Find Academic Supervisors

• Uncertainty Reasoning and Decision Making in Artificial Intelligence
• Electronic Business
• Systems Analysis and Design
• +7 more

Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Bradley OlsenView profile

Professor

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +6 more

Bradley Olsen is a Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT), holding the Alexander and I. Michael (1960) Kasser Chair in Chemical Engineering. He is affiliated with MIT's School of Engineering and directs research in the Plastics and the Environment Program. His academic career spans over two decades with numerous prestigious appointments and recognitions. Olsen earned his S.B. from MIT in 2003 followed by a Ph.D. from the University of California Berkeley in 2007. His educational background is complemented by postdoctoral fellowships including NIH and Beckman Institute Postdoctoral Fellowships (2008-2009) and the Hertz Fellowship (2003-2007). Research Interests Professor Olsen's research focuses on designing materials to address important challenges while understanding the fundamental science necessary for materials design. His primary research areas include block copolymers, soft condensed matter physics, protein-based materials, and bioelectronics. His group specializes in polymer networks, protein-polymer conjugates, self-assembly phenomena, and sustainable polymer development. The research has significant implications for biomaterials, sustainable polymers, and advanced materials design. Publication Trends Analysis of Professor Olsen's recent publications reveals a strong focus on polymer network topology, protein-polymer conjugates, and sustainable materials. His work increasingly integrates computational methods with experimental approaches, particularly in polymer characterization and data science applications to materials science. Recent publications show growing emphasis on biodegradable polymers, polymer informatics, and biomedical applications of advanced materials. Scientific Recognition Professor Olsen has received numerous prestigious awards throughout his career, including: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters/Biomacromolecules/Macromolecules Young Investigator Award (2021) AIChE Owens Corning Early Career Award (2019) American Physical Society Dillon Medal (2018) Alfred P. Sloan Research Fellow in Chemistry (2014) Advising and Funding Professor Olsen has secured significant research funding from multiple federal agencies including NSF, NIH, AFOSR, and DOE. His group has produced numerous high-impact publications across top journals in polymer science, materials science, and chemistry. He has advised multiple graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. The MIT OGE's Committed to Caring Honor (2019) recognizes his excellence in graduate student mentoring. Research Infrastructure Professor Olsen leads a research group with capabilities spanning polymer synthesis, protein engineering, materials characterization, and computational modeling. His lab maintains strong collaborations with other MIT departments, national laboratories, and international research institutions. The group participates in several interdisciplinary initiatives including the Plastics and the Environment Program and has developed significant data infrastructure for polymer science through projects like CRIPT and BigSMARTS.

Yannis PaschalidisView profile

Professor

• Optimization
• Control Systems
• Stochastic Systems
• +8 more

Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.

• Distributed Computing
• Wireless Networks
• Distributed Systems
• +4 more

Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.

Aleksandar JeremicView profile

Associate Professor

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +7 more

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

Srijan KumarView profile

Assistant Professor

• Data Science
• Applied Machine Learning
• Network Analysis
• +7 more

Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Robert Jenssen is a Professor in the Machine Learning Research Group at UiT The Arctic University of Norway and serves as the Director of Visual Intelligence , an 8-year Research Council of Norway-funded SFI center. His research focuses on solving societal challenges in healthcare, marine mapping, energy, and Earth observation through collaborations with industry and public stakeholders. Director, Visual Intelligence (SFI) Center Professor, UiT Adjunct Professor, Pioneer Centre for AI (University of Copenhagen) and Norwegian Computing Center His methodological expertise spans neural networks, information-theoretic learning, self-learning, and explainable AI (XAI). Recent work emphasizes multimodal learning, uncertainty estimation, and medical image analysis. Scientific awards include: Best Paper, Pattern Recognition Letters (2024) Dissertation Award, Norwegian AI Society (2023) Best Paper, Color and Visual Computing Symposium (2022) IEEE GRS Society Letters Prize (2013) Prize for Young Researchers, University of Tromsø (2007) He contributes to international leadership as a member of the Scientific Advisory Board (SAB) for the Max Planck Institute for Intelligent Systems, France's SequoIA AI Excellence Cluster, and Denmark's DIREC center.

Najim DehakView profile

Associate Professor

• Machine Learning
• Speech Processing
• Audio Classification
• +7 more

Najim Dehak is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, part of the Whiting School of Engineering. His research focuses on machine learning applied to speech processing, audio classification, and health applications. He is renowned for developing the I-vector representation for speaker recognition, introduced in 2008 during a workshop at Johns Hopkins’ Center for Language and Speech Processing. Prior to this role, he was a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory. Dehak holds a PhD from the School of Advanced Technology in Montreal (2009). He is a Senior Member of IEEE and contributes to the IEEE Speech and Language Technical Committee. His work bridges AI, healthcare, and signal processing, with notable contributions to neurodegenerative disease detection via speech and handwriting analysis. Research interests include adversarial attacks on speech systems, multimodal biomarker discovery, and robust speech processing across demographics. His lab’s tools, like the Hermespeech Recorder, enable scalable data collection for clinical and research applications. Education: PhD in Advanced Technology (2009), Montreal Affiliations: Johns Hopkins University, IEEE Labs/Teams: Center for Language and Speech Processing (CLSP) His recent work explores AI’s role in aging research, including Alzheimer’s and Parkinson’s disease detection through speech, eye tracking, and handwriting analysis. Ongoing projects address fairness in speaker verification and robustness against adversarial attacks in ASR systems.

Qianwen WangView profile

Assistant Professor

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +5 more

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

Jenna WiensView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Data Analytics
• +5 more

Jenna Wiens is an Associate Professor of Computer Science and Engineering at the University of Michigan's College of Engineering. She serves as Associate Director of the Artificial Intelligence Lab and co-Director of AI & Digital Health Innovation. Leading the MLD3 research group, her work focuses on machine learning and AI applications for healthcare data. PhD from MIT (2014) under John Guttag NSF CAREER Award recipient (2016) Humboldt Foundation Carl Friedrich von Siemens Award (2024) Her research addresses four key technical thrusts: Time-series analysis for predicting clinical outcomes Robust machine learning against spurious correlations Decision-making with causal inference and offline reinforcement learning Human-AI collaboration frameworks Notable methodological contributions include the FIDDLE preprocessing pipeline for clinical time-series data, foundational work on survival analysis, and novel approaches to model selection in healthcare reinforcement learning. Her work has led to real-world AI deployments in infection prevention and patient risk stratification. Scientific honors include: Forbes 30 Under 30 (2015) MIT Tech Review 35 Innovators Under 35 (2017) Sloan Research Fellowship in Computer Science (2020) Sarah Goddard Power Award (2023) Wiens collaborates with clinicians across disciplines, emphasizing clinician-in-the-loop AI systems and ethical implementation in healthcare workflows.

Bradley D. OlsenView profile

Professor

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +4 more

Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.

Stan AhaltView profile

Professor

• Data Science
• High-Performance Computing
• Biomedical Informatics
• +4 more

Dr. Stan Ahalt serves as the inaugural Dean of the UNC School of Data Science and Society (SDSS) and holds a professorship in the Department of Computer Science at UNC-Chapel Hill. He previously directed the Renaissance Computing Institute (RENCI) and currently acts as its executive advisor. Additionally, he is the associate director of informatics and data science at NC TraCS, the translational research institute at UNC. His academic journey includes a 22-year tenure as professor of electrical and computer engineering at Ohio State University and leadership roles at the Ohio Supercomputer Center. Ahalt’s research focuses on applying data science to societal challenges such as healthcare, climate, and judicial reform. Key initiatives include the National Consortium for Data Science and the iRODS project, which advanced enterprise data management systems. Ahalt has secured over $34.5M in grants to lead high-impact projects like the NHLBI BioData Catalyst (unifying biomedical data), the NCATS Translator (accelerating clinical discovery), and the NIH HEAL Initiative (ensuring FAIR data standards). He mentors students across doctoral and master’s programs, teaching data science fundamentals in courses like COMP 116: Introduction to Scientific Programming. His work emphasizes interdisciplinary collaboration, evidenced by roles in RENCI, NC TraCS, and the SDSS. Ahalt’s contributions span cyberinfrastructure development, educational innovation, and translational research, positioning UNC as a national leader in data science.

Allison BuchananView profile

Associate Professor

• Cone Beam Computed Tomography
• Digital Radiography
• Quality Assurance
• +2 more

Allison Buchanan is an Associate Professor at the Dental College of Georgia , part of Augusta University . She serves as Interim Chair and Associate Chair for Oral Biology, while teaching at both predoctoral and graduate levels. Her work focuses on Cone Beam Computed Tomography (CBCT) and Digital Radiography , with a special emphasis on Quality Assurance and Obstructive Sleep Apnea imaging. Recent publications address radiation safety, disinfection methods for imaging plates, and software quality control in dental radiology. 2024 : Outstanding Faculty Award, Augusta University 2021 : Honorable mention awardee for Oral and Maxillofacial Radiology (OMR) section 2020 : Inducted into PHI KAPPA PHI Honor Society 2018 : Faculty Research and Scholarship Achievement Award Buchanan serves on multiple committees including the Standards Committee on Dental Informatics (2022-present) and the American Dental Association (2021-present), with editorial board memberships since 2018. Her teaching portfolio includes courses like Radiology Clinic II and Diagnostic Sciences Conference .

Mobarak HoqueView profile

Senior Lecturer

• Biomedical Data Science
• Artificial Intelligence
• Medical Imaging
• +3 more

Dr Mobarak Hoque serves as a Senior Lecturer in the Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester. He concurrently holds an external position as Honorary Senior Research Fellow at University College London (UCL) from July 2025 to June 2028. Academic Background: Doctor of Philosophy in Representation Learning in Multimodal Spatiotemporal Image-Guided Medical Procedures, National University of Singapore (NUS), awarded 2019 Research Expertise: Dr Hoque's work centers on Biomedical Data Science and Artificial Intelligence with specialization in Multimodal Large Language Models , Vision-Language Models , and Safe and Responsible AI . His research directly addresses UN Sustainable Development Goal 3 (Good Health and Well-being) through innovations in surgical robotics, medical imaging, and endoscopic procedures including polyp segmentation and low-light enhancement. Publication Trends: Recent publications (2023-2025) reveal a cohesive research trajectory focused on robust AI for surgical applications. Key methodologies include adversarial feature disentanglement, semi-supervised learning frameworks, and multimodal integration of vision-language models. His work consistently targets real-world clinical challenges like instrument segmentation, surgical workflow recognition, and low-light endoscopy enhancement using advanced techniques such as wavelet attention and diffusion models. Academic Supervision: Dr Hoque actively mentors PhD candidates in Biomedical Data Science and AI, particularly welcoming students interested in multimodal learning and responsible AI deployment for healthcare applications.