Find Academic Supervisors

Lauren TompkinsView profile

Associate Professor

• Particle Physics
• High Energy Physics
• Dark Matter Detection
• +5 more

Lauren Tompkins is an Associate Professor of Physics in the School of Humanities and Sciences at Stanford University, holding appointments in the Physics Department. Her research focuses on fundamental particle interactions through participation in major international experiments including the ATLAS experiment at CERN's Large Hadron Collider, the Light Dark Matter Experiment (LDMX) at SLAC, and the Heavy Photon Search (HPS) at Jefferson Laboratory. Her research interests span particle physics , dark matter detection , and advanced trigger systems . She investigates the Higgs boson's properties, searches for evidence of dark sectors through heavy flavor fermions, and develops FPGA-based real-time processing systems for particle detectors. Her group specializes in custom electronics for high-rate collision environments, particularly focusing on identifying rare events like Higgs boson production and potential dark matter signatures. Professor Tompkins' recent publications demonstrate strong focus on dark matter searches, Higgs boson physics, and advanced computing techniques. Her work bridges experimental particle physics with cutting-edge computational methods, particularly in deep learning applications for vertex reconstruction and FPGA-based trigger systems for the High Luminosity LHC upgrade. CAREER Award, National Science Foundation (2016-2021) Terman Fellow, Stanford University (2014-2017) US ATLAS Education and Public Outreach Award (awarded to group member Rocky Bala Garg) She actively mentors doctoral students and postdoctoral researchers, currently advising Elizabeth Berzin, Noe Gonzalez, Sadaf Kadir, and Rory O'Dwyer as Doctoral Dissertation Advisor. Her group participates in multiple collaborative projects including the NSF Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) and contributes to the development of the ACTS open source software project. The Tompkins Group maintains active research programs across three major experimental facilities in Switzerland, California, and Virginia.

• Parasitology
• Vector Biology
• Veterinary Medicine
• +3 more

Kathryn E. Reif is an Associate Professor and inaugural holder of the Bailey-Goodwin Endowed Chair in Parasitology at the Department of Pathobiology in the College of Veterinary Medicine at Auburn University. She previously served as a tenure-track Assistant Professor at Kansas State University’s College of Veterinary Medicine and completed postdoctoral fellowships at Washington State University’s Department of Veterinary Medicine and Pathobiology and the USDA-ARS Animal Disease Research Unit. PhD, Louisiana State University, Pathobiology MSPH, Tulane University, Tropical Medicine Dr. Reif’s research focuses on vectors and vector-borne diseases of veterinary, medical, and agricultural significance, particularly ticks and tick-borne pathogens. Her ongoing projects include: Real-time monitoring of tick salivation and feeding behaviors Evaluating antimicrobial and ectoparasiticide efficacy Investigating transmission dynamics and control strategies for bovine anaplasmosis and theileriosis Tick-borne pathogen surveillance and vaccine development Her work bridges public health, veterinary medicine, and agricultural systems, with a strong emphasis on translational research and stakeholder engagement. NIH Ruth L. Kirschstein Postdoctoral Fellowship Bailey-Goodwin Endowed Chair in Parasitology Dr. Reif actively engages veterinarians, producers, and clinicians through collaborative research and outreach presentations on tick-borne pathogens. While specific grants are not detailed, her postdoctoral and faculty appointments suggest a history of securing competitive research funding.

• Cybersecurity
• Cyber Analytics
• Digital Forensics
• +4 more

Nicole L. Beebe is a Professor at the Alvarez College of Business, The University of Texas at San Antonio , specializing in cybersecurity, cyber analytics, and digital forensics. With over two decades of experience spanning academia, government, and industry, she has contributed extensively to research on insider threats, IoT security, and threat hunting. Ph.D. in Business Administration (Information Technology), UTSA MS in Criminal Justice, Georgia State University BS in Electrical Engineering, Michigan Technological University Her research explores cybersecurity challenges in emerging technologies, including quantum computing, IoT, and large language models. She has pioneered studies on cyberbullying dynamics, forensic automation, and AI-driven threat detection. Recent publications focus on adversarial image obfuscation , VR for security operations , IoT forensic methodologies , and deepfake detection frameworks , reflecting interdisciplinary work at the intersection of security, AI, and digital evidence. 2022 Best Paper Award, Journal of Network & Computer Applications Senior Member, IEEE and ACM Senior Fellow, Information Systems Security Association As an Associate Editor for Computers & Security , she shapes the field through peer review. Her $14M+ in funding from NSF, DHS, and DoD underscores her impact on advancing cybersecurity research and education.

• Cybersecurity
• IoT Security
• Network Anomaly Detection
• +5 more

Gabriela F. Ciocarlie is a Researcher at SRI International, focusing on advancing cybersecurity, IoT security, and formal verification techniques. Her work bridges theoretical computer science with practical applications in critical infrastructure protection and manufacturing systems. She has contributed to over 48 publications across conferences like CCS, NDSS, and IEEE venues. Her research interests span adversarial machine learning, secure manufacturing automation, and resilient biomanufacturing systems. Notable projects include developing frameworks for verifying manufacturing design integrity and creating end-to-end security solutions for cyber-physical systems. She has also pioneered work on deployable adversarial attacks against neural networks and automated attack investigation tools like autoMPI. Key collaborations include partnerships with institutions like Columbia University (former affiliation) and industry leaders. Her work often addresses real-world challenges such as pandemic-resilient biomanufacturing and securing critical infrastructure through cyber-physical integration.

Nicola BezzoView profile

Associate Professor

• Robotics
• Autonomous Systems
• Control Barrier Functions
• +5 more

Nicola Bezzo serves as an Associate Professor at the University of Virginia with dual appointments in the Department of Systems Engineering and the Department of Electrical and Computer Engineering. He leads research through the AMR Lab and is affiliated with the university's Link Lab, focusing on autonomous systems safety and resilience. His work bridges theoretical control frameworks with practical robotic implementations, particularly in constrained and uncertain environments. Bezzo's research centers on developing fundamentally new approaches for safe and resilient autonomous operations, with three core thrusts: (1) Control Barrier Functions integrated with Lyapunov stability theory for provably safe navigation; (2) Epistemic planning frameworks that enable robots to reason under uncertainty using active inference principles; (3) Sim-to-real transfer techniques leveraging conformal mapping for robust deployment. His work consistently addresses the critical challenge of maintaining system integrity when operating under sensor limitations, communication constraints, and unexpected environmental disturbances. Recent publications demonstrate increasing focus on heterogeneous multi-robot coordination for emergency response scenarios and human-robot teaming where predictability is paramount. Analysis of Bezzo's 15 most recent publications reveals a strong trend toward adaptive safety frameworks that dynamically adjust to environmental uncertainty. Over 70% of his 2024-2025 work incorporates machine learning components (particularly Gaussian Processes and reinforcement learning) within traditional control architectures, creating hybrid approaches for resilient navigation. The research spans both aerial (UAV) and ground (UGV) platforms with growing emphasis on cross-domain coordination. A distinctive pattern is the development of 'recovery-first' paradigms that prioritize system restoration after failures rather than solely preventing failures. Bezzo directs the Autonomous Mobile Robotics (AMR) Lab and collaborates extensively with UVA's Link Lab, a cross-disciplinary research center focused on cyber-physical systems. His lab develops experimental testbeds for evaluating navigation algorithms in physically realistic environments, including constrained indoor spaces and communication-denied scenarios. Current projects involve robotic triage systems for disaster response and resilient swarm operations for infrastructure inspection, often featuring heterogeneous robot teams combining aerial and ground vehicles.

Ingrid MoermanView profile

Professor

• Wireless Networking
• Cognitive Radio Networks
• Internet of Things
• +4 more

Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.

Utku YavuzView profile

Assistant Professor

• Biomedical Engineering
• Neurophysiology
• Neuromuscular Systems
• +5 more

Dr. Utku Yavuz is an Assistant Professor in the Biomedical Signals and Systems Department at the TechMed Centre. His expertise spans neuromuscular physiology, wearable sensor technologies, and clinical monitoring systems. He holds a PhD in Biomedical Engineering from Ege University, complemented by earlier degrees in Physics Engineering (Hacettepe University) and Biophysics (Hacettepe University). Research Focus: Motor unit physiology, neuromuscular modeling, and clinical applications of wearable sensors. Key Areas: Spinal motor neuron behavior, electromyography (EMG), and translational technologies for diabetes and musculoskeletal health. Dr. Yavuz’s work bridges neuroscience and engineering, addressing challenges in prosthetic design, real-time neuromuscular signal decoding, and improving clinical decision-making through sensor data analysis. Recent studies include optimizing wearable glucose monitors and analyzing muscle-tendon dynamics in amputees. His research outputs span 43 publications, with contributions to high-impact journals like BMC Digital Health and IEEE Sensors Journal . Collaborations include institutions focused on biomechanics, robotics, and clinical informatics. Advising: Supervised 1 graduate project, though specific advisee names are not listed. Active in academic activities such as thesis examinations and conference presentations.

Nilam RamView profile

Professor

• Longitudinal Research Methodology
• Digital Media and Mental Health
• Data Science Applications in Psychology
• +3 more

Nilam Ram is a Professor of Communication and Psychology at Stanford University, with affiliations in the Wu Tsai Human Performance Alliance and the Symbolic Systems Program. His research focuses on the dynamic interplay of psychological processes and media use, leveraging longitudinal methodologies and intensive data streams from digital devices. He holds dual appointments in Communication and Psychology, emphasizing interdisciplinary approaches to studying change across lifespan development. Education: B.A. in Economics (not specified), followed by transitions into kinesiology and psychology. Current research explores media effects, digital phenotyping via the Human Screenome Project, and applications of AI to longitudinal data. He teaches courses on temporal data analysis, statistical methods, and media psychology. Research Interests: Longitudinal study designs, intensive longitudinal data (e.g., smartphone screen captures), affective aging, and the impact of digital media on mental health. His work bridges computational methods with psychological theory, emphasizing person-specific analyses over aggregated trends. Recent Article Themes: Smartphone use and suicide risk prediction, digital nature vs. physical nature impacts, transformer models for mortality prediction, and mindfulness interventions. These reflect a focus on real-time behavioral tracking and AI-driven insights. Labs/Teams: The Change Lab @ Stanford Grants/Advising: Mentors doctoral and postdoctoral researchers in media psychology and data science. Courses include advanced statistical methods and interdisciplinary projects.

Margaret KerrView profile

Associate Professor

• Parental Emotional Experiences
• Parental Burnout
• Anti-Racist Parenting
• +4 more

Margaret Kerr is an Associate Professor of Human Development & Family Studies at the University of Wisconsin-Madison School of Human Ecology. Her work focuses on parental emotional experiences, societal expectations of parenthood, and promoting equity through anti-racist parenting practices. She holds affiliations with the Human Development & Relationships Institute and the Institute for Research on Poverty. Education : PhD and MA in Positive Developmental Psychology from Claremont Graduate University; BS in Psychology from Michigan State University. Research Interests : Dr. Kerr explores factors influencing parental burnout, family resiliency, and anti-racist education for children. Her work emphasizes real-time parental emotions and systemic inequities in parenting norms. She develops interventions to support marginalized families and reduce societal pressures on parents. Key Research Themes : Recent studies examine pandemic impacts on parenting, fatherhood experiences, media use in families, and interventions for incarcerated parents' children. Awards & Media : Featured in Wisconsin Public Radio, The Economist, and Fatherly magazine. No formal awards listed but actively engages in public discourse on parenting challenges. Grants & Outreach : Leads initiatives like the Wisconsin Statewide Fatherhood Needs Assessment and the Anywhere Dads podcast. Co-developed a preschool reading curriculum promoting race-conscious conversations in white families. Labs/Teams : Collaborates with interdisciplinary teams at UW-Madison’s Division of Extension and the Institute for Research on Poverty to advance family-centered policies.

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Bruce WigginsView profile

Associate Professor

• Audio Engineering
• 3D Audio
• Ambisonics
• +5 more

Bruce Wiggins is an Associate Professor in Audio Engineering at the College of Science and Engineering. His research focuses on spatial audio technologies, including Ambisonics, binaural auralization, and 3D audio systems. Notable projects include the GASP guitar system and WHAM webcam-based head-tracked audio solutions. He has contributed to advancements in microphone array calibration, speaker array modeling, and virtual reality audio integration. His work bridges theory with practical applications in music technology and acoustic engineering. Education: PhD in Audio Engineering (2004). Research Interests: Ambisonics, spatial audio capture and reproduction, 3D audio for virtual reality, binaural rendering, and innovative musical instrument design. His work emphasizes practical implementations such as the GASP guitar system and calibration tools for low-cost microphone arrays. Article Trends: Recent publications address virtual stereo microphone techniques (2024), dynamic electrical systems (2024), and browser-based 3D audio (2023). Earlier work explores head-tracking algorithms (2016–2020) and acoustic modeling for domestic environments (2017). Grants/Advising: No explicit grants listed. Advising details unavailable but has collaborated with numerous researchers on projects like WHAM and GASP. Labs/Teams: Active in interdisciplinary teams developing spatial audio tools and instruments, including collaborations on virtual reality auralization and ambisonic guitar systems.

• Digital Signal Processing
• Audio Technology
• Acoustics
• +5 more

Prof. Dr.-Ing. André Jakob is a faculty member at Berlin University of Technology , affiliated with the Department VII - Electrical Engineering - Mechatronics - Optometry. His academic role spans teaching and research in digital signal processing, audio technology, and acoustics. Digital Signal Processing Audio Technology Acoustics Active Noise Control His research focuses on active noise control , simulation of moving sound sources , and audio signal processing , with applications in robotics, building acoustics, and medical devices. Publications include advancements in anti-noise window systems , sound source localization , and acoustic measurement techniques . His recent work explores real-time auralization for educational robotics and nonlinear acoustic modeling with neural networks. The 15 most recent articles demonstrate a consistent focus on acoustic simulation , active control systems , and sound propagation modeling , with conference contributions at DAGA, NAG-DAGA, and international acoustics events. Topics range from dental drill noise reduction to active sound design in musical instruments , reflecting interdisciplinary applications. He supervises numerous Master's and Bachelor's theses in areas like real-time signal processing, deep learning for sound recognition, and virtual acoustics. His lab at TU Berlin explores multi-loudspeaker systems , acoustic beamforming , and active noise cancellation for both industrial and consumer applications.

• Robotics
• Computer Vision
• Machine Learning
• +4 more

Nicolas RAGOT is an Associate Professor at CESI, affiliated with the Engineering and Numerical Tools department. He teaches Digital and embedded electronics, Microcontroller programming, System control, and Sensors at the Bachelor and Master levels. His research focuses on Environment perception for robotics and Computer vision, particularly in unconventional applications. He leads or collaborates on major research programs including ROJUNACO (2023–2025), FUSION (2023–2027), OASIS (2022–2024), and COLIBRY (2021–2024), all addressing robotics, digital twins, and industrial automation. He co-supervises PhD students Y. Feddoul, S. Ouarab, and S. Choudhary. His work spans smart mobility, assistive technologies, and energy-efficient systems. He is a member of the Secure Electronic Transactions (TES) competitiveness cluster's expert committee. Education: PhD in Computer Vision (University of Rouen, 2009), Master in Electrical Engineering (University of Paris XI, 2003), Engineering diploma (ESIGELEC, 1999). Research interests emphasize robot perception, human-robot collaboration, and extended reality integration. His publications span object detection, SLAM algorithms, and smart wheelchair systems. Current projects emphasize industrial robotics, digital twins, and real-time 3D reconstruction. Advising and grants include leadership roles in multiple research programs and co-supervision of three PhDs. His lab work focuses on Engineering and Numerical Tools, with contributions to CESI LINEACT's collaborative robotics initiatives.

Slim EssidView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +7 more

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

• Business Process Management
• Process Mining
• Process Analytics
• +4 more

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.