Find Academic Supervisors

Vassilios TzerposView profile

Associate Professor

• Audio Processing
• Machine Learning
• Digital Signal Processing
• +3 more

Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.

Axel SchülzgenView profile

Professor

• Fiber Lasers
• Fiber Optic Sensing
• Optical Communications
• +6 more

Axel Schülzgen is a Professor of Optics at CREOL, The College of Optics and Photonics, University of Central Florida. He also holds an Adjunct Research Professor position at the University of Arizona's College of Optical Sciences. His research focuses on fiber fabrication, nano-structured fibers, nonlinear materials, and applications in fiber lasers, sensing, and communications. He earned a PhD in Physics from Humboldt-University of Berlin, Germany. His expertise spans optical fiber devices, components, and structures, with a strong emphasis on advancing high-power laser delivery and sensing technologies. Research Interests: - Development of hollow-core fibers for low-loss light transmission - Anti-resonant fiber designs for multi-mode guidance - Nonlinear optical materials for fiber lasers - Applications in fiber optic sensing and medical imaging - Disordered media imaging using optical fibers Awards & Honors: OSA Fellow (Optical Society of America) SPIE Fellow (International Society for Optics and Photonics) 2021 Excellence in Graduate Teaching Award 2015 CREOL Excellence in Research Award Advising & Labs: - Current advisees: Ameen Alhalemi, Caleb Dobias, Md Abu Sufian - Notable alumni: Xiaowen Hu (2022), Stefan Gausmann (2021), and Jian Zhao (2019) - Research Group: Focuses on fiber fabrication technology, nanotechnology in fibers, and photonics applications

Andrew ErwinView profile

Assistant Professor

• Physical Human-Robot Interaction
• Rehabilitation Robotics
• Bio-inspired Robotics
• +3 more

Dr. Andrew Erwin is an Assistant Professor in Mechanical Engineering at the University of Cincinnati, focusing on robotics, human-robot interaction, and rehabilitation engineering. He holds a PhD and MS from Rice University (2018, 2014) and a BS from the University of Massachusetts Amherst (2012). Prior to UC, he was a postdoc at the University of Southern California and the Jet Propulsion Laboratory. His research explores how forces and movements are executed in healthy individuals, and how robotic devices can assist or restore function post-injury. Key areas include rehabilitation robotics, bio-inspired systems, haptic interfaces, and motor learning. He has received prestigious awards such as the NASA Postdoctoral Program Fellowship (2018) and the IEEE/ASME Transactions on Mechatronics Best Paper Award (2017). Dr. Erwin’s work integrates biomechanics, control systems, and neurophysiology. His lab develops devices like the SE-AssessWrist for wrist assessment and explores planetary seismometers for space missions. He maintains an active Google Scholar profile with over 25 publications. Education: PhD, Mechanical Engineering, Rice University, 2018 MS, Mechanical Engineering, Rice University, 2014 BS, Mechanical Engineering, University of Massachusetts Amherst, 2012 His current research emphasizes curriculum design for robotics learning, human-robot collaboration, and adaptive control systems. He offers a PhD position for Fall 2025 focusing on these areas.

Matthew HoldenView profile

Associate Professor

• Surgical Data Science
• Machine Learning
• Computer-Assisted Surgery
• +3 more

Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.

Mustafa BilgicView profile

Professor

• Artificial Intelligence
• Machine Learning
• Active Learning
• +5 more

Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.

Salman DurraniView profile

Professor

• Internet of Things networks
• Satellite and UAV communications
• Machine learning for wireless communications
• +2 more

Salman Durrani is a Professor and Associate Director Education at the School of Engineering, Australian National University. He holds a PhD in Electrical Engineering from the University of Queensland and a BSc (First Class Honours) from the University of Engineering & Technology, Lahore, Pakistan. His research spans Internet of Things networks, satellite/UAV communications, machine learning applications in wireless systems, early wildfire detection, and backscatter communications. Current projects focus on terahertz communication security, UAV-assisted networks, and IoT-based environmental monitoring. His recent publications demonstrate strong emphasis on wireless security, terahertz technology optimization, UAV network design, and IoT applications for environmental protection. Technological innovations include novel beamforming techniques and lightweight authentication protocols. AI 2000 Internet of Things Most Influential Scholar (2020, 2022, 2023) IEEE ComSoc Asia Pacific Outstanding Paper Award (2016) Australian Council of Graduate Research Excellence Award (2019) ANU Vice-Chancellor's Awards for Supervision (2018) and Education (2012) He has supervised 15 PhD students to completion and secured $1.9M in research funding as chief investigator for six grants. Current projects include participation in the ANU Optus Bushfire Research Centre of Excellence.

Hatice GunesView profile

Professor

• Affective Computing
• Affective Robotics
• Social Robotics
• +5 more

Prof. Hatice Gunes is a Full Professor of Affective Intelligence and Robotics at the University of Cambridge's Department of Computer Science and Technology, leading the Affective Intelligence and Robotics Lab (AFAR Lab). She holds an EPSRC Fellowship and is an EPSRC Fellow. Her research focuses on multimodal affective and social intelligence for AI systems, particularly embodied agents and robots, integrating Machine Learning, Affective Computing, and Human Nonverbal Behaviour Understanding. Key projects include the CHANSE initiative (2025–2028) for child mental health assessment via social robotics, the EPSRC Fellowship on robotic EQ for wellbeing (2019–2025), and the EU Horizon 2020 WorkingAge project. Prof. Gunes has pioneered systems like the EU SEMAINE project's SAL system, recognized with Best Demo and Paper Awards, and co-founded SensingFeeling, a spin-out company from the Innovate UK Sensing Feeling project. Her work emphasizes ethical AI and fairness, earning awards like the Best Paper Award in Responsible Affective Computing (IEEE ACII'23). She has delivered keynote talks at IEEE FG’19 and ICPR’22 and collaborates with the Department of Psychiatry and wellbeing professionals. Education: PhD in Computer Science from University of Technology Sydney (UTS) under Australian Government IPRS Scholarship Postdoctoral Research at Imperial College London (SEMAINe project) Research Interests: Multimodal affective computing, social robotics for mental wellbeing, fairness in AI systems, human-robot interaction (HRI), and ethical deployment of AI technologies. Her lab develops robots for workplace wellbeing coaching and child mental health assessment, with over 700 media coverages and partnerships with industry and healthcare sectors. Notable Achievements: Runner-up Collaboration Award (2023 VC Research Impact) Better Future Award (2023 Hall of Fame) Finalist RSJ/KROS Interdisciplinary Research Award (2021) Shortlisted Sony Women in Technology Award 2025 Labs & Teams: AFAR Lab drives interdisciplinary research in Cambridge, focusing on socially intelligent robots and AI systems that address critical societal challenges in wellbeing and mental health.

• Artificial Intelligence
• Computational Biology
• Graph Neural Networks
• +5 more

Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.

Richard AllenView profile

Professor

• Seismology
• Earthquake Early Warning Systems
• Structural Health Monitoring
• +9 more

Richard Allen is a Professor and the Class of 1954 Endowed Chair at the University of California, Berkeley, serving as Director of the Berkeley Seismological Laboratory. His work focuses on seismology, earthquake early warning systems, and seismic hazard mitigation. Research interests include earthquake rupture mechanisms, regional seismic structure and dynamics, mantle upwelling processes, fault interaction analysis, stress modeling in seismology, and machine learning applications in seismic data analysis. He pioneered smartphone-based seismic networks like MyShake and advanced technologies such as distributed acoustic sensing (DAS) for offshore monitoring. His recent publications highlight trends in earthquake early warning algorithms (EPIC, bEPIC), real-time ground-motion modeling, ShakeAlert system performance, and integration of multimodal data (e.g., social media, LLMs, DAS) for hazard mitigation. Collaborative efforts include global smartphone networks and cloud computing for seismic datasets. Allen leads the Berkeley Seismological Laboratory, driving innovations in seismic monitoring, structural health assessment, and public alerting systems to enhance disaster resilience.

Fatma DenizView profile

Professor

• Computational Neuroscience
• Neuroscience of Language
• Cognitive Science
• +3 more

Fatma Deniz is a Full Professor (W3) of Computer Science at Technische Universität Berlin, supported by the Berlin Equal Opportunities Program. She leads the Chair of Language and Communication in Biological and Artificial Systems, and is a member of the Berlin Bernstein Center for Computational Neuroscience. Her roles include membership in TU Berlin's Executive Board and the Berlin University Alliance Steering Committee. She holds a Ph.D. (Dr. rer. nat.) from TU Berlin and a Diploma in Computer Science from Technische Universität München, with research training at Caltech and postdoctoral work at UC Berkeley. Her research focuses on understanding neural mechanisms of language processing, integrating computational neuroscience, cognitive science, and artificial intelligence. Key areas include semantic representation dynamics, cross-modal neural alignment, and language learning in bilingual contexts. She has pioneered studies showing the brain's invariant semantic processing across reading and listening modalities. Her grants include an ERC Starting Grant (2023-2028) for studying language learning shifts and a NSF-BMBF CRCNS grant on bilingual representations. She co-edited The Practice of Reproducible Research: Case Studies in Data Science (UC Press, 2017) and contributed to foundational work on reproducible data science methodologies. She has advised projects in neuroimaging, AI ethics, and computational linguistics, and collaborates with institutions like UCSF and the German Academic Exchange Service. Her lab explores neural correlates of language through fMRI, MEG, and machine learning techniques.

• Computational Linguistics
• Cognitive Science
• Machine Learning
• +3 more

Lena Jäger is a Professor in the Department of Computational Linguistics at the University of Zurich (UZH). Her research focuses on the intersection of linguistics, computational cognitive science, and machine learning, particularly analyzing cognitive mechanisms underlying human language processing through experimental psycholinguistics, computational modeling, and NLP methods. She holds an MA in Chinese Language and Culture, an MSc in Experimental and Clinical Linguistics, a PhD in Cognitive Science, and a BSc in Computer Science. Prior to UZH, she led a Machine Learning Junior Research Group funded by the German Federal Ministry of Education and Research (2020) and conducted postdoctoral research at the University of Potsdam. Her work emphasizes developing machine learning methods for analyzing eye-tracking data to uncover cognitive processes reflected in eye movements. Notable contributions include creating multilingual eye-tracking corpora (e.g., MultiplEYE, PoTeC) and advancing tools like pymovements for data processing. Her research spans applications in language comprehension, biometric identification, and clinical diagnostics (e.g., ADHD detection via eye movements). Education: BA/MA: Chinese Language and Culture (University of Freiburg, Tongji University, Beijing Language and Culture University, Université Paris 7) MSc: Experimental and Clinical Linguistics (University of Potsdam) PhD: Cognitive Science (University of Potsdam) BSc: Computer Science (concurrent with PhD) Awards: Machine Learning Junior Research Group Grant (2020). Labs/Teams: Leads computational linguistics and machine learning research groups at UZH, collaborating on projects like ScanDL and CoLAGaze. Her recent work bridges AI and cognitive science, exploring how language models emulate human reading behaviors and developing frameworks for ethical AI applications. Ongoing projects include improving fairness in biometric identification systems and analyzing individual differences in reading through synthetic data.

Jay PujaraView profile

Academic

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

Jay Pujara is a Research Associate Professor in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering and the Information Sciences Institute (ISI). He directs the Center on Knowledge Graphs and leads research in artificial intelligence, specializing in knowledge graph construction, scalable machine learning, and probabilistic models. Education : PhD in Computer Science (University of Maryland, 2016), MS and BS in Computer Science from Carnegie Mellon University (2005 and 2004), with minors in Robotics, Mathematical Sciences, and Logic & Computation. Research Interests : His work focuses on probabilistic models for dynamic data, knowledge graph construction, entity resolution, and applications in NLP and social network analysis. He emphasizes scalable algorithms and real-world impact in domains like finance, climate science, and healthcare. Awards : Includes the SWSA Ten-Year Award (2023), Best Paper Awards at IUI 2019 and ISWC 2013, and grants totaling over $9M from DARPA, NSF, and industry partners. Grants & Mentorship : Principal Investigator on projects like "Artificial Domain-Understanding and Collaborative Agency" (DARPA) and "Explainable and Robust AI Agents" (NSF). Mentored over 50 students in PhD, MS, and undergraduate programs, focusing on knowledge graphs, NLP, and machine learning. Labs & Teams : Leads ISI’s Knowledge Graph and Neurosymbolic AI teams, coordinating the Open Knowledge Network (OKN) and tools like KGTK. Active in academic service, including roles on PhD admissions committees and ISI’s Space Management Committee.

David EatonView profile

Professor

• Induced Seismicity
• Microseismic Monitoring
• Distributed Acoustic Sensing
• +2 more

David Eaton is a Professor and former NSERC/Chevron Industrial Research Chair in Microseismic System Dynamics at the University of Calgary's Department of Geoscience. He holds a PhD in Geophysics from the University of Calgary (1992) and has published the textbook 'Passive Seismic Monitoring of Induced Seismicity'. Educational Background: PhD Geophysics, University of Calgary, 1992 MSc Geophysics, University of Calgary, 1988 BSc Geology and Physics, Queen's University, 1984 His research focuses on induced seismicity characterization, microseismic monitoring technology development, distributed acoustic sensing applications, physics-informed machine learning approaches, and lithospheric structure analysis. Current projects investigate earthquake triggering mechanisms during hydraulic fracturing and geothermal energy development. Publications show consistent focus on induced seismicity source characterization, monitoring methodologies, and geophysical applications for energy resource development. Recent work integrates machine learning with seismic monitoring to understand geological controls on induced seismicity. Scientific Awards: NSERC Synergy Award for Innovation (2020) J. Tuzo Wilson Medal, Canadian Geophysical Union (2020) CSEG Distinguished Lecturer (2019) Schulich School of Engineering Distinguished Collaborator (2019) University of Calgary Great Supervisor Award (2016) He leads the CREATE-REDEVELOP program training future leaders in responsible resource development and directs the microseismic research laboratory.

João Paulo CosteiraView profile

Associate Professor

• Computer Vision
• 3D Reconstruction
• Structure from Motion
• +3 more

João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.

Kyle DeMarsView profile

Associate Professor

• Space situational awareness
• Navigation, guidance, and control
• Bayesian filtering
• +6 more

Kyle DeMars is an Associate Professor and Associate Department Head for Theoretical and Computational Research in the Department of Aerospace Engineering at Texas A&M University. He holds a Ph.D. from The University of Texas at Austin (2010) and has expertise in space situational awareness, navigation systems, Bayesian filtering, and information theory. His work focuses on advanced estimation techniques for spacecraft autonomy and space surveillance. Dr. DeMars' research emphasizes robust nonlinear filtering, multitarget tracking, and information-theoretic approaches to orbital dynamics. He has developed innovative methods for spacecraft navigation, including terrain-relative systems and anonymous feature processing. His contributions address challenges in uncertainty quantification, sensor fusion, and cislunar space domain awareness. Education: Ph.D./M.S.E./B.S. in Aerospace Engineering (UT Austin, 2004–2010) Awards: AIAA Young Professional Award (2017), NASA Innovation Award (2014), and multiple teaching/research recognitions Labs/Teams: Active in space situational awareness, guidance & control, and probabilistic navigation systems Key trends in his publications include: Advances in particle flow and Gaussian mixture methods for nonlinear estimation Cislunar trajectory analysis and resonance-based surveillance strategies Development of fault-resistant and anonymous navigation frameworks Integration of information theory into sensor tasking and uncertainty management His work bridges theoretical developments with practical applications in planetary landing navigation, space traffic management, and autonomous spacecraft systems.