime trend prediction using social media data. He has received several awards, including the NSERC Postgraduate Scholarship and the SBP Challenge Award (2012). His work spans over 50 peer-reviewed publications in journals like Expert Systems with Applications, Social Network Analysis and Mining, and Web Intelligence, with contributions to conferences such as GECCO and IEEE/ACM. Dr. Makrehchi’s publications highlight innovations in feature selection, text segmentation, and AI-driven frameworks for requirements elicitation. His recent work explores coherence graphs for text segmentation, generative AI in education, and bias detection in medical datasets. He has also contributed to patent applications related to sentiment analysis and text extraction systems.

Laks V.S. LakshmananView profile

Professor

• Data Management
• Graph Computing
• Machine Learning
• +5 more

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

• AI & Neuroscience
• Biomedical Engineering
• Neurotechnology
• +7 more

Aldo Faisal is a Professor of AI & Neuroscience at Imperial College London's Department of Bioengineering and Faculty of Engineering. He is also the Founding Director of the UKRI Centre for Doctoral Training in AI for Healthcare (£20M), leading the Faisal Lab. His roles include associate investigator at the MRC London Institute of Medical Sciences and affiliation with the Gatsby Computational Neuroscience Unit (UCL). His research bridges AI, neuroscience, and healthcare, focusing on neurotechnology, human behavior analysis, and clinical applications. Education: Studied Computer Science and Physics in Germany, followed by Biology at the University of Cambridge (Emmanuel College). Earned a PhD in Neuroscience under Simon Laughlin, and postdoctoral work with Daniel Wolpert on sensorimotor control. Prior professional experience includes roles at McKinsey & Co. and Credit Suisse. Research Interests: Combines cross-disciplinary approaches to study brain-behavior relationships, developing technologies for neurological disorders and amputees. Key labs include the Brain & Behaviour Lab (neurotechnology) and Behaviour Analytics Lab (behavioral data science). Techniques include machine learning, robotics, and neuroimaging (EEG/fNIRS). Publications: Over 100+ peer-reviewed articles, spanning AI in healthcare, motor learning, and neurotechnology. Recent work emphasizes safety of AI in critical care, wearable biomarkers for neuromuscular diseases, and human-AI collaboration. Awards: UKRI Turing AI Fellowship, Toyota Mobility Prize ($50k), Rosetree Interdisciplinary Award (£300k), and fellowships from German National Merit Foundation and Böhringer-Ingelheim Foundation. Elected to Global Futures Council (WEF, 2016). Grants & Labs: Leads £20M CDT in AI for Healthcare, £2M Turing Fellowship project, and manages labs at Imperial and University of Bayreuth (Germany). Collaborates with institutions like CRUK Convergence Science Centre and Data Science Institute.

Yan WangView profile

Professor

• Trust Management
• Recommender Systems
• Social Networks
• +7 more

Yan Wang is a Full Professor in the School of Computing at Macquarie University, Australia. He obtained his PhD from Harbin Institute of Technology (HIT), China, and has been at Macquarie since 2003 following a postdoctoral fellowship at the National University of Singapore. His research focuses on trust management, recommender systems, social networks, and services computing, with a strong emphasis on applications in cyber security and data analytics. Wang has published extensively in top-tier venues like AAAI, IJCAI, and IEEE Transactions, and has led over 25 research projects, including current initiatives like Trust-Oriented Data Analytics in Online Social Networks (DP230100676) and Combating Fake News on Social Media (2020–2023). He has received prestigious awards such as the IEEE TCSVC Outstanding Service Award (2017) and the Vice-Chancellor’s Excellence in Research Supervision Award (2014). He serves on editorial boards of journals like IEEE Transactions on Services Computing and has organized major conferences including IEEE ATC2013 and IEEE CLOUD2017. His work bridges theoretical advancements in machine learning and practical challenges in social computing, edge computing, and federated learning. Affiliations: School of Computing, Data Horizons Research Centre, Frontier AI Research Centre Key Projects: Trust-Oriented Data Analytics in Online Social Networks (2023–2026) Combating Fake News on Social Media (2020–2023) Research Themes: Recommender systems, graph learning, fake news detection, and trust management in distributed systems. Wang’s recent work explores causal representation learning, cross-domain recommendations, and privacy-preserving techniques in edge computing environments.