Find Academic Supervisors

SangWoo ParkView profile

Assistant Professor

• Power Systems
• Supply Chain Resilience
• Optimization Algorithms
• +3 more

SangWoo Park is an Assistant Professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology (NJIT). He holds a B.S. in Environmental Engineering from Johns Hopkins University (2016), and M.S. and Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley (2017 and 2022, respectively). His research focuses on power systems optimization, supply chain resilience, and AI-driven engineering solutions. He is a recipient of the 2020 American Control Conference Best Student Paper Award. Education Background: Ph.D., Industrial Engineering and Operations Research, UC Berkeley (2022) M.S., Industrial Engineering and Operations Research, UC Berkeley (2017) B.S., Environmental Engineering, Johns Hopkins University (2016) Research interests include: Power grid operations and resiliency Circular supply chain modeling Advanced optimization algorithms Machine learning applications in energy systems His work bridges theoretical optimization with practical engineering challenges, such as anomaly detection in smart grids and post-disaster supply chain recovery. Awards: Best Student Paper Award, 2020 American Control Conference Professional Activities: Office Hours: Monday 10:30 AM–12:00 PM Website: https://sangwoopark-njit.github.io/

Daniel RomeroView profile

Professor

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +5 more

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Sheldon Howard JacobsonView profile

Professor

• Operations Research
• Aviation Security
• Optimization
• +3 more

Sheldon Howard Jacobson is a Founder Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He holds cross-appointments in Electrical and Computer Engineering, Industrial and Enterprise Systems Engineering, Biomedical and Translational Sciences, Mathematics, and Statistics. His research focuses on operations research, optimization, and security systems, with notable contributions to aviation security, pediatric vaccines, and homeland security. Jacobson has been recognized with prestigious awards including the AAAS Fellowship (2019), George E. Kimball Medal (2020), and Guggenheim Fellowship (2003). His work bridges theoretical and applied domains, addressing real-world challenges in public health, policy, and technology. He has contributed to media discussions on topics like pandemic impacts and coronavirus safety measures. In research, Jacobson emphasizes interdisciplinary approaches, combining mathematical modeling with policy analysis. His recent work includes optimizing political redistricting and analyzing viral transmission risks in aviation. Collaborations span multiple disciplines, reflecting his role as a bridge between academia and practical problem-solving.

Abdussalam AlawiniView profile

Academic

• Database Systems
• Applied Machine Learning
• Computer Science Education
• +3 more

Abdussalam Alawini is a Teaching Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign . He holds a Ph.D. in Computer Science from Portland State University (2016) and has held academic positions including a postdoctoral fellowship at the University of Pennsylvania (2016–2018) and roles as a Teaching Assistant Professor (2018–2023) and Adjunct Faculty (2016). Education : Bachelor's in Computer Science (2002), University of Tripoli MSc. Computer Science and MSc. Engineering & Technology Management (2011), Portland State University Ph.D. Computer Science (2016), Portland State University Research Interests focus on databases, applied machine learning, and education. Key areas include: Improving classroom experiences via machine learning Next-generation data management systems (data provenance, citation) Developing tools like TriQL for database learning Awards include the 2024 Campus Award for Excellence in Undergraduate Teaching and the 2022 A3E Education Innovation Fellowship. His work has been recognized for contributions to database education and student success. Service & Leadership : Chair of Instructional Faculty Recruiting Committee (2023–2024) Co-Chair of SIGMOD 2020 and DataEd 2025 Member of committees on Broadening Participation in Computing and Online Education Teaching includes courses like Database Systems and Data Management in the Cloud, emphasizing practical learning through tools like MongoDB Atlas.

• Artificial Intelligence (AI)
• Machine Learning Algorithms
• AI Ethics
• +4 more

Daswin De Silva is a Full Professor of AI and Analytics at La Trobe University, Australia, and Deputy Director of the Centre for Data Analytics and Cognition (CDAC). He also holds an Adjunct Professor position at Lulea University of Technology, Sweden. His expertise spans AI ethics, algorithm development, and applications in healthcare, energy, and education. He leads major initiatives like the La Trobe Energy AI Platform for net-zero emissions and the OptusU AI Micro-credentials program. Education: PhD in AI (Monash University, 2011). Awards include the Australian Awards for University Teaching (2021), Vice-Chancellor’s Teaching Award (2019), and Mid-Career Research Excellence Award (2018). Editor of five journals including IEEE Transactions on Industrial Informatics and Springer Discover AI. Research focuses on generative AI, ethical AI systems, and vector symbolic architectures. Recent work includes AI applications in healthcare diagnostics, energy efficiency, and smart cities. He has secured AU$14M in research funding and supervised 15 PhD completions with 10 current students. Leadership roles include Deputy Chair of La Trobe’s Research & Graduate Studies Committee and chairmanship of IEEE committees on Responsible AI and Web/Information Systems. Keynote speaker at global conferences like IEEE HSI, INDIN, and ETFA. Media engagements include ABC News, Forbes, and The Conversation.

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.

Tiziano De MatteisView profile

Assistant Professor

• Parallel and Distributed Computing
• High-Performance Computing
• Energy Efficiency in Computing
• +4 more

Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar

Bettina KemmeView profile

Professor

• Distributed Systems
• Cloud Computing
• Database Systems
• +5 more

Bettina Kemme is a Professor in the School of Computer Science at McGill University, Montreal, Canada. She leads the Distributed Information Systems Lab (DISL) and specializes in large-scale data management, distributed systems, and cloud computing. Her academic roles include teaching COMP 512 (Distributed Systems) and COMP 421 (Database Systems). Education: Diplom (M.Sc. equivalent) in Computer Science, Friedrich-Alexander University, Erlangen, Germany (1996) PhD in Computer Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2000) Research Interests: Distributed systems, cloud-native data management, in-database analytics (AIDA project), monitoring-as-a-service frameworks, and scalable pub/sub systems for online games. Current projects focus on integrating machine learning with databases, cloud performance monitoring using SDN, and sustainable data systems for data science. Lab & Collaborations: Leads the Distributed Information Systems Lab (DISL) with active projects in distributed databases, cloud computing, and game systems. Collaborates on EU-Canada initiatives like the SustainSys program for sustainable data infrastructure. Advising: Supervises PhD and M.Sc. students in topics like monitoring frameworks (Mona ElSaadawy), in-database ML (Winnie He), and distributed systems (Maximilian Schiedermeier). Alumni include over 50 researchers from PhD candidates to undergraduate researchers.

Francesca SpezzanoView profile

Associate Professor

• Social Network Analysis
• Misinformation Detection and Mitigation
• Information Diffusion
• +5 more

Francesca Spezzano is an Associate Professor in the Computer Science Department at Boise State University, part of the College of Engineering. She holds a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), with postdoctoral research at the University of California Santa Cruz and the University of Maryland Institute for Advanced Computer Studies. Her research focuses on social network analysis, misinformation detection, information diffusion, and national security, with emphasis on applying machine learning and graph-based methods to combat online deception. Education: Ph.D. in Computer Science Engineering, University of Calabria, Italy (2012) Visiting Scholar, UC Santa Cruz Database Group (2010–2011) Postdoctoral Research Associate, University of Maryland (2013–2015) Research Interests: Misinformation detection and mitigation Social network analysis and mining Graph databases and link prediction National security applications Awards & Grants: NSF CAREER Award (2020) for studying misinformation diffusion Funding from the National Science Foundation, U.S. Army Research Office, and National Security Agency Service & Leadership: General Chair of WSDM 2026, CIKM 2024, and MISDOOM 2022 PC Co-Chair of ASONAM 2019 and FOSINT-SI 2020 Recipient of the Best Paper Award at FOSINT-SI 2018

Ernesto Jiménez-RuizView profile

Senior Lecturer

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +5 more

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

Jeff HeflinView profile

Associate Professor

• Semantic Web
• Ontology Development
• Artificial Intelligence
• +3 more

Jeff Heflin is an Associate Professor in the Department of Computer Science and Engineering at Lehigh University, and Director of the Cognitive Science Program. He holds a PhD from the University of Maryland, College Park, and a B.S. from The College of William and Mary. His research focuses on AI for Data Management, Scalable Reasoning, and Data Exploration, with significant contributions to Semantic Web technologies like SHOE, DAML+OIL, and OWL. Education: Ph.D. in Computer Science, University of Maryland, 2001 M.S. in Computer Science, University of Maryland, 1999 B.S. in Computer Science, The College of William and Mary, 1992 Research Interests: Heflin’s work advances Semantic Web infrastructure, including ontology development, scalable reasoning systems, and data integration. His contributions include foundational benchmarks like LUBM and innovations in entity matching (DAME), table search (StruBERT), and neural-augmented logical reasoning. Awards: Winner of the Billion Triple Challenge (ISWC 2012) Best Paper Award, ISWC 2004 Ruth and Joel Spira Award for Excellence in Teaching (2017) Service & Leadership: Served as Vice-President of the Semantic Web Science Association (SWSA), Editorial Board member of the Artificial Intelligence Journal , and General Chair of ISWC 2017. Active in conference organization (CIKM 2020, COLING 2022) and journal special issues on Semantic Web evaluation. Labs & Collaborations: Leads the Semantic Web and Agent Technologies (SWAT) Lab at Lehigh, focusing on AI-driven data management and scalable reasoning systems. Collaborates on NSF-funded projects like CRISPS (cell-centric image similarity search) and Domain-Agnostic Dataset Search.

• Database Systems
• Data Management
• Graph Databases
• +5 more

Alin Deutsch is a Professor of Computer Science at the University of California, San Diego (UCSD), specializing in database systems, graph databases, and formal verification. He has contributed significantly to research areas including query optimization, data integration, and privacy-preserving systems. His work spans theoretical foundations and practical implementations, such as the Linked Data Benchmark Council (LDBC) and the TigerGraph database system. He co-authored over 100 papers and has been involved in major conferences like SIGMOD and VLDB. Research interests include graph query processing, parallel computing, data-centric business processes, and automated system verification. Recent work focuses on scalable hybrid analytics and graph databases. Deutsch is also active in database education, co-authoring a paper on UCSD's database curriculum. He has led projects in privacy-aware systems, such as policy-aware location-based services, and contributed to tools like CLIDE for interactive query formulation in service-oriented architectures. His collaborations involve industry partners like TigerGraph and academic institutions globally.

• Databases
• Data Provenance
• XML
• +5 more

Peter Buneman is a prominent academic affiliated with the University of Edinburgh, UK. His research primarily focuses on databases, data provenance, XML, and semistructured data. He has held significant roles in advancing computational models for data citation, provenance tracking, and distributed systems. Key areas of expertise include database systems, graph algorithms, and collaborative data curation platforms. Buneman's work integrates theoretical foundations with practical applications in pharmacology data, digital libraries, and scientific data management. His contributions to data provenance and citation systems have shaped standards in academic and industrial settings. Notable projects include the Database Wiki platform for collaborative data management and the MITra framework for graph traversal. Buneman collaborates extensively with co-authors like Susan Davidson, Wenfei Fan, and Val Tannen. Publications span 1974–2025, emphasizing methodologies for data integration, distributed computing, and computational biology. His work bridges theoretical computer science with real-world applications in biomedicine and information systems.

Alex KontorovichView profile

Professor

• Automorphic Forms and Representations
• Homogeneous Dynamics
• Harmonic Analysis
• +5 more

Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.

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).